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Hadoop or Spark: Which Big Data Framework to Choose?

Posted on June 25, 2018May 23, 2020 by Dexlab

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!

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.

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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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A Comprehensive Article on Apache Spark: the Leading Big Data Analytics Platform

Posted on May 12, 2018May 21, 2018 by Dexlab

Speedy, flexible and user-friendly, Apache Spark is one of the main distributed processing frameworks for big data in the world. This technology was developed by a team of researchers at U.C. Berkeley in 2009, with the aim to speed up processing in Hadoop systems. Spark provides bindings to programming languages, like Java, Scala, Python and R and is a leading platform that supports SQL, machine learning, stream and graph processing. It is extensively used by tech giants, like Apple, Microsoft and IBM, telecommunications industry and games organizations.

Databricks, a firm where the founding members of Apache Spark are now working, provides Databricks Unified Analytics Platform. It is a service that includes Apache Spark clusters, streaming and web-based notebook development. To operate in a standalone cluster mode, one needs Apache Spark framework and JVM on each machine in a cluster. To reap the advantages of a resource management system, running on Hadoop YARN is the general choice. Amazon EMR and Google Cloud Dataproc are fully-managed cloud services for running Apache Spark.

Working of Apache Spark:

Apache Spark has the power to process data from a variety of data storehouses, such as Hadoop Distributed File System (HDFS) and NoSQL databases. It is a platform that enhances the functioning of big data analytics applications through in-memory processing. It is also equipped to carry out regular disk-based processing in case of large data sets that are unable to fit into system memory.

Spark Core:

Apache Spark API (Application Programming Interface) is more developer-friendly compared to MapReduce, which is the software framework used by earlier versions of Hadoop. Apache Spark API hides all the complicated processing steps from developers, like reducing 50 lines of MapReduce code for counting words in a file to only a few lines of code in Apache Spark. Bindings to well-liked programming languages, like R and Java, make Apache Spark accessible to a wide range of users, including application developers and data analysts.

Spark RDD:

Resilient Distributed Dataset is a programming concept that encompasses an immutable collection of objects for distribution across a computing cluster. For fast processing, RDD operations are split across a computing cluster and executed in a parallel process. A driver core process divides a Spark application into jobs and distributes the work among different executor processes. The Spark Core API is constructed based on RDD concept, which supports functions like merging, filtering and aggregating data sets. RDDs can be developed from SQL databases, NoSQL stores and text files.

Apart from Spark Core engine, Apache Spark API includes libraries that are applied in data analytics. These libraries are:

Spark SQL:

Spark SQL is the most commonly used interface for developing applications. The data frame approach in Spark SQL, similar to R and Python, is used for processing structured and semi-structured data; while SQL2003-complaint interface is for querying data. It supports reading from and writing to other data stores, like JSON, HDFS, Apache Hive, etc. Spark’s query optimizer, Catalyst, inspects data and queries and then produces a query plan that performs calculations across the cluster.

Spark MLlib:

Apache Spark has libraries that can be utilized for applying machine learning techniques and statistical operation to data. Spark MLlib allows easy feature extractions, selections and conversions on structured datasets; it includes distributed applications of clustering and classification algorithms, such as k-means clustering and random forests.

Spark GraphX:

This is a distributed graph processing framework that is based on RRDs; RRD being immutable makes GraphX inappropriate for graphs that need to be updated, although it supports graph operations on data frames. It offers two types of APIs, Pregel abstraction and a MapReduce style API, which help execute parallel algorithms.

Spark Streaming:

Spark streaming was added to Apache Spark to help real-time processing and perform streaming analytics. It breaks down streams of data into mini-batches and performs RDD transformations on them. This design facilitates the set of codes written for batch analytics to be used in stream analytics.

Future of Apache Spark:

The pipeline structure of MLlib allows constructing classifiers with a few lines of code and applying Tensorflow graphs and Keras models on data. The Apache Spark team is working to improve streaming performance and facilitate deep learning pipelines.

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

Posted on April 16, 2018May 23, 2020 by Dexlab

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.

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.

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.

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.

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.

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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How Data Analytics Is Shaping and Developing Improved Storage Solutions

Posted on March 16, 2018May 23, 2020 by Dexlab

Technology has penetrated deep into our lives – the last 5 decades of IT sector have been characterized by intense development in electronic storing solutions for recordkeeping.

Today, every file, every document is stored and archived safely and efficiently – rows of data are tabled in spreadsheets and stored in SQL relational databases for smooth access anytime by anyone, of course the authorized persons. Data is omnipresent. It is being found in data warehouses, data lakes, data mines and in pools. It is so much large in volume nowadays, that it can even be calculated in something like a Brontobyte.

Information is power. Data stored in archives are used to make accurate forecasts. And the data evaluation has begun within a subset of mathematics powered by a discipline named probability and statistical analysis.

Slowly, this discipline evolved into Business Intelligence that further into Data Science. The latter is the most sought after and well-paid career option for today’s tech-inspired generation. Grab a data science certification in Gurgaon and push your career to success.

Big Data Storage Challenges and Solutions

The responsibility of storage, ensuring security and provide accessibility for data is huge. Managing volumes and volumes of data is posing a challenge in itself – for example, even powering and cooling enough HDD RAID arrays to keep an Exabyte of raw data tends to break the bank for many companies.

Software-defined storage and flash devices are being deployed for big data storage. They promise of better direct business benefit. Also, increasingly Apache Spark Hadoop or simply Spark is taking care of the software side of big data analytics. Whether your big data cluster is developed on these open-source architectures or some other big data frameworks, it will for sure impact your storage decisions.

Hadoop is in this business of storage for big data for quite some time now. It is a robust open-source framework opted for suave processing of big data. It led to the emergence of server clusters and Facebook is known to have the largest Hadoop cluster containing millions of nodes.

Now, the question remains where and how you proceed with Hadoop – there are so many differing opinions about how you approach Hadoop clusters, at times it may leave you exasperated. For that, we can help you here.

With a huge array of data at play, we suggest to deploy a dedicated processing, storage and networking system in different racks to avoid latency or performance issues. It is for the same reasons, we ask you to stay away running Hadoop in a virtual environment.

Instead, implement HDFS (Hadoop Distributed File System) – it is perfect for distributed storage and processing with the help of commodity hardware. The structure is simple, tolerant, expandable and scalable.

Besides, the cost of data storage should also be given a look at – cost should be kept low and data compression features should likely to be implemented.

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The Takeaway

Times are changing, and so are we. Big data analytics are becoming more real-time, hence better you scale up to real-time analytics. Today, data analytics have gone way beyond the conventional desktop considerations – it has now become a lot more, and to keep pace with the analytics evolution, you need to have sound storage infrastructure, where possible upgrades to computing, storage and networking is easily available and implementable.

To answer about big data or Hadoop, power yourself up with a good certification in Big Data Hadoop from DexLab Anlaytics – such intensive big data courses do help!

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Latest Open Source Tools in Data Analytics Beyond Apache Spark

Posted on August 26, 2016February 12, 2019 by Dexlab

In the IT world change is always in the air, but especially in the realm of data analytics, profound change is coming up as open source tools are making a huge impact. Well you may already be familiar with most of the stars in the open source space like Hadoop and Spark. But with the growing demand for new analytical tools which will help to round up the data holistically within the analytical ecosystem. A noteworthy point about these tools is the fact that they can be customized to process streaming data.

With the emergence of the IoT (Internet of things) that is giving rise to numerous devices and sensors which will add to this stream of data production, this forms one of the key trends why we need more advanced data analytics tools. The use of streaming data analysis is used for enhanced drug discovery, and institutes like SETI and NASA are also collaborating with each other to analyze terabytes of data, that are highly complex and stream deep in space radio signals.

The Apache Hadoop Spark software has made several headlines in the realm of data analytics that allowed billions of development funds to be showered at it by IBM along with other companies. But along with the big players several small open source projects are also on the rise. Here are the latest few that grabbed our attention:

Apache Drill:

This open source analytics tool has had quite good impact on the analytics realm, so much so that companies like MapR have even included it into their Hadoop distribution systems. This project is a top-level one at Apache and is being leveraged along with the star Apache Spark in many streaming data analytics scenarios.

Like at the New York Apache Drill meeting in January this year, the engineers at MapR system showed how Apache Spark and Drill could be used in tandem in a use cases that involve packet capture and almost real-time search and query.

But Drill is not ideal for streaming data application because it is a distributed schema free SQL engine. People like IT personnel and developers can use Drill to interactively explore data in Hadoop and NoSQL databases for things such as HBase and MongoDB. There is no need to explicitly describe the schemas or maintain them because the Drill has the ability to automatically leverage the structure which is embedded in the data. It is capable of streaming the data in memory between operators and minimizes the use of disks unless you need to complete a query.

Grappa:

Both big and small organizations are constantly working on new ways to cull actionable insights from their data streaming in constantly. Most of them are working with data that are generated in clusters and are relying on commodity hardware. This puts a premium label on affordable data centric work processes. This will do wonders to enhance the functionality and performance of tools such as MapReduce and even Spark. With the open source project Grappa that helps to scale the data intensive applications on commodity clusters and will provide a new type of abstraction which will trump the existing distributed shared memory (DSM) systems.

Grappa is available for free on the GitHub under a BSD license. And to use Grappa one can refer to its quick start guide that is available readily on the README file to build and execute it on a cluster.

These were the latest open source data analytics tools of 2017. For more such interesting news on Big Data analytics and information about analytics training institute follow our daily uploads from DexLab Analytics.

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