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StarRocks

StarRocks offers the next generation of real-time SQL engines for enterprise-scale analytics. Learn how we make it easy to deliver real-time analytics.

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Type
OLAP Database
Category
Data Warehouse
Pricing
Free (open source)
Deployment
Cloud or self-hosted
Last updatedSeptember 21, 2026Open Source

Editor's Take

We recommend StarRocks for data teams that need low-latency, real-time SQL analytics at enterprise scale and want to start with a free option. Its stated focus on real-time analytics makes it a better fit than batch-oriented warehouse workflows, but the available context does not provide evidence on enterprise adoption, total operating costs, or required team size. Corrected 14 September 2026: an earlier version reported a capped free tier and a four-figure monthly paid plan. Those were CelerData's terms, not the project's, and CelerData has since rebranded.

— Egor Burlakov, Editor

Evaluate StarRocks

StarRocks Pricing
Plans, costs & free tier
Top Alternatives
Substitutes, and when to pick them
Best OLAP Databases
Ranked by public evidence
OLAP Databases landscape
7 published products in this market
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Every published tool in the category
Stack Recommender
Build a stack around StarRocks

Popular comparisons

vs ClickHouse
Head-to-head comparison
vs SingleStore
Head-to-head comparison
vs Databricks
Head-to-head comparison
vs Snowflake
Head-to-head comparison
See all 6 StarRocks comparisons
vs Google BigQuery
Head-to-head comparison
vs Imply Cloud
Head-to-head comparison

StarRocks: product and architecture

This StarRocks review reaches a clear verdict: we recommend StarRocks for teams that need a single SQL analytics engine for mutable real-time data and open-lakehouse queries, and that can accept the operational and design discipline of an MPP OLAP database. Its strongest case is not generic warehouse replacement; it is delivering fresh, low-latency analytics without forcing every workload through denormalized copies or batch-only pipelines.

StarRocks positions itself as a next-generation, sub-second MPP OLAP database for multidimensional analytics, real-time analytics, ad hoc queries, and lakehouse scenarios. The project is Apache-2.0 licensed, its GitHub repository has 11,987 stars, and version 4.0.13 was released on July 21, 2026. Those are useful public adoption and maintenance signals, but they are not proof that the product fits every enterprise operating model.

Overview

StarRocks is an open-source high-performance data warehouse built around sub-second analytics for end users and AI agents. Its stated product scope combines real-time data, lakehouse querying, and AI-oriented query serving in one engine. That breadth matters because many analytics teams otherwise maintain separate systems for streaming updates, BI queries, and data-lake access.

The central value proposition is consistent: query fresh data in real time or query directly from the lakehouse, while maintaining performance at scale. StarRocks explicitly supports complex multi-table SQL and presents itself as a system that can run analytics on normalized schemas rather than requiring pre-aggregation or denormalization. For data teams, that can reduce the number of duplicated analytic models and the delay between a source update and a usable query result.

Its technical identity is MPP OLAP rather than a general-purpose transactional database. That distinction should guide evaluation. StarRocks is designed around analytical scans, joins, aggregations, concurrency, and data freshness; teams looking primarily for row-by-row application transactions should look elsewhere.

The project also has credible open-source signals. It received InfoWorld’s 2023 BOSSIE Award for best open source software, its primary repository language is Java, and its repository topics include analytics, big-data, cloudnative, datalake, Delta Lake, and Hudi. The latest repository push listed in the supplied data was August 13, 2026, which indicates current public development activity but does not by itself establish support quality or deployment maturity for a particular organization.

Key Features and Architecture

StarRocks combines real-time ingestion, MPP SQL execution, and lakehouse access in a unified architecture. Its primary-key table design resolves data changes during ingestion, which is intended to keep updates immediately queryable while preserving read performance. The product describes this capability as supporting sub-ten-second freshness on mutable data, a meaningful design point for operational analytics where records change after initial arrival.

Streaming and CDC ingestion are another core feature. StarRocks can stream inserts and updates directly from Flink and Kafka, then apply those changes in real time rather than waiting for a batch job. This is particularly relevant when analysts need current customer, order, inventory, or event records and cannot tolerate stale replicas created by periodic transformations.

The execution engine is columnar, fully vectorized, SIMD-optimized, and built in C++. Vectorized operators and columnar storage are designed to make scans and aggregations use modern CPU instructions efficiently. The cost is that teams must still model and distribute analytical data sensibly; vectorization does not remove the need to understand workload shape, data skew, or join behavior.

For SQL planning, StarRocks uses a cost-based optimizer that relies on table and column statistics to select join order, pruning, and pushdown. Its stated aim is stable plans for complex queries without manual tuning. That is valuable for analytics engineers maintaining evolving models, although reliable optimizer behavior still depends on having useful statistics and query patterns.

The MPP layer supports large fact-to-fact joins and high-cardinality aggregations. StarRocks states that it can query normalized schemas directly without pre-aggregation or denormalization, which can shorten delivery cycles and reduce redundant derived datasets. The trade-off is that normalized, multi-table workloads make query planning and physical data layout more consequential than in a deliberately simplified serving model.

For multi-tenant workloads, StarRocks provides resource-group isolation and skew-aware data layouts. These capabilities are intended to stabilize p95 and p99 latency by spreading hotspots and reducing wasted scans under load. This is a practical feature for shared analytics platforms, though it introduces resource-governance work that smaller teams may prefer to avoid.

Lakehouse delivery is another differentiator. StarRocks can query Apache Iceberg, Delta Lake, and Apache Hudi directly, using open table formats rather than mandatory data copies and ingestion pipelines. Its shared-data architecture persists data on object storage such as S3 and separates compute from storage, giving teams elasticity and potentially lower long-term storage cost while retaining open-format governance.

Finally, StarRocks explicitly targets AI-agent query serving. The product states that it can serve agents with low latency and high concurrency, including unoptimized SQL. That is strategically useful for teams exposing governed analytical data to agent workflows, but the supplied data does not provide benchmark figures, concurrency limits, or security-control details for this use case. Evaluate those requirements directly before treating agent support as a production guarantee.

Ideal Use Cases

StarRocks is best for a data platform team supporting a real-time analytical product where updates and deletes matter as much as initial event ingestion. Consider a retail, marketplace, logistics, or customer-operations team that receives mutable order, inventory, shipment, or account records through Kafka and Flink. Primary-key tables and real-time application of changes provide a direct fit when the organization needs queryable updates within sub-ten-second freshness rather than a delayed batch reconciliation.

A second strong scenario is an analytics engineering team with a lakehouse built on Apache Iceberg, Delta Lake, or Apache Hudi. If that team wants SQL analytics on open tables without creating another copied serving layer, StarRocks’ direct open-table-format querying is compelling. We recommend StarRocks for teams that value retaining their lakehouse governance model and can benefit from separating compute from object storage such as S3.

A third fit is a shared data product with many analytical consumers and uneven demand. Resource-group isolation and skew-aware layouts address a common platform problem: one heavy workload can destabilize interactive queries for everyone else. Organizations building internal dashboards, embedded analytics, or AI-agent access can evaluate StarRocks when predictable tail latency matters alongside high concurrency.

StarRocks is also a credible option for teams whose SQL workloads involve complex multi-table joins and high-cardinality aggregations. Its MPP engine, cost-based optimizer, and vectorized execution are explicitly oriented toward that workload. This is not merely a faster extract-and-load destination; it is a system designed to execute analytical SQL at scale.

Do not use StarRocks if the main requirement is general-purpose transactional processing rather than OLAP. Avoid it as well if your team cannot operate an MPP database, maintain relevant statistics and data layouts, or validate performance on representative workloads. The supplied data supports strong feature claims, but it does not provide deployment staffing requirements, managed-service operational details, security certifications, or workload benchmark results; those missing decision inputs should be resolved in a proof of concept.

Strengths & Trade-offs

StarRocks has a focused set of strengths that matter for modern analytical systems:

  • Mutable-data analytics is a first-class design point. Primary-key tables resolve changes during ingestion, and the product states that updates become immediately queryable without impacting query performance. That is more relevant than append-only ingestion when records are corrected, deleted, or updated after arrival.

  • It integrates streaming changes directly from Flink and Kafka. Teams can stream inserts and updates into StarRocks without designing every freshness requirement around batch jobs. The benefit is current analytical records; the trade-off is that streaming and CDC pipelines still require careful operational ownership.

  • The SQL engine is built for demanding analytical queries. Columnar storage, fully vectorized operators, SIMD optimization, and an MPP architecture target fast scans, complex joins, and high-cardinality aggregations. This is a concrete technical advantage for analytical SQL, not a generic usability claim.

  • It can query Iceberg, Delta Lake, and Hudi directly. That supports open-format analytics without mandatory ingestion pipelines, data copies, or denormalized serving models. For teams committed to a lakehouse, this can reduce duplication and preserve existing governance patterns.

  • It includes workload-stability mechanisms. Resource-group isolation and skew-aware data layouts are intended to protect p95 and p99 latency in multi-tenant environments. This is valuable when interactive dashboards and heavier analytical users share a platform.

  • Its public project evidence is substantive. StarRocks is Apache-2.0 licensed, has 11,987 GitHub stars, received the 2023 BOSSIE Award, and released version 4.0.13 on July 21, 2026. These are positive public signals of activity and interest, though they should not replace a technical evaluation.

The limitations are equally important:

  • StarRocks is weak as a fit for transactional application workloads. Its stated identity is an MPP OLAP database for analytics, not a general-purpose transaction database. Teams should not select it simply because they need a database with SQL.

  • The advertised performance claims are not accompanied by supplied benchmark detail. StarRocks states sub-second latency, stable p95/p99 behavior, and performance at scale, but the available data does not include workload definitions, dataset sizes, cluster sizes, or comparative benchmark results. Require proof on your own joins, concurrency, and update patterns.

  • Self-hosting is the only path the project prices, and it prices it at zero. There is no ingestion ceiling to plan around, and no paid tier to grow into; what scales is the cluster you run. A managed offering is a separate purchase from a separate vendor.

  • The architecture creates operational responsibility. Primary-key ingestion, statistics-driven optimization, resource groups, skew-aware layouts, streaming changes, and shared data all offer leverage, but they also require capable platform ownership. A small team seeking a minimal-configuration analytics service may find the operational surface area disproportionate.

StarRocks pricing

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Alternatives to StarRocks

The reviewed substitutes for StarRocks among the OLAP databases, and what would make each one the better answer.

Direct alternatives

Reviewed substitutes: products bought for the same job, where a team picks one.

ClickHouse
Choose ClickHouse if you prioritize ecosystem maturity, a large community for troubleshooting, and raw aggregation speed on append-only analytical workloads where updates are infrequent.Applies to: Choosing the analytical database that will serve fast queries over large event data.
Apache Druid
Choose Druid if your primary workload involves streaming event data that needs to be queryable immediately, especially time-series and high-cardinality analytics where Druid's pre-aggregation at ingestion time reduces both storage and query latency.Applies to: Choosing between these two for the real time analytical databases decision.
Apache Pinot
Choose Pinot if you are building user-facing analytical applications that demand extreme query concurrency and consistently low latencies across large-scale datasets.Applies to: Choosing between these two for the real time analytical databases decision.
SingleStore
Two MPP analytical databases with MySQL-compatible interfaces, both positioned on join performance and real-time ingestion, and listed side by side on db-engines and in 2026 OLAP comparisons. They compete for the same workload.Applies to: Choosing an analytical database that must join tables at query time and ingest in real time.

Other approaches

A different approach to the same problem. Each substitutes only for the workload named beside it.

Databricks
A lakehouse platform and an MPP OLAP database reach analytical queries by different architectures, the same choice as Databricks against any query-latency engine: platform breadth against sub-second response. Organisations commonly run both, with the lakehouse doing engineering and the OLAP store serving dashboards.Applies to: Serving low-latency analytical queries, and whether the same platform must also carry ETL and ML.
Imply Cloud
StarRocks is chosen instead of Imply Cloud for lakehouse analytics workloads requiring complex multi-table SQL and frequent updates or deletes. **StarRocks** is a sub-second MPP OLAP database for real-time analytics, ad hoc queries, and data lakehouse scenarios.
Google BigQuery
Both answer the same need from different architectures, so the decision is how the stack is shaped rather than which product is better, and organisations commonly run both. Recorded against external comparison content rather than against this site's own verdict, which is what the earlier derived approval rested on.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
Snowflake
Both answer the same need from different architectures, so the decision is how the stack is shaped rather than which product is better, and organisations commonly run both. Recorded against external comparison content rather than against this site's own verdict, which is what the earlier derived approval rested on.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.

Head to head

See detailed alternatives analysis

StarRocks is an open-source, sub-second MPP OLAP database built for real-time analytics, multi-dimensional analysis, and ad-hoc queries across data lakehouse and warehouse scenarios. Licensed under Apache 2.0 with over 11,500 GitHub stars, StarRocks delivers a fully vectorized execution engine written in C++, a cost-based optimizer, and MySQL wire protocol compatibility. While StarRocks excels at real-time mutable data workloads and complex multi-table joins, its relative youth as a project, smaller community compared to established competitors, and operational complexity for self-hosted deployments mean that several StarRocks alternatives merit evaluation depending on your specific requirements.

Top Alternatives Overview

ClickHouse is the most widely adopted open-source columnar OLAP database, with nearly 47,000 GitHub stars and a mature ecosystem built over many years of production use at companies handling petabyte-scale workloads. ClickHouse uses a MergeTree storage engine family with aggressive compression and vectorized query execution, optimized for append-heavy analytical workloads. Users highlight high performance, easy configuration, and data replication as strengths, though data manipulation (updates and deletes) remains a recognized weakness. ClickHouse Cloud offers a managed service with usage-based pricing. Choose ClickHouse if you prioritize ecosystem maturity, a large community for troubleshooting, and raw aggregation speed on append-only analytical workloads where updates are infrequent.

Apache Druid is a distributed real-time analytics data store that merges concepts from data warehouses, time-series databases, and search systems. Druid features native streaming ingestion from Apache Kafka and Amazon Kinesis, sub-second OLAP queries, and automatic columnar storage with bitmap indexing. It carries a 9.9/10 rating across 3 reviews and supports high concurrency through its segment-based architecture with configurable tiering and quality of service. Choose Druid if your primary workload involves streaming event data that needs to be queryable immediately, especially time-series and high-cardinality analytics where Druid's pre-aggregation at ingestion time reduces both storage and query latency.

Trino (formerly PrestoSQL) is a distributed SQL query engine with over 12,700 GitHub stars, designed for federated analytics across heterogeneous data sources. Unlike StarRocks, which requires data ingestion, Trino queries data in place across S3, Hadoop, MySQL, Cassandra, PostgreSQL, MongoDB, Kafka, Elasticsearch, and dozens of other systems using standard ANSI SQL. The community edition is free and open-source under Apache 2.0. Choose Trino if you need to query data across multiple storage systems without copying or moving it, or if your organization follows a data lake strategy where data remains in its original format.

Apache Pinot is a real-time distributed OLAP datastore with over 6,000 GitHub stars, purpose-built for low-latency, user-facing analytics. It supports pluggable indexing options including StarTree, inverted, range, and geospatial indexes, and handles streaming ingestion natively. Choose Pinot if you are building user-facing analytical applications that demand extreme query concurrency and consistently low latencies across large-scale datasets.

Dremio is a data lakehouse platform that enables SQL-based analytics directly on Apache Iceberg, Delta Lake, and Parquet files without data movement or ETL. Dremio uses usage-based pricing starting at $0.20 per query. Choose Dremio if your strategy centers on open table formats and you want to run analytics directly on your data lake without ingesting data into a separate OLAP engine.

Starburst is an enterprise analytics platform built on Trino that adds managed infrastructure, fine-grained access controls, and streaming ingest capabilities. Starburst offers a free tier (up to 3 clusters), with Pro tier starting at $0.50/credit and Enterprise tier at $0.75/credit. Choose Starburst if you want Trino's federated query capabilities with enterprise-grade security, governance, and managed infrastructure support.

Architecture and Approach Comparison

StarRocks uses a shared-data architecture where data persists on object storage like S3 while compute scales independently. Its fully vectorized execution engine, built in C++, leverages SIMD instruction sets for maximum CPU throughput on columnar data. The cost-based optimizer uses table and column statistics to determine join order, pruning, and pushdown strategies. StarRocks' primary key table design resolves data changes at ingestion time, enabling sub-ten-second data freshness for mutable workloads without impacting query performance. It also supports streaming and CDC ingestion directly from Flink and Kafka.

ClickHouse employs a shared-nothing architecture with its MergeTree engine family, storing data in sorted columnar format with aggressive compression. While both StarRocks and ClickHouse are vectorized columnar engines, ClickHouse is more mature and offers a broader set of specialized table engines (ReplacingMergeTree, AggregatingMergeTree, CollapsingMergeTree) that encode data modeling decisions directly into storage. StarRocks takes a different approach with its cost-based optimizer and primary key tables, making updates more straightforward but offering less specialized storage-level optimization.

Apache Druid and Apache Pinot both use segment-based architectures designed specifically for real-time event analytics. Druid pre-aggregates data during ingestion using rollup, trading raw row-level detail for reduced storage and faster queries. Pinot preserves raw data and relies on pluggable indexes for query acceleration. Both integrate tightly with streaming platforms. Compared to StarRocks, which provides a general-purpose OLAP engine, Druid and Pinot are more specialized for event-driven, high-concurrency, user-facing analytics.

Trino and Dremio represent the query federation approach. Neither stores data; they push computation to underlying sources. Trino connects to over 50 data source types through its connector-based architecture, while Dremio focuses specifically on data lakehouse formats like Iceberg and Parquet. StarRocks can also query open table formats (Iceberg, Delta Lake, Hudi) directly, but it primarily functions as a storage-plus-compute engine rather than a pure federation layer. Starburst extends Trino with enterprise features, managed infrastructure, and advanced autoscaling.

Pricing Comparison

StarRocks and most of its alternatives are open-source for self-hosting, but their managed offerings and commercial tiers vary considerably.

ToolSelf-Hosted CostCloud/Managed Starting PricePricing Model
StarRocksFree (Apache 2.0)No published pricingOpen source
ClickHouseFree (Apache 2.0)Usage-based (ClickHouse Cloud)Open Source + Cloud
Apache DruidFree (Apache 2.0)Vendor-dependent (Imply)Open Source
TrinoFree (Apache 2.0)From $12/month (cloud version)Open Source + Cloud
Apache PinotFree (Apache 2.0)Vendor-dependent (StarTree)Open Source
DremioN/AFrom $0.20 per queryUsage-Based
StarburstFree tier (up to 3 clusters)From $0.50/credit (Pro)Freemium + Credit-Based
FireboltN/AFrom $0.35 (usage-based)Usage-Based
MotherDuckFree tier (1 user)From $25/month (Pro)Freemium

All self-hosted open-source options are free to run, with costs limited to infrastructure and operational headcount. For managed services, Trino's cloud offering provides the lowest entry point. Starburst's credit-based model scales with compute usage, making costs predictable for consistent workloads. Dremio's per-query pricing suits intermittent analytical workloads. Managed StarRocks is sold by third parties under their own brands, so price it against whichever vendor you would buy it from.

When to Consider Switching

You need maximum ecosystem maturity and community support. ClickHouse has nearly four times the GitHub stars of StarRocks and a sizable user community. If troubleshooting resources, third-party integrations, and battle-tested production deployments at massive scale are your priority, ClickHouse offers more community backing and more extensive documentation.

Your primary requirement is federated querying across data sources. If your data lives across S3, relational databases, NoSQL stores, and streaming platforms, Trino or Starburst let you query everything with a single SQL statement without ingesting into StarRocks first. This eliminates data duplication and ETL pipeline maintenance.

You are building user-facing applications demanding extreme concurrency. Apache Pinot and Apache Druid are purpose-built for serving analytical queries to thousands of concurrent end users. Their segment-based architectures and specialized indexing deliver more predictable latencies under heavy concurrent load than a general-purpose OLAP engine.

Your workload is streaming-first with event data. While StarRocks supports Flink and Kafka ingestion, Apache Druid and Apache Pinot were designed from the ground up for streaming data. Their native integrations with Kafka, Pulsar, and Kinesis require less configuration and provide tighter end-to-end streaming pipelines.

You want serverless or embedded analytics without infrastructure. MotherDuck (powered by DuckDB) provides serverless SQL analytics with no infrastructure to manage. For teams that do not need distributed real-time processing, this approach eliminates operational overhead entirely, with a free tier for individual users and Pro plans starting at $25/month.

Migration Considerations

StarRocks uses ANSI SQL with MySQL protocol compatibility, so most analytical queries translate directly to alternatives like ClickHouse, Trino, and Dremio with moderate rewriting effort. ClickHouse is the closest architectural peer, meaning data models and query patterns transfer with the least restructuring, though ClickHouse's specialized MergeTree engine variants may require rethinking how updates and aggregations are handled at the storage layer.

For data migration, StarRocks can export to standard formats that most alternatives consume natively. Exporting to Parquet on S3 provides a universal migration path, as ClickHouse, Trino, Dremio, Apache Pinot, and Starburst all read Parquet efficiently. StarRocks' support for querying Iceberg, Delta Lake, and Hudi tables means data already in these formats can be accessed by the target system without any conversion.

The operational learning curve differs across alternatives. ClickHouse requires learning its engine-specific data modeling concepts and distributed deployment patterns. Trino and Starburst use standard ANSI SQL, making the query layer familiar, but require understanding coordinator-worker topology for deployment. Apache Druid and Apache Pinot each have their own ingestion specifications and segment management paradigms that require dedicated ramp-up time for teams unfamiliar with their architectures.

StarRocks' primary key table design, which resolves data changes at ingestion for mutable workloads, does not have a direct equivalent in ClickHouse or Druid. Teams relying heavily on this capability will need to evaluate whether ClickHouse's ReplacingMergeTree or Pinot's upsert support provides comparable functionality, and plan for differences in how updates are applied and queried. Testing with representative production workloads before committing to migration is essential for validating both performance and correctness.

Public signals

About these signals

Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.

1.3k GitHub commits 90d12.1k GitHub stars0 vulnerabilities across 1 package

See all signals from 8 sources
Source
Signals
Last updated
GitHub
Commits 90d:1.3k↓12Stars:12.1k↑27
September 21, 2026
Docker Hub
Pulls:906.6k↑31.9k
September 21, 2026
PyPI
Weekly downloads:131.3k↑45.8k
September 21, 2026
Google Trends
Search interest:Top 50%overallTop 61%in Data Warehouse
September 21, 2026
Hacker News
Matching stories, 90d:0
September 21, 2026
Product Hunt
Comments:0Reviews:0Votes:2
September 21, 2026
Stack Overflow
Questions:11
September 21, 2026
OSV
Package vulnerabilities:0 vulnerabilitiesacross 1 package

PyPI · starrocks@1.3.4

September 21, 2026

Discussed on Hacker News

Recent Hacker News threads mentioning StarRocks.

StarRocks product dashboard and interface

Frequently asked questions

What is StarRocks?

StarRocks is a high-performance analytical database designed for real-time analytics, providing fast query performance and scalability.

Is StarRocks free to use?

Yes, StarRocks offers a free pricing model, making it accessible to users without incurring costs.

How does StarRocks compare to Amazon Redshift?

StarRocks is designed for real-time analytics and has better query performance compared to Amazon Redshift, but the choice ultimately depends on your specific use case and requirements.

Can I use StarRocks for data warehousing and business intelligence?

Yes, StarRocks is suitable for data warehousing and business intelligence applications due to its high-performance analytics capabilities and scalability features.

What are the system requirements for running StarRocks?

The exact system requirements depend on your specific use case and cluster configuration, but generally, a minimum of 4-8 cores, 16-32 GB RAM, and 1-2 TB storage is recommended.

Is StarRocks suitable for large-scale enterprise applications?

Yes, StarRocks is designed to handle large-scale enterprise workloads with its high-performance analytics capabilities, scalability features, and support for big data processing.

Related OLAP Databases

Other OLAP databases in the catalog. Same kind of product, not a substitution recommendation.

Data Warehouse

Apache Pinot

Real-time distributed OLAP datastore

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Apache Druid

Apache Druid is an open source distributed data store.

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SingleStore

SingleStore aims to enable organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads in one unified platform.

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View all OLAP Databases
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