Why Does Apache Spark – Hadoop Integration Matters

Hadoop – A set of open-source software applications provide a suitable platform for extracting, processing, and analyzing an enormous amount of data. It offers an efficient software framework for constructive processing of both structured and unstructured data, with the support of the MapReduce programming model. Even though Hadoop features some of the most advanced benefits such as scalability, flexibility, and cost-effectiveness, however, it might become even much better platform with the support of robust components.

Labeled as “Lightning Fast Cluster Computing”, Apache Spark is a unified analytics framework utilized for managing large-scale data analytics and is capable of handling both, batch/real-time analytics as well as data processing workloads. The flexibility, scalability, and easy-to-write makes Spark the “Next Big Thing” in the IT industry.

The innovation of Apache Spark implementation is regarded as the most significant turning point in the domain of Big Data and has been embraced by most of the firms all over the world with a substantial amount of success as well as impact.

Let’s have a quick look at Apache Spark’s core features: This Gen-next tool for Big Data processing offers some of the awesome core features over its competitors.

  • Swift Data Processing – Spark helps to achieve a high data processing speed by reducing the total number of read-write’s – approx. 10x faster on the disk and 100x faster in the memory.
  • In-memory Computation - As the data is being cached, Spark saves time that has been utilized for fetching data from the disk every time that results in accelerating of processing speed.
  • Re-usability – The code of the Spark can be used for other purposes such as distributed batch processing and running of ad-hoc queries over stream state.
  • Fault Tolerance – With the help of RDD (Resilient Distributed Datasets), Spark offers efficient fault tolerance, which ensures minimum loss of data in case any of the work node in the cluster goes down.
  • Dynamic Nature – Backed with 80 high-level operators, Spark provides an effective platform to develop collateral applications without putting much effort.
  • Hadoop Integration – Spark is capable of working with the files that are stored in the Hadoop Distributed File System (HDFS) and also supports applications related to Big Data Analytics.
  • Analytic Suite – Spark comes along tools that meant for real-time analysis, interactive query analysis, and large-scale graph processing as well as analysis.

Advantages of Working Together: Apache Spark and Hadoop

Termed as the most advanced technological application, Apache Spark is outfitted with some of the state-of-art features and offers enhanced functionality alongside the Hadoop’s applicability.

  • Spark is well suitable for Hadoop’s community of open source and can easily work with Hadoop Distributed File System (HDFS). In addition, Spark assures performance approx. 100x faster in comparison to Hadoop’s MapReduce for some of the applications.
  • Spark provides essential features for in-memory computing that enables loading of enormous data into the memory of a cluster and analyze it on a frequent basis.
  • Spark has the ability to generate a number of tasks associated with data analysis that can operate 100 times quicker than that of a standard Hadoop MapReduce which only permits the execution of batch data processing.
  • Spark is an alternative platform for MapReduce and successfully executes a variety of jobs like an analysis of live-streaming data and more computation-oriented jobs revolving around graph processing and machine learning.
  • Spark supports the writing of data analysis tasks in multiple languages such as Java, Python, and/or Scala with the help of around 80 high-level operators.
  • Spark’s in-built libraries will complement all types of data processing with the support of latest Hadoop’s deployments – MLlib executes a large number of the most common machine learning algorithms, Streaming allows high-speed data processing from various sources, while GraphX enables computation on the graphical data.  
  • Spark provides a powerful Application Programming Interface, through which developers are able to interact with its support team via their own applications.
  • Spark SQL component, which is the only available alpha in the present market, permits the data to be examined along with unstructured data in the analysis process. Extracting of data from the Hadoop with the help of SQL queries is another key feature of real-time interrogation functionality.
  • Spark is perfectly suitable for Hadoop’s Distributed File System, Yet Another Resource Negotiator as well as HBase distributed-database – which is an added advantage.

Adopters of Spark in the IT industry

Several prominent IT firms such as NASA, Cloudera, Yahoo, IBM, Pivotal, MapR, and Intel have incorporated Spark within their Hadoop framework for data associated day-to-day operations. Founded by the creators of Apache Spark, Databricks offers support for the clients who are into the processing task of cloud-based Big Data.

Conclusion 

The integration of Spark with Hadoop might turn as the biggest advantage for the users and vendors of Hadoop. The users who are thinking of implementing the Hadoop as well as who already brought Hadoop into their systems are in fact attracted to the excellent notion of utilizing Hadoop as both batch and real-time processing systems.

The advanced Spark technology offers some of the cutting-edge features to build or support Big Data applications. However, at present, some of the well-established vendors of Hadoop such as Cloudera (through Cloudera Enterprise) and Hortonworks (through its Hadoop distribution) are already providing support for Spark. Of course, the adoption of Spark by some of the large-scale firms imply that it might turn around as the most eminent platform for Big Data processing.

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