Presto vs Apache Spark: What are the differences?
Presto: Distributed SQL Query Engine for Big Data. Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes; 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.
Presto and Apache Spark can be primarily classified as "Big Data" tools.
"Works directly on files in s3 (no ETL)" is the top reason why over 9 developers like Presto, while over 45 developers mention "Open-source" as the leading cause for choosing Apache Spark.
Presto and Apache Spark are both open source tools. It seems that Apache Spark with 22.3K GitHub stars and 19.3K forks on GitHub has more adoption than Presto with 9.22K GitHub stars and 3.12K GitHub forks.
Slack, Shopify, and SendGrid are some of the popular companies that use Apache Spark, whereas Presto is used by Repro, Airbnb, and Netflix. Apache Spark has a broader approval, being mentioned in 263 company stacks & 111 developers stacks; compared to Presto, which is listed in 19 company stacks and 11 developer stacks.
What is Presto?
What is Apache Spark?
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What are the cons of using Presto?
What tools integrate with Apache Spark?
Spark is good at parallel data processing management. We wrote a neat program to handle the TBs data we get everyday.