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Apache Drill vs Apache Spark
June 04, 2023 | Author: Michael Stromann
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Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage. Get faster insights without the overhead (data loading, schema creation and maintenance, transformations, etc.). Analyze the multi-structured and nested data in non-relational datastores directly without transforming or restricting the 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.
Apache Drill and Apache Spark are both powerful open-source distributed computing frameworks, but they have key differences in terms of their primary use cases and data processing capabilities.
Apache Drill is designed for self-service data exploration and analysis. It enables users to perform ad-hoc queries on a variety of data sources, including structured, semi-structured, and unstructured data, without requiring predefined schemas or data transformations. Drill's schema-free nature allows for on-the-fly schema discovery and flexible querying across heterogeneous data sources, making it suitable for exploratory data analysis and data discovery scenarios.
Apache Spark, on the other hand, is a general-purpose distributed computing framework that excels at processing and analyzing large-scale data sets. Spark provides a unified computing engine that supports batch processing, real-time streaming, machine learning, and graph processing. It offers a rich set of libraries and APIs for various data processing tasks, making it suitable for a wide range of use cases, including data analytics, machine learning, and ETL (Extract, Transform, Load) processes.
See also: Top 10 Big Data platforms
Apache Drill is designed for self-service data exploration and analysis. It enables users to perform ad-hoc queries on a variety of data sources, including structured, semi-structured, and unstructured data, without requiring predefined schemas or data transformations. Drill's schema-free nature allows for on-the-fly schema discovery and flexible querying across heterogeneous data sources, making it suitable for exploratory data analysis and data discovery scenarios.
Apache Spark, on the other hand, is a general-purpose distributed computing framework that excels at processing and analyzing large-scale data sets. Spark provides a unified computing engine that supports batch processing, real-time streaming, machine learning, and graph processing. It offers a rich set of libraries and APIs for various data processing tasks, making it suitable for a wide range of use cases, including data analytics, machine learning, and ETL (Extract, Transform, Load) processes.
See also: Top 10 Big Data platforms
Apache Drill 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.
