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Types Of Big Data

Big Data is creating a revolution in the IT field, every year the use of analytics is increasing drastically every year. We are creating 2.5 quintillion bytes of data every day hence the field is expanding in B2C apps. Big Data has entered almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the Globe. Let us first discuss- “What is Big Data?” “Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search will show. The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis. Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data) Types of Big Data: Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are- Structured Unstructured Semi-structured 1. Structured data Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities. There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few. Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications. Let’s understand Structured data with an example. Top 3 players who have scored most runs in international T20 matches are as follows: Player Country Scores No of Matches played                Brendon McCullum New Zealand                                 2140                                           71                    Rohit Sharma India     2237          90 Virat Kohli  India      2167          65 2. Unstructured data While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually. Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology. Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data. Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on. The following image will clearly help you to understand what exactly Unstructured data is The Unstructured data is further divided into – Captured User-Generated data a. Captured data: It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output. b. User-generated data: It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc. 3. Semi-structured data: The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily. Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity. Diagram showing Semi-structured data Difference between Structured, Semi-structured and Unstructured data       Factors      Structured data       Semi-structured data     Unstructured data Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage .
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Types Of Big Data

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Types Of Big Data

Big Data is creating a revolution in the IT field, every year the use of analytics is increasing drastically every year. We are creating 2.5 quintillion bytes of data every day hence the field is expanding in B2C apps. Big Data has entered almost every industry today and is a dominant driving force behind the success of enterprises and organizations across the Globe.

Let us first discuss- “What is Big Data?”

“Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick google search will show.

The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big Data increases exponentially- more than 500 terabytes of data are uploaded to Facebook alone, in a single day- it represents a real problem in terms of analysis.

Before we jump into the article, let's have a visual introduction on what is Big data and its types. (Structured Data, Semi-Structured & Unstructured Data)

Types of Big Data:

Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are-

Types of Big Data:

  • Structured
  • Unstructured
  • Semi-structured

1. Structured data

Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for about 20% of the total existing data and is used the most in programming and computer-related activities.

There are two sources of structured data- machines and humans. All the data received from sensors, weblogs, and financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage statistics captured by servers and applications and the huge amount of data that usually move through trading platforms, to name a few.

Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be used by companies to figure out their customer behavior and make the appropriate decisions and modifications.

Let’s understand Structured data with an example.

Top 3 players who have scored most runs in international T20 matches are as follows:

Structured data

Player Country Scores No of Matches played               
Brendon McCullum New Zealand                                 2140                                           71                   
Rohit Sharma India     2237          90
Virat Kohli  India      2167          65


2. Unstructured data

While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the data a person encounters belong to this category- and until recently, there was not much to do to it except storing it or analyzing it manually.

Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various facets of technology.

Human-generated unstructured data is found in abundance across the internet since it includes social media data, mobile data, and website content. This means that the pictures we upload to Facebook or Instagram handle, the videos we watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data.

Examples of unstructured data include text, video, audio, mobile activity, social media activity, satellite imagery, surveillance imagery – the list goes on and on.

The following image will clearly help you to understand what exactly Unstructured data is

The Unstructured data is further divided into –

  • Captured
  • User-Generated data

a. Captured data:

It is the data based on the user’s behavior. The best example to understand it is GPS via smartphones which help the user each and every moment and provides a real-time output.

b. User-generated data:

It is the kind of unstructured data where the user itself will put data on the internet every movement. For example, Tweets and Re-tweets, Likes, Shares, Comments, on Youtube, Facebook, etc.

3. Semi-structured data:

The line between unstructured data and semi-structured data has always been unclear since most of the semi-structured data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data, but contains some organizational properties which make it easier to process, are included in semi-structured data. For example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to process the document easily.

Big Data analysis has been found to have definite business value, as its analysis and processing can help a company achieve cost reductions and dramatic growth. So it is imperative that you do not wait too long to exploit the potential of this excellent business opportunity.

Diagram showing Semi-structured data

Semi-structured data:

Difference between Structured, Semi-structured and Unstructured data

      Factors      Structured data       Semi-structured data     Unstructured data
Flexibility It is dependent and less flexible It is more flexible than structured data but less than flexible than unstructured data It is flexible in nature and there is an absence of a schema
Transaction Management Matured transaction and various concurrency technique The transaction is adapted from DBMS not matured No transaction management and no concurrency
Query performance Structured query allow complex joining Queries over anonymous nodes are possible An only textual query is possible
Technology It is based on the relational database table It is based on RDF and XML This is based on character and library data

Big data is indeed a revolution in the field of IT. The use of Data analytics is increasing every year. In spite of the demand, organizations are currently short of experts. To minimize this talent gap many training institutes are offering courses on Big data analytics which helps you to upgrade skills set needed to manage and analyze big data. If you are keen to take up data analytics as a career then taking up Big data training will be an added advantage
.

KnowledgeHut

KnowledgeHut

Author

KnowledgeHut is a fast growing Management Consulting and Training firm that is a source of Intelligent Information support for businesses and professionals across the globe.


Website : http://www.knowledgehut.com/

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2 comments

Tony 20 Apr 2017

Hi, Thanks for sharing the information. These information will really help us a lot.

Rebheka 31 May 2018

This data is useful

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This engine has been developed in Spark, mixes MLLib and own implementations, and is currently into production serving more than 5M customers daily.The challenges that the BBVA technology team faced while building this ML were many:They did not know the data source in advanceThey did not have a labelled setA fraction of texts is useless (detection rather than classification)Distribution of categories is imbalancedPrefer false negatives over false positivesVery short text, language not even syntactically correctThe engineers solved these problems using the Spark MLlib pipeline using some other NLP tools like word2vec.TF-IDF features + linear classifier (98% precision, 21% recallFurther tests with word2vec + Vector of Locally Aggregated Descriptors (VLAD)Implemented in Spark/Scala, using MLlib classesOwn classes implemented for Multi-class Logistic Regression, VLADScala dependency injection useful to quickly setup variants of the above stepsHealthCare:Healthcare industry is the newest in adopting advanced technologies like big data and machine learning to provide hi-tech facilities to their patients. Apache Spark is penetrating fast and is becoming the heartbeat in the latest Healthcare applications. Hospitals use these Spark enabled healthcare applications to analyze patients medical history to identify possible health issues based on history and learning.Also, healthcare produces massive amounts of data and to process so much of the data in quick time and provide insights based on that itself was a challenge which Spark solves with ease.Another very interesting problem in hospitals is when working with Operating Room(OR) scheduling within a hospital setting is that it is difficult to schedule and predict available OR block times. This leads to empty and unused operating rooms leading to longer waiting times for patients for their procedures.Let’s see a use case. For a basic surgical procedure, it costs around $15-20 per minute. So, OR is a scarce and valuable resource and it needs to be utilized carefully and optimally. OR efficiency differs depending on the OR staffing and allocation, not the workload. So the loss of efficiency means a loss for the patient. So time and management are the utmost importance here.Spark and MLlib solve the problem by developing a predictive model that would identify available OR time 2 weeks in advance, allows hospitals to confirm waitlist cases two weeks in advance instead of when blocks normally release 4 days out. This OR Scheduling can be done by getting the historical data and running then linear regression model with multiple variables.This model works because:Can coordinate waitlist scheduling logistics with physicians and patients within 2 weeks of surgery.Plan staff scheduling and resources so there are less last-minutes staffing issues for nursing and anaesthesiaUtilization metrics show where elective surgical schedule and level demand can be maximized.Retail: Big retail chains have this usual problem of optimising their supply chain to minimize cost and wastage, improve customer service and gain insights into customer’s shopping behaviour to serve better and in the process optimize their profit.To achieve these goals these retail companies have a lot of challenges like to keep the inventory up to date based on sales and also to predict sales and inventory during some promotional events and sale seasons. Also, they need to keep a track on customer’s orders transit and delivery. All these pose huge technical challenges. Apache Spark and MLlib is being used by a lot of these companies to capture real-time sales and invoice data, ingest it and then figure out the inventory. The technology can also be used to identify in real-time the order’s transit and delivery status. Spark MLlib analytics and predictive models are being used to predict sales during promotions and sale seasons to match the inventory and be ready for the event. The historical data on customer’s buying behaviour is also used to provide the customer with personalized suggestions and improve customer satisfaction. A lot of stores have started using sensors to get data on customer’s location within the store, their preferences, shopping behaviour, etc to provide on-the-spot suggestions and help to find, buy a product by sending messages, using displays etc.Travel: Airline customer segmentation is a challenging field to understand due to customer’s complex behaviour. Amadeus is one of the main IT solution providers in the airline industry. It has the resources and infrastructure to manage all the ticketing and booking data as well as understanding the Airline needs and market particularities. By combining different data sources produced by different airline systems, they have applied unsupervised machine learning techniques to improve our understanding of customer behaviour.Challenges in the airline industry are to understand the health of the business:Are any segments growing or shrinkingHow is the yield developingTune marketing to specific interests within segmentsOptimize product offers using fare structures and media offersTraditional approaches for segmentation were based on business intuition and manually crafter rules set. But these approaches have limitations and prejudices which can sometimes be negative for the business. On the contrary, the data-driven approach is resilient against turn-over, prejudices and market change.With a data-driven approach and using Spark and MLlib, the model is able to extract actionable insights on typical customer behaviour and intentions. Supervised and supervised learning using Spark MLlib techniques at scale are used to train models for prediction. These are then used to assist the customer in deploying the newfound insights into day-to-day operations.Media: Media companies Netflix, Hotstar etc are using Apache Spark at the heart of their technology engine to drive their business. When a user turns on Netflix, he is able to see his favourite content playing automatically. This is achieved through recommendation engines built on Machine learning algorithms and Spark MLlib. Netflix uses historical data from users content selection, trains its ML algorithms, tests it offline and then deploys it live and checks if it works in Production as well.Netflix has built an engine something called Time Travel using Apache Spark and other big data technologies to: Snapshot online services and use the snapshot data offline to generate features and share facts and features between experiments without calling live systems.If someone is interested in exploring the details of the use case, one can look at the below link:Energy: Apache Spark is spreading its roots everywhere. A common man not related to software industry may not realise it but there are applications running or extracting data from his home environment and processed in Spark to make his life better and easier. An example we will discuss below is the British Gas.British Gas is a 200-year-old company. Connected Homes is BG’s IoT “startup”. It is a leader in the UK’s connected home market. Connected Homes is trying to predict the usage consumption patterns of the electricity, gas at the homes and provide consumers with insights so they can smartly use their devices and reduce energy consumption and save energy and money. Connected homes use Apache Spark at the core of its Data Engineering and ML engine.The challenges are there are millions of electric and gas meters and the meters are read every 30 minutes.There are:Gas and electricity meter readingsThermostat temperature dataConnected boiler dataReal-time energy consumption dataIntroducing motion sensors, window and door sensors, etcApache Spark MLlib is used to apply machine learning to these data for disaggregation, similar home comparison and smart meters used in indirect algorithms for non-smart customers.The analytics engine is used to show customers how they have spent energy, what are their top 3 spends, how can they reduce their energy consumption by showing patterns from smart consumers and smart meters etc. This gives customers a lot of insight and educates customers on optimally using energy at their homes.Gaming:Online Gaming industry is another beneficiary of the Apache Spark technology.Riot Games uses Spark for Combating abusive language in chat in the team games. The challenges in online gaming are:1% of all players are consistently unsportsmanlike2% of all games infected by serious toxicityIn-Game Toxicity 95% of all serious toxicity comes from players who are otherwise sportsmanlikeTo solve this the game developers tried to predict the words used by the gamers in the context of the game or the scenario. They used the “Word2Vec” a neural model which has 256 dimensions embedding months of chat logs. Each word in the chat is document split in spaces and lowercase. The model was trained on NLP for acronyms, short forms, colloquial words etc and the deviations could be huge. The team built a model trained to predict bad/toxic language. The gaming company has 100+ million users every month and so the data is huge. They used Spark MLlib to train their models using different algorithms, one of them Logistic Regression Random Forest Gradient Boosted Trees. The results were impressive for them as they tuned their models for better precision.Benefits of having Apache Spark for Individual companiesMany of the companies across industries have been benefiting from Apache Spark. The reasons could be different:Speed of executionMulti-language supportMachine learning libraryGraph processing libraryBatch processing as well as Stream & Structured stream processingApache Spark is beneficial for small as well as large enterprise. Spark offers a complete solution to many of the common problems like ETL and warehousing, Stream data processing, common use case of supervised and unsupervised learning for data analytics and predictive modelling. So with Apache Spark, the technology team does not require to look out for different technology stack and multiple vendors for a solution. This reduces the learning curve for additional development and maintenance. Also, since Spark has support for multiple languages Scala, Java, Python & R, it is easy to find developers.Limitations:Though there are so many benefits of Apache Spark as we have seen above, there are few limitations which Apache Spark has. We should be aware of these limitations before we decide to adopt any technology.Apache Spark does not come with an inbuilt file system and it has to depend on HDFS in most of the use cases. If not, it has to be used with some cloud-based data platform.Even though Spark has Stream processing feature it is not exactly real-time processing. It processes in batches which are called micro-batches.Apache Spark is expensive as it catches a lot of data and memory is not cheap.Spark faces issues while working with HDFS which has a very large number of small files.Though Spark MLlib provides machine learning capabilities, it does not come with a very exhaustive list of algorithms. It can solve a lot of ML problems but not all.Apache Spark does not have automatic code optimization process in place and so the code needs to be optimized manually.Reasons why you should learn Apache SparkWe have seen the wide impact and use cases if Apache Spark. So we know that Spark has become a buzzword these days. We should now also understand why we should learn Spark.Spark offers a complete package for developers and can act as a unified analytics engine. Hence it increases the productivity for the developers. So it ROI for any firm is high and most of the companies dependent on technology are aware of the fact and also willing to put their money in Spark.Learning Spark can help explore the world of Big data and data science. Both these technology fields are the future and bringing transformational changes in almost all industries. So getting exposed to Spark is becoming a necessity for all firms.With fast-paced Spark adoption by organizations, it is opening up new prospects in business and many of the applications have proved that instead of business driving technology it is becoming vice versa now, that technology is driving business.According to a survey, there is a huge demand for Spark engineers. Today, there are well over 1,000 contributors to the Apache Spark project across 250+ companies worldwide. Recently, Indeed.com listed over 2,400 full-time open positions for Apache Spark professionals across various industries including enterprise technology, e-commerce/retail, healthcare, and life sciences, oil and gas, manufacturing, and more.Apache Spark developers earn the highest average salary among all other programmers. So this is another and one of the major incentives one can get to learn and expertise Spark.One can look at an old survey by Databricks to understand the importance and impact of Apache Spark by 2016. By 2019 these number would have grown much bigger.ConclusionApache Spark has capabilities to process huge amount of data in a very efficient manner with high throughput. It can solve problems related to batch processing, near real-time processing, can be used to apply lambda architecture, can be used for Structured streaming. Also, it can solve many of the complex data analytics and predictive analytics problems with the help of the MLlib component which comes out of the box. Apache Spark has been making a big impact on the whole data engineering and data science gamut at scale.
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Apache Spark Use Cases & Applications
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Apache Spark Use Cases & Applications

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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 VThis 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?
Rated 4.5/5 based on 11 customer reviews
What is Big Data — An Introductory Guide
Blogs
6632
What is Big Data — An Introductory Guide

The massive world of Big DataIf one strolls around... Read More