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The ABC Basics of Apache Spark

The ABC Basics of Apache Spark

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

Introducing Apache Spark

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

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

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Key elements of Spark are:

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

Spark core

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

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

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

  • Transformations
  • Actions

Spark SQL

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

Spark Streaming:

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

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

MLib

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

GraphX

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

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The blog has been sourced fromwww.toptal.com/spark/introduction-to-apache-spark
 

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

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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How Data Analytics Is Shaping and Developing Improved Storage Solutions

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.

 
How Data Analytics Is Shaping and Developing Improved Storage Solutions
 

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.

 
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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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