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What is Big Data — An Introductory Guide

The massive world of Big DataIf one strolls around any IT office premises, over every decade (nowadays time span is even lesser, almost every 3-4 years) one would overhear professionals discussing new jargons from the hottest trends in technology. Around 5 -6 years ago, one such word has started ruling IT services is ‘BIG data’ and still has been interpreted by a layman to tech geeks in various ways.Although services industries started talking about big data solutions widely from 5-6 years, it is believed that the term was in use since the 1990s by John Mashey from Silicon Graphics, whereas credit for coining the term ‘big data’ aligning to its modern definition goes to Roger Mougalas from O’Reilly Media in 2005.Let’s first understand why everyone going gaga about ‘BIG data’ and what are the real-world problems it is supposed to solve and then we will try to answer what and how aspects of it.Why is Big Data essential for today’s digital world?Pre smart-phones era, internet and web world were around for many years, but smart-phones made it mobile with on-the-go usage. Social Media, mobile apps started generating tons of data. At the same time, smart-bands, wearable devices ( IoT, M2M ), have given newer dimensions for data generation. This newly generated data became a new oil to the world. If this data is stored and analyzed, it has the potential to give tremendous insights which could be put to use in numerous ways.You will be amazed to see the real-world use cases of BIG data. Every industry has a unique use case and is even unique to every client who is implementing the solutions. Ranging from data-driven personalized campaigning (you do see that item you have browsed on some ‘xyz’ site onto Facebook scrolling, ever wondered how?) to predictive maintenance of huge pipes across countries carrying oils, where manual monitoring is practically impossible. To relate this to our day to day life, every click, every swipe, every share and every like we casually do on social media is helping today’s industries to take future calculated business decisions. How do you think Netflix predicted the success of ‘House of Cards’ and spent $100 million on the same? Big data analytics is the simple answer.Talking about all this, the biggest challenge in the past was traditional methods used to store, curate and analyze data, which had limitations to process this data generated from newer sources and which were huge in volumes generated from heterogeneous sources and was being generated  really fast(To give you an idea, roughly 2.5 quintillion data is generated per day as on today – Refer infographic released by Domo called “Data Never Sleeps 5.0.” ), Which given rise to term BIG data and related solutions.Understanding Big Data: Experts’ viewpoint BIG data literally means Massive data (loosely > 1TB) but that’s not the only aspect of it. Distributed data or even complex datasets which could not be analyzed through traditional methods can be categorized into ‘Big data’ and hence Big data theoretical definition makes a lot of sense with this background:“Gartner (2012) defines, Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”Generic data possessing characteristics of big data are 3Vs namely Variety, Velocity, and VolumeBut due to the changing nature of data in today’s world and to gain most insights of it, 3 more Vs are added to the definition of BIG DATA, namely Variability, Veracity and Value.The diagram below illustrates each V in detail:Diagram: 6 V’s of Big DataThis 6Vs help understanding the characteristics of “BIG Data” but let’s also understand types of data in BIG Data processing.  “Variety” of above characteristics caters to different types of data can be processed through big data tools and technologies. Let’s drill down a bit for understanding what those are:Structured ex. Mainframes, traditional databases like Teradata, Netezza, Oracle, etc.Unstructured ex. Tweets, Facebook posts, emails, etc.Semi/Multi structured or Hybrid ex. E-commerce, demographic, weather data, etc.As the technology is advancing, the variety of data is available and its storage, processing, and analysis are made possible by big data. Traditional data processing techniques were able to process only structured data.Now, that we understand what big data and limitations of old traditional techniques are of handling such data, we could safely say, we need new technology to handle this data and gain insights out of it. Before going further, do you know, what were the traditional data management techniques?Traditional Techniques of Data Processing are:RDBMS (Relational Database Management System)Data warehousing and DataMartOn a high level, RDBMS catered to OLTP needs and data warehousing/DataMart facilitated OLAP needs. But both the systems work with structured data.I hope. now one can answer, ‘what is big data?’ conceptually and theoretically both.So, it’s time that we understand how it is being done in actual implementations.only storing of “big data” will not help the organizations, what’s important is to turn data into insights and business value and to do so, following are the key infrastructure elements:Data collectionData storageData analysis andData visualization/outputAll major big data processing framework offerings are based on these building blocks.And in an alignment of the above building blocks, following are the top 5 big data processing frameworks that are currently being used in the market:1. Apache Hadoop : Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.First up is the all-time classic, and one of the top frameworks in use today. So prevalent is it, that it has almost become synonymous with Big Data.2 Apache Spark : unified analytics engine for large-scale data processing.Apache Spark and Hadoop are often contrasted as an "either/or" choice,  but that isn't really the case.Above two frameworks are popular but apart from that following 3 are available and are comparable frameworks:3. Apache Storm : free and open source distributed real-time computation system. You can also take up Apache Storm training to learn more about Apache Storm.4. Apache Flink : streaming dataflow engine, aiming to provide facilities for distributed computation over streams of data. Treating batch processes as a special case of streaming data, Flink is effectively both batch and real-time processing framework, but one which clearly puts streaming first.5. Apache Samza : distributed Stream processing framework.Frameworks help processing data through building blocks and generate required insights. The framework is supported by the whopping number of tools providing the required functionality.Big Data processing frameworks and technology landscapeBig data tools and technology landscape can be better understood with layered big data architecture. Give a good read to a great article by Navdeep singh Gill on XENONSTACK for understanding the layered architecture of big data.By taking inspiration from layered architecture, different available tools in the market are mapped to layers to understand big data technology landscape in depth. Note that, layered architecture fits very well with infrastructure elements/building blocks discussed in the above section.Few of the tools are briefed below for further understanding:  1. Data Collection / Ingestion Layer Cassandra: is a free and open-source, distributed, wide column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failureKafka: is used for building real-time data pipelines and streaming apps. Event streaming platformFlume: log collector in HadoopHBase: columnar database in Hadoop2. Processing Layer Pig: scripting language in the Hadoop frameworkMapReduce: processing language in Hadoop3. Data Query Layer Impala: Cloudera Impala:  modern, open source, distributed SQL query engine for Apache Hadoop. (often compared with hive)Hive: Data Warehouse software for data Query and analysisPresto: Presto is a high performance, distributed SQL query engine for big data. Its architecture allows users to query a variety of data sources such as Hadoop, AWS S3, Alluxio, MySQL, Cassandra, Apache Kafka, and MongoDB4. Analytical EngineTensorFlow: n source machine learning library for research and production.5. Data storage LayerIgnite: open-source distributed database, caching and processing platform designed to store and compute on large volumes of data across a cluster of nodesPhoenix: hortonworks: Apache Phoenix is an open source, massively parallel, relational database engine supporting OLTP for Hadoop using Apache HBase as its backing storePolyBase: s a new feature in SQL Server 2016. It is used to query relational and non-relational databases (NoSQL). You can use PolyBase to query tables and files in Hadoop or in Azure Blob Storage. You can also import or export data to/from Hadoop.Sqoop: ETL toolBig data in EXCEL: Few people like to process big datasets with current excel capabilities and it's known as Big Data in Excel6. Data Visualization LayerMicrosoft HDInsight: Azure HDInsight is a Hadoop service offering hosted in Azure that enables clusters of managed Hadoop instances. Azure HDInsight deploys and provisions Apache Hadoop clusters in the cloud, providing a software framework designed to manage, analyze, and report on big data with high reliability and availability. Hadoop administration training will give you all the technical understanding required to manage a Hadoop cluster, either in a development or a production environment.Best Practices in Big Data  Every organization, industry, business, may it be small or big wants to get benefit out of “big data” but it's essential to understand that it can prove of maximum potential only if organization adhere to best practices before adapting big data:Answering 5 basic questions help clients know the need for adapting Big Data for organizationTry to answer why Big Data is required for the organization. What problem would it help solve?Ask the right questions.Foster collaboration between business and technology teams.Analyze only what is required to use.Start small and grow incrementally.Big Data industry use-cases We talked about all the things in the Big Data world except real use cases of big data. In the starting, we did discuss few but let me give you insights into the real world and interesting big data use cases and for a few, it’s no longer a secret ☺. In fact, it’s penetrating to the extent you name the industry and plenty of use cases can be told. Let’s begin.1. Streaming PlatformsAs I had given an example of ‘House of Cards’ at the start of the article, it’s not a secret that Netflix uses Big Data analytics. Netflix spent $100mn on 26 episodes of ‘House of Cards’ as they knew the show would appeal to viewers of original British House of Cards and built in director David Fincher and actor Kevin Spacey. Netflix typically collects behavioral data and it then uses this data to create a better experience for the user.But Netflix uses Big Data for more than that, they monitor and analyze traffic details for various devices, spot problem areas and adjust network infrastructure to prepare for future demand. (later is action out of Big Data analytics, how big data analysis is put to use). They also try to get insights into types of content viewers to prefer and help them make informed decisions.   Apart from Netflix, Spotify is also a known great use case.2. Advertising and Media / Campaigning /EntertainmentFor decades marketers were forced to launch campaigns while blindly relying on gut instinct and hoping for the best. That all changed with digitization and big data world. Nowadays, data-driven campaigns and marketing is on the rise and to be successful in this landscape, a modern marketing campaign must integrate a range of intelligent approaches to identify customers, segment, measure results, analyze data and build upon feedback in real time. All needs to be done in real time, along with the customer’s profile and history, based on his purchasing patterns and other relevant information and Big Data solutions are the perfect fit.Event-driven marketing is also could be achieved through big data, which is another way of successful marketing in today’s world. That basically indicates, keeping track of events customer are directly and indirectly involved with and campaign exactly when a customer would need it rather than random campaigns. For. Ex if you have searched for a product on Amazon/Flipkart, you would see related advertisements on other social media apps you casually browse through. Bang on, you would end up purchasing it as you anyway needed options best to choose from.3. Healthcare IndustryHealthcare is one of the classic use case industries for Big Data applications. The industry generates a huge amount of data.Patients medical history, past records, treatments given, available and latest medicines, Medicinal latest available research the list of raw data is endless.All this data can help give insights and Big Data can contribute to the industry in the following ways:Diagnosis time could be reduced, and exact requirement treatment could be started immediately. Most of the illnesses could be treated if a diagnosis is perfect and treatment can be started in time. This can be achieved through evidence-based past medical data available for similar treatments to doctor treating the illness, patients’ available history and feeding symptoms real-time into the system.  Government Health department can monitor if a bunch of people from geography reporting of similar symptoms, predictive measures could be taken in nearby locations to avoid outbreak as a cause for such illness could be the same.   The list is long, above were few representative examples.4. SecurityDue to social media outbreak, today, personal information is at stake. Almost everything is digital, and majority personal information is available in the public domain and hence privacy and security are major concerns with the rise in social media. Following are few such applications for big data.Cyber Crimes are common nowadays and big data can help to detect, predicting crimes.Threat analysis and detection could be done with big data.  5. Travel and TourismFlight booking sites, IRCTC track the clicks and hits along with IP address, login information, and other details and as per demand can do dynamic pricing for the flights/ trains. Big Data helps in dynamic pricing and mind you it’s real time. Am sure each one of us has experienced this. Now you know who is doing it :DTelecommunications, Public sector, Education, Social media and gaming, Energy and utility every industry have implemented are implementing several of these Big Data use cases day in and day out. If you look around am sure you would find them on the rise.Big Data is helping everyone industries, consumers, clients to make informed decisions, whatever it may be and hence wherever there is such a need, Big Data can come handy.Challenges faced by Big Data in the real world for adaptationAlthough the world is going gaga about big data, there are still a few challenges to implement and adopt Big Data and hence service industries are still striving towards resolving those challenges to implement best Big Data solution without flaws.An October 2016 report from Gartner found that organizations were getting stuck at the pilot stage of their big data initiatives. "Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 per cent)," the firm said.Let’s discuss a few of them to understand what are they?1. Understanding Big Data and answering Why for the organization one is working with.As I started the article saying there are many versions of Big Data and understanding real use cases for organization decision makers are working with is still a challenge. Everyone wants to ride on a wave but not knowing the right path is still a struggle. As every organization is unique thus its utmost important to answer ‘why big data’ for each organization. This remains a major challenge for decision makers to adapt to big data.2. Understanding Data sources for the organizationIn today’s world, there are hundreds and thousands of ways information is being generated and being aware of all these sources and ingest all of them into big data platforms to get accurate insight is essential. Identifying sources is a challenge to address.It's no surprise, then, that the IDG report found, "Managing unstructured data is growing as a challenge – rising from 31 per cent in 2015 to 45 per cent in 2016."Different tools and technologies are on the rise to address this challenge.3. Shortage if Big Data Talent and retaining themBig Data is changing technology and there are a whopping number of tools in the Big Data technology landscape. It is demanded out of Big Data professionals to excel in those current tools and keep up self to ever-changing needs. This gets difficult for employees and employers to create and retain talent within the organization.The solution to this would be constant upskilling, re-skilling and cross-skilling and increasing budget of organization for retaining talent and help them train.4. The Veracity V This V is a challenge as this V means inconsistent, incomplete data processing. To gain insights through big data model, the biggest step is to predict and fill missing information.This is a tricky part as filling missing information can lead to decreasing accuracy of insights/ analytics etc.To address this concern, there is a bunch of tools. Data curation is an important step in big data and should have a proper model. But also, to keep in mind that Big Data is never 100% accurate and one must deal with it.5. SecurityThis aspect is given low priority during the design and build phases of Big Data implementations and security loopholes can cost an organization and hence it’s essential to put security first while designing and developing Big Data solutions. Also, equally important to act responsibly for implementations for regulatory requirements like GDPR.  6. Gaining Valuable InsightsMachine learning data models go through multiple iterations to conclude on insights as they also face issues like missing data and hence the accuracy. To increase accuracy, lots of re-processing is required, which has its own lifecycle. Increasing accuracy of insights is a challenge and which relates to missing data piece. Which most likely can be addressed by addressing missing data challenge.This can also be caused due to unavailability of information from all data sources. Incomplete information would lead to incomplete insights which may not benefit to required potential.Addressing these discussed challenges would help to gain valuable insights through available solutions.With Big Data, the opportunities are endless. Once understood, the world is yours!!!!Also, now that you understand BIG DATA, it's worth understanding the next steps:Gary King, who is a professor at Harvard says “Big data is not about the data. It is about the analytics”You can also take up Big Data and Hadoop training to enhance your skills furthermore.Did the article helps you to understand today’s massive world of big data and getting a sneak peek into it Do let us know through the comment section below?

What is Big Data — An Introductory Guide

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What is Big Data — An Introductory Guide

The massive world of Big Data

If one strolls around any IT office premises, over every decade (nowadays time span is even lesser, almost every 3-4 years) one would overhear professionals discussing new jargons from the hottest trends in technology. Around 5 -6 years ago, one such word has started ruling IT services is ‘BIG data’ and still has been interpreted by a layman to tech geeks in various ways.

Although services industries started talking about big data solutions widely from 5-6 years, it is believed that the term was in use since the 1990s by John Mashey from Silicon Graphics, whereas credit for coining the term ‘big data’ aligning to its modern definition goes to Roger Mougalas from O’Reilly Media in 2005.

Let’s first understand why everyone going gaga about ‘BIG data’ and what are the real-world problems it is supposed to solve and then we will try to answer what and how aspects of it.

Why is Big Data essential for today’s digital world?

Pre smart-phones era, internet and web world were around for many years, but smart-phones made it mobile with on-the-go usage. Social Media, mobile apps started generating tons of data. At the same time, smart-bands, wearable devices ( IoT, M2M ), have given newer dimensions for data generation. This newly generated data became a new oil to the world. If this data is stored and analyzed, it has the potential to give tremendous insights which could be put to use in numerous ways.

You will be amazed to see the real-world use cases of BIG data. Every industry has a unique use case and is even unique to every client who is implementing the solutions. Ranging from data-driven personalized campaigning (you do see that item you have browsed on some ‘xyz’ site onto Facebook scrolling, ever wondered how?) to predictive maintenance of huge pipes across countries carrying oils, where manual monitoring is practically impossible. To relate this to our day to day life, every click, every swipe, every share and every like we casually do on social media is helping today’s industries to take future calculated business decisions. How do you think Netflix predicted the success of ‘House of Cards’ and spent $100 million on the same? Big data analytics is the simple answer.

Talking about all this, the biggest challenge in the past was traditional methods used to store, curate and analyze data, which had limitations to process this data generated from newer sources and which were huge in volumes generated from heterogeneous sources and was being generated  really fast(To give you an idea, roughly 2.5 quintillion data is generated per day as on today – Refer infographic released by Domo called “Data Never Sleeps 5.0.” ), Which given rise to term BIG data and related solutions.

Understanding Big Data: Experts’ viewpoint 

BIG data literally means Massive data (loosely > 1TB) but that’s not the only aspect of it. Distributed data or even complex datasets which could not be analyzed through traditional methods can be categorized into ‘Big data’ and hence Big data theoretical definition makes a lot of sense with this background:

“Gartner (2012) defines, Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Generic data possessing characteristics of big data are 3Vs namely Variety, Velocity, and Volume

But due to the changing nature of data in today’s world and to gain most insights of it, 3 more Vs are added to the definition of BIG DATA, namely Variability, Veracity and Value.

The diagram below illustrates each V in detail:

 6 V’s of Big Data

Diagram: 6 V’s of Big Data

This 6Vs help understanding the characteristics of “BIG Data” but let’s also understand types of data in BIG Data processing.  
“Variety” of above characteristics caters to different types of data can be processed through big data tools and technologies. Let’s drill down a bit for understanding what those are:

  1. Structured ex. Mainframes, traditional databases like Teradata, Netezza, Oracle, etc.
  2. Unstructured ex. Tweets, Facebook posts, emails, etc.
  3. Semi/Multi structured or Hybrid ex. E-commerce, demographic, weather data, etc.

As the technology is advancing, the variety of data is available and its storage, processing, and analysis are made possible by big data. Traditional data processing techniques were able to process only structured data.

Now, that we understand what big data and limitations of old traditional techniques are of handling such data, we could safely say, we need new technology to handle this data and gain insights out of it. Before going further, do you know, what were the traditional data management techniques?

Traditional Techniques of Data Processing are:

  1. RDBMS (Relational Database Management System)
  2. Data warehousing and DataMart

On a high level, RDBMS catered to OLTP needs and data warehousing/DataMart facilitated OLAP needs. But both the systems work with structured data.

I hope. now one can answer, ‘what is big data?’ conceptually and theoretically both.

So, it’s time that we understand how it is being done in actual implementations.

only storing of “big data” will not help the organizations, what’s important is to turn data into insights and business value and to do so, following are the key infrastructure elements:

  • Data collection
  • Data storage
  • Data analysis and
  • Data visualization/output

All major big data processing framework offerings are based on these building blocks.

Traditional Techniques of Data Processing

And in an alignment of the above building blocks, following are the top 5 big data processing frameworks that are currently being used in the market:

1. Apache Hadoop : Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.First up is the all-time classic, and one of the top frameworks in use today. So prevalent is it, that it has almost become synonymous with Big Data.

2 Apache Spark : unified analytics engine for large-scale data processing.

Apache Spark and Hadoop are often contrasted as an "either/or" choice,  but that isn't really the case.

Above two frameworks are popular but apart from that following 3 are available and are comparable frameworks:

3. Apache Storm : free and open source distributed real-time computation system. You can also take up Apache Storm training to learn more about Apache Storm.

4. Apache Flink : streaming dataflow engine, aiming to provide facilities for distributed computation over streams of data. Treating batch processes as a special case of streaming data, Flink is effectively both batch and real-time processing framework, but one which clearly puts streaming first.

5. Apache Samza : distributed Stream processing framework.

Frameworks help processing data through building blocks and generate required insights. The framework is supported by the whopping number of tools providing the required functionality.

Big Data processing frameworks and technology landscape

Big data tools and technology landscape can be better understood with layered big data architecture. Give a good read to a great article by Navdeep singh Gill on XENONSTACK for understanding the layered architecture of big data.

By taking inspiration from layered architecture, different available tools in the market are mapped to layers to understand big data technology landscape in depth. Note that, layered architecture fits very well with infrastructure elements/building blocks discussed in the above section.

 Framework and technology landscape

Few of the tools are briefed below for further understanding:  

1. Data Collection / Ingestion Layer 

  • Cassandra: is a free and open-source, distributed, wide column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure
  • Kafka: is used for building real-time data pipelines and streaming apps. Event streaming platform
  • Flume: log collector in Hadoop
  • HBase: columnar database in Hadoop

2. Processing Layer 

  • Pig: scripting language in the Hadoop framework
  • MapReduce: processing language in Hadoop

3. Data Query Layer 

  • Impala: Cloudera Impala:  modern, open source, distributed SQL query engine for Apache Hadoop. (often compared with hive)
  • Hive: Data Warehouse software for data Query and analysis
  • Presto: Presto is a high performance, distributed SQL query engine for big data. Its architecture allows users to query a variety of data sources such as Hadoop, AWS S3, Alluxio, MySQL, Cassandra, Apache Kafka, and MongoDB

4. Analytical Engine

  • TensorFlow: n source machine learning library for research and production.

5. Data storage Layer

  • Ignite: open-source distributed database, caching and processing platform designed to store and compute on large volumes of data across a cluster of nodes
  • Phoenix: hortonworks: Apache Phoenix is an open source, massively parallel, relational database engine supporting OLTP for Hadoop using Apache HBase as its backing store
  • PolyBase: s a new feature in SQL Server 2016. It is used to query relational and non-relational databases (NoSQL). You can use PolyBase to query tables and files in Hadoop or in Azure Blob Storage. You can also import or export data to/from Hadoop.
  • Sqoop: ETL tool
  • Big data in EXCEL: Few people like to process big datasets with current excel capabilities and it's known as Big Data in Excel

6. Data Visualization Layer

  • Microsoft HDInsight: Azure HDInsight is a Hadoop service offering hosted in Azure that enables clusters of managed Hadoop instances. Azure HDInsight deploys and provisions Apache Hadoop clusters in the cloud, providing a software framework designed to manage, analyze, and report on big data with high reliability and availability. Hadoop administration training will give you all the technical understanding required to manage a Hadoop cluster, either in a development or a production environment.

Best Practices in Big Data  

Every organization, industry, business, may it be small or big wants to get benefit out of “big data” but it's essential to understand that it can prove of maximum potential only if organization adhere to best practices before adapting big data:

Answering 5 basic questions help clients know the need for adapting Big Data for organization

  1. Try to answer why Big Data is required for the organization. What problem would it help solve?
  2. Ask the right questions.
  3. Foster collaboration between business and technology teams.
  4. Analyze only what is required to use.
  5. Start small and grow incrementally.

Big Data industry use-cases 

We talked about all the things in the Big Data world except real use cases of big data. In the starting, we did discuss few but let me give you insights into the real world and interesting big data use cases and for a few, it’s no longer a secret ☺. In fact, it’s penetrating to the extent you name the industry and plenty of use cases can be told. Let’s begin.

1. Streaming Platforms

As I had given an example of ‘House of Cards’ at the start of the article, it’s not a secret that Netflix uses Big Data analytics. Netflix spent $100mn on 26 episodes of ‘House of Cards’ as they knew the show would appeal to viewers of original British House of Cards and built in director David Fincher and actor Kevin Spacey. Netflix typically collects behavioral data and it then uses this data to create a better experience for the user.

But Netflix uses Big Data for more than that, they monitor and analyze traffic details for various devices, spot problem areas and adjust network infrastructure to prepare for future demand. (later is action out of Big Data analytics, how big data analysis is put to use). They also try to get insights into types of content viewers to prefer and help them make informed decisions.   

Streaming Platforms

Apart from Netflix, Spotify is also a known great use case.

2. Advertising and Media / Campaigning /Entertainment

For decades marketers were forced to launch campaigns while blindly relying on gut instinct and hoping for the best. That all changed with digitization and big data world. Nowadays, data-driven campaigns and marketing is on the rise and to be successful in this landscape, a modern marketing campaign must integrate a range of intelligent approaches to identify customers, segment, measure results, analyze data and build upon feedback in real time. All needs to be done in real time, along with the customer’s profile and history, based on his purchasing patterns and other relevant information and Big Data solutions are the perfect fit.

Event-driven marketing is also could be achieved through big data, which is another way of successful marketing in today’s world. That basically indicates, keeping track of events customer are directly and indirectly involved with and campaign exactly when a customer would need it rather than random campaigns. For. Ex if you have searched for a product on Amazon/Flipkart, you would see related advertisements on other social media apps you casually browse through. Bang on, you would end up purchasing it as you anyway needed options best to choose from.

Advertising and Media

3. Healthcare Industry

Healthcare is one of the classic use case industries for Big Data applications. The industry generates a huge amount of data.

Patients medical history, past records, treatments given, available and latest medicines, Medicinal latest available research the list of raw data is endless.

All this data can help give insights and Big Data can contribute to the industry in the following ways:

  1. Diagnosis time could be reduced, and exact requirement treatment could be started immediately. Most of the illnesses could be treated if a diagnosis is perfect and treatment can be started in time. This can be achieved through evidence-based past medical data available for similar treatments to doctor treating the illness, patients’ available history and feeding symptoms real-time into the system.  
  2. Government Health department can monitor if a bunch of people from geography reporting of similar symptoms, predictive measures could be taken in nearby locations to avoid outbreak as a cause for such illness could be the same.   

The list is long, above were few representative examples.

4. Security

Due to social media outbreak, today, personal information is at stake. Almost everything is digital, and majority personal information is available in the public domain and hence privacy and security are major concerns with the rise in social media. Following are few such applications for big data.

  1. Cyber Crimes are common nowadays and big data can help to detect, predicting crimes.
  2. Threat analysis and detection could be done with big data.  

5. Travel and Tourism

Flight booking sites, IRCTC track the clicks and hits along with IP address, login information, and other details and as per demand can do dynamic pricing for the flights/ trains. Big Data helps in dynamic pricing and mind you it’s real time. Am sure each one of us has experienced this. Now you know who is doing it :D

Telecommunications, Public sector, Education, Social media and gaming, Energy and utility every industry have implemented are implementing several of these Big Data use cases day in and day out. If you look around am sure you would find them on the rise.

Big Data is helping everyone industries, consumers, clients to make informed decisions, whatever it may be and hence wherever there is such a need, Big Data can come handy.

Challenges faced by Big Data in the real world for adaptation

Challenges faced by Big Data in the real world for adaptation

Although the world is going gaga about big data, there are still a few challenges to implement and adopt Big Data and hence service industries are still striving towards resolving those challenges to implement best Big Data solution without flaws.

An October 2016 report from Gartner found that organizations were getting stuck at the pilot stage of their big data initiatives. "Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 per cent)," the firm said.

Let’s discuss a few of them to understand what are they?

1. Understanding Big Data and answering Why for the organization one is working with.

As I started the article saying there are many versions of Big Data and understanding real use cases for organization decision makers are working with is still a challenge. Everyone wants to ride on a wave but not knowing the right path is still a struggle. As every organization is unique thus its utmost important to answer ‘why big data’ for each organization. This remains a major challenge for decision makers to adapt to big data.

2. Understanding Data sources for the organization

In today’s world, there are hundreds and thousands of ways information is being generated and being aware of all these sources and ingest all of them into big data platforms to get accurate insight is essential. Identifying sources is a challenge to address.

It's no surprise, then, that the IDG report found, "Managing unstructured data is growing as a challenge – rising from 31 per cent in 2015 to 45 per cent in 2016."

Different tools and technologies are on the rise to address this challenge.

3. Shortage if Big Data Talent and retaining them

Big Data is changing technology and there are a whopping number of tools in the Big Data technology landscape. It is demanded out of Big Data professionals to excel in those current tools and keep up self to ever-changing needs. This gets difficult for employees and employers to create and retain talent within the organization.

The solution to this would be constant upskilling, re-skilling and cross-skilling and increasing budget of organization for retaining talent and help them train.

4. The Veracity V 

This V is a challenge as this V means inconsistent, incomplete data processing. To gain insights through big data model, the biggest step is to predict and fill missing information.

This is a tricky part as filling missing information can lead to decreasing accuracy of insights/ analytics etc.

To address this concern, there is a bunch of tools. Data curation is an important step in big data and should have a proper model. But also, to keep in mind that Big Data is never 100% accurate and one must deal with it.

5. Security

This aspect is given low priority during the design and build phases of Big Data implementations and security loopholes can cost an organization and hence it’s essential to put security first while designing and developing Big Data solutions. Also, equally important to act responsibly for implementations for regulatory requirements like GDPR.  

6. Gaining Valuable Insights

Machine learning data models go through multiple iterations to conclude on insights as they also face issues like missing data and hence the accuracy. To increase accuracy, lots of re-processing is required, which has its own lifecycle. Increasing accuracy of insights is a challenge and which relates to missing data piece. Which most likely can be addressed by addressing missing data challenge.

This can also be caused due to unavailability of information from all data sources. Incomplete information would lead to incomplete insights which may not benefit to required potential.

Addressing these discussed challenges would help to gain valuable insights through available solutions.

With Big Data, the opportunities are endless. Once understood, the world is yours!!!!

Also, now that you understand BIG DATA, it's worth understanding the next steps:

Gary King, who is a professor at Harvard says “Big data is not about the data. It is about the analytics”

You can also take up Big Data and Hadoop training to enhance your skills furthermore.

Did the article helps you to understand today’s massive world of big data and getting a sneak peek into it Do let us know through the comment section below?

Shruti

Shruti Deshpande

Blog Author

10+ years of data-rich experience in the IT industry. It started with data warehousing technologies into data modelling to BI application Architect and solution architect.


Big Data enthusiast and data analytics is my personal interest. I do believe it has endless opportunities and potential to make the world a sustainable place. Happy to ride on this tide.


*Disclaimer* - Expressed views are the personal views of the author and are not to be mistaken for the employer or any other organization’s views.

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2 comments

shivkumar 01 Jun 2019 1 likes

Thanks for sharing this amazing blog.It is really an informative post.

Nisha 18 Jun 2019 1 likes

The article looks good and the way of presentation is nice.

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How To Become a Data Analyst

With the increase in the generation of data, Data Analysis has become one of the major functions in any organization. Since the past few years, the job of a ‘Data Analyst’ has evolved immensely and is considered to be one of the most sought-after roles after ‘Data Scientist’.  However, there are several questions that you as an aspiring data analyst may have. What is Data Analytics? What are the roles and responsibilities of a Data Analyst? How does one become a Data Analyst and what are the skills required? And many more. Our primary focus, in this article, will be to answer all these questions and take you a step further towards your dream of becoming a Data Analyst.Let us first get a clear understanding of what Data Analytics is.What is Data Analytics?In layman’s terms, the term ‘analyze’ means to examine something in a systematic manner to gain meaningful insights from it. Data Analytics or Data Analysis refers to the process of analyzing the raw data to gather useful information. This data can be in the form of some corporate information, product innovations, or market trends.Let us understand this more precisely with an example. We can compare Data Analytics with a jigsaw puzzle. The first thing is to collect all the pieces of the puzzle and fit them correctly to reveal the final picture. Similarly, in the process of data analytics, we collect the raw data from several sources, analyze it and transform it into some meaningful information that can be interpreted by humans.  Thus, we can define Data Analysis as the process that helps to discover new and significant patterns by cleaning, summarizing, transforming, and modeling data which later can be used to make informative decisions. The collected data can be of any of the three forms – structured, semi-structured, or unstructured and can be represented visually in the form of graphs and charts. The visualized information enhances the precision and allows an individual to have a clearer view of the final analysis.Organizations sniff around to recruit individuals who can perform the task of converting raw data into useful information which in turn helps in their business growth. There are numerous job roles in this field and out of all these, a Data Analyst’s career journey is the most satisfying and amazing.Now, let us understand the role and responsibilities of a Data Analyst in the field of Data Science.What is the role of a Data Analyst?Organizations, in recent times, with the help of huge chunks of data often try to optimize their strategies for efficient business growth. In order to derive useful information from this massive collection of data, they require a highly qualified professional who can make sense of the data and help others understand. That is where a Data Analyst comes in.  A Data Analyst collects, processes, and performs analysis of these large amounts of data. Every business organization, be it small or big generates and collects data which can be in the form of accounts, logistics, marketing research, customer feedback, etc.  A Data Analyst processes the data and generates significant indicators useful for decision making depending upon the customers, or the products, or the performance of the company. These indicators help companies to decide what products should be offered to their customers, what type of marketing strategy is to be implemented, how to reduce transportation costs, or what changes are to be made to enhance the process of production.Mostly data analysts collaborate with IT teams, the management, or data scientists to mine, clean, and then analyze and interpret information with the help of statistical tools. Their prime focus is to determine trends, correlations, and patterns in large and complex data sets which in turn allows companies to identify new ways for process improvement.  Let us now understand the basic responsibilities of a Data Analyst.What are the responsibilities of a Data Analyst?The first step towards becoming a Data Analyst is to understand the numerous responsibilities they need to undertake in their journey. Some of the most common responsibilities are as follows:Understanding the GoalThe first and foremost task of a Data Analyst is to identify the goal of the organization by evaluating the resources, understanding the business problem, and then collecting the proper data.QueryingData Analysts also write SQL queries in order to collect, store, and derive information from databases like Microsoft SQL Server, Oracle, and MySQL.Data MiningData Analysts mine data from multiple sources and structure them to build data models which help in enhancing the system’s efficiency.Data TestingWith the help of analytical and statistical techniques, Data Analysts perform a logical examination of data.Interpretation of Data TrendsUsing different libraries and packages, Data Analysts identify trends and patterns from complex data thereby providing important insights to the organization.Preparation of ReportsThe leading teams of the organization are able to make timely decisions with the help of the summary reports prepared by Data Analysts. They perform this task using Data Visualization tools like Tableau or Google Charts, etc.Let us now take a look at the most popular industries that hire Data Analysts.What are the top industries hiring Data Analysts?There are around 82 thousand job openings worldwide in 2021 that require skills in data analysis, but there is a huge shortage of data talent. Almost every industry requires data to be analyzed and data jobs are diverging into a variety of fields.  The top industries that hire data analyst are as follows:Business Intelligence It is one of the leading industries that hire data analysts and according to a survey by Indeed, 20% of Data Analysts are from this sector. The most posted job vacancies for data analysts in the US and Europe are primarily from the Business Intelligence sector.Finance It is one of the earliest industries to be associated with data science and takes advantage of big data to make business ventures more efficient. Finance organizations such as investment banks, consumer banks, capital firms are responsible for generating a large number of data analytics jobs.Healthcare It is one of those industries that were dependent on paper data systems for many centuries. However, the importance of Data Analysis in this field is growing at a good pace and it is certain that they benefit the most. Most, Data Analysts in this sector are termed healthcare data analysts.Sharing-based economyThis industry has blossomed in recent years. Nearly every urban area be it small or big hires data talent. eBay is considered to be the first global marketplace that successfully launched the service economy services. Data analytics can be a game changer in this field.EntertainmentThis sector is evolving very fast. Global streaming networks like Netflix and Amazon are major players in this market and are using data insights to boost their growth. Data Analysts along with messaging analytics engineers or marketing intelligence analysts are very common positions in this industry. What are the technical skills required to master as a Data AnalystsThe most essential task of the data analyst is to parse through a good quantity of raw information and then develop meaningful insights in the entire process. The other tasks also include removing corrupted data, understanding the quality of data, and preparing various reports. All of these tasks involve knowledge of certain technologies and technical skills. Let us focus on a few of them.1. Data VisualizationData Visualizations revolve around a person’s ability to present data findings via graphics or other illustrations. It allows a data analyst to understand data-driven insights and helps the business decision-makers (who may lack advanced analytical training) to identify patterns and understand complex ideas at a glance.Data visualization may even allow you to accomplish more than data analysts traditionally have. As one writer for SAS Insights notes, “Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights — look at data differently, more imaginatively. Data visualization will promote creative data exploration.”Already, data visualization has become a necessary skill. According to a recent study conducted by LinkedIn Learning, “recent graduates are much more likely to learn hard skills when they first enter the workforce. And these hard skills revolve around analyzing data and telling stories with insights gleaned from the data.” Get yourself enrolled for the Data Visualization course offered by KnowledgeHut.2. Data CleaningIt is believed that cleaning is an invaluable part of achieving success. Similarly, data cleaning is one of the most critical steps in assembling a functional machine learning model and consumes a good amount of time in a data analyst’s day. Any uncleaned data may result in misleading patterns and incorrect conclusions. However, a thoroughly cleaned dataset is capable of generating remarkable insights. Data Analysts should necessarily have proper data cleaning skills.3. RR is one of the most pervasive and well-used languages in data analytics. The structure and syntax were specifically created in order to support analytical work. It comprises several built-in, easy-to-use commands. R can easily handle large and complex quantities of data. As an aspiring data analyst, considering the popularity and functionality of R, it is very essential to learn R. Learn more about R programming language from the course offered by KnowledgeHut.4. PythonPython is among the most popular programming languages for data analysis. It is an essential language to be learnt by would-be analysts. It offers a large number of specialized libraries and built-in functions.  Python is a cross-functional, maximally interpreted language that has lots of advantages to offer. It is easy to learn, well supported, flexible- a fantastic option for data processing, scalable, and has a huge collection of libraries. Python Certification course offered by KnowledgeHut will assist you in mastering the concepts of Python and its libraries like SciPy, Matplotlib, Scikit-Learn, Pandas, NumPy, Lambda functions, and Web Scraping. You will also learn how to write Python Programming for Data Analytics.5. Linear Algebra and CalculusIn data analytics, one thing that is non-negotiable is having advanced mathematical skills. Some data analysts even choose to major in mathematics or statistics during their undergraduate years just to gain a better understanding of the theory that underpins real-world analytical practice!  Two specific fields of mathematical study rise to the forefront in analytics: linear algebra and calculus. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.  6. Microsoft ExcelWhile Excel is a great application to learn, it must be noted that the operations Excel can perform, other programming languages like R and Python can perform much faster. Excel is clunky in comparison to other platforms. However, Spreadsheets are still relevant and a great tool to learn about data. While it’s not the only or most fitting solution for all data projects, but it remains a reliable and affordable tool for analytics. It’s a foundational structure for intelligent data because it deepens your understanding of the analytics process. Many industries and businesses continue to emphasize the importance of Excel skills because it remains an intelligent way to extract actionable insights. Revenue patterns, operations, marketing trends, and more can be analyzed through Excel spreadsheets, but the real advantage is the process.7. Critical ThinkingIt’s not enough to simply look at data; you need to understand it and expand its implications beyond the numbers alone. As a critical thinker, you can think analytically about data, identifying patterns and extracting actionable insights from the information you have at hand. It requires you to go above and beyond and apply yourself to thinking, as opposed to only processing.8. CommunicationAt the end of the day, you need to be able to explain your findings to others. It doesn’t matter if you’re the most talented, insightful data analyst on the planet — if you can’t communicate the patterns you see to those without technical expertise, you’ve fallen short.  Being a good data analyst effectively means becoming “bilingual.” You should have the capability to address highly technical points with your trained peers, as well as provide clear, high-level explanations in a way that supports — rather than confuses — business-centered decision-makers. If you can’t do so, you may still need to build your skill set as a data analyst.What are the Data Analysts’ salaries around the world?According to a report by Forbes, around 92 percent of organizations worldwide gain effective marketing insights by analyzing data. As an individual in the technical field, becoming a Data Analyst is a pretty amazing career opportunity.  In recent times, every business organization extracts information from sales or marketing campaigns and uses this data to gather insights. These insights allow the business to answer questions like what worked well, what did not, and what to do differently in the future. Thus, businesses can make more informed decisions with the right and organized data.  The salaries of Data Analysts depend on several factors like which industry they are working in, how many years of experience they have, what is the size of the organization, and so on. However, one big advantage of being a Data Analyst is they are always in demand globally and if you get bored of working in a particular city or a particular country, you always have the option of moving somewhere else because of the freedom and flexibility that this role offers.  Let us now look at the highest paying countries and their average annual salary of a Data Analyst:CountryAverage Annual SalaryIndiaThe average annual Data Analyst salary in India is over INR 4,45,000USAThe average annual Data Analyst salary in the USA is around USD 65,000GermanyThe average annual Data Analyst salary in Germany is around €44,330United KingdomThe average annual Data Analyst salary in the UK is around £26933CanadaThe average annual Data Analyst salary in Canada is around CAD 55000AustraliaThe average annual Data Analyst salary in Australia is over AUD 82,000DenmarkThe average annual Data Analyst salary in Denmark is around DKK 881,794SingaporeThe average annual Data Analyst salary in Singapore is around SGD 55,000What factors affect the salary of a Data Analyst in India?According to Payscale, around 78 percent of Data Analysts in India have a salary ranging between 0 – 6 Lakhs. A Data Analyst in India with experience between 1 – 4 years has net earnings of around 3,96,125 INR. On the other hand, an individual with experience of 5 – 9 years makes up to 6,00,000 INR per annum and someone with more experience than that can earn up to 9 Lakhs INR per annum. However, there are several factors that are also associated while deciding the salary of a Data Analyst.Every company, big or small, around the world now considers data analytics as an important sector and looks upon its potential to change the market trends. The decision-making authorities of the companies are focusing more on technology and people.  Now, let us understand what are the significant factors that affect the salary of a Data Analyst in India.1. Based on ExperienceAccording to a survey by Zippia, an entry-level Data Analyst in the USA having a bachelor’s degree and 2 years of experience, has an average annual salary of $54,000. A couple more years of experience can help them earn up to $70,000. A senior analyst gets an annual salary of $88,000 with experience of 6 years. However, someone with a specialization in the field can get a salary of around $100,000.Let’s see how experience affects the salary of a Data Analyst in India:The average annual salary of an Entry-Level Data Analyst in India is ₹325,616.The average annual salary of a mid-Level Data Analyst in India is ₹635,379The average annual salary of an experienced Data Analyst in India is ₹852,516.2. Based on IndustrySince every industry around the world recruits Data Analysts, there has been a significant increase in individuals who are choosing this career path, which in turn adds a lot of value to this field.  In an organization, the Data Analysts are directly responsible for some of the decision-making process and they perform this task with the help of the analyzed data using statistical tools like Excel, Tableau, and SQL. The progress impacts the salaries of these Data Analysts, which range between $54,000 to $70,000 for entry-level professionals.  Financial accounting companies hire financial analysts to predict the company’s performance and study the macro and microeconomic trends. The analysts in this industry are responsible for creating economic models and forecasts using the data. In 2017, Robert Half made a survey on the salary of entry-level financial analysts. The survey showed that their average annual salary ranges between $52,700 to $66,000.Source: Data Analysts  Salary Trends in India By IndustryMarketing research analysts use sales data, customer surveys, and competitor research to optimize the targeting and positioning efforts of their products. This industry has a pay scale ranging from $51,000 to $65,000 at the entry-level.Similarly, the Data Analysts working in the healthcare industry whose job is to maintain the daily administrative advancements and operations get an average annual salary of $46,000 to $80,000.3. Based on LocationThe number of Data Analysts and the average annual data salary in India is the highest in the Silicon Valley of India, that is Bangalore.Source: Data Analysts  Salary Trends in India By LocationBangalore, Pune, and Gurgaon offer 19.2%, 9.8%, and 9.5% more than the average annual salary in India respectively. On the other hand, Data Analysts working in Mumbai get 5.2% lesser than the national average. Hyderabad and New Delhi receive 4.85 and 2.8% lesser than the national average respectively.4. Based on CompanyThe top recruiters of Data Analysts in India are tech giants like Tata Consultancy Services, Accenture, and Earnest & Young whereas, according to reports, salaries offered are highest at HSBC which is around 7 Lakhs.Source: Data Analyst Salary Based on Company5. Based on SkillsSkill is an important factor while deciding the salary of a Data Analyst in India. You need to go beyond the qualifications of a Master’s degree and gather more knowledge of the respective languages and software. Some useful insights are as follows:The most important skill is to have a clear understanding of Python. A python programmer in India alone earns around 10 Lakhs per annum.  There is an increase of around 25 percent in the salary of a Data Analyst in India when you get familiar with Big Data and Data Science.  Experts in Statistical Package for Social Sciences or SPSS get an average salary of 7.3  Lakhs whereas experts in Statistical Analysis Software or SAS have an earning of around 9 Lakhs to 10.8 Lakhs.A Machine Learning expert in India alone can earn around 17 Lakhs per year. Along with being a Data Analyst, if you also have Machine Learning and Python skills, you can reach the highest pay in this field.How KnowledgeHut can helpAll these free resources are a great place to start your Data Analytics journey. Beside these there are many other free resources on the internet, but they may not be organized and may not have a structured approach.  This is where KnowledgeHut can make a difference and serve as a one stop shop alternative with its comprehensive Instructor-led live classes. The courses are taught by Industry experts and are perfect for aspirants who wish to become Data Analyst.Links for some of the popular courses by KnowledgeHut are appended below-Big Data Analytics CertificationR Programming Language TrainingPython Certification TrainingData Visualization with Tableau TrainingIn this article we attempt to understand about Data Analytics and the major roles of a Data Analyst. We also learnt about the responsibilities of a Data Analyst, the various industries offering jobs to Data Analysts and also the technical skills required to master to be a Data Analyst.  If you are inspired by the opportunities provided by Data Analytics, enroll in our  Data Analytics Courses for more lucrative career options in this field.
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How To Become a Data Analyst

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Overview of Deploying Machine Learning Models

Machine Learning is no longer just the latest buzzword. In fact, it has permeated every facet of our everyday lives. Most of the applications across the world are built using Machine Learning and their applications extend further when they are combined with other cutting-edge technologies like Deep Learning and Artificial Intelligence. These latest technologies are a boon to mankind, as they simplify tasks, helping to complete work in lesser time. They boost the growth and profitability of industries and organizations across sectors, which in turn helps in the growth of the economy and generates jobs.What are the fields that machine learning extends into?Machine Learning now finds applications across sectors and industries including fields like Healthcare, defense, insurance, government sectors, automobile, manufacturing, retail and more. ML gives great insights to businesses in gaining and retaining customer loyalty, enhances efficiency, minimizes the time consumption, optimizes resource allocation and decreases the cost of labor for a specific task.What is Model Deployment?It’s well established that ML has a lot of applications in the real world. But how exactly do these models work to solve our problems? And how can it be made available for a large user base? The answer is that we have to deploy the trained machine learning model into the web, so that it can be available for many users.When a model is deployed, it is fully equipped with training and it knows what are the inputs to be taken by the model and what are the outputs given out in return. This strategy is used to advantage in real world applications. Deployment is a tricky task and is the last stage of our ML project.Generally, the deployment will take place on a web server or a cloud for further use, and we can modify the content based on the user requirements and update the model. Deployment makes it easier to interact with the applications and share the benefits to the applications with others.With the process of Model Deployment, we can overcome problems like Portability, which means shifting of software from one machine to the other and Scalability, which is the capacity to be changed on a scale and the ability of the computation process to be used in a wide range of capabilities.Installing Flask on your MachineFlask is a web application framework in Python. It is a lightweight Web Server Gateway Interface (WSGI) web framework. It consists of many modules, and contains different types of tools and libraries which helps a web developer to write and implement many useful web applications.Installing Flask on our machine is simple. But before that, please ensure you have installed Python in your system because Flask runs using Python.In Windows: Open command prompt and write the following code:a) Initially make the virtual environment using pip -- pip install virtualenv And then write mkvirtualenv HelloWorldb) Connect to the project – Create a folder dev, then mkdir Helloworld for creating a directory; then type in cd HelloWorld to go the file location.c) Set Project Directory – Use setprojectdir in order to connect our virtual environment to the current working directory. Now further when we activate the environment, we will directly move into this directory.d) Deactivate – On using the command called deactivate, the virtual environment of hello world present in parenthesis will disappear, and we can activate our process directly in later steps.e) Workon – When we have some work to do with the project, we write the command  “workon HelloWorld” to activate the virtual environment directly in the command prompt.The above is the set of Virtual Environment commands for running our programs in Flask. This virtual environment helps and makes the work easier as it doesn’t disturb the normal environment of the system. The actions we perform will reside in the created virtual environment and facilitate the users with better features.f) Flask Installation – Now you install flask on the virtual environment using command pip install flaskUnderstanding the Problem StatementFor example, let us try a Face Recognition problem using opencv. Here, we work on haarcascades dataset. Our goal is to detect the eyes and face using opencv. We have an xml file that contains the values of face and eyes that were stored. This xml file will help us to identify the face and eyes when we look into the camera.The xml data for face recognition is available online, and we can try this project on our own after reading this blog. For every problem that we solve using Machine Learning, we require a dataset, which is the basic building block for the Model development in ML. You can generate interesting outcomes at the end like detecting the face and eyes with a bounding rectangular box. Machine learning beginners can use these examples and create a mini project which will help them to know much about the core of ML and other technologies associated with it.Workflow of the ProjectModel Building: We build a Machine Learning model to detect the face of the human present in front of the camera. We use the technology of Opencv to perform this action which is the library of Computer Vision.Here our focus is to understand how the model is working and how it is deployed on server using Flask. Accuracy is not the main objective, but we will learn how the developed ML model is deployed.Face app: We will create a face app that detects your face and implements the model application. This establishes the connection between Python script and the webpage template.Camera.py: This is the Python script file where we import the necessary libraries and datasets required for our model and we write the actual logic for the model to exhibit its functionality.Webpage Template: Here, we will design a user interface where the user can experience live detection of his face and eyes in the camera. We provide a button on a webpage, to experience the results.Getting the output screen: when the user clicks the button, the camera will open directly and we can get the result of the machine learning model deployed on the server. In the output screen you can see your face. Storage: This section is totally optional for users, and it is based on the users’ choice of storing and maintaining the data. After getting the outputs on the webpage screen, you can store the outputs in a folder on your computer. This helps us to see how the images are captured and stored locally in our system. You can add a file path in the code, that can store the images locally on your system if necessary.This application can be further extended to a major project of “Attendance taking using Face Recognition Technique”, which can be used in colleges and schools, and can potentially replace normal handwritten Attendance logs. This is an example of a smart application that can be used to make our work simple.Diagrammatic Representation of the steps for the projectBuilding our Machine Learning ModelWe have the XML data for recognizing face and eyes respectively. Now we will write the machine learning code, that implements the technique of face and eyes detection using opencv. Before that, we import some necessary libraries required for our project, in the file named camera.py # import cv2 # import numpy as np # import scipy.ndimage # import pyzbar.pyzbar as pyzbar # from PIL import Image Now, we load the dataset into some variables in order to access them further. Haarcascades is the file name where all the xml files containing the values of face, eye, nose etc are stored. # defining face detector# face_cascade = cv2.CascadeClassifier("haarcascades/haarcascade_frontalface_default.xml") # eye_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_eye.xml')The xml data required for our project is represented as shown below, and mostly consists of numbers.Now we write the code for opening the camera, and releasing of camera in a class file. The “def” keyword is the name of the function in Python. The functions in Python are declared using the keyword “def”.The function named “def __init__” initiates the task of opening camera for live streaming of the video. The “def __del__” function closes the camera upon termination of the window.# class VideoCamera(object):#    def __init__(self):        # capturing video#       self.video = cv2.VideoCapture(0) #  def __del__(self):#        # releasing camera#        self.video.release()Next, we build up the actual logic for face and eyes detect using opencv in Python script as follows. This function is a part of class named videocamera.# class VideoCamera(object):#    def __init__(self):#        # capturing video#        self.video = cv2.VideoCapture(0)#    def __del__(self):#        # releasing camera#        self.video.release()#    def face_eyes_detect(self):#        ret, frame = self.video.read()#        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)#        faces = face_cascade.detectMultiScale(gray, 1.3, 5)#        c=0#        for (x,y,w,h) in faces:#            cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0), 2)#            roi_gray = gray[y:y+h, x:x+w]#            roi_color = frame[y:y+h, x:x+w]#            eyes = eye_cascade.detectMultiScale(roi_gray)#            for (ex,ey,ew,eh) in eyes:#                cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)#            while True:#                k = cv2.waitKey(1000) & 0xFF#                print("Image "+str(c)+" saved")#                file = 'C:/Users/user/dev/HelloWorld/images/'+str(c)+'.jpg'#                cv2.imwrite(file, frame)#                c += 1            # encode Opencv raw frame to jpg and display it#        ret, jpeg = cv2.imencode('.jpg', frame)#        return jpeg.tobytes()The first line in the function “ret, frame” reads the data of live streaming video. The ret takes the value “1”, when the camera is open, else it takes “0” as input. The frame captures the live streaming video from time to time. In the 2nd line, we are changing the color of image from RGB to Grayscale, i.e., we are changing the values of pixels. And then we are applying some inbuilt functions to detect faces. The for loop, illustrates that it is having some fixed dimensions to draw a bounding rectangular box around the face and eyes, when it is detected. If you want to store the captured images after detecting face and eyes, we can add the code of while loop, and we can give the location to store the captured images. When an image is captured, it is saved as Image 1, Image 2 saved, etc., for confirmation.All the images will be saved in jpg format. We can mention the type of format in which the images should be stored. The method named cv2.imwrite stores the frame in a particular file location.Finally, after capturing the detected picture of face and eyes, it displays the result at the user end. Creating a WebpageWe will create a webpage, in order to implement the functionality of the developed machine learning model after deployment using Flask. Here is the design of our webpage.The above picture represents a small webpage demonstrating “Video Streaming Demonstration” and a link “face-eyes-detect”. When we click the button on the screen, the camera gets opened and the functionality will be displayed to the users who are facing the camera.The code for creating a webpage is as follows:If the project contains only one single html file, it should be necessarily saved with the name of index. The above code should be saved as “index.html” in a folder named “templates” in the project folder named “HelloWorld”, that we have created in the virtual environment earlier. This is the actual format we need to follow while designing a webpage using Flask framework.Connecting Webpage to our ModelTill now we have developed two separate files, one for developing the machine learning model for the problem statement and the other for creating a webpage, where we can access the functionality of the model. Now we will try to see how we can connect both of them.This is the Python script with the file name saved as “app.py”. Initially we import the necessary libraries to it, and create a variable that stores the Flask app. We then guide the code to which location it needs to be redirected, when the Python scripts are executed in our system. The redirection is done through “@app.route” followed by a function named “home”. Then we include the functionality of model named “face_eyes_detect” to the camera followed by the function definition named “gen”. After adding the functionality, we display the response of the deployed model on to the web browser. The outcome of the functionality is the detection of face and eyes in the live streaming camera and the frames are stored in the folder named images. We put the debug mode to False. # from flask import Flask, render_template, Response,url_for, redirect, request.# from flask import Flask, render_template, Response,url_for, redirect, request  # from camera import VideoCamera  # import cv2  # import time  # app = Flask(__name__)  # @app.route("/")  # def home():  #     # rendering web page  #     return render_template('index.html')  # def gen(camera):  #     while True:  #         # get camera frame  #         frame = camera.face_eyes_detect()  #         yield(b'--frame\r\n'  #                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')  # @app.route("/video_feed")  # def video_feed():  #     return Response(gen(VideoCamera()),  #           mimetype='multipart/x-mixed-replace; boundary=frame')  # if __name__ == '__main__':  #     # defining server ip address and port  #     app.run(debug=False)Before running the Python scripts, we need to install the libraries like opencv, flask, scipy, numpy, PIL, pyzbar etc., using the command prompt with the command named “pip install library_name” like “pip install opencv-python”, ”pip install flask”, “pip install scipy” etc.When you have installed all the libraries in your system, now open the python script “app.py” and run it using the command “f5”. The output is as follows:Image: Output obtained when we run app.py fileNow we need to copy the server address http://127.0.0.1:5000/ and paste it on the web browser, and we will get the output screen as follows:Now when we click the link “face-eyes-detect”, we will get the functionality of detecting the face and eyes of a user, and it is seen as follows:Without SpectaclesWith SpectaclesOne eye closed by handone eye closedWhen these detected frames are generated, they are similarly stored in a specified location of folder named “images”. And in the Python shell we can observe, the sequence of images is saved in the folder, and looks as follows:In the above format, we get the outcomes of images stored in our folder.Now we will see how the images were stored in the previously created folder named “images” present in the project folder of “HelloWorld.”Now we can use the deployed model in real time. With the help of this application, we can try some other new applications of Opencv and we can deploy it in the flask server accordingly.  You can find all the above code with the files in the following github repository, and you can make further changes to extend this project application to some other level.Github Link.ConclusionIn this blog, we learnt how to deploy a model using flask server and how to connect the Machine Learning Model with the Webpage using Flask. The example project of face-eyes detection using opencv is a pretty common application in the present world. Deployment using flask is easy and simple.  We can use the Flask Framework for deployment of ML models as it is a light weight framework. In the real-world scenario, Flask may not be the most suitable framework for bigger applications as it is a minimalist framework and works well only for lighter applications.
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How Big Data Can Solve Enterprise Problems

Many professionals in the digital world have become familiar with the hype cycle. A new technology enters the tech world amid great expectations. Undoubtedly, dismay sets in and retrenchment stage starts, practice and process catch up to assumptions and the new value is untied. Currently, there is apparently no topic more hyped than big data and there is already no deficit of self-proclaimed pundits. Yet nearly 55% of big data projects fail and there is an increasing divide between enterprises that are benefiting from its use and those who are not. However, qualified data scientists, great integration across departments, and the ability to manage expectations all play a part in making big data work for your organization. It is often said that an organization’s future is dependent on the decisions it takes. Since most of the business decisions are backed by data available at hand. The accurate the information, the better they are for the business. Gone are the days when data was only used as an aid in better decision making. But now, with big data, it has actually become a part of all business decisions. For quite some time now, big data has been changing the way business operations are managed, how they collect data and turn it into useful and accurate information in real-time. Today, let’s take a look at solving real-life enterprise problems with big data. Predictive Analysis Let’s assume that you have a solid knowledge of the emerging trends and technologies in the market or when your infrastructure needs good maintenance. With huge amounts of data, you can easily predict trends and your future needs for the business. This sort of knowledge gives you an edge over your peers in this competitive world. Enhancing Market Research Regardless of the business vertical, market research is an essential part of business operations. With the ever-changing needs and aspirations of your customers, businesses need to find ways to get into the mind of customers with better and improved products and services. In such scenarios, having large volumes of data in hand will let you carry out detailed market research and thus enhancing your products and services. Streamlining Business Process For any enterprise, streamlining the business process is a crucial link to keeping the business sustainable and lucrative. Some effective modifications here and there can benefit you in the long run by cutting down the operational costs. Big data can be utilized to overhaul your whole business process right from raw material procurement to maintaining the supply chain. Data Access Centralization It is an inevitable fact that the decentralized data has its own advantages and one of the main restrictions arises from the fact that it can build data silos. Large enterprises with global presence frequently encounter such challenges. Centralizing conventional data often posed a challenge and blocked the complete enterprise from working as one team. But big data has entirely solved this problem, offering visibility of the data throughout the organization. How are you navigating the implications of all that data within your enterprise? Have you deployed big data in your enterprise and solved real-life enterprise problems? Then we would love to know your experiences. Do let us by commenting in the section below.
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How Big Data Can Solve Enterprise Problems

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