Cloud native, real-time SQL engine delivers sub-second OLAP analysis and supports ad hoc queries 3-5x faster than other open source OLAP products
StarRocks, a performance leader in real-time enterprise analytics, today announced a cloud-native version of its SQL engine. StarRocks Cloud, a fully managed software as a service (SaaS) platform, greatly simplifies the delivery of real-time analytics projects and reduces the time to business value for enterprises. This new cloud-based offering enables the democratization of real-time analytics to help further accelerate digital transformation strategies.
Founded in 2020, StarRocks project came out of the work being done on the Apache Doris OLAP database. The company has raised more than $60 million in venture capital; with its Series B financing closing in February 2021. StarRocks is being used by leading organizations in the US, Europe and Asia, including market leaders, Airbnb and Lenovo.
“The demand for faster insights poses several challenges for data infrastructure teams, including the non-stop growth of data, the proliferation of citizen analysts and data scientists, and cross environment data pipelines that are increasingly brittle and complex,” said Mike Leone, senior analyst, ESG. “StarRocks’ strategy is perfectly aligned to address these challenges. By harnessing the data, they have collected through their digital channels to gain timely insights, enterprise organizations can benefit from data freshness and responsiveness – critical ingredients for digital transformation success.”
Next Generation Cloud Native Database
StarRocks is a next-gen cloud-native sub-second OLAP database. Its architecture is purposely designed to support real-time data analysis and a large number of concurrent users; with multi-table join queries. With StarRocks, data engineers can flexibly build various types of models including flat tables, star and snowflake schemas. With a unified analytics platform, enterprises can easily analyze historical and real-time data and make timely business decisions. Providing modern analytics applications where the latest transaction data is combined with historical records affords the best in-app recommendation and decision making.
“The growing complexity of data volumes and data pipelines along with the proliferation of citizen data scientists, are major challenges holding enterprises back from being able to fully utilize the massive amounts of data they’re now able to collect,” said James Li, co-founder and CEO, StarRocks. “With StarRocks, customers can unite real-time analytics, an OLAP database, and data lake analytics onto one engine, with one data pipeline.”
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A Single Solution, Faster Analytics
As enterprises expand the number of data users across their organization, StarRocks supports that growth with its high concurrency that can support more than 10,000 queries per second. StarRocks offers comprehensive real-time data support including data ingestion at speeds of 100MB/s per node.
Benefits of the Cloud Offering Include:
- Reduced Administrative Overhead and Lower TCO – StarRocks Cloud is a fully managed StarRocks SaaS platform that eliminates the administrative work that needs to be performed by customers
- Fastest Time to Value – Customers do not need to provision the hardware infrastructure or download software
- Powerful SQL Analytics Engine as a Service – StarRocks Cloud provides industry leading query performance – with benchmark testing results showing a 3x to 5x performance gain compared to other vendors
- Extending Simplicity and Ease of Use – StarRocks Cloud greatly simplifies the commonly complex requirements of real time analytics in the cloud, by eliminating complex data pipelines and denormalized tables
- BYOC (Bring your own Cloud) – Safely integrates with existing data infrastructure for seamless and secure operations
StarRocks brings all of this together in a single solution that makes enterprise-scale analytics faster, real-time capable, and cost-effective – supported by the following innovations:
- 360 Real-time data with data ingestion from streaming (Apache Kafka), Real-time update support, Realtime pre-aggregation using materialized view, and real-time query performance
- Cost Based Optimizer – The cornerstone for distributed join multiple tables in distributed environments in query execution
- Native vectorized query engine – Delivers an effective pipeline execution leveraging massive parallelism of CPU cores and a memory and storage layer which enables 3x to 5x faster than ClickHouse, Apache Druid, and Apache Pinot
- Pipeline execution – Fully leverage CPU cores for parallel processing
- Intelligent materialized view – Transparent query acceleration
- Resource management – No single runaway query can bring down the cluster
- Data Lake Analytics – High performance analytics on data lake without ETL is also possible and is 3x faster than other major open-source products
- 100% SQL compatible with MySQL client protocol – Out of box support for all major BI tools
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