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HBase

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TLDR: HBase, introduced in 2008, is a distributed, open-source NoSQL database designed for real-time processing of large-scale structured and semi-structured data. Built on top of Hadoop, it provides low-latency access to vast amounts of key-value and column-oriented data types. HBase excels in scenarios requiring high write throughput and random read access, making it ideal for real-time data analytics, IoT workloads, and large-scale transaction processing in industries such as telecommunications and e-commerce.

HBase integrates seamlessly with the Hadoop ecosystem, offering advanced features like automatic sharding, replication, and failover. It supports the HBase Shell as its query language and integrates with Apache Spark and Apache Hive for advanced analytics and batch processing. Its compatibility with popular programming terms such as Java, Python, and Scala enables developers to build robust and scalable applications. Widely used in hybrid and cloud database environments, HBase ensures reliable performance and scalability for enterprise-grade data science and operational analytics.

https://github.com/apache/hbase

https://hbase.apache.org

https://en.wikipedia.org/wiki/Apache_HBase

Snippet from Wikipedia: Apache HBase: HBase is an open-source non-relational distributed database modeled after Google's Bigtable and written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System) or Alluxio, providing Bigtable-like capabilities for Hadoop. That is, it provides a fault-tolerant way of storing large quantities of sparse data (small amounts of information caught within a large collection of empty or unimportant data, such as finding the 50 largest items in a group of 2 billion records, or finding the non-zero items representing less than 0.1% of a huge collection).
HBase features compression, in-memory operation, and Bloom filters on a per-column basis as outlined in the original Bigtable paper. Tables in HBase can serve as the input and output for MapReduce jobs run in Hadoop, and may be accessed through the Java API but also through REST, Avro or Thrift gateway APIs. HBase is a wide-column store and has been widely adopted because of its lineage with Hadoop and HDFS. HBase runs on top of HDFS and is well-suited for fast read and write operations on large datasets with high throughput and low input/output latency.
HBase is not a direct replacement for a classic SQL database, however Apache Phoenix project provides a SQL layer for HBase as well as JDBC driver that can be integrated with various analytics and business intelligence applications. The Apache Trafodion project provides a SQL query engine with ODBC and JDBC drivers and distributed ACID transaction protection across multiple statements, tables and rows that use HBase as a storage engine.
HBase is now serving several data-driven websites but Facebook's Messaging Platform migrated from HBase to MyRocks in 2018. Unlike relational and traditional databases, HBase does not support SQL scripting; instead the equivalent is written in Java, employing similarity with a MapReduce application.
In the parlance of Eric Brewer's CAP Theorem, HBase is a CP type system.

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