Change analytics databases when the workload, not raw speed, is the problem. Embedded engines that run on one machine suit analysts working on files. Other real-time OLAP engines suit user-facing dashboards, time-series databases suit metrics and sensor data, and managed warehouses suit teams that don't want a cluster to look after.
ClickHouse lists its flagship as a real-time analytical database, and the people looking for alternatives are usually data or backend engineers who run dashboards or event analytics on it. Some have outgrown the way they run it; others are starting fresh and want to see what else exists before they commit.
ClickHouse now sells more than one database
Before you plan a migration, look at what ClickHouse itself now offers, because the line-up has widened. Its site lists Managed Postgres, described as an enterprise-grade transactional database, alongside the analytics engine. It also publishes chDB, an in-process version of ClickHouse.
That changes two common reasons for leaving. If you were drawn to an engine that runs inside your own process, test chDB beside DuckDB rather than assuming you must switch vendor. If your pain is running a separate transactional database next to your analytics, one supplier now offers both, which may suit you if you'd rather deal with one company.
Equally, if you're leaving because you want less dependence on a single vendor, the wider product line is a reason to look harder at the independent projects below.
Embedded analytics on one machine
If your analysts mostly work in notebooks against files on a laptop or a single server, a whole cluster is overhead. An engine that runs where the data already sits removes a network hop and a service to babysit.
DuckDB
An analytical database system you can deploy anywhere from edge devices to servers. It's MIT-licensed and governed by the independent DuckDB Foundation, which matters if governance is part of your decision.
Real-time OLAP engines and open warehouses
These are closest in spirit to what you're leaving: built for fast queries on large tables, often behind dashboards your own customers see. If that's your case, test with the number of people you expect on a busy day, not a quiet one.
Apache Doris
Doris describes itself as an open-source real-time analytics and search database, so it's worth a look if search sits alongside your analytics.
Apache Druid
Druid's own page calls it a real-time analytics database that delivers sub-second queries on both streaming and batch data. Treat that as its claim and test it on your own data.
Apache Pinot
Pinot is an open-source distributed OLAP database for user-facing and agent-facing real-time analytics, aimed squarely at numbers shown to end users or AI agents.
StarRocks
StarRocks presents itself as an open source, high-performance data warehouse. Shortlist it if you want warehouse-style workloads you can run yourself.
Time-series databases
If most of your rows are timestamped readings, a database built for that shape may serve you better than a general analytics engine. Metrics, sensor data and logs with a clock attached tend to be written once and then read in time windows, which is the pattern these two are aimed at.
QuestDB
A time-series database with both an open source edition and an Enterprise edition, so you can begin with the open version and look at the other if your needs grow.
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TimescaleDB
TimescaleDB runs time-series, real-time analytics and events on Postgres, with an open source edition. It suits teams who already know Postgres and would like to stay there.
A fully managed warehouse
Here the trade is control for convenience: someone else handles the servers, and you accept their way of doing things. One warning if you go this way: changing the product and the operating model at once doubles the risk, and it makes it hard to tell afterwards which change helped. Move one dashboard first and compare it with the original before you move the rest.
Snowflake
Snowflake markets itself as the AI Data Cloud, a fully managed platform integrated across data types and clouds. Choose this route if running a cluster is the job no one wants. Our direct comparison of the two goes into that trade-off.
All eight at a glance
| Tool | Group | Pitched at | Start your trial with |
|---|---|---|---|
| DuckDB | Embedded | Edge devices up to servers | A folder of local files |
| Apache Doris | OLAP engine | Analytics with search alongside | A dashboard that also needs text lookup |
| Apache Druid | OLAP engine | Fast answers on live and batch loads | Mixed live and nightly loads |
| Apache Pinot | OLAP engine | Queries from end users and AI agents | Numbers your customers see |
| StarRocks | Self-run warehouse | Warehouse speed you host yourself | Warehouse jobs you host |
| QuestDB | Timestamped data | Timestamped data, two editions | Sensor readings |
| TimescaleDB | Timestamped data | Time-series on Postgres | Metrics kept beside relational tables |
| Snowflake | Hands-off warehouse | Hands-off data cloud | A team with nobody to run clusters |
Notice how many of these pitches promise speed on live data. That's why the trial column matters more than the pitch: the same phrase can mean a customer dashboard to one project and a sensor feed to another, so start each trial with the job you have rather than the label on the box. A pitch tells you what a project wants to be good at; only your own queries, on your own data and at your own busiest hour, tell you whether it is.
Run your own queries, not someone else's benchmark
A speed comparison built on someone else's data tells you little about yours. An afternoon with your own tables tells you far more.
- Export a realistic slice of production data, including your widest table and your messiest one.
- Pull the ten queries your dashboards run most, plus the one everyone complains about.
- Load the slice into two candidates from the same group and note how much schema work each needed.
- Run the queries cold and warm, and at the number of users your dashboards really see.
- Test ingestion: stream or batch new data the way production does and watch how fresh results stay.
- Write down the operational work: upgrades, backups, scaling and monitoring.
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Frequently asked questions
Is an embedded database enough for production dashboards?
For a single user or a scheduled report, it can be. For many people querying at once, test concurrency carefully before you commit.
Should analytics run in my transactional database?
Only for modest volumes. Keeping heavy analytical queries off the database that runs your app protects both.
How much SQL rewriting should I expect?
Some, almost always. Each engine has its own functions and quirks, so set aside time to adapt your heaviest queries.
Can I keep one engine and add another?
Yes. You can leave the dashboards that work where they are and trial a second engine on the workload that hurts.
Which of these options build on Postgres?
TimescaleDB is the one that runs on Postgres, so teams who know that database keep their skills. Check how it handles your heaviest queries before assuming it behaves like plain Postgres.
Does Pinot only serve human users?
No. Pinot describes itself as built for agent-facing as well as user-facing analytics, so AI agents querying live data are part of its pitch.
