The best real-time analytics database depends on how much of it you want to run yourself. Open-source engines give you control and a server to look after, managed services turn the same engines into API endpoints, and enterprise platforms aim at user-facing analytics at scale. Decide who'll operate it before you compare speed claims.
A dashboard that's five minutes behind is fine for a monthly board pack and useless for a customer watching their own orders arrive. That gap is why real-time analytics databases exist: they're built to answer analytical questions on fresh data while lots of people ask at once. Here are ten of them, in no particular order, grouped by how you'd run them rather than by whose benchmark looks best.
Three camps, and why the homepages tell you which one you're in
Read the vendor homepages side by side and the field splits into three camps. Most of the confusion when shortlisting comes from comparing a tool in one camp with a tool in another as if they did the same job.
Open-source engines you run yourself
ClickHouse, StarRocks and Apache Doris are open-source engines. You can download them, run them on your own servers and tune them to your data. That's real control, and it's also real work: someone on your team owns upgrades, scaling and the late-night page when a node falls over. If you've got that person, this camp gives you the most freedom. If you haven't, look at the next group first.
Managed services built on an engine
Tinybird sits on top of a managed version of one of those open-source engines, so what you ship is an API endpoint rather than a cluster you babysit. Firebolt, SingleStore and RisingWave are also offered as services you consume rather than servers you build, though each has its own angle: an analytical database for engineers, one engine for transactions and analytics together, and a streaming database that speaks PostgreSQL. This camp suits small product teams who want fast analytics inside their app without hiring a database specialist.
Enterprise real-time platforms
StarTree, built on Apache Pinot, and Kinetica, with its hardware-accelerated engine, both sell analytics at scale for user-facing and operational workloads. Imply comes from the Apache Druid side of the field, and its own homepage now leads with a security data platform called Lumi rather than general analytics. That doesn't rule it out, but it's a clear sign to check it still fits an analytics brief before you put it on a shortlist.
Ten engines at a glance
| Tool | Kind of engine | Good for |
|---|---|---|
| Apache Doris | Open-source analytics and search database | Analytics and search together in one open-source engine |
| ClickHouse | Open-source column-oriented database | Real-time analytical reports written in plain SQL |
| Firebolt | Analytical database for engineers | Engineering teams mixing AI agents, analytics and ELT |
| Imply | Druid-lineage data platform | Security data work; check the fit for general analytics |
| Kinetica | Hardware-accelerated analytics database | SQL, vector, graph, time-series and spatial in one engine |
| RisingWave | Streaming SQL database | Serving live streams with PostgreSQL-compatible SQL |
| SingleStore | Real-time database for apps and analytics | One engine for transactions and analytics |
| StarRocks | Open-source analytical database | Enterprise-scale real-time SQL you can run yourself |
| StarTree | Apache Pinot query engine | Customer-facing analytics and observability workloads |
| Tinybird | Managed analytics API layer | Shipping analytics features without running a cluster |
The ten, alphabetically
Unranked and alphabetical. Every line is based on what the vendor says about its own product.
Apache Doris
Best for: Teams who want analytics and search in one open-source data foundation, which is how the Apache Software Foundation project describes itself.
ClickHouse
Best for: Running analytical reports in real time with SQL on an open-source, column-oriented engine; speed is the vendor's headline claim.
Firebolt
Best for: Engineers who want one analytical database for AI agents, real-time analytics and efficient ELT, which is how the vendor lists its uses.
Imply
Best for: Security data work first: the vendor's homepage now leads with Lumi, its agentic security data platform, so confirm it fits an analytics brief.
Kinetica
Best for: Workloads that mix SQL, vector, graph, time-series and spatial data in one distributed engine; the vendor claims answers in milliseconds.
RisingWave
Best for: Ingesting, processing and serving live streaming data with PostgreSQL-compatible SQL; the vendor promises sub-second freshness for analytics and decisions.
SingleStore
Best for: Apps that need live reads and writes plus analytics in one engine, which the vendor pitches for AI, apps and analytics together.
StarRocks
Best for: Open-source, enterprise-scale analytics; the vendor describes next-generation real-time SQL engines that make real-time analytics easier to deliver.
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StarTree
Best for: Customer-facing analytics and observability on Apache Pinot; the vendor says it's built for revenue-critical workloads at scale.
Tinybird
Best for: Product teams who'd rather ship analytical features as API endpoints than run a database cluster themselves; the vendor builds on managed open-source infrastructure.
My advice before you pick an engine
Work out who'll be on call for it. That one question decides more than any speed chart, because a fast engine nobody on your team can operate is slower in practice than a managed one that just works. Small teams without a dedicated data engineer will usually get to a working feature sooner with a managed service, and teams with strict data rules or deep in-house skills often prefer to run an open-source engine themselves.
Next, be honest about what real time means for you. A customer-facing dashboard where thousands of people query their own data at once is a very different job from an internal report that refreshes every minute. The first needs an engine built for lots of concurrent queries; the second may not need a specialist database at all. And treat every speed claim on a vendor's homepage as a starting point for your own test, not as an answer.
If you're also weighing a warehouse for slower, heavier reporting, my guide to data warehouse tools covers that side, and if most of your data is sensor or metric readings over time, the list of time-series databases may be a closer match. Teams moving away from a cloud warehouse can also compare Snowflake alternatives.
Questions to answer in a proof of concept
- Load a realistic slice of your own data, not a vendor sample set, and keep it the same across every candidate.
- Write the five queries your users will run most often and time them with the number of users you expect at peak.
- Check how fast new data shows up in query results after it arrives.
- Ask who handles upgrades, backups and scaling, and what happens when a node fails.
- Try connecting the tools you already use, such as your BI layer and your app's backend, before you commit.
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Frequently asked questions
What is a real-time analytics database?
It's a database built to run analytical queries, like totals, trends and breakdowns, on data that arrived seconds ago, often while many people query it at once. Classic warehouses are tuned for heavier, slower reporting instead.
Do I need one, or will my normal database do?
If you're running a few internal reports, your existing database or warehouse may be enough. Specialist engines earn their place when customers query their own data in your app, or when results need to reflect events within seconds.
Is open source less work to run?
The software can be free to download, but running it takes staff time. Weigh the hours your team will spend on upgrades, scaling and monitoring against the convenience of a managed service.
What's the difference between streaming and real-time analytics?
Streaming tools process data as it flows in, while real-time analytics databases make that fresh data quick to query. Some products do both, so check which part of the job each one covers.
Can I use SQL with these databases?
Most engines in this category use SQL or a close variant, and some describe themselves as PostgreSQL-compatible. Check the exact dialect against the queries your team already writes.
How should I compare speed claims?
Run the same queries on the same data across your shortlist. Vendor benchmarks use their own data and setups, so your own test is the only result that tells you how it'll behave for you.
