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Here’s All You Need to Know about Apache Spark 2.4

Posted on January 28, 2019January 28, 2019 by Dexlab

Here’s All You Need to Know about Apache Spark 2.4

Apache Spark 2.4 has joined the data bandwagon recently – and it is incredible. It brings experimental support for Scala 2.12. Join us as we dig into the features of the latest Spark version – what else it has to offer to our big data developers – apart from a brand new barrier execution mode supporting Databricks Runtime5.0!

Of late, as we were all busy tapping IoT revolution and latest discoveries in the domain of AI, Apache Spark rolled out a new array of exciting goodies in terms tech features to enhance the data experience for data scientists and developers. The power package is Apache Spark 2.4 – it boasts of a dozen improved features and upgrades that tackle large-scale data processing in a jiffy. Known to all, Apache Spark is a powerful analytics engine that is designed to deal with humongous volumes of data with speed and efficiency. Under the Apache Software umbrella, Spark is one of the most successful projects and the most active open source big data programs.

The latest Spark version is a combination of its erstwhile goals, such as ease of use, efficiency and speed, along with stability and refinement. On a positive note, Project Hydrogen is finally panning out as expected. Designed to ensure better coordination between big data and AI, deep learning frameworks work well. The barrier mode bolsters up better integration with distributed deep learning architecture. The present architecture of Spark is a bit intricate because elaborate communication patterns result in frequent snags and blockages.

However, thanks to the latest barrier execution mode, Spark can seamlessly initiate training tasks like MPI tasks and promptly restart everything when task failures occur. Also, this Spark has introduced a new process of fault tolerance for barrier tasks – whenever barrier task breaks down, Spark mindfully aborts all tasks and initiates the stage.

In addition, Spark 2.4 also comes with built-in advanced functions such as map and array. The latest high-in-order functions permit developers to tackle challenging types directly. Also, these much-improved functions have the ability to manipulate highly advanced values with an anonymous lambda function.

The new Spark offers experimental support for Scala 2.12- owing to this, the developers can now write entire Spark applications with Scala 2.12 just focusing on the 2.12 reliability. It is also equipped with improved interoperability with Java 8 resulting in better serialization of lambda functions.

This latest Spark variant also features built-in support for Apache Avro, the widely recognized data serialization format. As a result, today, the developers can write and read their Avro data within Spark itself. It first started off as a Databricks Project and today it boasts of a host of new functions and superb logical support.

Moreover, Apache Spark 2.4 highlights refined Kubernetes integration in 3 particular ways, and they are as follows:

Aids running containerized PySpark and SparkR on Kubernetes,
Client Mode is on offer,
A higher number of mounting options is made available for increasing Kubernetes volumes.

Besides, other improvements to be noted are:

Pandas UDF upgrades,
Prompt ascertainment of DataFrames in notebooks,
Elimination of 2GB-block size limitation.

Additionally, the new release supports Databricks Runtime 5.0.

Want to know more? Check out our Apache Spark training courses in Delhi. They are well curated and student-friendly. DexLab Analytics is not only touted for its best Scala training Delhi but also our Spark training courses are highly advanced and industry-relevant.

The blog has been sourced from ― jaxenter.com/apache-spark-2-4-overview-151623.html

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Introducing Scala and Spark for Seamless Big Data Analysis

Posted on January 2, 2019May 23, 2020 by Dexlab

Introducing Scala and Spark for Seamless Big Data Analysis

Application of Big Data through network clusters has become the order of the day. Multiple industries are embracing this new trend. The elaborate use of Hadoop and MapReduce justifies the popularity of this evolving phenomenon. What’s more, the rise of Apache Spark, an incredible data processing engine written in Scala programming language also lends proof.

Introducing Scala

Somewhat similar to Java programming, Scala is a generic object-oriented programming language. Also known as Scalable Language, Scala is a multi-purpose language with capabilities to grow along the lines of many requirements. The capabilities range from an ordinary scripting language to a mission-critical language for complex applications. A wide number of technologies are being built on this robust platform.

Why Scala?

It supports functional programming equipped with features, such as immutability, pattern matching, type interference, lazy evaluation and currying.
It includes an advanced type system – with algebraic data types.
It helps you explore features that are not available in Java, including raw strings, operator overloading and named parameters.

Besides, Scala runs on Java Virtual Machine (JVM) and endorses cluster computing on Spark.

Introducing Apache Spark

An open source big data processing framework, Apache Spark offers a sound interface for fast processing of huge datasets. It aids in programming data clusters using fault tolerance and data parallelism.

Since 2009, more than 200 companies and 1000 developers have been leveraging Apache Spark and the numbers are still on the rise.

Features of Spark

Comprehensive Framework

Apache Spark is a unified framework ideal for managing big data processing. It also aids a diverse range of datasets, such as batch data, text data, graphical data and real-time streaming data.

Easy to Use

Spark lets programmers write Scala, Java or Python applications – thanks to its built-in set of more than 80 A-grade operators.

Fast and Effective

Talking of speed, Spark runs programs up to 100 X faster than Hadoop clusters in memory and 10 X quicker while running on disk. Powered by a cutting-edge DAG (Directed Acrylic Graph) execution engine, Spark enhances cyclic data flow and in-memory data sharing across DAGs for smoother execution of different jobs but with similar data.

Robust Support

Along with managing MapReduce operations, Spark offers support for streaming data, graphic data processing, SQL queries and machine learning.

Flexibility

Besides Scala programming language, programmers can leverage Python, R, Java and Clojure for developing ace applications using Spark.

Platform-independent

Spark applications are run either in the cloud or on a distinctive cluster mode. Spark can be employed as an individual server or as a part of the distributed framework, like YARN or MESOS. It gives access to versatile data structures, such as HBase, HDFS, Hive, Cassandra and similar Hadoop data sources.

Encompassing Library Support

Are you a Spark programmer? Fuse together additional libraries within the same application and enhance big data and analytics capabilities.

Some of the supported libraries are as follows:

Spark SQL
Spark GraphX
BlinkDB
Spark MLib
Tachyon
Spark R
Spark Cassandra Connector

As parting thoughts, Apache Spark is the perfect alternative to MapReduce – for installations. The former effortlessly tackles humongous volumes of data that need low latency processing.

DexLab Analytics is a refined Apache Spark training institute in Gurgaon. The comprehensive courses, on-point faculty and flexible batch timings make this institute the best pick for Apache Spark training Gurgaon. For more information, reach us at dexlabanalytics.com.

The blog has been sourced from — www.knowledgehut.com/blog/big-data/analysis-of-big-data-using-spark-and-scala

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Top Things to Know About Scala Programming Language

Posted on December 27, 2018May 23, 2020 by Dexlab

Top Things to Know About Scala Programming Language

Scalable Language, Scala is a general-purpose programming language, both object-oriented and highly functional programming language. It is easy to learn, simple and aids programmers in writing codes in a simple, sophisticated and type-safe manner. It also enables developers and programmers in being more productive.

Even though Scala is a relatively new language, it has garnered enough users and has wide community support – because it’s touted as the most user-friendly language.

About Scala and Its Features

Scala is a completely object-oriented programming language

In Scala, everything is treated as an object. Even, the operations you conduct are termed as a method call. Scala lets you add new operations to already existing classes – thanks to the implicit classes.

One of the best things about Scala is that it makes it effortlessly easy to interact with Java code. You can easily write a Java code inside Scala class – interesting, isn’t it? The Scala makes way for hi-tech component architectures with the help of classes and traits.

Scala is a functional language

No wonder, Scala has implemented top-notch functional programming concepts – in case you don’t know, in functional programming, each and every computation is regarded as a mathematical function. Following are the characteristics of functional programming:

Simplicity
Power and flexibility
Suitable for parallel processing

Not interpreted, Scala is a compiler-based language

As Scala is a compiler based language, its execution is relatively faster than its tailing competitor, Python. The latter is an interpreted language. The compiler in Scala functions just like a Java compiler. It taps the source code and launches Java byte-code that’s executable across any standard JVM (Java Virtual Machine).

Pre-requisites for Mastering Scala

Scala is a fairly simple programming language and there are minimal prerequisites for learning it. If you possess some basic knowledge of C/C++, you can easily start acing Scala. As it is developed upon Java, the fundamental programming functions of Scala are somewhat similar to Java.

Now, if you happen to know about Java syntax or OOPs concept, it would prove better for you to work in Scala.

Basic Scala Terms to Get Acquainted With

Object

An entity which consists of state and behavior is defined as an Object. Best examples – person, table, car etc.

Class

Described as a template or a blueprint for designing different objects that reflects its behavior and properties, a Class is a widely popular term.

Method

It is reckoned as a behavior of a class, where a class may include one or more methods. For example, a deposit can be reckoned as a method of bank class.

Closure

It is defined as any function that ends within the environment in which it’s defined. A closure return value is determined based on the value of one or more variables declared outside the closure.

Traits

These are used to determine object types by mentioning the signature of the supported methods. It is similar to a Java interface.

Things to Remember About Scala

Scala is case sensitive
When saving a Scala program, use “.scala”
Scala execution process begins from main() methods
Never can an identifier name start with numbers. For an instance, the variable name “789salary” is not valid.

Now, if you are interested in understanding the intricacies and subtle nuances of Apache Spark in detail, you have to enroll for Scala certification Training Gurgaon. Such intensive Scala training programs not only help you master the programming language but ensure secure placement assistance. For more information, reach us at DexLab Analytics, a premier Scala Training Institute in Gurgaon.

The blog has been sourced from ― www.analyticsvidhya.com/blog/2017/01/scala

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Databricks Supports Apache Spark 2.4 and Adds ML Runtime

Posted on December 18, 2018December 18, 2018 by Dexlab

Databricks Supports Apache Spark 2.4 and Adds ML Runtime

Databricks recently embraced the Apache Spark 2.4, a latest version. They are integrating it into their platform of analytics. Also, the company is on its way to unveil another runtime feature that would simplify the intricacies of deep learning.

Needless to say, Databricks is one of the most powerful supporters of version 2.4 of Spark, the notable stream processing framework. The latest upgraded version features improvement in the performance of machine learning framework running on Spark as well as distributed deep learning. It also includes modifications that would instantly address dependency issues related to deep learning tasks.

Project Hydrogen is an ambitious initiative; it’s under this tag the Spark upgrades were fused and introduced as a new scheduling mode, known as ‘barrier execution’. It encourages developers to embed training in lieu of distributed deep learning posed as an Apache Spark workload.

In context to above, Reynold Xin, a staunch Spark contributor and co-founder at Databricks said, “This is the largest change to Spark’s scheduler since the inception of the project.” He further mentioned that the upgrades will actually help reduce the complexities of machine learning structures and ensure high efficacy.

The latest runtime detail categorized HorovodRunner is developed to rationalize scaling and streamlining of distributed deep learning workloads. It is performed from a single machine to huge clusters. Previously, drifting from single-node workloads to huge distributed training on GPU or CPU clusters needed a bunch of full code rewrites – it was exceedingly challenging enough. Undeniably, HorovodRunner reduces training as well as programming time cutting down them from hours to a few minutes. This was claimed by the professionals working at Databricks.

Besides Horovod, Databricks is found to be saying that its platform offers native integration with TensorFlow, Kera and several other machine learning programs coupled with MLib and GraphFrames super machine learning algorithms.

On top of all this, a few weeks back, Databricks associated itself with a versatile cloud data integrator Talend with a sole aim to integrate the cloud service with their own data analytics platform to allow data scientists leverage the cluster computing framework – it would help process large data sets at scale.

About Apache Spark:

Apache Spark is a robust, well-integrated analytics engine efficient in processing large datasets. Crafted for high speed, productivity and generic use, it is considered as one of the most popular projects in motion under Apache software umbrella. It is also one of the most volatile and active open source big data projects.

DexLab Analytics is a top-notch Apache Spark training institute in Gurgaon. It provides top of the line in-demand skill training on a plethora of new-age IT related courses, such as data science, data analytics courses, big data, risk analytics and more.

The blog was sourced from ― www.datanami.com/2018/11/19/databricks-upgrades-spark-support-adds-ml-runtime

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It’s Cracked: Now Increase Your Salary as an IT Professional

Posted on November 19, 2018November 19, 2018 by Dexlab

It’s Cracked: Now Increase Your Salary as an IT Professional

Keen to increase your salary – perhaps you’ve accomplished a difficult task and in a position to ask for a salary-hike? Or maybe, it’s time you want to make a switch?

Whatever be the reason, in both the abovementioned cases, the crux is a salary hike – but how to do it well? Salary negotiations are one of the toughest battle fought inside the boardrooms. Interestingly, only 39 percent of professionals even tried to negotiate a higher salary during their last job offer, says a 2018 survey of close to 3,000 people conducted by global staffing firm Robert Half.

Below, we’ve handpicked few of the best ways to enhance your salary without raising an eyebrow – scroll below for such key pieces of advice:

Never Lose Your Calm

Emotional intelligence is to be demonstrated. Not impatience. You are yet to get that job, and your salary negotiation skill is a reflection how you are going to do business, while remaining calm under stressful situations.

Do Your Homework

“Be confident in your own skin! Your salary negotiations can deeply suffer owing to a lack of preparation,” says Jim Johnson, senior vice president at Robert Half Technology. This firm generates an annual salary guide for more than 75 positions in IT field, with data!

In addition, Mr. Johnson supports weighing the competitiveness of your current pay. That’s important. Not only subject to your role or designation, but also to your respective skills, vertical industry and area – including security and data analytics.

Certifications Help

Today, an array of certified and non-certified in-demand skills is available in the market. As a result, IT professionals are found shelling extra pounds for these certifications – an average of 7.6 percent of base salary for a single certification and 9.4 percent of base salary on average for certain single, non-certified skills.

Amidst all, Apache Spark Progamming Training, Data Science, Cryptography and Penetration Testing are the hottest in line. Python Course in Delhi NCR, Artificial Intelligence and Risk Analytics are next to follow.

Other than that, open source skills are quite popular – especially those that concerns DevOps, cloud and containers.

Imbibe Soft Skills As Much As You Can

Developing soft skills is an art! And in this tough age of digital transformation, IT professionals have to constantly to work in cross-functional teams with fellows from different arenas of the business, as well as clients and partners who have zero tech skills.

For this and more, you have to have a good command over English, undying patience and understand people, what they have to say! No wonder, many IT bigwigs say these soft skills are not as soft as they sound – sometimes, it’s really hard to explain and teach people from different parts of the industry.

“It’s funny that we even talk about these skills as ‘soft,’ because they are very hard to master and are frequently the cause of more trouble than lack of ‘hard’ skills,” shares Anders Wallgren, CTO at Electric Cloud.

Care to nurture your data analytics skill? The expert guys at DexLab Analytics are here!

The blog has first appeared on ― enterprisersproject.com/article/2018/11/what-best-way-increase-your-salary-it-professional

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The Success Story of Big Data Tooling

Posted on September 17, 2018May 23, 2020 by Dexlab

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.

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

Posted on June 26, 2018 by Dexlab

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.

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?

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.

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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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.

For knowledge on how to create data pipelines and cutting edge machine learning models, join Apache Spark programming training in Gurgaon at Dexlab Analytics. Our experienced consultants ensure that you receive the best apache spark certification training.

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