Open-source Azure Data Explorer alternatives

ClickHouse is an open-source database management system built to generate analytical reports in real time. Its column-oriented storage makes scans and aggregations over very large tables efficient, which is why it is widely used for event data, logs, metrics and product analytics. It is written in C++ and queried with SQL.

The project's topics describe it as an OLAP, massively parallel and distributed system, and it is used for big data workloads where query latency matters. You can install it on Linux, macOS or FreeBSD, follow a tutorial that sets up a small cluster, or skip installation by trying ClickHouse Cloud, a managed service built by its creators. Releases arrive monthly, with a community call for each one, and there are Slack and Telegram channels for help.

ClickHouse is Apache-2.0 licensed. It is a good fit for analytics engineers and platform teams who need fast aggregations over billions of rows and are comfortable operating a database, or who prefer to hand that operation to the vendor's cloud.

InfluxDB is an open-source time series database for collecting, processing, transforming and storing event and metrics data. The current Core edition of InfluxDB 3 is written in Rust and builds on Apache Arrow, DataFusion and Parquet, and it targets workloads where data must be ingested continuously and queried quickly enough to drive dashboards, interactive interfaces and automation.

The README lists typical uses such as sensor monitoring, server and network monitoring, application performance monitoring, financial market analytics and behavioral analytics. Feature highlights include a diskless architecture that can store data in object storage or on local disk with no extra dependencies, an embedded Python virtual machine for plugins and triggers, and persistence in Parquet files. The project quotes very low query latency for last-value lookups, though real numbers depend on your hardware and data.

InfluxDB is Apache-2.0 licensed and can be run on your own servers. InfluxData, the company behind it, also sells hosted and commercial editions. The topics list monitoring, metrics and time series, and it commonly sits behind visualization tools such as Grafana in observability stacks.

TimescaleDB is a PostgreSQL extension that turns Postgres into a database for time-series and event data, aimed at real-time analytics. Because it lives inside PostgreSQL, you keep SQL, existing tools and the broader Postgres ecosystem while gaining storage and query features designed for data that arrives continuously with timestamps.

The quick start walks through running it locally with a one-line install script or Docker, creating a hypertable with the columnstore enabled, inserting data directly into the columnstore and running analytical queries. It suggests Docker, around 8 GB of RAM and a psql or other PostgreSQL client, and warns that the install script is meant for local development and testing, not production. Topics list uses such as IoT, financial analysis and historian data.

The code is written in C, and its license is listed as 'Other' on GitHub because some features sit under Timescale's own license rather than a standard open-source one, so check which apply to you. The company, now branded TigerData, also runs a hosted cloud service. It suits teams who want time-series features without leaving PostgreSQL.

QuestDB is an open-source time-series database aimed at high-speed ingestion and fast analytical queries, with a focus on use cases such as capital markets, tick data and real-time analytics. One SQL engine covers ingestion, stream processing and long-term storage, so teams query live data and years of history the same way.

The engine is written in Java that avoids garbage collection, with C++ and Rust used on performance-critical paths. Data is held in memory-mapped, time-partitioned columns, and queries run across all CPU cores using SIMD and JIT-compiled filters. No third-party libraries sit on the data path. Data moves through three storage tiers: Apache Parquet files, native columnar partitions, and a write-ahead log that is written in parallel. In the open-source edition you convert partitions to Parquet manually, while the Enterprise edition automates tiering to object storage. Because the data stays in open formats (Parquet, Iceberg and Arrow), other tools can read it, and the README publishes benchmark figures for ingestion and query streaming.

QuestDB is Apache-2.0 licensed and runs on your own servers, with documentation also available in Chinese. It suits teams building financial, industrial or telemetry platforms who need very fast time-series ingestion.

GreptimeDB is an open-source observability database in which metrics, logs and traces share one columnar engine over object storage and one table model of tags, timestamp and fields. When signals share identifiers like service name, host or trace ID, they can be correlated with SQL joins rather than shuttling data between databases.

Ingestion works through OpenTelemetry, Prometheus Remote Write, the Loki push API or Elasticsearch Bulk, and queried with SQL across all signals and PromQL for metrics, so ingestion can migrate one signal at a time without rebuilding collectors. The README lists reasons to use it: replacing a Prometheus plus Loki or Elasticsearch combination with one backend, outgrowing Prometheus on cardinality or retention without the Thanos or Mimir complexity, long retention on object storage, and storing GenAI telemetry next to infrastructure signals.

GreptimeDB is written in Rust, with an Apache-2.0 licensed core, and is run self-hosted on your own infrastructure. Stable, canary and nightly builds are offered. It suits platform and SRE teams wanting a consolidated, cost-conscious observability backend.