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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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The Soaring Importance of Apache Spark in Machine Learning: Explained Here

Posted on January 22, 2019May 23, 2020 by Dexlab

The Soaring Importance of Apache Spark in Machine Learning: Explained Here

Apache Spark has become an essential part of operations of big technology firms, like Yahoo, Facebook, Amazon and eBay. This is mainly owing to the lightning speed offered by Apache Spark – it is the speediest engine for big data activities. The reason behind this speed: Rather than a disk, it operates on memory (RAM). Hence, data processing in Spark is even faster than in Hadoop.

The main purpose of Apache Spark is offering an integrated platform for big data processes. It also offers robust APIs in Python, Java, R and Scala. Additionally, integration with Hadoop ecosystem is very convenient.

Why Apache Spark for ML applications?

Many machine learning processes involve heavy computation. Distributing such processes through Apache Spark is the fastest, simplest and most efficient approach. For the needs of industrial applications, a powerful engine capable of processing data in real time, performing in batch mode and in-memory processing is vital. With Apache Spark, real-time streaming, graph processing, interactive processing and batch processing are possible through a speedy and simple interface. This is why Spark is so popular in ML applications.

Apache Spark Use Cases:

Below are some noteworthy applications of Apache Spark engine across different fields:

Entertainment: In the gaming industry, Apache Spark is used to discover patterns from the firehose of real-time gaming information and come up with swift responses in no time. Jobs like targeted advertising, player retention and auto-adjustment of complexity levels can be deployed to Spark engine.

E-commerce: In the ecommerce sector, providing recommendations in tandem with fresh trends and demands is crucial. This can be achieved because real-time data is relayed to streaming clustering algorithms such as k-means, the results from which are further merged with various unstructured data sources, like customer feedback. ML algorithms with the aid of Apache Spark process the immeasurable chunk of interactions happening between users and an e-com platform, which are expressed via complex graphs.

Finance: In finance, Apache Spark is very helpful in detecting fraud or intrusion and for authentication. When used with ML, it can study business expenses of individuals and frame suggestions the bank must give to expose customers to new products and avenues. Moreover, financial problems are indentified fast and accurately. PayPal incorporates ML techniques like neural networks to spot unethical or fraud transactions.

Healthcare: Apache Spark is used to analyze medical history of patients and determine who is prone to which ailment in future. Moreover, to bring down processing time, Spark is applied in genomic data sequencing too.

Media: Several websites use Apache Spark together with MongoDB for better video recommendations to users, which is generated from their historical data.

ML and Apache Spark:

Many enterprises have been working with Apache Spark and ML algorithms for improved results. Yahoo, for example, uses Apache Spark along with ML algorithms to collect innovative topics than can enhance user interest. If only ML is used for this purpose, over 20, 000 lines of code in C or C++ will be needed, but with Apache Spark, the programming code is snipped at 150 lines! Another example is Netflix where Apache Spark is used for real-time streaming, providing better video recommendations to users. Streaming technology is dependent on event data, and Apache Spark ML facilities greatly improve the efficiency of video recommendations.

Spark has a separate library labelled MLib for machine learning, which includes algorithms for classification, collaborative filtering, clustering, dimensionality reduction, etc. Classification is basically sorting things into relevant categories. For example in mails, classification is done on the basis of inbox, draft, sent and so on. Many websites suggest products to users depending on their past purchases – this is collaborative filtering. Other applications offered by Apache Spark Mlib are sentiment analysis and customer segmentation.

Conclusion:

Apache Spark is a highly powerful API for machine learning applications. Its aim is wide-scale popularity of big data processing and making machine learning practical and approachable. Challenging tasks like processing massive volumes of data, both real-time and archived, are simplified through Apache Spark. Any kind of streaming and predictive analytics solution benefits hugely from its use.

If this article has piqued your interest in Apache Spark, take the next step right away and join Apache Spark training in Delhi. DexLab Analytics offers one the best Apache Spark certification in Gurgaon – experienced industry professionals train you dedicatedly, so you master this leading technology and make remarkable progress in your line of work.

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Top 3 Reasons to Learn Scala Programming Language RN

Posted on January 15, 2019May 23, 2020 by Dexlab

Top 3 Reasons to Learn Scala Programming Language RN

Highly scalable general-purpose programming language, Scala is a wonder tool for newbie developers as well as seasoned professionals. It provides both object-oriented and functional programming assistance. The key to such wide level popularity of Scala lies in the sudden explosive growth of Apache Spark, which is actually written in Scala – thus making later a powerful programming language best suited for machine learning, data processing and streaming analytics.

Below, we have enumerated top three reasons why you should learn Scala and tame the tides of success:

For Better Coding

The best part is that you would be able to leverage varied functional programming techniques that will stabilize your applications and mitigate challenges that might arise due to unforeseen side effects. Just by shifting from mutable data structures to immutable ones, and from traditional methods to purely functional strategies that have zero effect on their environment, you can stay rest assured that your codes would be more stable, safer and easy to comprehend.

Inarguably, your code would be simple and expressive. If you are already working on languages, such as JavaScript, Python or Ruby, you already know about the power of a simple, short and expressive syntax. Hence, use Scala to shed unnecessary punctuations, explicit types and boilerplate code.

What’s more, your code would support multiple inheritance and myriad capabilities, and would be strongly-typed. Also, in case of any incompatibilities, it would be soon caught even before the code runs. So, developers in both dynamic and statically typed languages should embrace Scala programming language – it ensures safety with performance along with staying as expressive as possible.

To Become a Better Engineer

He who can write short but expressive codes as well as ensure a type-safe and robust-performance application is the man for us! This breed of engineers and developers are considered immensely valuable, they impress us to the core. We suggest take up advanced Scala classes in Delhi NCR and take full advantage of its high-grade functional abilities. Not just learn how to deliver expressive codes but also be productive for your organization and yourself than ever before.

Mastering a new programming language or upgrading skills is always appreciable. And, when it comes to learning a new language, we can’t stop recommending Scala – it will not only shape your viewpoint regarding concepts, like data mutability, higher-order functions and their potential side effects, but also will brush your coding and designing skills.

It Enhances Your Code Proficiency

It’s true, Scala specialization improves your coding abilities by helping you read better, debug better and run codes pretty faster. All this even makes you write codes in no time – thus making you proficient, and happy.

Now, that you are into all-things-coding, it’s imperative to make it interesting and fun. Scala fits the bill perfectly. If you are still wondering whether to imbibe the new-age skill, take a look at our itinerary on advanced Scala Training in Delhi displayed on the website and decide for yourself. The world of data science is evolving at a steadfast rate, and it’s high time you learn this powerful productive language to be on the edge.

The blog has been sourced from – www.oreilly.com/ideas/3-simple-reasons-why-you-need-to-learn-scala

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Introducing Scala: A Concise Overview

Posted on January 9, 2019January 9, 2019 by Dexlab

Introducing Scala: A Concise Overview

Developed on Java Virtual Machine, Scala is a remarkable, advanced programming language finding acceptance amongst a fueling developer’s community worldwide. It functions parallel to Java. It has a lot of differences as well as similarities with the Java programming language. Its source code is compiled and exhibits functional programming.

The scope and capabilities of Scala are versatile. From writing web applications to parallel batch processing and data analysis, Scala can be leveraged for a plethora of high-end purposes. But, before going into such nuances, we would advise you to take a brief look at the below-mentioned questions with answers: they will help you grasp the intricacies of Scala and grab the hottest job in town.

Explain Scala?

Scala is a fantastic concoction of object-oriented and functional programming. Together, it combines to construct a cutting-edge programming language that is highly scalable, hence the name ‘Scala’.

Highlight the advantages of using Scala.

Swearing allegiance to its name, Scala is a highly scalable language – supported by maintainability, testability and productivity features – which it makes it an obvious choice over its tailing rivals.
Companion and Singleton objects in Scala offers an improvised solution in contrary to other static in other JVM languages, including Java.
It has the striking ability to eliminate the need to possess a ternary operator.

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Define a Scala Map.

Scala Map is a cluster of key-value pairs, wherein the values can easily be retrieved using the keys. In the map, the values are not unique but the keys are.

Scala supports two types of maps, namely immutable and mutable. By default, Scala endorses immutable map, but no worries, if you want to leverage mutable map, you need to import scala.collection.mutable.Map class, explicitly.

Name the Scala library ideal for functional programming.

Best suited, Scalaz library is hailed perfect for functional programming. Equipped with functional data structures complementing quintessential Scala library parameters, it hosts a healthy stream of pre-determined foundational type classes, including Functor, Monad, etc.

Highlight the difference between ‘Unit’ and ‘()’ in Scala.

Unit is a subset of scala.anyval, which is just a replica of Java void offering Scala with an abstraction of the Java platform. On the other hand, empty tuple, represented as () in Scala defined as a unit value.

What distinguishes concurrency from parallelism?

Most of the laymen confuse the terms concurrency and parallelism. To clear it up, concurrency is a phenomenon when numerous computations perform sequentially at overlapping time periods, while parallelism refers to when processes occur simultaneously. Futures, Parallel collection and Async library are a few examples when parallelism is achieved in Scala.

Define Monad in Scala.

The best way to explain a monad would be by comparing it with a wrapper: just how you wrap a present with a shiny wrapping paper finished with ribbons to make it look attractive, Monad in Scala is used to wrap class objects and fulfill two significant tasks:

Determine through ‘unit’ in Scala
Bind through ‘flatmap’ in Scala

Why do you use Scala’s App?

The App is a trait reflected in Scala package termed as ‘scala.App’, and it determines the main method. When a class or an object goes beyond this trait, they automatically become Scala executable programs, because they acquire the main method directly from the application. No one needs to write the main method when using the App.

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