Engineered for LOw latency

Execute thousands of queries concurrently without compromising speed.

Achieve unparalleled cost efficiency with industry-leading price-to-performance ratios.

Scale seamlessly to handle 100ʼs of TBs of datasets with minimal performance impact.

Ease to use with simplicity and familiarity of SQL, which follows PostgreSQL dialect closely.

400x

Faster Query
Performance

300

Queries Per Second
(Single Cluster)

High efficiency at low cost

Optimize both speed and cost efficiency for low-latency, data-intensive analytics workloads in production.

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CREATE AGGREGATING INDEX idx_by_day ON uservisits (visitdate, countrycode,languagecode, useragent, MAX(visitdate), SUM(adrevenue), MAX(adrevenue), COUNT(*));

SELECT
visitdate,"countrycode","languagecode","useragent",sum("adrevenue"),
FROM uservisits
WHERE "visitdate" BETWEEN '2024-08-01' AND '2024-08-31'
GROUP BY visitdate, "countrycode","languagecode","useragent";

Plan:
\_[3] [Filter] (idx_by_day.visitdate BETWEEN DATE '2024-08-01’ AND DATE '2024-08-31')
\_[4] [StoredTable] Name: "idx_by_day", used 6/8 column(s) AGGREGATING_INDEX_TABLE

SELECT sourceIP, AVG(adRevenue) AS avgRevenue
FROM uservisits
GROUP BY sourceIP
ORDER BY avgRevenue DESC
LIMIT 10;

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Scaling-out to 100’s of terabytes of data

Achieve unparalleled elasticity and efficiency for modern data warehousing needs and scale instantly as your data grows without performance bottlenecks.

Up to 4,200 QPS with concurrency scaling

Maintain optimal query performance even under heavy workloads with concurrency scaling. Instantly add new clusters on-demand to manage sudden spikes in concurrent queries.

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Ease of SQL

SQL-first simplicity, robust database fundamentals and PostgreSQL compatibility allow you to run SQL queries directly on structured or semi-structured data without compromising speed or efficiency.