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The ABC Basics of Apache Spark

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The ABC Basics of Apache Spark

Amazon, Yahoo and eBay has embraced Apache Spark. It’s a technology worth taking a note of. A bulk of organizations prefers running Spark on clusters along with thousands of nodes. Till date, the biggest known cluster consists of more than 8000 nodes.

Introducing Apache Spark

Spark is basically an Apache project tagged as ‘lightning fast cluster computing’. It features a robust open-source community and is the most popular Apache project right now.

Spark is equipped with a faster and better data processing platform. It runs programs faster in memory as well as on disk as compared to Hadoop. Furthermore, Spark lets users write code as quickly as possible – after all, you’ve more than 80 high-level operators for coding!

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Key elements of Spark are:

  • It offers APIs in Java, Scala and Python in support with other languages
  • Seamlessly integrates with Hadoop ecosystem and other data sources
  • It runs on clusters controlled by Apache Mesos and Hadoop YARN

Spark core

Ideal for wide-scale parallel and distributed data processing, Spark Core is responsible for:

  • Communicating with storage systems
  • Memory management and fault recovery
  • Arranging, assigning and monitoring jobs present in a cluster

The nuanced concept of RDD (Resilient Distributed Dataset) was first initiated by Spark. An RDD is an unyielding, fault-tolerant versatile collection of objects that are easily operational in parallel. It can include any kind of object, and supports mainly two kinds of operations:

  • Transformations
  • Actions

Spark SQL

A major Spark component, SparkSQL queries data either through SQL or through Hive Query Language. It first came into operations as an Apache Hive port to run on top of Spark, replacing MapReduce, but now it’s being integrated with Spark Stack. Along with providing support to numerous data sources, it also fabricates several SQL queries with code transformations, which makes it a very strong and widely-recognized tool.

Spark Streaming:

Ideal for real time processing of streaming data – Spark Streaming receives input data streams, which is then divided into batches only to be processed by Spark engine to unleash final stream of results, all in batches.

Look at the picture below:

The Spark Streaming API resembles Spark Core – as a result, it becomes easier for programmers to tackle for batch and streaming data, effortlessly.

MLib

MLib is a versatile machine learning library that comprises of numerous fetching algorithms that are designed to scale out on a cluster for regression, classification, clustering, collaborative filtering and more. In fact, some of these algorithms specialize in streaming data, such as linear regression using ordinary least squares or k-means clustering.

GraphX

An exhaustive library for fudging graphs and performing graph-parallel operations, GraphX is the most potent tool for ETL and other graphic computations.

Want to learn more on Apache Spark? Spark Training Course in Gurgaon fits the bill. No wonder, Spark simplifies the intensive job of processing high levels of real-time or archived data effortlessly integrating associated advanced capabilities, such as machine learning – hence Apache Spark Certification Training can help you process data faster and efficiently.

 
The blog has been sourced from ― www.toptal.com/spark/introduction-to-apache-spark
 

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The Success Story of Big Data Tooling

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The Success Story of Big Data Tooling

The world of hadoop data tooling is flourishing. It’s being said, Hadoop is shifting from possible data warehousing to an accomplished big data analytics set-up.

Back in the day, right after Hadoop at Yahoo was first invented, proponents of big data asserted its potential for substituting enterprise data warehouses, framed on business intelligence.

Open source Hadoop data tooling became a preferred choice more as an alternative to those insanely expensive existing systems – as a result, over time, the focus shifted to expanding existing data warehouses and more. Intricate Hadoop applications today are known as data lakes and of late big data tooling is found swelling beyond meager data warehouses.

“We are seeing increasing capabilities on the Hadoop and open source side to take over more and more of the corporation’s data and workloads, including BI,” said Mike Matchett, an analyst and founder of the Small World Big Data consultancy.

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Self Service and Big Data

In August, Cloudera launched Workload XM management services designed exclusively for cloud-based analytics. Alternatively, the company built a hybrid Cloudera Data Warehouse and a Cloudera Altus Data Warehouse, capable of running over both Microsoft Azure clouds and AWS.

The main objective of management services is to bring forth some visibility into various data workloads. Workload XM is constructed to aid administrators in presenting reliable service-level agreements for self-service analytics applications – says Anupam Singh, GM of Analytics at Cloudera, Palo Alto, Calif.

Importantly, Singh also mentioned that the cloud warehouse offers encryption for data both at still and in motion, and provides a better view into the trajectory of data sets in analytics workloads. Such potentials have gained momentum and recognition as well as GDPR and other programs.

However, all these discussions boil down to one point, which is how to increase the use of big data analytics. “Customers don’t look at buzzwords like Hadoop and cloud. But they do want more business units to access the data,” he added.

Data on the Wheels

Hadoop player, Hortonworks is a Cloud aficionado. In June, the company broadened its Google Cloud existence with Google Cloud Storage support. Enhancing real-time data analytics and management is a priority.

Meanwhile, in August, Hortonworks churned out Streams Messaging Manager (SMM) with an objective of handling data streaming and provide administrators comprehensive views into Kafka messaging clusters. They have increasingly become popular amongst big data pipelines.

These management tools are crucial for moving Hadoop-inspired big data analytics into production capacities, where in data warehouses fails performing – thus, recommendation engines and fraud detection appears to be a saving grace!

Meanwhile, Kafka-related capabilities in SMM are going on getting advanced and with recently released Hortonworks DataFlow 3.2, the performance for data streaming amplified.

R Adaptability

Similar to its competitors, MapR has bolstered its capabilities beyond its original scope of being used as a mere data warehouse replacement. Early this year, the organizers released a new version of its MapR Data Platform equipped with better streaming data analytics and new item data services that would easily work on cloud as well as premises.

As final thoughts, the horizon of Hadoop is expanding, while data tooling keeps modifying. However, today, unlike before, Hadoop is not only the sole choice for doing data analytics – the choice includes Apache Spark and Machine Learning. All being extremely superior and effective when put to use.

If you are looking for Apache Spark Certification, drop by DexLab Analytics. Their Apache Spark Training program is extremely well-crafted and in sync with industry demands. For more, visit the site.

 

The article has been sourced from — searchdatamanagement.techtarget.com/news/252448331/Big-data-tooling-rolls-with-the-changing-seas-of-analytics

 

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An ABC of Apache Spark Streaming

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Estimator Procedure under Simple Random Sampling: EXPLAINED

Apache Spark has become one the most popular technologies. It is accompanied with a powerful streaming library, which has quite a few advantages over other technologies. The integration of Spark streaming APIs with Spark core APIs provides a dual purpose real-time and batch analytical platform. Spark Streaming can also be combined with SparkSQL, SparkML and GraphX when complex cases need to be handled. Famous organizations that prevalently use Spark Streaming are Netflix, Uber and Pinterest. Spark Streaming’s fame in the world of data analytics can be attributed to its fault tolerance, ability to process live streams, scalability and high throughput.

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Need for Streaming Analytics:

Companies generate enormous amounts of data on a daily basis. Transactions happening over the internet, social network platforms, IoT devices, etc. generate large volumes of data that need to be leveraged in real-time. And this process shall gain more important in future. Entrepreneurs consider real-time data analysis as a great opportunity to scale up their businesses.

Spark streaming intakes live data streams, Spark engine processes and divides it and the output is in the form of batches.

Architecture of Spark Streaming:

Spark streaming breaks the data stream into micro batches (known as discretize stream processing). First of all, the receivers accept data in parallel and hold it in worker nodes as buffer. Then the engine runs brief tasks and sends the result to other systems.

Spark tasks are allocated to workers dynamically, that depends on the resources available and the locality of data. The advantages of Spark Streaming are many, including better load balancing and speedy fault recovery. Resilient distributed dataset (RDD) is the basic concept behind fault tolerant datasets.

Useful features of Spark streaming:

Easy to use: Spark streaming supports Java, Scala and Python and uses the language integrated API of Apache Spark for stream processing. Stream jobs can be written in a similar manner in which batch jobs are written.

Spark Integration: Since Spark streaming runs on Spark, it can be utilized for addressing unplanned queries and reusing similar codes. Robust interactive applications can also be designed.

Fault tolerance: Work that has been lost can be recovered without additional coding from the developer.

Benefits of discretized stream processing:

Load balancing: In Spark streaming, the job load is balanced across workers. While, some workers handle more time-consuming tasks, others process tasks that take less time. This is an improvement from traditional approaches where one task is processed at a time. This is because if the task is time-taking then it behaves like a bottle neck and delays the whole pipeline.

Fast recovery: In many cases of node failures, the failed operators need to be restarted on different nodes. Recomputing lost information involves rerunning a portion of the data stream. So, the pipeline gets halted until the new node catches up after the rerun. But in Spark, things work differently. Failed tasks can be restarted in parallel and the recomputations are distributed across different nodes evenly. Hence, recovery is much faster.

Spark streaming use cases:

Uber: Uber collects gigantic amounts of unstructured data from mobile users on a daily basis. This is converted to structured data and sent for real time telemetry analysis. This data is analyzed in an ETL pipeline build using Spark streaming, Kafka and HDFS.

Pinterest: To understand how Pinterest users are engaging with pins globally, it uses an ETL data pipeline to provide information to Spark through Spark streaming. Hence, Pinterest aces the game of showing related pins to people and providing relevant recommendations.

Netflix: Netflix relies on Spark streaming and Kafka to provide real-time movie recommendations to users.

Apache foundation has been inaugurating new techs, such as Spark and Hadoop. For performing real-time analytics, Spark streaming is undoubtedly one of the best options.

As businesses are swiftly embracing Apache Spark with all its perks, you as a professional might be wondering how to gain proficiency in this promising tech. DexLab Analytics, one of the leading Apache Spark training institutes in Gurgaon, offers expert guidance that is sure to make you industry-ready. To know more about Apache Spark certification courses, visit Dexlab’s website.

This article has been sources from: https://intellipaat.com/blog/a-guide-to-apache-spark-streaming-tutorial

 

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Hadoop or Spark: Which Big Data Framework to Choose?

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Hadoop or Spark: Which Big Data Framework to Choose?

Feeling confused?

Of late, Spark has overtaken Hadoop for being the most active open source big data project. Though they have their differences, they both have many common uses.

To begin, they both are incredible big data frameworks. For some years, Hadoop has been leading the open source big data framework clusters but recently highly advanced Spark tends to have captured the market. The latter has become increasingly popular and for all the right reasons. But that is not to say, Hadoop is losing its significance entirely.

They don’t perform exactly the similar tasks. Neither are they mutually exclusive. Though it’s been heard that Spark can work 100X faster than Hadoop in some scenarios, it doesn’t come with its own distributed storage system, which is quite fundamental to big data projects. Distributed storage offers elaborate multi-petabyte dataset storage solution across almost infinite number of computer hard drives. As compared to expensive machinery customization which holds everything in one device, distributed system is cheap as well as scalable, which means as many devices can be added if the network of data set ever grows.

Moreover, Spark doesn’t have its own file system; it cannot organize files in a distributed way without help from third party. This is the reason why several companies think of installing Spark after Hadoop, so that superior analytical applications of Spark can employ data stored using HDFS.

So, what makes Spark win over Hadoop? It’s the SPEED. Spark is a champion of handling a large chunk of its operations ‘in memory’- this reduces a lot of time and effort, indeed. Thanks to MapReduce!

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MapReduce writes of the data right to its physical storage medium after each activity. The main purpose of this was to ensure a fully recovery if something goes wrong – nevertheless, Spark organizes data in Resilient Distributed Datasets, where data can be easily recovered following failure or any kind of mishap.

The main driving factor behind growth of Spark lies in its adept functionality for tackling advanced data processing tasks, including machine learning and real-time stream processing. Real-time processing stands for feeding data into analytical applications the moment it’s seized, and insights are right away directed back to the users through a dashboard to inspire action. This kind of processing is nowadays very much used in big data, thus making Spark enjoy an upper hand against its Hadoop counterpart.

The technology of machine learning is right at the kernel of digital revolution – artificial intelligence and creating far-fetched algorithms is an area of analytics Spark excels at. Its speed and the sound capability to tackle streaming data are the reasons behind. Spark has its own machine learning libraries, known as MLib, while Hadoop needs to collaborate with third-party machine learning library, for example Apache Mahout.

As closing thoughts, though it appears that the two big data frameworks are stiff competitors of each other, yet this is really not the case in the reality. The corporate uses offers both the application services, letting the buyer decide which one they prefer to pick, subject to their functionality and need.

DexLab Analytics Presents #BigDataIngestion

DexLab Analytics Presents #BigDataIngestion

 

The good news is that DexLab offers both Hadoop and Apache Spark Certification Training. What’s more, a recent admission drive is ongoing #BigDataIngestion. Enroll now and enjoy 10% discount on big data certification training courses.

 

The blog originally was published on – www.forbes.com/sites/bernardmarr/2015/06/22/spark-or-hadoop-which-is-the-best-big-data-framework/2/#714061d161d6

 

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Foster your Machine Learning Efforts with these 5 Best Open Source Frameworks

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Foster your Machine Learning Efforts with these 5 Best Open Source Frameworks

Machine Learning is rapidly becoming the mainstream and changing the way we carry out tasks. While many factors have contributed to this current boom in machine learning, the most important reason is the wide availability of open source frameworks.

’Open source’ refers to a program that is created as a collaborative effort in which programmers improve the code and share the changes within the community. Open source sprouted in the technological community in response to proprietary software owned by corporations. The rationale for this movement is that programmers not concerned with proprietary ownership or financial gain will produce a more useful product for everyone to use. 

Framework: It refers to a cluster of programs, libraries and languages that have been manufactured for use in application development. The key difference between a library and a framework is ‘’inversion of control’’. When a method is summoned from a library, the user is in control. With a framework the control is inverted- the framework calls the user.

If you are plunging full-fledged into machine learning, then you clearly need relevant resources for guidance. Here are the top 5 frameworks to get you started.

  1. TensorFlow:

TensorFlow was developed by the Google Brain Team for handling perceptual and language comprehending tasks. It is capable of conducting research on machine learning and deep neural networks. It uses a Python-based interface. It’s used in a variety of Google products like handling speech recognition, Gmail, photos and search.

A nifty feature about this framework is that it can perform complex mathematical computations and observe data flow graphs. TensorFlow grants users the flexibility to write their own libraries as well. It is also portable. It is able to run in the cloud and on mobile computing platforms as well as with CPUs and GPUs.

  1. Amazon Machine Learning (AML):

AML comes with a plethora of tools and wizards to help create machine learning models without having to delve into the intricacies of machine learning. Thus it is a great choice for developers. AML users can generate predictions and utilize data services from the data warehouse platform, Amazon Redshift. AML provides visualization tools and wizards that guide developers. Once the machine learning models are ready  AML makes it easy to obtain predictions using simple APIs.

  1. Shogun:

 Abundant in state-of-the-art algorithms, Shogun makes for a very handy tool. It is written in C++ and provides data structures for machine learning problems. It can run on Windows, Linux and MacOS. Shogun also proves very helpful as it supports uniting with other machine learning libraries like SVMLight, LibSVM, libqp, SLEP, LibLinear, VowpalWabbit and Tapkee to name a few.

  1. NET:

Accord.NET is a machine learning framework which possesses multiple libraries to deal with everything from pattern recognition, image and signal processing to linear algebra, statistical data processing and much more. What makes Accord so valuable is its ability to offer multiple things which includes 40 different statistical distributions, more than 30 hypothesis tests, and more than 38 kernel functions.

  1. Apache Signa, ApacheSpark MLibApache, and Apache Mahout:

These three frameworks have plenty to offer. Apache Signa is widely used in natural language processing and image recognition. It is also adept in running a varied collection of hardware.

Mahout provides Java libraries for a wide range of mathematical operations. Spark MLlib was built with the aim of making machine learning easy. It unites numerous learning algorithms and utilities, including classification, clustering, dimensionality reduction and many more.

 With the advent of open source frameworks, companies can work with developers for improved ideas and superior products. Open source presents the opportunity to accelerate the process of software development and meet the demands of the marketplace.

Boost your machine learning endeavors by enrolling for the Apache Spark training course at DexLab Analytics where experienced professionals ensure that you become proficient in the field of machine learning.

 

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