Spark has made it as a top-level Apache project, going from incubation to graduation in record time. It is also one of Apache’s most active projects with hundreds of contributors. This is because of its superior architecture and timeliness of engineering choices. With that plus appropriate care and feeding, Apache Spark will have a bright future even as it evolves and adapts to changing technology and business drivers.
What is Apache Spark?
Apache Spark is an open-source cluster computing framework which works as a standalone technology or a Hadoop-compatible technology. It was developed at AMPLabs in UC Berkeley as part of Berkeley Data Analytics Stack (BDAS).
Usually, there are three different types of use cases in big data processing, namely batch (ad-hoc queries), real-time streaming and interactive (querying historical data).
There are different tools to deal with these specific use cases within a Hadoop ecosystem. Apache Spark solves this issue as it provides a common framework for working with all types of data sets in any use case scenario.
Apache Spark: Bringing New Efficiencies to Big Data Analysis
Data analysis performed in near-time and real-time also needs solutions that can rapidly process large data sets. Apache Spark, an in-memory data processing framework, is increasingly the solution of choice.
Spark is a framework providing parallel, distributed data processing. Spark can be deployed through Apache Hadoop via Yarn, Apache Mesos, or its own standalone cluster manager. It can serve as a foundation for other data processing frameworks, and supports programming languages including Scala, Java, and Python. Data can be accessed in HDFS, Cassandra, HBase, Hive, Tachyon, and any Hadoop data source.
Data sets can be pinned in memory with Spark, which boosts application performance noticeably. Spark also provides speed improvements for applications running on disk and enables MapReduce to support interactive queries and stream processing far more efficiently.
And Spark eliminates the need for separate, distributed systems to process, for example, batch applications, interactive queries, iterative algorithms, and/or streaming. With Spark, all of these processing types are supported by the same engine, reducing management chores and making the processes easier to combine.
Businesses can count on Spark’s benefits over the long-term. Apache top level projects, which include Hadoop, Spark, and httpd, is a designation that indicates a project has strong community backing from developers and users-and has proved its worth.