Quasar AI Successfully Achieves SOC 2 Compliance
New York, United States – November 19, 2024 – Quasar AI, a leader in real-time data intelligence platforms, proudly announces the successful completion of its System and Organization Controls (SOC) 2 Type II audit, reaffirming its dedication to maintaining top-tier customer data security standards.
The SOC 2 standard, developed by the American Institute of Certified Public Accountants (AICPA), validates controls for security, availability, processing integrity, confidentiality, and privacy. Quasar’s achievement underscores its commitment to safeguarding customer data and delivering a trusted, secure data environment.
The audit was conducted by Johanson Group LLP, a recognized certification body specializing in SOC 2 audits. Johanson Group attested to Quasar’s compliance with leading security standards for time series and real-time data intelligence solutions, reflecting Quasar’s rigorous security protocols and operational excellence.
SOC 2 Type II compliance ensures robust organizational practices to protect customer data. As a key player providing mission-critical data and identity management solutions across industries such as manufacturing, oil and gas, energy, mining, and finance, Quasar remains dedicated to upholding the highest standards of data security and integrity.
About Quasar AI
Quasar AI, founded in 2014, is a software publisher whose mission is to transform data into powerful insights that propel businesses forward. Quasar captures, normalizes, and stores an unlimited volume of data in the most efficient way possible. Our real-time data intelligence Platform enables businesses to find opportunities in their data and make smarter business decisions faster.
Quasar was recognized as “Cool Vendor in Data Management” by Gartner in 2020.
Quasar’s main offices are based in Paris and New York. Trusted by industry leaders in manufacturing, capital markets, and telecom, Quasar is redefining what is possible with real-time data intelligence.
Contact Info
Vicki Formosa
Quasar AI
QuasarDB Successfully Achieves SOC 2 Compliance
New York City, NY – Feb 21st, 2024 – QuasarDB (quasar.ai), a leading high-performance time-series database designed for real-time data processing and analytics, announce the successful completion of its System and Organization Controls (SOC) 2 Type I audit, achieving compliance with the leading industry standards for customer data security. This report shows QuasarDB’s ongoing commitment to providing a secure data environment for its customers.
Developed by the American Institute of Certified Public Accountants (AICPA), a SOC 2 information security standard is a report that validates controls relevant to security, availability, integrity, confidentiality, and privacy. The audit was completed with the help of Johanson Group LLP, a premier certification body helping organizations to obtain and maintain global compliance standards.
Johanson Group attested to QuasarDB’s information security controls meeting the leading industry standards for time series databases. Johanson Group specializes in SOC 2 audits and provides audit and professional services to public and private companies, large and small, in a variety of industries.
SOC 2 has a rigorous requirement on how companies handle customer data and information, so compliance guarantees there are established and implemented organizational practices in place to safeguard customer data.
At its core, QuasarDB is committed to providing mission-critical data to industries such as manufacturing, financial services, energy and various others. Data integrity and security is a fundamental part of how QuasarDB manages user identity. SOC2 Type I compliance represents a commitment that secure systems and controls are maintained by the organization on an ongoing basis.
About QuasarDB
QuasarDB, founded in 2014, is a software publisher whose mission is to make real-time intel ubiquitous, for a safer and smarter world. Quasar captures, normalizes, and stores an unlimited volume of events in the most efficient way possible, and enables businesses to deriver better insights from this data.
Quasar was recognized as “Cool Vendor in Data Management” by Gartner in 2020.
Quasar’s main offices are based in Paris and New York. Quasar is proud to be trusted by global leaders in telecoms, manufacturing, and capital markets.
More info: contact us
QuasarDB "Seneca" 3.14.1 Released
We are thrilled to announce the immediate availability of QuasarDB 3.14.1, an update from 3.14.0.
This update brings major new features while being 100% compatible with 3.14.0. Features such as streaming aggregation (named aggregated tables) and automatic data expiration are now available to all users.
Streaming aggregations enable you to aggregate the data as it comes and store the aggregate, unlocking ultra-low latency analytics while crushing disk usage.
In addition, we’ve added numerous performance enhancements, query functionalities, and bug fixes, that make the upgrade to 3.14.1 highly recommended!
Change log
(w.r.t. 3.14.0)
- Protocol version 48
- [arm64] Optimized several functions to leverage ARM Neon SIMD
- [compression] Slight improvement in (de)compression speed
- [daemon] Upsert now properly replaces all duplicates
- [kernel] Add support for TTL in tables
- [kernel] General availability of AGGREGATED tables (streaming analytics)
- [kernel] Overall improvement of insertion performance across the board
- [linux] Fixed stack trace symbol dump
- [orderbook] Can now generate order books on a list of timestamps and offsets
- [orderbook] Significant performance improvement of the Currenex order book engine
- [orderbook] The account ID column is now properly displayed
- [query] Add round, ceil, and floor functions
- [query] Add support for complex ASOF joins across multiple tables
- [query] Add support for count($timestamp)
- [query] Add support for interpolation in GROUP BY queries
- [query] Add support for JSON operators
- [query] Add support for LEAD() and LAG() for sliding window queries
- [query] ASOF now has a consistent behavior when there are NULL values
- [query] Can now drop a column that has a name made of a single character
- [query] Columns with different symbol tables can now be compared
- [query] Fix invalid first() and last() result for certain complex queries
- [query] Significant improvement for GROUP BY spanning thousands of tables
- [query] Support composition for correlation
- [query] Support for HAVING with PIVOT
QuasarDB "Seneca" 3.14.0 Released
We are thrilled to announce the immediate availability of QuasarDB 3.14.0, an update from 3.13.7.
Multiple performance and fixes have been added in this release, as well as support for multiple readers from a single S3 bucket, improved S3 backup speed, and, last but not least, initial support for windowing functions with the SQL OVER syntax.
This version sets a new standard in terms of speed, compression, and analytical power for timeseries databases and has been battle tested over multiple petabytes data sets in use case as diverse as asset health monitoring, orderbook analysis, cybersecurity, and electric grid simulations.
Get QuasarDB 3.14.0 here!
Change log
(w.r.t. 3.13.7)
- Protocol version 48
- [api] Add a function to trim a single entry
- [client] Compiler, build, cpu, and allocator are now in the –version output
- [daemon] Add support for multiple instances over the same S3 bucket (one writer, many readers)
- [daemon] Add support for short-term credentials for S3 authentication
- [daemon] Sensitive parameters are now hidden from the log
- [daemon] Significantly increased S3 backup speed
- [daemon] Trimming multiple entries is now done in a dedicated background thread
- [kernel] Fix an issue where old buckets could not be updated after a new column was added to a table
- [kernel] Trimming a timeseries now includes the root
- [odbc] Add support for log size limit and rotation
- [odbc] Log verbosity is now configurable, and the default value has been lowered
- [orderbook] Currenex ORDERBOOK function is more tolerant of errors
- [orderbook] Fix a bug related to time ranges and Currenex data
- [orderbook] Greatly improved the performance of the ORDERBOOK functions for Currenex data
- [persistence] Reduces the chances for data loss in case of an operating system failure during a trimming operation
- [persistence] S3 Backups can now be done using multiple threads
- [query] Initial support for Windowing functions (SQL OVER())
Quasar Selected by National Renewable Energy Laboratory to Help with Energy System De-risking and Optimization
New York City, NY – June 6th, 2023 – Quasar, a high-performance time-series database designed for real-time data processing and analytics, has been selected by the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) to enhance their digital real-time simulation capabilities. NREL will use Quasar to enable the digital-twin simulation of various power-grid use cases.
The selection of Quasar reflects NREL's need for a high-performance time-series database capable of capturing and analyzing large data sets. The data comes from tens of thousands of power-grid devices, many transmitting voltage data as a waveform of more than 20,000 points per second. Quasar's real-time ingestion and streaming analytics capabilities will enable NREL to understand better and predict the behavior of the electric power system in response to changing energy demand and supply conditions.
"As our next-generation power systems become increasingly integrated, we must incorporate more advanced data processing and analytics into our digital-twin emulation platform, to ensure the de-risking and optimization of the power grid and to provide insights into the design and operation of future energy systems," said Dr. Rob Hovsapian, Research Advisor at National Renewable Energy Laboratory. "And that's why we chose Quasar as our trusted data solution provider, due to its high performance, scalability, track record, and world-class engineering support."
"We are humbled to partner with the NREL and support their efforts to accelerate the transition to a clean energy future," said Edouard Alligand, Founder and CEO of Quasar. "Quasar's unlimited ingestion and storage capabilities, along with the industry-leading streaming analytics, make it an ideal data solution for organizations like the NREL who need speed and volume. Quasar is the only solution on the market that allows for a very short feedback loop on vast data sets, unlocking a new level of productivity and insights."
The NREL underwent a rigorous evaluation and validation process with several time-series databases before selecting Quasar. From the assessment, Quasar came up on top in many aspects, including ingestion speed, latency, compression ratio, and user experience.
"The rise of electric vehicles and the need to coordinate that across all data points within the utility sector increase the grid complexity. That’s why our needs for data ingestion and analysis grow exponentially. Quasar's solution will be key to NREL’s mission in stabilizing the future power grid." said Dr. Sayonsom Chanda, Senior Scientist at the NREL, "Quasar’s low latency in ingestion and outstanding compression ratio will help us save a lot on data storage costs as the data volume grows. Quasar has what it takes to be the leading solution provider for data streaming and analytics in the energy industry.”
Quasar's selection by the NREL represents a significant milestone in the company's mission to empower organizations to make better decisions through real-time data processing and analytics. Companies across the world have successfully deployed Quasar's powerful database architecture in various industries, including finance, telecommunications, manufacturing IoT, and more.
Quasar is an AI-powered, real-time data platform for capturing and analyzing high-volume time-series data. Quasar's patented compression algorithm and unique database architecture enable organizations to process and analyze large data sets in real time, empowering them to make better decisions and gain competitive advantages while saving huge data storage costs.
Important update to the Quasar Python API
We just pushed an important bug fix to the Python API for 3.13.5. The bug could result in data being rendered incorrectly in the client (data in the server is not affected). All users of 3.13.5 are strongly encouraged to update.
The new version is 3.13.5.post2
To install explicitly:
pip install quasardb==3.13.5.post2
Updating should automatically fetch the new version:
pip install -U quasardb
For further information, refer to the documentation or contact us.