Open-source analytical databases compared

How the three engines are built differently

Which database fits which analytics scenario

This table maps common analytics workloads to the database most likely to fit, with the condition that makes each choice hold. Read it as a starting point: the right answer depends on data volume, query shape, concurrency and how much database operations work your team can absorb.

Each option in brief, with its license

Each card names one candidate, its license category and the release facts that affect upkeep. All three are OSI-approved open source, so the license alone does not separate them. The choice turns on support windows, managed options and what your team must operate.

What running ClickHouse replication and backups requires

How to choose and pilot an analytics database

This sequence turns a vague wish for faster analytics into a documented decision: whether PostgreSQL is still enough, and if not, which engine has been tested against your own data, queries and operating model.

1. Map query patterns and concurrency

   The analytics owner and the platform team list the top queries by business question, noting how many users run them at once, how wide the scans are, how many columns they touch and whether results must be ad hoc or fixed dashboards. Include the peak concurrency you expect, not only today's average. The step is done when the list is short enough to test and each query has an owner who can explain its purpose. Column-oriented engines such as ClickHouse favor filtering and aggregation over many rows, while single-row lookups are a weaker fit, so this list is the first input to every later comparison.

2. Measure current PostgreSQL performance

   The DBA or database engineer runs the listed queries against the existing PostgreSQL estate using realistic data volumes, recording execution time, resource use, index design and how replicas behave under read load. Record which queries miss the target and why, such as missing indexes, table bloat, or contention with transactional workloads. The output is a baseline report that states clearly whether tuning, partitioning, read replicas or a separate engine is the next lever. PostgreSQL remains a credible option for many analytics workloads, so a well-tuned baseline can close the question without a new platform.

3. Define freshness and ingest requirements

   The data engineering lead writes down how fresh each dataset must be, from nightly batch to seconds, and where the data comes from, whether a stream such as Apache Kafka or Amazon Kinesis, periodic batch files, or change data capture from transactional databases. Note expected event volumes, late-arriving data, and whether updates or deletes are common. Druid, for example, reads streams directly through supervisors and loads batch through native tasks or SQL INSERT and REPLACE statements, while other engines have different loading paths. The step is complete when each source has a freshness target and a named ingest method to test.

4. Shortlist engines and confirm licenses

   The architecture lead narrows the field to two or three candidates, typically PostgreSQL plus one columnar or real-time engine, and records each license and the edition that includes the features you need. Confirm the license from the project repository rather than from secondary summaries, and check whether any capability you depend on (such as authentication options, backup tooling or high availability) is in the open-source build or only in a managed or enterprise offering. The output is a one-page table of candidates with license, supported versions and gaps to verify with the vendor.

5. Run a pilot on representative data

   The platform team deploys each shortlisted engine in a pre-production environment loaded with representative data and the real query set from step one, following each project's production guidance. Include the ingest path from step three and run concurrency tests at the peak level you defined. Record query latency, ingest lag, resource consumption, failure behavior and the operator steps needed for routine tasks. The pilot is done when every candidate has results against the same workload and the team can state which requirements each one met or missed.

6. Read the changelog and plan upgrades

   Before any upgrade decision, the platform engineer reads the release notes and changelog for every version under consideration, checking for backward-incompatible changes, deprecations and security fixes. ClickHouse, for example, recommends reading the changelog before upgrading because minor backward-incompatible changes can occur, and it publishes stable and LTS lines with separate support windows. Confirm which versions the vendor currently supports with security updates, and write down the upgrade path you would follow. The output is a dated upgrade plan that names the support window for the chosen line.

7. Compare operating effort and restore testing

   The operations lead estimates the recurring work for each candidate: cluster topology, coordination services, replication and failover design, monitoring, backup configuration and version upgrades. Run a restore test on a spare cluster for each engine, because backups that have never been restored often fail when needed. Ask the vendor or your provider to confirm how automatic failover is handled, which tools are required, and what is included in support. The step is complete when the team has a written comparison of operating effort and a verified restore for the candidate it is considering.

8. Record the decision and its exit criteria

   The engineering and security leads write a decision record that states the chosen engine, the workload it serves, the pilot results, the open questions, and the conditions that would trigger a re-evaluation, such as growth in concurrency or a new ingest source. Include the authentication, access control and audit requirements confirmed during the pilot, and name the person responsible for each recurring task. The record is the artifact that keeps the decision reviewable when staff or requirements change.

What do buyers ask before choosing ClickHouse, Druid or PostgreSQL?

What it costs to move analytics off PostgreSQL later

Licensing and paid editions to check in a quote

This sequence separates what the open-source engines license for free from what managed services and vendor support cost, so the quote covers the right components. Each step ends with a written answer you can file alongside the scope.

1. Confirm the license of each engine

   Start by recording the license for ClickHouse, Apache Druid and PostgreSQL in your own file. ClickHouse server is Apache-2.0 licensed according to its GitHub repository, Apache Druid is licensed under the Apache License 2.0, and PostgreSQL is released under the PostgreSQL License, a liberal license similar to BSD or MIT. Your security or legal reviewer should read the license text in the repository for the exact version you plan to deploy. The step is done when each engine has a license entry that names the source you checked.

2. Separate managed services from the open-source build

   Ask each vendor which offerings are self-hosted open-source software and which are commercial products. ClickHouse Inc. sells ClickHouse Cloud as a managed service, so a quote for that product is a different contract from the one for the self-hosted server. Ask whether the vendor's managed service carries features, regions or compliance attestations that the self-hosted build does not include. The output is a two-column list: components you will run yourselves, and components you would pay a vendor to run.

3. Ask about support windows and release lines

   Request the published support policy for each engine and get the dates in writing. For ClickHouse, the production FAQ states that LTS releases ship twice a year and are supported for a year, and the security policy lists which versions receive security updates. For PostgreSQL, the versioning policy gives an end-of-life date for each major version. For Druid, ask the project which release lines receive fixes, since the community does not promise support windows the way a vendor contract does. The check is complete when every engine has a support end date your upgrade plan can meet.

4. Ask which features are paid or vendor-only

   Ask each vendor to list features that appear in paid editions, enterprise tiers or managed plans and are absent from the open-source build. Examples to raise include authentication options, replication or failover tooling, backup management and monitoring. Note that PostgreSQL does not include automatic failover software, so any failover tool you add may be an external product with its own license. Record each feature with the edition that includes it. The output is a feature gap list you can compare against your requirements.

5. Request support terms and their scope

   For any vendor support contract, ask for the response model, the products and versions covered, what the contract excludes, and how upgrades are handled. Confirm whether a support contract covers configuration review, incident response on your infrastructure, or only the vendor's own software. Ask for the terms as a document, not a verbal summary. The step is done when you can state, for each engine, who answers a production incident and under which written terms.

6. Map cost components to who is paid

   Build a table with one row per cost: the open-source software, infrastructure you run, implementation services, a managed or support contract, and internal staff time. Mark which items are one-time (initial build, migration, first restore test) and which recur (infrastructure, support, upgrades, licenses for add-ons). Note what drives each recurring item up or down, such as data volume, number of users, or upgrade frequency. The output is a cost map that a quote can be checked against line by line.

7. Write the quote request from the findings

   Turn the license, support and feature findings into a written request that specifies the engines, versions, deployment location, the managed services to price separately, and the support terms required. Ask DeployOpen for a quote based on that written scope, since fixed-scope engagements are agreed in writing before work starts and DeployOpen does not publish a price list. Ask the vendors for their own written quotes on any managed or support product you are considering. The step closes when the request and the scope document are on file for comparison.