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5 Best Data Processing Frameworks

“Big data Analytics” is a phrase that was coined to refer to amounts of datasets that are so large, traditional data processing software simply can’t manage them. For example, big data is used to pick out trends in economics, and those trends and patterns are used to predict what will happen in the future. These vast amounts of data require more robust computer software for processing, best handled by data processing frameworks. These are the top preferred data processing frameworks, suitable for meeting a variety of different needs of businesses. Hadoop This is an open-source batch processing framework that can be used for the distributed storage and processing of big data sets. Hadoop relies on computer clusters and modules that have been designed with the assumption that hardware will inevitably fail, and those failures should be automatically handled by the framework. There are four main modules within Hadoop. Hadoop Common is where the libraries and utilities needed by other Hadoop modules reside. The Hadoop Distributed File System (HDFS) is the distributed file system that stores the data. Hadoop YARN (Yet Another Resource Negotiator) is the resource management platform that manages the computing resources in clusters, and handles the scheduling of users’ applications. The Hadoop MapReduce involves the implementation of the MapReduce programming model for large-scale data processing. Hadoop operates by splitting files into large blocks of data and then distributing those datasets across the nodes in a cluster. It then transfers code into the nodes, for processing data in parallel. The idea of data locality, meaning that tasks are performed on the node that stores the data, allows the datasets to be processed more efficiently and more quickly. Hadoop can be used within a traditional onsite datacenter, as well as through the cloud. Apache Spark Apache Spark is a batch processing framework that has the capability of stream processing, as well, making it a hybrid framework. Spark is most notably easy to use, and it’s easy to write applications in Java, Scala, Python, and R. This open-source cluster-computing framework is ideal for machine-learning, but does require a cluster manager and a distributed storage system. Spark can be run on a single machine, with one executor for every CPU core. It can be used as a standalone framework, and you can also use it in conjunction with Hadoop or Apache Mesos, making it suitable for just about any business. Spark relies on a data structure known as the Resilient Distributed Dataset (RDD). This is a read-only multiset of data items that is distributed over the entire cluster of machines. RDDs operate as the working set for distributed programs, offering a restricted form of distributed shared memory. Spark is capable of accessing data sources like HDFS, Cassandra, HBase, and S3, for distributed storage. It also supports a pseudo-distributed local mode that can be used for development or testing. The foundation of Spark is Spark Core, which relies on the RDD-oriented functional style of programming to dispatch tasks, schedule, and handle basic I/O functionalities. Two restricted forms of shared variables are used: broadcast variables, which reference read-only data that has to be available for all the nodes, and accumulators, which can be used to program reductions. Other elements included in Spark Core are: Spark SQL, which provides domain-specific language used to manipulate DataFrames. Spark Streaming, which uses data in mini-batches for RDD transformations, allowing the same set of application code that is created for batch analytics to also be used for streaming analytics. Spark MLlib, a machine-learning library that makes the large-scale machine learning pipelines simpler. GraphX, which is the distributed graph processing framework at the top of Apache Spark. Apache Storm This is another open-source framework, but one that provides distributed, real-time stream processing. Storm is mostly written in Clojure, and can be used with any programming language. The application is designed as a topology, with the shape of a Directed Acyclic Graph (DAG). Spouts and bolts act as the vertices of the graph. The idea behind Storm is to define small, discrete operations, and then compose those operations into a topology, which acts as a pipeline to transform data. Within Storm, streams are defined as unbounded data that continuously arrives at the system. Sprouts are sources of data streams that are at the edge of the topology, while bolts represent the processing aspect, applying an operation to those data streams. The streams on the edges of the graph direct data from one node to another. These bolts and sprouts define sources of information and allow batch, distributed processing of streaming data, in real-time. Samza Samza is another open-source framework that offers near a real-time, asynchronous framework for distributed stream processing. More specifically, Samza handles immutable streams, meaning transformations create new streams that will be consumed by other components without any effect on the initial stream. This framework works in conjunction with other frameworks, using Apache Kafka for messaging and Hadoop YARN for fault tolerance, security, and management of resources. Samza uses the semantics of Kafka to define how it handles streams. Topic refers to each stream of data that enters a Kafka system. Brokers are the individual nodes that are combined to make a Kafka cluster. A producer is any component that writes to a Kafka topic, and a consumer is any component that reads from a Kafka topic. Partitions are used to divide incoming messages in order to distribute a topic among the different nodes. Flink Flink is a hybrid framework, open-source, and stream processes, but can also manage batch tasks. It uses a high-throughput, low-latency streaming engine that is written in Java and Scala, and the runtime system that is pipelined allows for the execution of both batch and stream processing programs. The runtime also supports the execution of iterative algorithms natively. Flink’s applications are all fault-tolerant and can support exactly-once semantics. Programs can be written in Java, Scala, Python, and SQL, and Flink offers support for event-time processing and state management. The components of the stream processing model in Flink include streams, operators, sources, and sinks. Streams are immutable, unbounded datasets that go through the system. Operators are functions that are used on data streams to create other streams. Sources are the entry points for streams that enter into the system. Sinks are places where streams flow out of the Flink system, either into a database or into a connection to another system. Flink’s batch processing system is really just an extension of the stream processing model. Flink does not provide its own storage system, however, so that means you will have to use it in conjunction with another framework. That should not be a problem, as Flink is able to work with many other frameworks. Data processing frameworks are not intended to be one-size-fits-all solutions for businesses. Hadoop was originally designed for massive scalability, while Spark is better with machine learning and stream processing. A good IT services consultant can evaluate your needs and offer advice. What works for one business may not work for another, and to get the best possible results, you may find that it’s a good idea to use different frameworks for different parts of your data processing.
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5 Best Data Processing Frameworks

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5 Best Data Processing Frameworks

Big data Analytics” is a phrase that was coined to refer to amounts of datasets that are so large, traditional data processing software simply can’t manage them. For example, big data is used to pick out trends in economics, and those trends and patterns are used to predict what will happen in the future. These vast amounts of data require more robust computer software for processing, best handled by data processing frameworks.

These are the top preferred data processing frameworks, suitable for meeting a variety of different needs of businesses.

Hadoop

This is an open-source batch processing framework that can be used for the distributed storage and processing of big data sets. Hadoop relies on computer clusters and modules that have been designed with the assumption that hardware will inevitably fail, and those failures should be automatically handled by the framework.

There are four main modules within Hadoop. Hadoop Common is where the libraries and utilities needed by other Hadoop modules reside. The Hadoop Distributed File System (HDFS) is the distributed file system that stores the data. Hadoop YARN (Yet Another Resource Negotiator) is the resource management platform that manages the computing resources in clusters, and handles the scheduling of users’ applications. The Hadoop MapReduce involves the implementation of the MapReduce programming model for large-scale data processing.

Hadoop operates by splitting files into large blocks of data and then distributing those datasets across the nodes in a cluster. It then transfers code into the nodes, for processing data in parallel. The idea of data locality, meaning that tasks are performed on the node that stores the data, allows the datasets to be processed more efficiently and more quickly. Hadoop can be used within a traditional onsite datacenter, as well as through the cloud.

Apache Spark

Apache Spark is a batch processing framework that has the capability of stream processing, as well, making it a hybrid framework. Spark is most notably easy to use, and it’s easy to write applications in Java, Scala, Python, and R. This open-source cluster-computing framework is ideal for machine-learning, but does require a cluster manager and a distributed storage system. Spark can be run on a single machine, with one executor for every CPU core. It can be used as a standalone framework, and you can also use it in conjunction with Hadoop or Apache Mesos, making it suitable for just about any business.

Spark relies on a data structure known as the Resilient Distributed Dataset (RDD). This is a read-only multiset of data items that is distributed over the entire cluster of machines. RDDs operate as the working set for distributed programs, offering a restricted form of distributed shared memory. Spark is capable of accessing data sources like HDFS, Cassandra, HBase, and S3, for distributed storage. It also supports a pseudo-distributed local mode that can be used for development or testing.

The foundation of Spark is Spark Core, which relies on the RDD-oriented functional style of programming to dispatch tasks, schedule, and handle basic I/O functionalities. Two restricted forms of shared variables are used: broadcast variables, which reference read-only data that has to be available for all the nodes, and accumulators, which can be used to program reductions. Other elements included in Spark Core are:

  • Spark SQL, which provides domain-specific language used to manipulate DataFrames.
  • Spark Streaming, which uses data in mini-batches for RDD transformations, allowing the same set of application code that is created for batch analytics to also be used for streaming analytics.
  • Spark MLlib, a machine-learning library that makes the large-scale machine learning pipelines simpler.
  • GraphX, which is the distributed graph processing framework at the top of Apache Spark.

Apache Storm

This is another open-source framework, but one that provides distributed, real-time stream processing. Storm is mostly written in Clojure, and can be used with any programming language. The application is designed as a topology, with the shape of a Directed Acyclic Graph (DAG). Spouts and bolts act as the vertices of the graph. The idea behind Storm is to define small, discrete operations, and then compose those operations into a topology, which acts as a pipeline to transform data.

Within Storm, streams are defined as unbounded data that continuously arrives at the system. Sprouts are sources of data streams that are at the edge of the topology, while bolts represent the processing aspect, applying an operation to those data streams. The streams on the edges of the graph direct data from one node to another. These bolts and sprouts define sources of information and allow batch, distributed processing of streaming data, in real-time.

Samza

Samza is another open-source framework that offers near a real-time, asynchronous framework for distributed stream processing. More specifically, Samza handles immutable streams, meaning transformations create new streams that will be consumed by other components without any effect on the initial stream. This framework works in conjunction with other frameworks, using Apache Kafka for messaging and Hadoop YARN for fault tolerance, security, and management of resources.

Samza uses the semantics of Kafka to define how it handles streams. Topic refers to each stream of data that enters a Kafka system. Brokers are the individual nodes that are combined to make a Kafka cluster. A producer is any component that writes to a Kafka topic, and a consumer is any component that reads from a Kafka topic. Partitions are used to divide incoming messages in order to distribute a topic among the different nodes.

Flink

Flink is a hybrid framework, open-source, and stream processes, but can also manage batch tasks. It uses a high-throughput, low-latency streaming engine that is written in Java and Scala, and the runtime system that is pipelined allows for the execution of both batch and stream processing programs. The runtime also supports the execution of iterative algorithms natively. Flink’s applications are all fault-tolerant and can support exactly-once semantics. Programs can be written in Java, Scala, Python, and SQL, and Flink offers support for event-time processing and state management.

The components of the stream processing model in Flink include streams, operators, sources, and sinks. Streams are immutable, unbounded datasets that go through the system. Operators are functions that are used on data streams to create other streams. Sources are the entry points for streams that enter into the system. Sinks are places where streams flow out of the Flink system, either into a database or into a connection to another system. Flink’s batch processing system is really just an extension of the stream processing model.

Flink does not provide its own storage system, however, so that means you will have to use it in conjunction with another framework. That should not be a problem, as Flink is able to work with many other frameworks.

Data processing frameworks are not intended to be one-size-fits-all solutions for businesses. Hadoop was originally designed for massive scalability, while Spark is better with machine learning and stream processing. A good IT services consultant can evaluate your needs and offer advice. What works for one business may not work for another, and to get the best possible results, you may find that it’s a good idea to use different frameworks for different parts of your data processing.

Blake

Blake Davies

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Blake Davies is an IT specialist and a growth hacker. He often writes on topics of IT services support and general implications of IT in business. He's been in the industry for over five years.

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3 comments

Shivangi 28 May 2018

Great presentation of Python form of blog and Python tutorial. Very helpful for beginners like us to understand Python course. if you're interested to have an insight on Python training do watch this amazing tutorial. https://www.youtube.com/watch?v=YJ8xP33nTOA

priya 10 Jul 2018

There is no doubt that Java is one of the programming languages that is most in demand these days.

Datainox Services 10 Jan 2019

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Why a Career in Big Data Is the Right Choice for You?

Are you in that job market where the Big Data skills are more appreciated? Confused about whether to make a career shift in Big Data or not? What will be the next career options available for me after Big Data? Just spend some time reading this blog and know the answers to all these questions and the reasons for making Big Data as a career choice.  “Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming.” – Chris LynchReasons to Must-Have Big Data in your career1. Increased Job Opportunities for Big Data professionalsWith the technology reaching greater heights, undoubtedly Big Data is becoming a buzz word and a growing need for the organizations in the upcoming years. But, as Jeanne Harris, a senior executive at Accenture Institute said- “Data is useless without the skill to analyze it.”Today, Big Data professionals have a soaring demand across organizations worldwide. Organizations are making huge use of Big Data to stay ahead of the competitive market. The candidates with Big Data skills and expertise are in high demand. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.2. Salary GrowthThe strong demand for Big Data professionals is affecting the wages for qualified professionals. According to Glassdoor, the salary provided by various organizations based on the employees working in these organizations in the US region are as follows:CompanySalaryJ.P. Morgan$93K – $100KCognizant Technology Solutions$92K – $98KCSAA Insurance Group$133K – $144KZipRecruiter$81K – $89KThe salary of Big Data professionals is directly proportional to the factors like the skills earned, education, experience in the domain, knowledge of technology, etc. Also, one needs to understand and solve the real-world Big Data problems and a good grasp of tools and technologies.   3. Massive Big Data adoptionForbes stated that- Big data adoption in enterprises is increased from 17% in 2015 to 59% in 2018, reaching a Compound Annual Growth Rate (CAGR) of 36%. Big Data is steadily spreading its wings across numerous sectors including sales, marketing, research and development, logistics,  strategic management, etc.According to the 'Peer Research – Big Data Analytics' survey by Intel, the decision has incurred that- Big Data is one of the top priorities of the enterprises taking part in the survey as they believe that it improves the performance of their organizations. From the survey, it is found that 45% of the respondents trust that Big Data will offer more business benefits to rank on the top of the Big data market.    “Bigiota Insight out forecasted that the Big Data market is expected to grow to $80 billion from current $40 billion making a revenue of $187 billion.”4. Various options in job titles and responsibilitiesBig Data professionals have an array of job titles open depending on the skills they have achieved so far. The options for the Big Data job aspirants are many where they are free to align their career paths based on their career interests. Some of the job roles Big Data professionals can play are as follows:Data EngineerBusiness Analyst,Visualization SpecialistMachine Learning ExpertAnalytics ConsultantSolution ArchitectBig Data Solution ArchitectBig Data Analyst5. Usage Across numerous firms/industriesToday, Big Data is used almost in every firm. The top 5 industries recruiting Big Data professionals widely are Professional, Scientific and Technical Services (27%), Information Technology (19%), Manufacturing (15%), Finance and Insurance (9%), Retail Trade (9%) and Others 21%.The career path of a Big Data professionalAlthough the term Big Data is used commonly nowadays, there are many career paths available for the Big Data professionals to stand out in the industries that can be explored as per one’s potentiality and interest. The career paths that Big Data professionals can play are:Data ScientistBig Data EngineerBig Data AnalystData Visualization DeveloperMachine Learning EngineerBusiness Intelligence EngineerBusiness Analytics SpecialistMachine Learning ScientistLet us see them in details:1. Data Scientist:This is the most sought-after career path in Big Data careers. The Data Scientists are the individuals who use their technical and analytical skills to extract meaning from data. They are responsible for collecting, cleaning, and manipulating data.2. Big Data Engineer:Big Data Engineer is a well-known and more demanding career option. Data Engineers are the professionals responsible for building the designs created by Solution Architects. They are responsible for developing, testing, managing, and maintaining the big data solutions in the enterprises.3. Big Data Analyst:Being a command on the big data technologies like Hadoop, Hive, Pig, etc. and analytics skills, Data Analyst finds out relevant information from the datasets. This is also most demanding in Big Data career.4. Data Visualization Developer:The data visualization developers have the responsibilities of designing, conceptualizing, developing the graphics or data visualization, and supporting the data visualization activities. They should have strong technical skills for implementing visualization using tools.5. Machine Learning Engineer:Today, Machine Learning has become a crucial part of Big Data. Being an expert in machine learning (Machine Learning Engineer) responsible for building the data analysis software to run the product code without human intervention.6. Business Intelligence Engineer:Business Intelligence Engineer is in more demand today as around 90 percent of IT professionals are planning to increase spending on BI tools, as stated in the Forbes report. BI engineers are responsible for managing the big data warehouses with the help of Big Data tools and solving complex issues related to Big Data.7. Business Analytics Specialist:Business Analytics Specialist is an expert in Business Analytics field who aids in developing the scripts to test scripts and carrying out testing. They are also responsible for taking up business research activities to analyze the issues for developing cost-effective solutions.8. Machine Learning Scientist:Machine Learning Scientist work most probably in the research and development department. They are responsible for developing the algorithms to use in adaptive systems, adding product suggestions, and forecasting the demand for the same.Conclusion:As per Entrepreneur, Businesses that use Big Data saw a profit increase from 8 to10 percent and almost 10% reduction in overall cost. Another survey from Forbes states that IBM predicts demand For Data Scientists will reach 28% by the year 2020. As the data pours in, many high-rated companies like Google, Apple, NetApp, Qualcomm, Intuit, FactSet, The MITRE Corporation, Adobe, Salesforce, and so on are investing in Big Data.   According to the most recent McKinsey report, companies based in the U.S. are seeking for hiring 1.5 million Managers and Data Analysts with the strong knowledge and experience in Big Data. One can attain the most in-demand Big Data skills by taking specialized training in Big Data to go for any of the Big Data careers available in the job market.With the rising demand that industries are witnessing, it is an ideal time to add Big data skills to your curriculum vitae and offer yourself the wings to fly in the job market with the ample of Big Data jobs available today!  
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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?
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What is Big Data — An Introductory Guide

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Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. Comparison Table: Apache Spark Vs. Apache StormSr. NoParameterApache SparkApache Storm1.Processing  ModelBatch ProcessingMicro-batch processing2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.3.Stream SourcesHDFSSpout4.MessagingAkka, NettyZeroMQ, Netty5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints7.Stream PrimitivesDStreamTuple, Partition8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.9.State ManagementSupports State ManagementSupports State Management as well10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.12.Throughput100k records per node per second10k records per node per second13.PersistenceMapStatePer RDD14.ProvisioningBasic monitoring using GangliaApache AmbariApache Spark: Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. Apache Storm:Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. Apache Spark Vs. Apache Storm1. Processing Model: Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. 2. Programming Language:Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.3. Stream Sources:For Storm, the source of stream processing is Spout, while that for Spark is HDFS. 4. Messaging:Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. 5. Resource Management:Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. 6. Low Latency: Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.7. Stream Primitives:Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). 8. Development Cost:It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  9. State Management: The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. 10. Message Delivery Guarantees (Handling the message level failures):Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. 11. Fault-Tolerant:Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.12. Ease of Development: In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. Summing Up: Apache Spark Vs Apache StormApache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 
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Apache Spark Vs Apache Storm - Head To Head Compar...

In today’s world, the need for real-time data st... Read More

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