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Presto
Presto

106
176
+ 1
41
RocksDB
RocksDB

44
52
+ 1
10
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Presto vs RocksDB: What are the differences?

Developers describe Presto as "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. On the other hand, RocksDB is detailed as "Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team". RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

Presto and RocksDB are primarily classified as "Big Data" and "Databases" tools respectively.

"Works directly on files in s3 (no ETL)" is the primary reason why developers consider Presto over the competitors, whereas "Very fast" was stated as the key factor in picking RocksDB.

Presto and RocksDB are both open source tools. It seems that RocksDB with 14.3K GitHub stars and 3.12K forks on GitHub has more adoption than Presto with 9.29K GitHub stars and 3.15K GitHub forks.

According to the StackShare community, Presto has a broader approval, being mentioned in 19 company stacks & 11 developers stacks; compared to RocksDB, which is listed in 6 company stacks and 7 developer stacks.

What is Presto?

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.

What is RocksDB?

RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.
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      What are some alternatives to Presto and RocksDB?
      Stan
      A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.
      Apache Spark
      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.
      Amazon Athena
      Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
      Apache Flink
      Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
      Apache Hive
      Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
      See all alternatives
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