87+ Analytics Databases Ranked & Compared

Compare OLAP and analytics databases ranked by GitHub stars, query speed, and analytical power.

Last updated: April 16, 2026
78 databases
1Elasticsearch
Elasticsearch
76.5k+378 30d

Distributed search and analytics engine built on Apache Lucene for full-text search, observability, and security

Search·2010·Elastic-2.0·Java
2ClickHouse
ClickHouse
46.9k+542 30d

Blazing-fast open-source column-oriented database for real-time analytics and OLAP

Analytics·2016·Apache-2.0·C++
3Apache Spark SQL
Apache Spark SQL
43.1k+190 30d

Distributed SQL query engine within Apache Spark for structured data processing at scale

Analytics·2014·Apache-2.0·Scala, Java, Python, R
4TiDB
TiDB
40.0k+182 30d

MySQL-compatible distributed SQL database for hybrid transactional and analytical workloads

Relational·2015·Apache-2.0·Go
5DuckDB
DuckDB
37.5k+728 30d

Fast in-process analytical database with rich SQL support and zero dependencies

Analytics·2019·MIT·C++
6Apache Flink
Apache Flink
25.9k+99 30d

Stateful stream processing framework for real-time and batch data at any scale

Streaming·2011·Apache-2.0·Java, Scala
7Presto
Presto
16.7k+30 30d

Distributed SQL query engine for running interactive analytic queries against data sources of all sizes

Analytics·2012·Apache-2.0·Java, C++
8Apache Doris
Apache Doris
15.2k+122 30d

High-performance real-time analytical database for sub-second queries on large-scale data

Analytics·2017·Apache-2.0·Java, C++
9Apache Druid
Apache Druid
14.0k+20 30d

High-performance real-time analytics database for sub-second OLAP queries at scale

Analytics·2012·Apache-2.0·Java
10Trino
Trino
12.7k+97 30d

Fast distributed SQL query engine for big data analytics across heterogeneous data sources

Analytics·2019·Apache-2.0·Java
11Citus
Citus
12.4k+64 30d

Distributed PostgreSQL as an extension for multi-tenant SaaS and real-time analytics at scale

Relational·2016·AGPL-3.0·C
12StarRocks
StarRocks
11.6k+115 30d

High-performance MPP analytics engine for real-time and batch data warehousing

Analytics·2021·Apache-2.0·Java, C++
13Quickwit
Quickwit
11.1k+107 30d

Cloud-native search engine for observability, built on object storage with sub-second latency

Search·2021·Apache-2.0·Rust
14OceanBase
OceanBase
10.1k+52 30d

Distributed relational database for high-performance transactional, analytical, and AI workloads at scale

Relational·2010·Mulan PubL v2·C++
15Databend
Databend
9.3k+69 30d

Cloud-native data warehouse built in Rust for analytics, search, and AI on object storage

Analytics·2021·Apache-2.0·Rust
16Apache Pinot
Apache Pinot
6.1k+12 30d

Real-time distributed OLAP datastore for ultra low-latency analytics at high throughput

Analytics·2015·Apache-2.0·Java
17Apache Hive
Apache Hive
6.0k−15 30d

Data warehouse software for reading, writing, and managing large datasets in distributed storage using SQL

Analytics·2010·Apache-2.0·Java
18AliSQL
AliSQL
5.8k+40 30d

Alibaba's battle-tested MySQL branch with built-in DuckDB analytics and vector search

Relational·2016·GPL-2.0·C++, C
19Apache Kylin
Apache Kylin
3.8k+3 30d

Distributed OLAP engine with sub-second query performance via pre-calculated cubes on Hadoop

Analytics·2015·Apache-2.0·Java
20AresDB
AresDB
3.1k0 30d

GPU-powered real-time analytics storage and query engine by Uber

Analytics·2019·Apache-2.0·Go, C++, CUDA
21HeavyDB
HeavyDB
3.1k0 30d

GPU-accelerated SQL analytics engine for interactive exploration of massive datasets

Analytics·2017·Apache-2.0·C++, CUDA, Java
22Apache HoraeDB
Apache HoraeDB
2.8k+4 30d

High-performance distributed cloud-native time-series database for analytics and time-series workloads

Time-Series·2022·Apache-2.0·Rust
23chDB
chDB
2.7k+28 30d

Embedded in-process OLAP SQL engine powered by ClickHouse for Python analytics

Embedded·2023·Apache-2.0·C++, Python
24Apache Sedona
Apache Sedona
2.3k+9 30d

Cluster computing framework for large-scale geospatial data processing on Spark, Flink, and Snowflake

Analytics·2017·Apache-2.0·Scala, Java, Python, Rust
25YTsaurus
YTsaurus
2.2k+21 30d

Exabyte-scale distributed storage and processing platform for big data from Yandex

Multi-Model·2023·Apache-2.0·C++
26Apache Drill
Apache Drill
2.0k+3 30d

Schema-free SQL query engine for Hadoop, NoSQL, and cloud storage with dynamic schema discovery

Analytics·2015·Apache-2.0·Java
27MatrixOne
MatrixOne
1.8k−14 30d

Cloud-native HTAP database with MySQL compatibility, Git-style data versioning, and AI-native capabilities

Relational·2021·Apache-2.0·Go
28RisingLight
RisingLight
1.8k+10 30d

Educational OLAP database system written in Rust with columnar storage

Analytics·2022·Apache-2.0·Rust
29FrostDB
FrostDB
1.5k+11 30d

Embeddable columnar database in Go using Apache Arrow and Parquet for observability workloads

Analytics·2022·Apache-2.0·Go
30Apache Impala
Apache Impala
1.3k+3 30d

Native analytic SQL engine for Apache Hadoop and open data formats with low-latency queries

Analytics·2012·Apache-2.0·C++, Java
31Apache Cloudberry

Advanced open-source MPP analytics database forked from Greenplum with a modern PostgreSQL kernel

Analytics·2023·Apache-2.0·C, C++
32MyScale
MyScale
1.0k+5 30d

SQL vector database built on ClickHouse for high-performance AI applications with filtered search

Vector·2023·Apache-2.0·C++
33Arc
Arc
573+15 30d

High-performance columnar analytical database built on DuckDB SQL, Parquet storage, and Arrow format

Analytics·2024·AGPL-3.0·Go
34MonetDB
MonetDB
460+3 30d

Pioneering open-source column-store database for high-performance analytics and data warehousing

Analytics·2004·MPL-2.0·C
35OpenTenBase
OpenTenBase
242+45 30d

Enterprise-level distributed HTAP database based on PostgreSQL for hybrid transactional and analytical workloads

Relational·2019·BSD-3-Clause·C
36Firebolt
Firebolt
199+5 30d

Sub-second analytics cloud data warehouse built for high-concurrency, data-intensive applications

Analytics·2021·proprietary·C++
37Splice Machine

Dual-engine HTAP database combining HBase transactions with Spark analytics and ANSI SQL

Relational·2014·AGPL-3.0·Java, Scala
38RayforceDB
RayforceDB
119+6 30d

SIMD-accelerated columnar database for analytics written in pure C with zero dependencies

Analytics·2023·MIT·C
39Greenplum
Greenplum
112+2 30d

Massively parallel processing analytics database built on PostgreSQL for large-scale data warehousing

Analytics·2005·proprietary·C, C++, Python
40DolphinDB
DolphinDB
570 30d

High-performance time-series database with built-in analytics for finance and IoT

Time-Series·2018·Proprietary·C++
41Exasol
Exasol
60 30d

High-performance in-memory MPP analytics database delivering up to 1000x faster analytical queries

Analytics·2000·Commercial·C++
421010data

Cloud-based columnar analytics platform for massive-scale data discovery and ad hoc analysis

Analytics·2000·proprietary·K
43Actian Vector

Vectorized columnar analytics database with SIMD-optimized query execution

Analytics·2010·proprietary·C, C++
44Alibaba Cloud AnalyticDB for MySQL

Cloud-native real-time data warehouse with MySQL compatibility for petabyte-scale analytics

Analytics·2017·proprietary·C++
45Alibaba Cloud AnalyticDB for PostgreSQL

MPP cloud data warehouse with PostgreSQL compatibility and vector search capabilities

Analytics·2016·proprietary·C, C++
46Alibaba Cloud Log Service

Cloud-native observability platform for PB-scale log collection, analysis, and visualization

Analytics·2016·proprietary
47Alibaba Cloud MaxCompute

Fully managed petabyte-scale data warehouse with serverless SQL, MapReduce, and graph computation

Analytics·2010·proprietary
48Amazon Redshift

Petabyte-scale cloud data warehouse with columnar storage and massively parallel processing

Analytics·2013·proprietary
49Microsoft Azure Data Explorer

Fast and scalable data analytics service for real-time analysis of streaming and time-series data using Kusto Query Language

Analytics·2019·proprietary
50BigObject

High-speed time-series analytics engine with SQL support and in-data computing

Time-Series·2014·proprietary·C++
51Brytlyt

GPU-accelerated analytics database built on PostgreSQL for millisecond queries on billions of rows

Analytics·2016·proprietary·C++, C
52DaggerDB

Real-time OLAP analytics engine with hybrid column store and .NET query API

Analytics·2014·proprietary·C#
53GBase

Chinese enterprise database platform with leading analytical and transactional database products

Analytics·2004·proprietary·C, C++
54Google BigQuery

Serverless, highly scalable multi-cloud data warehouse with built-in ML and real-time analytics

Analytics·2010·proprietary
55IBM Db2

Enterprise-grade relational database with AI-powered optimization and hybrid cloud deployment

Relational·1983·proprietary·C, C++, Assembly
56IBM Db2 Warehouse

Cloud-native analytics data warehouse with in-memory columnar processing and AI integration

Analytics·2006·proprietary·C, C++, Java
57Kinetica

GPU-accelerated real-time analytics database for spatial, temporal, graph, and AI workloads at scale

Analytics·2016·proprietary·C++
58Kyligence Enterprise

AI-augmented OLAP analytics platform delivering sub-second queries on petabyte-scale data, built on Apache Kylin

Analytics·2016·proprietary·Java
59Microsoft Azure Synapse Analytics

Integrated analytics platform combining data warehousing, big data, and data integration with serverless and provisioned options

Analytics·2019·proprietary
60Netezza

Purpose-built analytics appliance for high-performance data warehousing and advanced analytics

Analytics·2002·Proprietary·C, C++
61Oracle Essbase

Market-leading multidimensional OLAP database for enterprise performance management and analytics

Analytics·1992·Oracle Commercial License·C, C++
62OushuDB

Cloud-native MPP data warehouse built on Apache HAWQ for petabyte-scale interactive analytics

Analytics·2016·proprietary·C, C++
63PieCloudDB

Cloud-native virtual data warehouse with elastic massive parallel processing (eMPP) architecture

Analytics·2022·proprietary·C++
64Sadas Engine

Columnar in-memory DBMS for high-speed big data analytics and business intelligence

Analytics·2010·Commercial·C++
65SAP HANA

In-memory relational database for real-time analytics and transactional processing in enterprise environments

Relational·2010·proprietary·C++
66SAP IQ

Column-oriented analytics database for high-performance big data queries with extreme compression

Analytics·1996·SAP Commercial License·C, C++
67SciDB

Array database for multidimensional data management and complex analytics in scientific computing

Analytics·2010·AGPL-3.0·C++
68SingleStore

Distributed SQL database for data-intensive applications combining transactions, analytics, and AI workloads

Relational·2013·SingleStore Commercial License·C++
69Snowflake

Cloud-native data warehouse with automatic scaling, separation of storage and compute, and near-zero administration

Analytics·2014·proprietary
70SpaceTime

Spatiotemporal relational database optimized for analytical workloads on moving objects with JIT compilation

Analytics·2020·proprietary·C++
71SQream DB

GPU-accelerated columnar analytics database for petabyte-scale SQL queries

Analytics·2010·proprietary·C++, CUDA
72Teradata

Enterprise-grade parallel data warehouse for large-scale analytics and business intelligence

Analytics·1984·proprietary·C, C++
73Transbase

Lightweight high-performance relational SQL database with a sub-10MB footprint for edge to enterprise

Relational·1987·Commercial·C
74Transwarp ArgoDB

Distributed analytical database replacing Hadoop+MPP with unified SQL analytics and real-time data processing

Analytics·proprietary
75Valentina Server

Multi-paradigm database server unifying ValentinaDB, SQLite, and DuckDB with integrated reporting

Multi-Model·2000·proprietary·C++
76Vertica

High-performance columnar analytics database for petabyte-scale real-time analytics and machine learning

Analytics·2005·Proprietary·C++
77XtremeData

Cloud-scale parallel SQL analytics engine with vectorized execution for complex analytical workloads

Analytics·2005·proprietary·C++
78Yellowbrick

Modern enterprise cloud data warehouse built on Kubernetes for extreme speed and concurrency

Analytics·2014·proprietary·C++

What is an Analytics Database?

An analytics database (also called OLAP or columnar database) is optimized for analytical queries — aggregations, groupings, and scans across large datasets. Unlike row-oriented relational databases that read entire rows, columnar databases store data by column, enabling massive compression and fast scans over specific fields. This makes queries like 'total revenue by region for the last quarter' run orders of magnitude faster than in PostgreSQL or MySQL. ClickHouse, DuckDB, Apache Druid, and Redshift are among the most popular, each targeting different scale and deployment needs.

When to Use an Analytics Database

Use an analytics database when you need to run complex analytical queries over large datasets — business intelligence dashboards, product analytics, ad-hoc data exploration, or log analysis. They excel at queries that scan millions or billions of rows with aggregations (COUNT, SUM, AVG, percentiles) and GROUP BY operations. ClickHouse is ideal for real-time analytics at scale. DuckDB is perfect for analytical queries embedded in applications or run locally on your laptop. Apache Druid targets sub-second interactive analytics on streaming data. Consider a relational database if your workload is primarily transactional (OLTP) with small, targeted queries.

Frequently Asked Questions

What is the difference between OLTP and OLAP databases?
OLTP (Online Transaction Processing) databases like PostgreSQL and MySQL are optimized for many small, fast transactions — inserting an order, updating a user record, reading a single row. OLAP (Online Analytical Processing) databases like ClickHouse and DuckDB are optimized for few large, complex queries — aggregating millions of rows, computing metrics across time ranges, generating reports. OLTP databases store data in rows (fast for reading/writing complete records); OLAP databases store data in columns (fast for scanning specific fields across many records).
What is DuckDB and why is it gaining popularity?
DuckDB is an in-process OLAP database — it runs embedded inside your application (like SQLite) but is optimized for analytical queries instead of transactions. It can query CSV, Parquet, and JSON files directly without importing, integrates natively with Python and R, and runs on laptops without any server setup. It has gained massive popularity because it makes analytical queries accessible to individual developers and data scientists who previously needed a full data warehouse. DuckDB is ideal for local analytics, ETL scripts, and embedded BI.
Is ClickHouse better than PostgreSQL for analytics?
For analytical workloads, yes — significantly. ClickHouse is a columnar database designed specifically for OLAP queries. On typical analytical queries (aggregations over millions of rows), ClickHouse can be 100-1000x faster than PostgreSQL. However, ClickHouse is not designed for transactional workloads — it doesn't support UPDATE/DELETE efficiently and lacks full ACID transactions. The common pattern is to use PostgreSQL for transactional data and replicate to ClickHouse for analytics and dashboards.
Can I use an analytics database for real-time data?
Yes. ClickHouse supports real-time ingestion and can query data seconds after it arrives. Apache Druid is specifically designed for real-time analytics on streaming data. Materialized views and continuous aggregation in ClickHouse and TimescaleDB allow pre-computed analytics that update in real time. DuckDB is less suited for real-time streaming since it's designed for batch analytical queries. For real-time dashboards, ClickHouse or Druid connected to a streaming source (Kafka) is the standard approach.
What is the difference between ClickHouse and Snowflake?
ClickHouse is an open-source, self-hosted (or ClickHouse Cloud managed) columnar database optimized for real-time analytics with very fast query performance. Snowflake is a fully managed cloud data warehouse with a consumption-based pricing model, strong data sharing features, and separation of storage and compute. ClickHouse offers better raw performance and cost control; Snowflake offers easier management and better enterprise collaboration features. For startups and engineering-heavy teams, ClickHouse is often preferred. For enterprise data teams, Snowflake's managed approach is attractive.

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