Apache Spark vs Sqoop: What are the differences?
Apache Spark: Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning; Sqoop: A tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores. It is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases of The Apache Software Foundation.
Apache Spark can be classified as a tool in the "Big Data Tools" category, while Sqoop is grouped under "Database Tools".
Apache Spark is an open source tool with 22.9K GitHub stars and 19.7K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.
Uber Technologies, Slack, and Shopify are some of the popular companies that use Apache Spark, whereas Sqoop is used by Auto Trader, Adaptly, and Kobalt Music. Apache Spark has a broader approval, being mentioned in 356 company stacks & 564 developers stacks; compared to Sqoop, which is listed in 9 company stacks and 6 developer stacks.
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