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With data exploding multifold every year, companies are looking at ways to drive value from the growing pool of information in their backyard. Part of the effort has been directed toward leveraging real-time data – information on events as they take place. It is the new holy grail, with a number of organizations processing and reacting to event streams for use cases such as detecting outages.
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 grows multifold when dozens try to query data at the same time.
StarRocks’ unified engine
StarRocks solves these issues with a solution that unites real-time analytics, an OLAP database, and data lake analytics into a single engine, with one data pipeline.
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“This is the first analytical database in the industry that addressed the critical technical challenges in real-time analytics, such as the need to denormalize data, the inability to process updates, and the challenge of supporting large numbers of concurrent users. We did this by creating a brand new query engine with many breakthrough technologies,” Li Kang, VP of strategy at StarRocks, told Venturebeat.
The engine has been purposely 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 data from streaming stores at 100MB/s per node and run more than 10,000 queries per second. This eventually allows enterprises 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.
Now, with the new cloud-native version, available as a fully managed SaaS platform, StarRocks is making its product more lucrative for enterprises.
Basically, StarRocks Cloud allows organizations to integrate existing data infrastructure in the cloud and do away from regular engineering and administrative tasks required for real-time analytics, starting from setting up the servers/virtual machines to deploying the software.
In addition to this, cloud support also brings in various cloud-specific features such as separation of compute and storage, automated resource management, etc, which not only reduces cost but also gives data teams more time to focus on query experience and improve the time to insight for end-users.
Currently, multiple organizations are looking at the real-time analytics space, including ClickHouse, Imply (Apache Druid), StarTree (Apache Pinot) and Rockset (RocksDB). However, StarRocks claims to offer a much better price-performance ratio in comparison.
“StarRocks has better performance – we have published benchmark testing results showing we have 3x to 5x performance advantages over our competitors… In addition, StarRocks is more cost-effective – with our industry-leading query engine, we are able to get better query performance without complex data pipelines and heavy indexing processes, resulting in a much better price-performance ratio. And lastly, StarRocks is more flexible. Our unique design can handle frequent updates to past transactions while still maintaining high query performance in real-time. This allows us to support use cases previously considered not suitable for real-time analytics,” Kang said.
StarRocks Cloud will be generally available in Q3 2022 on Amazon AWS, with support for Google Cloud Platform coming in later.
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