The Apache Cassandra Project develops a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model. Cassandra is suitable for applications that can't afford to lose data. Data is automatically replicated to multiple nodes for fault-tolerance. 

 Cassandra provides support for Scale out, load balancing, cluster growth, Flexible schema, Key-oriented queries and CAP theorem (Consistency, Availability, Partition tolerance). It is in use at Digg, Facebook, Twitter, Reddit, Rackspace, Cloudkick, Cisco and more companies.

The Apache Cassandra Project develops a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model. Cassandra is suitable for applications that can't afford to lose data. Data is automatically replicated to multiple nodes for fault-tolerance.

Cassandra - Scalable Distributed Database

LucidDB is the RDBMS built entirely for data warehousing and business intelligence. It is based on architectural cornerstones such as column-store, bitmap indexing, hash join/aggregation, and page-level multiversioning. Every component of LucidDB was designed with the requirements of flexible, high-performance data integration and sophisticated query processing in mind. 

 Rather than throwing hardware at data warehousing problems by relying on expensive clusters or specialized "appliances", the scalability offered by LucidDB's unique architecture allows you to achieve great performance using only a single off-the-shelf Linux or Windows server. Besides keeping costs down, this also minimizes maintenance and administration hassles. 
 <UL>
	<LI>Support of both 32 and 64 bit platforms</LI>
	<LI>Integrated with Pentaho Business Intelligence product</LI>
	<LI>Automatically adapts to either bitmap or btree representation depending on data distribution</LI>
	<LI>Versioning at page-level is much more efficient than transactional databases</LI>
	<LI>Supports incremental backup</LI>
	<LI>High performance and greater cache and disk effectiveness</LI>
	<LI>Allows flat files (e.g. BCP or CSV format) to be queried as foreign tables via LucidDB</LI>
 </UL>

LucidDB is the RDBMS built entirely for data warehousing and business intelligence. It is based on architectural cornerstones such as column-store, bitmap indexing, hash join/aggregation, and page-level multi versioning. Every component of LucidDB was designed with the requirements of flexible, high-performance data integration and sophisticated query processing in mind.

LucidDB - RDBMS built entirely for Data Warehousing and Business Intelligence

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations. Druid can load both streaming and batch data. 

Druid IO - Real Time Exploratory Analytics on Large Datasets

Pinot is a realtime distributed OLAP datastore, which is used at LinkedIn to deliver scalable real time analytics with low latency. It can ingest data from offline data sources (such as Hadoop and flat files) as well as online sources (such as Kafka). Pinot is designed to scale horizontally, so that it can scale to larger data sets and higher query rates as needed.
 
Its features include: 
<ul>
<li>A column-oriented database with various compression schemes such as Run Length, Fixed Bit Length</li>
<li>Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index</li>
<li>Ability to optimize query/execution plan based on query and segment metadata .</li>
<li>Near real time ingestion from Kafka and batch ingestion from Hadoop</li>
<li>SQL like language that supports selection, aggregation, filtering, group by, order by, distinct queries on fact data.</li>
<li>Support for multivalued fields</li>
<li>Horizontally scalable and fault tolerant</li>
</ul>

Pinot is a realtime distributed OLAP datastore, which is used at LinkedIn to deliver scalable real time analytics with low latency. It can ingest data from offline data sources (such as Hadoop and flat files) as well as online sources (such as Kafka). Pinot is designed to scale horizontally, so that it can scale to larger data sets and higher query rates as needed.

Pinot - A realtime distributed OLAP datastore

Cloudata is Distributed Large scale Structured Data Storage, and open source project implementing Google's Bigtable. It's DBMS(Database Management System), but not Relational DBMS. It can store more than Peta bytes.

Cloudata - Structured Data Storage implementing Google's Bigtable.

Discover open source projects across all platforms

Projects

Cassandra - Scalable Distributed Database

LucidDB - RDBMS built entirely for Data Warehousing and Business Intelligence

Druid IO - Real Time Exploratory Analytics on Large Datasets

Pinot - A realtime distributed OLAP datastore

Cloudata - Structured Data Storage implementing Google's Bigtable.

Tagcloud

License

Suggested keywords:

Projects

Cassandra - Scalable Distributed Database

LucidDB - RDBMS built entirely for Data Warehousing and Business Intelligence

Druid IO - Real Time Exploratory Analytics on Large Datasets

Pinot - A realtime distributed OLAP datastore

Cloudata - Structured Data Storage implementing Google's Bigtable.

Tagcloud

License