Find Open-Source Alternatives
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Compare community-driven replacements for Snowflake in data warehouse & olap databases workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
These projects match the most common migration paths for teams replacing Snowflake.
Why teams pick it
Organizations with data spread across multiple clouds, regions, or on-premises systems
Run on infrastructure you control
Recent commits in the last 6 months
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to Snowflake.
Why teams pick it
Unified deployment: local Python install, Docker self-host, or managed cloud
Cloud-native lakehouse with ACID transactions and streaming upserts
Why teams choose it
Watch for
Requires PostgreSQL for metadata management, adding infrastructure dependency
Migration highlight
Real-Time MySQL Replication
Sync entire MySQL databases to cloud storage with auto table creation, DDL propagation, and exactly-once CDC guarantees for downstream analytics.
Geo-distributed federated metadata lake for unified data governance
Distributed relational database delivering high‑availability, linear scalability, and vector search.
Postgres-compatible analytical database with built-in data sync connectors
Scalable, fault-tolerant platform for big-data storage and processing
AI-native multimodal data warehouse with Snowflake-compatible SQL
Sub-second ad-hoc analytics across data lakes and warehouses
In-process SQL OLAP engine powered by ClickHouse
High-performance in-process analytical SQL database for fast queries
High-performance real-time analytical database with MPP architecture
Distributed SQL database for real-time analytics at scale
Cloud-native data warehouse with compute-storage separation for large-scale analytics
Teams replacing Snowflake in data warehouse & olap databases workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Snowflake.
Why teams choose it
Watch for
Windows builds are not currently supported
Migration highlight
Multi-Cloud Data Lake Federation
Unified metadata access across AWS S3, Azure Data Lake, and on-premises HDFS, enabling cross-cloud analytics without data migration.
Why teams choose it
Watch for
All‑in‑one deployment is Linux‑only
Migration highlight
Real‑time fraud detection
Processes billions of transactions per day while instantly querying vector embeddings to flag anomalies.
Why teams choose it
Watch for
Requires external Postgres database for catalog metadata management
Migration highlight
Centralized analytics without ETL complexity
Query data from multiple Postgres databases and SaaS platforms through a single endpoint without building custom pipelines
Why teams choose it
Watch for
Complex deployment may require Kubernetes expertise
Migration highlight
Real‑time clickstream analytics
Process billions of events per day with low latency using MapReduce and CHYT for instant dashboards.
Why teams choose it
Watch for
Dual licensing (Apache 2.0 + Elastic 2.0) may restrict certain commercial use cases
Migration highlight
Snowflake Migration with Cost Optimization
Maintain SQL compatibility while reducing cloud warehouse costs by up to 90% through S3-native storage and eliminating proprietary compute overhead
Why teams choose it
Watch for
Optimally runs on Linux/Unix environments only
Migration highlight
Business intelligence dashboards with sub‑second refresh
Analysts receive instant query results across multi‑dimensional data, enabling real‑time decision making.
Why teams choose it
Watch for
Limited to single-process execution without distributed query capabilities
Migration highlight
Ad-hoc Parquet Analysis
Query multi-gigabyte Parquet files directly from disk with SQL, returning results as Pandas DataFrames without ETL pipelines or database imports.
Why teams choose it
Watch for
Optimized for analytics, not transactional OLTP workloads
Migration highlight
Interactive Data Exploration
Analysts query multi-gigabyte Parquet datasets on laptops without loading data into separate databases, accelerating insight discovery.
Why teams choose it
Watch for
Storage-compute integrated architecture may limit independent scaling flexibility
Migration highlight
Real-Time Business Dashboards
Deliver sub-second reporting and decision-making dashboards with real-time data ingestion from transactional databases, enabling automated business processes and instant insights.
Why teams choose it
Watch for
Requires understanding of distributed database concepts for optimal deployment
Migration highlight
IoT Sensor Data Analytics
Ingest thousands of sensor readings per second and run real-time SQL queries for anomaly detection and trend analysis across distributed clusters.
Why teams choose it
Watch for
Requires FoundationDB client library dependency for operation
Migration highlight
Real-Time Analytics on Streaming Data
Ingest and query streaming events alongside historical batch data without maintaining separate systems, enabling unified analytics across all data sources.