MapD Core is an in-memory, column store, SQL relational database that was designed from the ground up to run on GPUs. MapD Core is the foundational element of a larger data exploration platform that emphasizes speed at scale. By taking advantage of the parallel processing power of the hardware, MapD Core can query billions of rows in milliseconds. Furthermore, by using the graphics pipelines of GPUs, MapD Core can render graphics directly from the server.
 
MapD can operate as a standalone database using the command line or its administration console. MapD can also output to its native interface, MapD Immerse, or to third-party software such as Tableau, Qlik, or Birst, using a variety of connectors. 
 
Within a single server, MapD Core can handle datasets 1.5-3TB in raw size in GPU RAM, or 10-15TB in CPU RAM. In distributed deployments, that number scales linearly.

 MapD Core is an in-memory, column store, SQL relational database that was designed from the ground up to run on GPUs. MapD Core is the foundational element of a larger data exploration platform that emphasizes speed at scale. By taking advantage of the parallel processing power of the hardware, MapD Core can query billions of rows in milliseconds. Furthermore, by using the graphics pipelines of GPUs, MapD Core can render graphics directly from the server.

MapD - The MapD Core database

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

EventQL is a distributed, column-oriented database built for large-scale event collection and analytics. It runs super-fast SQL and MapReduce queries. Its features include Automatic partitioning, Columnar storage, Standard SQL support, Scales to petabytes, Timeseries and relational data, Fast range scans and lot more.
 
Use case: 
<ul>
 <li>
 Storage and analysis of streaming event, timeseries or relational data
 </li>
 <li>
 High volume event and sensor data logging
 </li>
 <li>
 Joining and correlating of timeseries data with relational tables
 </li>
 </ul>

 EventQL is a distributed, column-oriented database built for large-scale event collection and analytics. It runs super-fast SQL and MapReduce queries. Its features include Automatic partitioning, Columnar storage, Standard SQL support, Scales to petabytes, Timeseries and relational data, Fast range scans and lot more.

EventQL - The database for large-scale event analytics

TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.
 
<ul><li>10x Faster on Insert/Query Speeds: Through the innovative design on storage, on a single-core machine, over 20K requests can be processed, millions of data points can be ingested, and over 10 million data points can be retrieved in a second. It is 10 times faster than other databases.</li><li>1/5 Hardware/Cloud Service Costs: Compared with typical big data solutions, less than 1/5 of computing resources are required. Via column-based storage and tuned compression algorithms for different data types, less than 1/10 of storage space is needed.</li><li>Full Stack for Time-Series Data: By integrating a database with message queuing, caching, and stream computing features together, it is no longer necessary to integrate Kafka/Redis/HBase/Spark or other software. It makes the system architecture much simpler and more robust.</li><li>Powerful Data Analysis: Whether it is 10 years or one minute ago, data can be queried just by specifying the time range. Data can be aggregated over time, multiple time streams or both. Ad Hoc queries or analyses can be executed via TDengine shell, Python, R or Matlab.</li><li>Seamless Integration with Other Tools: Telegraf, Grafana, Matlab, R, and other tools can be integrated with TDengine without a line of code. MQTT, OPC, Hadoop, Spark, and many others will be integrated soon.</li><li>Zero Management, No Learning Curve: It takes only seconds to download, install, and run it successfully; there are no other dependencies. Automatic partitioning on tables or DBs. Standard SQL is used, with C/C++, Python, JDBC, Go and RESTful connectors.</li></ul><div> </div>

TDengine is an open-source big data platform designed and optimized for Internet of Things (IoT), Connected Vehicles, and Industrial IoT. Besides the 10x faster time-series database, it provides caching, stream computing, message queuing and other functionalities to reduce the complexity and costs of development and operations.

TDengine -  Big data platform designed and optimized for the Internet of Things

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MapD - The MapD Core database

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TDengine - Big data platform designed and optimized for the Internet of Things

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