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Introducing Scala and Spark for Seamless Big Data Analysis

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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.

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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

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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.

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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

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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

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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:

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

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

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.

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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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Latest Open Source Tools in Data Analytics Beyond Apache Spark

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Latest Open Source Tools in Data Analytics Beyond Apache Spark

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.

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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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