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PipelineDB
PipelineDB

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RocksDB
RocksDB

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RocksDB vs PipelineDB: What are the differences?

RocksDB: 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; PipelineDB: The Streaming SQL Database. PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.

RocksDB and PipelineDB can be primarily classified as "Databases" tools.

Some of the features offered by RocksDB are:

  • Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM
  • Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory
  • Scales linearly with number of CPUs so that it works well on ARM processors

On the other hand, PipelineDB provides the following key features:

  • No Application Code
  • Runs on PostgreSQL
  • Eliminate ETL

RocksDB is an open source tool with 16K GitHub stars and 3.59K GitHub forks. Here's a link to RocksDB's open source repository on GitHub.

- No public GitHub repository available -

What is PipelineDB?

PipelineDB is an open-source relational database that runs SQL queries continuously on streams, incrementally storing results in tables.

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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        TimescaleDB
        TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.
        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.
        RethinkDB
        RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.
        InfluxDB
        InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.
        Kafka
        Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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