Open Source Amazon Redshift Alternatives

ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real-time. Analytical queries sweep billions of rows, and a row-oriented database has to read every column to answer them. That is the cost ClickHouse was built to remove.

Tables are stored as a collection of columns, with the values of each column stored sequentially one after the other, so a query touches only the columns it names. The documentation reports a sample query processing 100 million rows in 92 milliseconds, over a billion rows per second or just under 7 GB per second.

Several engineering decisions follow from that layout.

• SQL query language: a declarative language with GROUP BY, ORDER BY, subqueries, JOINs and window functions.
• Multi-master replication: an asynchronous scheme storing data redundantly on multiple nodes.
• Adaptive joins: the engine chooses between hash and merge algorithms per query.
• Accuracy traded for speed: approximate aggregations and sampling when an exact answer is not worth the wait.
• Role based access control: permissions modeled on the ANSI SQL standard.

Installing on Linux, macOS or FreeBSD is a single shell command from the project's site. The same engine runs as self-managed open source under Apache 2.0 or as ClickHouse Cloud, a managed service built by the creators and maintainers. Releases land monthly, each introduced by a public community call whose recording and slides stay online.

The time between an event arriving and a person acting on it is what Apache Druid sets out to shorten. It is a real-time analytics database designed for workflows where query speed and ingestion speed both matter, a pairing that general purpose warehouses tend to treat as two separate problems.

Druid does well behind user-facing interfaces, ad-hoc operational queries and workloads with high concurrency, and the project positions it as an open source alternative to a data warehouse for exactly those cases. Design documentation explains the segments, processes and architecture behind that behavior.

Interaction happens through APIs and a console that ships with the database.

• Query interfaces: HTTP and JDBC endpoints for loading, managing and querying data.
• Ingestion wizard: a point-and-click flow in the web console for streaming and batch ingestion, with monitoring for one-off tasks and supervisors.
• Cluster view: datasources, segments, ingestion tasks and services in one place, backed by SQL system tables whose underlying query you can read.
• Query workbench: somewhere to prototype DruidSQL and native queries before wiring up an external tool.
• Ecosystem: many third-party tools connect to Druid, and a separate druid-operator repository handles Kubernetes deployments.

Getting started means a local or Docker quickstart, both documented as tutorials. The documentation site carries manuals for the current release and for previous ones, and the project is developed at the Apache Software Foundation under the Apache License 2.0.

Databend is an enterprise data warehouse written in Rust that keeps analytics, vector search and full-text search inside one engine. Teams stop shuttling data between a warehouse, a vector store and a search index just to answer different kinds of questions.

Agent logic runs inside the warehouse through sandbox user defined functions. A control plane handles resource scheduling, permission validation and sandbox lifecycle, the execution plane orchestrates with SQL and issues requests over Arrow Flight, and isolated sandbox workers run your Python. Defining such a function and calling it across a table of tasks is ordinary SQL.

The engine is shaped by what enterprise workloads keep asking of it.

• Core analytics: large-scale SQL queries, transactions and automatic schema evolution.
• Search: vector and full-text search living beside the analytical tables.
• Branching: git-like data versioning so agents work on production snapshots safely.
• Elastic compute: cloud native scaling with S3, Azure or GCS underneath.
• Python sandboxes: isolated workers for agent logic, model calls and tool use.

Starting takes one of three forms, a managed cloud account, a local Python driver install for development on Python 3.12 or 3.13, or a Docker image that runs the full warehouse on your machine. Licensing splits between Apache 2.0 and Elastic 2.0 depending on the component.

Built by engineers at LinkedIn and Uber, Apache Pinot is a real-time distributed OLAP datastore for analytics that end users see directly, not only dashboards an analyst opens now and then. At LinkedIn it backs more than fifty user-facing products, ingesting millions of events per second and serving over a hundred thousand queries per second at millisecond latency.

Data arrives from batch sources such as Hadoop HDFS, Amazon S3, Azure ADLS and Google Cloud Storage, and from streams such as Apache Kafka, Apache Pulsar and AWS Kinesis. Batch and streaming sources combine into one table for querying, and upserts during real-time ingestion keep records consistent as they change.

The engine is column oriented, with the pieces interactive queries depend on.

• Query latency: filters and aggregations over petabyte datasets return with P90 latencies in the tens of milliseconds.
• Concurrency: hundreds of thousands of concurrent queries per second when applications query it directly.
• Pluggable indexing: timestamp, inverted, StarTree, Bloom filter, range, text, JSON and geospatial index types.
• Versatile joins: arbitrary fact to dimension and fact to fact joins across petabyte datasets.
• Multitenancy: isolated logical namespaces keep data separated and resources manageable.

Access is standard SQL, reachable from a built-in query editor and a REST API. Deployment is horizontally scalable and fault tolerant, with a Helm chart for clustered installs on Kubernetes. Pinot fits best where fast aggregations run over largely immutable data with many dimensions and metrics, and it handles time series of that shape particularly well.

Created by the original developers of Greenplum Database, Apache Cloudberry is a mature massively parallel processing database that evolved from the open source version of Pivotal Greenplum. It carries a newer PostgreSQL kernel and more advanced enterprise capabilities than the code it grew from, and it works both as a data warehouse and as an engine for large-scale analytics and AI or machine learning workloads.

The project is undergoing incubation at the Apache Software Foundation, sponsored by the Apache Incubator. Incubation is required of every newly accepted project until a review finds that its infrastructure, communications, and decision making have stabilized in a manner consistent with other successful ASF projects. That status says nothing about the completeness or stability of the code itself.

Getting started and taking part are both documented in the repository.

• Build from source: guides cover RHEL and Rocky Linux, Ubuntu, and macOS.
• Docker sandbox: a sandbox in the repository gives a first look at the features without a full deployment.
• Ecosystem repositories: separate projects hold the website and documentation, the backup utility, the Go libraries, and the Platform Extension Framework.
• Community channels: Slack for real-time chat, GitHub Discussions for questions and feature ideas, and a published events calendar.
• Contribution paths: code, proposals, and documentation each have their own guide, plus an AI guideline for agent-assisted work.

Cloudberry is licensed under the Apache License, Version 2.0. It suits organizations already running Greenplum who want a maintained successor on a newer kernel, and analytics teams who need MPP scale without a proprietary warehouse.