Data Management for Big Data and Analytics
eXtremeDB delivers accelerated storage to HPC infrastructure workloads.
eXtremeDB for High Performance Computing and Big Data is based on a blazingly fast storage engine delivering scalability, low latency and advanced analytical capabilities for HPC workloads.
Exploring large amounts of data to discover underlying patterns is a cornerstone of modern business practices. Statistics are applied everywhere from machine learning to finance to digital marketing to healthcare and communications. eXtremeDB HPC comes with a library of more than 150 functions for performing statistical analysis on time series data, such as grand, group, grid and window averages to name a few.
Learn more about what makes eXtremeDB ideal for Time Series Data
Learn more about what makes eXtremeDB ideal for Financial Systems
Learn more about the eXtremeDB database for Big Data and analytics in our HPC FAQ
Pipelining
Pipelining refers to a series of data processing steps where the output of one step is the input to the next step. The pipelining of vector‐based statistical functions is key to eXtremeDB’s ability to accelerate performance when working with IoT and capital markets’ time series data. eXtremeDB’s extensive library of math functions are the building blocks that are assembled into pipelines to minimize data transfers and maximally exploit the CPU’s L1/L2/L3 cache. Don’t see the function your analytics require? No problem, write it yourself and use it alongside the built-in functions seamlessly.
A database for Big Data and analytics should be the fastest available. Since 2014, eXtremeDB for HPC has set records, then broken our own records. See the stats.
Watch a 2 minute video on Pipelining.
Review the white paper: Pipelining Vector-Based Statistical Functions for In-Memory Analytics
Pipelining
Pipelining refers to a series of data processing steps where the output of one step is the input to the next step. The pipelining of vector‐based statistical functions is key to eXtremeDB’s ability to accelerate performance when working with IoT and capital markets’ time series data. eXtremeDB’s extensive library of math functions are the building blocks that are assembled into pipelines to minimize data transfers and maximally exploit the CPU’s L1/L2/L3 cache. Don’t see the function your analytics require? No problem, write it yourself and use it alongside the built-in functions seamlessly.
A database for Big Data and analytics should be the fastest available. Since 2014, eXtremeDB for HPC has set records, then broken our own records. See the stats.
Watch a 2 minute video on Pipelining.
Review the white paper: Pipelining Vector-Based Statistical Functions for In-Memory Analytics
Columnar and Row-oriented Storage
Combining analytics and online transaction processing (OLTP) queries is a common requirement for today’s high-performance database systems. eXtremeDB supports two storage formats: Vertical (columnar) storage for time-series data, which is ideal for working with tick streams and machine-generated IoT data, while traditional row-oriented storage is de rigueur for OLTP workloads.
eXtremeDB stores time series data with a columnar layout, and “normal” data with a conventional row-based layout. The result is higher performance for time series analytics.
Learn about the advantages of an in-memory database for financial analytics
Learn more about eXtremeDB and in-database analytics
Efficient Big Data processing frequently requires data partitioning. eXtremeDB offers ultra-fast, elastically scalable data management through sharding — the horizontal partitioning of data. Sharding allows distribution of data over multiple physical nodes, or partitions on the same node, maximizing CPU load and exploiting storage media I/O concurrency.
The developer can specify the storage (in-memory or persistent) for each table, which is ideal for handling streaming and historical data within a single database architecture.
Since 2014, eXtremeDB for HPC has set records, then broken its own records. See the stats.
Scalable distributed database contrast and compare. This table summarizes the primary purpose and characteristics of each distributed database option.
The developer can specify the storage (in-memory or persistent) for each table, which is ideal for handling streaming and historical data within a single database architecture.
Since 2014, eXtremeDB for HPC has set records, then broken our own records. See the stats.
Scalable distributed database contrast and compare. This table summarizes primary purpose and characteristics of each distributed database option.
High Availability Options
eXtremeDB offers a comprehensive set of high availability capabilities through master-slave and multi-master (Cluster) database configurations, and the advanced Active Replication Fabric. Depending on the applications’ requirements, these capabilities can be deployed individually or can seamlessly work together ensuring maximum availability of data and promoting query load balancing in complex heterogeneous environments.
Review a chart of all eXtremeDB distributed database options
Learn how eXtremeDB Active Replication Fabric solves connectivity issues for developers.
Learn more about eXtremeDB Cluster distributed database for big data and analytics
eXtremeDB provides a set of libraries written in the C language that enables you to access databases from within any C or C++ program, and language bindings for Python, Java, Scala, PHP, Rust and Lua, and a RESTful API for browser-based apps. Whether you prefer SQL or a native (nonSQL) API, eXtremeDB has you covered. Each language binding defines its own interface to access eXtremeDB databases. SQL and nonSQL APIs, and multiple programming languages can be used simultaneously with the same database.
Read the article, Is SQL Fast Enough for Tick Data?
Learn more about programming with eXtremeDB in our online documentation
Learn more about:
- eXtremeSQL
- eXtremeDB Java Native interface
- eXtremeDB Python interface
- The eXtremeDB type-safe API eliminates database corruption.
Read the article, Is SQL Fast Enough for Tick Data?
Learn more about programming with eXtremeDB in our online documentation
Learn more about:
- eXtremeSQL
- eXtremeDB Java Native interface
- eXtremeDB Python interface
- The eXtremeDB type-safe API eliminates database corruption.
eXtremeDB is database management wherever you need it. It’s used by innovative industry leaders in over 70,000,000 deployments world-wide in these markets and others.
Network & Telecom
Network gear developers build on proven eXtremeDB speed and reliability, combined in-memory and persistent data layouts, optimized access methods and unmatched flexibility.
Consumer electronics
eXtremeDB’s small code size (approximately 200K) reduces device hardware costs, while its unmatched speed delivers a better user experience.
Industrial Systems
eXtremeDB’s sophisticated event notification systems, time series data processing and high availability have powered its wide-spread adoption in SCADA, fleet management, smart building automation and other verticals.
Aerospace & Defense
Northrop Grumman, Lockheed Martin, British Aerospace, EADS and others depend on eXtremeDB’s reliability, unmatched performance and broad platform support.
Energy
eXtremeDB optimization technology can dramatically boost utilities’ power generation yields. Distribution networks can become self-healing and bi-directional, enabling end-users to contribute power back to the grid.
Finance
eXtremeDB’s unique hybrid row- and columnar-layout (OLTP and time series) couples with pipelined functions for statistical analysis and scalable distributed database architecture power record-setting STAC-M3 benchmark results.
Outstanding Structured Database
Best Big Data Analytics & Technology Provider
Related Resources
Articles for Professional Developers
“On Time Series Analysis and Big Data. Interview with Andrei Gorine ” – ODBMS.org or “A McObject Focus – What’s Changing in the Satellite Industry ” – SatMagazine
See a list of articles
White Papers for Professional Developers
We have been testing, improving on, and retesting our software from the beginning in 2001 in order to provide our clients with the best possible data management solutions. Read “Pipelining Vector-Based Statistical Functions for In-Memory Analytics” and more.
Review our research
Webinars for Professional Developers
Watch to on-demand Webinars, hosted by experts, about proven database management system practices. Watch “Embedded Databases: Building In Always On High Availability” and others.
Review our list of Webinars
Review the Benchmark Test Results
eXtremeDB sets speed records year after year. In multiple independently audited benchmark tests, eXtremeDB has broken its own earlier records for the best (lowest) mean response times and for lowest standard deviation of test results using the SQL database programming language.
