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Amazon EMR vs Apache Spark
July 21, 2023 | Author: Michael Stromann
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Amazon EMR is a service that uses Apache Spark and Hadoop, open-source frameworks, to quickly & cost-effectively process and analyze vast amounts of data.
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Apache Spark is a fast and general engine for large-scale data processing. Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Write applications quickly in Java, Scala or Python. Combine SQL, streaming, and complex analytics.
See also:
Top 10 Big Data platforms
Top 10 Big Data platforms
Amazon EMR (Elastic MapReduce) and Apache Spark are both powerful big data processing platforms, but they have distinct differences that set them apart. Amazon EMR is a managed service offered by AWS, allowing users to easily provision and scale Hadoop and Spark clusters on Amazon's cloud infrastructure. EMR supports various big data processing frameworks, including Apache Spark, Apache Hadoop, and more. It provides a convenient way for organizations to run Spark-based data processing and analytics tasks on the cloud without the need for manual cluster management. On the other hand, Apache Spark is an open-source distributed computing system designed for speed and ease of use. Spark provides in-memory data processing, enabling faster data analytics and machine learning capabilities. While EMR includes Spark as one of its supported frameworks, running Spark directly gives users more control over cluster configurations and allows for greater customization.
See also: Top 10 Big Data platforms
See also: Top 10 Big Data platforms
Amazon EMR vs Apache Spark in our news:
2015. IBM bets on big data Apache Spark project
IBM has made a significant announcement regarding its involvement in the open source big data project Apache Spark. The company plans to allocate a team of 3,500 researchers to this initiative. Additionally, IBM has unveiled its decision to open source its own IBM SystemML machine learning technology. These strategic moves are aimed at positioning IBM as a frontrunner in the domains of big data and machine learning. Cloud, big data, analytics, and security form the pillars of IBM's transformation strategy. In conjunction with this announcement, IBM has committed to integrating Spark into its core analytics products and partnering with Databricks, the commercial entity established to support the open source Spark project. IBM's participation in these endeavors goes beyond mere altruism. By actively engaging with the open source community, IBM aims to establish itself as a trusted contributor in the realm of big data. This, in turn, enhances its credibility among companies working on big data and machine learning projects using open source tools. The collaborative involvement with the community opens doors for IBM to offer consulting services and seize other business opportunities in this space.
2015. Google partners with Cloudera to bring Cloud Dataflow to Apache Spark
Google has announced a collaboration with Cloudera, the Hadoop specialists, to integrate its Cloud Dataflow programming model into Apache's Spark data processing engine. By bringing Cloud Dataflow to Spark, developers gain the ability to create and monitor data processing pipelines without the need to manage the underlying data processing cluster. This service originated from Google's internal tools for processing large datasets at a massive scale on the internet. However, not all data processing tasks are identical, and sometimes it becomes necessary to run tasks in different environments such as the cloud, on-premises, or on various processing engines. With Cloud Dataflow, data analysts can utilize the same system to create pipelines, regardless of the underlying architecture they choose to deploy them on.
