StarRocks Launches Cloud-Native SQL Engine for Faster Real-Time Analytics

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California-based StarRocks has announced a cloud-native version of its SQL engine to provide businesses with faster and more affordable real-time analytics.

With the explosion of data every year, companies are looking for ways to extract value from the growing pool of information in their backyard. Some of the effort has been directed towards leveraging real-time data – information about events as they happen. It's the new holy grail, with a number of organizations processing and reacting to event streams for use cases such as fault detection.

However, when it comes to analytics, which involves discovering patterns in data, working with real-time streams can be a major challenge. After all, one cannot run analytical queries when information is constantly being inserted, updated, and even deleted. The problem multiplies when dozens of people attempt to query data at the same time.

The Unified StarRocks Engine

StarRocks solves these problems with a solution that brings together real-time analytics, an OLAP database, and data lake analytics in a single engine, with a single data pipeline.

Event

Transform 2022

Join us at the leading Applied AI event for enterprise business and technology decision makers on July 19 and virtually July 20-28.

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"It is the first analytics database in the industry to address the critical technical challenges of real-time analytics, such as the need to denormalize data, the inability to process updates, and the difficulty of supporting a large number of concurrent users, and we achieved this by creating an all-new query engine with many breakthrough technologies,” Li Kang, vice president of strategy at StarRocks, told Venturebeat.

The engine was specifically designed to support real-time data and a large number of concurrent users, with multi-table join queries. According to the company, it can ingest streaming store data at 100MB/s per node and execute over 10,000 requests per second. This ultimately allows businesses to combine their latest streaming transaction data with historical records for effective recommendations and decision making.

More than 500 companies, including Airbnb, Trip.com and Lenovo, have already adopted the solution.

Cloud offer

With the new cloud-native version, available as a fully managed SaaS platform, StarRocks is making its product more lucrative for businesses.

Basically, StarRocks Cloud enables organizations to integrate existing data infrastructure into the cloud and dispense with the regular engineering and administration tasks required for real-time analytics, server configuration /virtual machines to software deployment.

In addition to this, cloud support also brings various cloud-specific features such as separation of compute and storage, automated resource management, etc., which not only saves cost but gives also giving data teams more time to focus on the query experience and improving time to insight for end users.

Competition

Currently, several organizations are investigating the real-time analytics space, including ClickHouse,

StarRocks Launches Cloud-Native SQL Engine for Faster Real-Time Analytics

We're excited to bring Transform 2022 back in person on July 19 and virtually from July 20-28. Join leaders in AI and data for in-depth discussions and exciting networking opportunities. Sign up today!

California-based StarRocks has announced a cloud-native version of its SQL engine to provide businesses with faster and more affordable real-time analytics.

With the explosion of data every year, companies are looking for ways to extract value from the growing pool of information in their backyard. Some of the effort has been directed towards leveraging real-time data – information about events as they happen. It's the new holy grail, with a number of organizations processing and reacting to event streams for use cases such as fault detection.

However, when it comes to analytics, which involves discovering patterns in data, working with real-time streams can be a major challenge. After all, one cannot run analytical queries when information is constantly being inserted, updated, and even deleted. The problem multiplies when dozens of people attempt to query data at the same time.

The Unified StarRocks Engine

StarRocks solves these problems with a solution that brings together real-time analytics, an OLAP database, and data lake analytics in a single engine, with a single data pipeline.

Event

Transform 2022

Join us at the leading Applied AI event for enterprise business and technology decision makers on July 19 and virtually July 20-28.

register here

"It is the first analytics database in the industry to address the critical technical challenges of real-time analytics, such as the need to denormalize data, the inability to process updates, and the difficulty of supporting a large number of concurrent users, and we achieved this by creating an all-new query engine with many breakthrough technologies,” Li Kang, vice president of strategy at StarRocks, told Venturebeat.

The engine was specifically designed to support real-time data and a large number of concurrent users, with multi-table join queries. According to the company, it can ingest streaming store data at 100MB/s per node and execute over 10,000 requests per second. This ultimately allows businesses to combine their latest streaming transaction data with historical records for effective recommendations and decision making.

More than 500 companies, including Airbnb, Trip.com and Lenovo, have already adopted the solution.

Cloud offer

With the new cloud-native version, available as a fully managed SaaS platform, StarRocks is making its product more lucrative for businesses.

Basically, StarRocks Cloud enables organizations to integrate existing data infrastructure into the cloud and dispense with the regular engineering and administration tasks required for real-time analytics, server configuration /virtual machines to software deployment.

In addition to this, cloud support also brings various cloud-specific features such as separation of compute and storage, automated resource management, etc., which not only saves cost but gives also giving data teams more time to focus on the query experience and improving time to insight for end users.

Competition

Currently, several organizations are investigating the real-time analytics space, including ClickHouse,

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