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Demystifying Apache Spark: Understanding the Big Data Analytics Platform

Posted In Webmaster - By Techtiplib on Tuesday, February 20th, 2018 With No Comments »

Apache Spark is the platform ranked first for large-scale structured query language (SQL), stream processing, and machine learning. This is because it is flexible, relatively fast, and developer friendly. Since its introduction, Apache Spark is becoming one of the most important Big Data processing frameworks across the entire world. Spark can be used in different ways as it allows native bindings for Java, Scala, and Python, apart from being supportive of the SQL. It has been widely used in banks, telecommunication companies, game companies, and governmental departments too. Other major companies who have made great technological advancements use Apache Spark too. Spark can work as an independent cluster that will just demand the Apache Spark framework and Java Virtual Machines (JVM) in every machine in your group. If you’re into a more managed solution, you can get apache spark consulting services that will help you in the manner you utilize and manage Big Data opportunities.

Incremental Database Synchronization

Spark vs Hadoop

It is important to note that making comparisons between Apache Spark and Hadoop is kind of misleading. In most cases you will get Spark included in the Hadoop distributions. However, Spark enjoys some advantages that have made it the framework of choice when it comes to data processing. As a result, it has overtaken the ancient MapReduce paradigm that made Hadoop prominent. Spark works at a high speed. It also has an in-memory data engine that can execute tasks at a speed that is 100 times faster than MapReduce.

Spark core

When compared to MapReduce or other Apache Hadoop components, Apache Spark brings less or no complexity to developers. It does away with the complexity of a distributed processing engine. For instance, Apache Spark can reduce 40 lines of MapReduce to just a few lines. Spark allows for binding to various languages for data analysis like Python, Java, and Scala. It allows anybody, ranging from app developers to data scientists, to explore its scalability and speed in a way which is accessible.

Spark SQL

Spark SQL has transformed to provide more features and is more crucial for most processes as far as the Apache Spark project is concerned. It is an interface widely employed in today’s world by those creating apps. Spark SQL is aimed at the processing of structured data, using an approach copied from R and Python. Spark SQL also has an interface which allows for querying of data, reading, and writing from and to other data stores. Apache Spark employs a query optimizer that examines queries and then provides an efficient query plan.

Spark streaming

This was a feature added to Apache Spark which made it more popular in an environment that dealt with real-time processing. Before, batch and stream processing with Apache and Hadoop were different things. You could use MapReduce for batch processing and Apache Spark for real-time streaming. Spark streaming has made it possible to extend the Apache Spark batch processing feature into streaming. This is achieved by shredding the streams into micro-batches using Apache Spark.

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