Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Doris provides batch data loading and real-time mini-batch data loading. It provides high availability, reliability, fault tolerance, and scalability. Its original name was Palo, developed in Baidu. 
 
<ul><li>Uses MySQL protocol to communicate. Users can connect to Doris cluster through MySQL client or MySQL JDBC</li><li>Support standard SQL language, compatible with MySQL protocol</li><li>Vectorized SQL executor</li><li>Getting result of a query within one second</li><li>Rollup, novel pre-computation mechanism
</li><li>Effective data model for aggregation</li><li>High performance, high availability, high reliability</li><li>Easy for operation, Elastic data warehouse for big data</li></ul>

Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Doris provides batch data loading and real-time mini-batch data loading. It provides high availability, reliability, fault tolerance, and scalability. Its original name was Palo, developed in Baidu. 

Apache Doris -  A fast MPP database for all modern analytics on big data

VoltDB was specifically designed for contemporary software applications that are pushed beyond their limits by high volume data sources. VoltDB provides the ability to capture, store and process incoming data at millions of read/write operations per second. And VoltDB’s relational model opens that data to be analyzed in real-time, using familiar Business Intelligence tools, to identify data patterns and trends, spot anomalies, or perform tracking and alerting. In addition to supporting real-time analytics, VoltDB seamlessly integrates with data warehousing and Hadoop infrastructures, feeding data to those environments for deep analytical exploration. Large database gets automatically sharded to multiple nodes, basically it supports database to scale out. VoltDB implements HA using K-Safety, a configurable, active-active durability model for high performance distributed systems. Client connectors are available in languages like Java, C++, Python, PHP, HTTP/JSON, Ruby and Erlang.

VoltDB was specifically designed for contemporary software applications that are pushed beyond their limits by high volume data sources. VoltDB provides the ability to capture, store and process incoming data at millions of read/write operations per second. And VoltDB’s relational model opens that data to be analyzed in real-time, using familiar Business Intelligence tools, to identify data patterns and trends, spot anomalies, or perform tracking and alerting.

VoltDB - Fast Scalable SQL DBMS with ACID

Apache Mnemonic is a non-volatile hybrid memory storage oriented library, it proposed a non-volatile/durable Java object model and durable computing service that bring several advantages to significantly improve the performance of massive real-time data processing/analytics. developers are able to use this library to design their cache-less and SerDe-less high performance applications.
<ul>
<li>In-place data storage on local non-volatile memory</li>
<li>Durable Object Model (DOM)</li>
<li>Durable Native Computing Model (DNCM)</li>
<li>Object graphs lazy loading &amp; sharing</li>
<li>Auto-reclaim memory resources and Mnemonic objects</li>
<li>Hierarchical cache pool for massive data caching</li>
<li>Extensible memory services for new device adoption and allocation optimization</li>
<li>Durable data structure collection(WIP)</li>
<li>Durable computing service</li>
<li>Minimize memory footprint of on-heap</li>
<li>Reduce GC Overheads as the following chart shown (collected from Apache Spark experiments)</li>
<li>Drop-in Hadoop MapReduce support</li>
<li>Drop-in Hadoop Spark support</li>
</ul>

Apache Mnemonic is a non-volatile hybrid memory storage oriented library, it proposed a non-volatile/durable Java object model and durable computing service that bring several advantages to significantly improve the performance of massive real-time data processing/analytics. developers are able to use this library to design their cache-less and SerDe-less high performance applications.

Apache Mnemonic -  Non-volatile hybrid memory storage oriented library

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

sidekick is a high-performance sidecar load-balancer. By attaching a tiny load balancer as a sidecar to each of the client application processes, you can eliminate the centralized loadbalancer bottleneck and DNS failover management. sidekick automatically avoids sending traffic to the failed servers by checking their health via the readiness API and HTTP error returns.

Sidekick - High Performance HTTP Sidecar Load Balancer


Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage). As an organization, Hudi can help you build an efficient data lake, solving some of the most complex, low-level storage management problems, while putting data into hands of your data analysts, engineers and scientists much quicker.
 
Features: 
<ul>
<li>Upsert support with fast, pluggable indexing</li>
<li>Atomically publish data with rollback support</li>
<li>Snapshot isolation between writer &amp; queries</li>
<li>Savepoints for data recovery</li>
<li>Manages file sizes, layout using statistics</li>
<li>Async compaction of row &amp; columnar data</li>
<li>Timeline metadata to track lineage</li>
<li>Optimize data lake layout with clustering</li>
</ul>

Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage). As an organization, Hudi can help you build an efficient data lake, solving some of the most complex, low-level storage management problems, while putting data into hands of your data analysts, engineers and scientists much quicker.

Apache Hudi - Streaming Data Lake Platform

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Apache Doris - A fast MPP database for all modern analytics on big data

VoltDB - Fast Scalable SQL DBMS with ACID

Apache Mnemonic - Non-volatile hybrid memory storage oriented library

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

Sidekick - High Performance HTTP Sidecar Load Balancer

Apache Hudi - Streaming Data Lake Platform

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Apache Doris - A fast MPP database for all modern analytics on big data

VoltDB - Fast Scalable SQL DBMS with ACID

Apache Mnemonic - Non-volatile hybrid memory storage oriented library

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

Sidekick - High Performance HTTP Sidecar Load Balancer

Apache Hudi - Streaming Data Lake Platform

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