Open Source Data Lake
The expensive mistake in a data lake is not the storage bill; it is the format. Write your tables in something only one engine can read and you have quietly rebuilt warehouse lock-in on top of cheap object storage, losing the whole reason you left. The open table formats, catalogs, and query engines here keep data in open, engine-agnostic files, so the same tables can be read and written by many tools at once and you can swap compute without rewriting a byte. Open source also lets you assemble the lake from parts you control - storage, ingestion, table format, and catalog - instead of accepting one vendor's bundle and its exit terms.
SeaweedFS
Distributed storage for object storage, file systems, and Iceberg tables with horizontal scaling
RustFS
Distributed object storage in Rust with S3 compatibility and OpenStack Swift support
Airbyte
Open-source ELT and data movement for moving data from APIs, databases, and files to warehouses, lakes, and AI apps
Apache Druid
High-performance real-time analytics database for fast ingest, ad hoc queries, and high concurrency
Citus
PostgreSQL extension that shards tables across a cluster for distributed SQL workloads
Apache Iceberg
Open table format that lets Spark, Trino, and Flink read the same huge analytic tables at once
Delta Lake
Open table format that adds ACID transactions to lakehouse tables across Spark, Trino, and more
Apache Hudi
Open data lakehouse platform for ingesting, indexing, and managing data on cloud storage
Apache Pinot
Real-time distributed OLAP datastore for low-latency analytics on streaming and batch data
Our picks
A lake is built from layers, so these span table format, query, versioning, and ingestion.
Open table format for multi-engine lakes: Apache Iceberg Iceberg gives large analytic datasets the reliability of SQL tables, letting Spark, Trino, Flink, Presto, Hive, and Impala query and write the same tables at once. It handles Parquet, Avro, and ORC files and integrates with the Hive metastore, with Go, Python, Rust, and C++ clients for non-JVM teams.
Low-latency user-facing analytics: Apache Pinot Pinot is a real-time OLAP datastore that ingests batch data plus Kafka, Pulsar, and Kinesis streams and answers standard SQL, filtering and aggregating petabyte data sets in milliseconds. Upserts during real-time ingestion and pluggable indexes suit interactive, user-facing dashboards.
Version control for the lake: lakeFS lakeFS turns object storage into a Git-like repository, giving every job a zero-copy branch of production data. Hooks enforce write-audit-publish gates before a merge, and time travel rolls back a bad write. It is S3-compatible and works with Spark, Hive, Athena, DuckDB, and Presto.
Getting data into the lake: Airbyte Airbyte moves data from APIs, databases, and files into lakes and warehouses, with 600+ connectors plus a no-code builder and a low-code CDK. Syncs orchestrate with Airflow, Dagster, or Kestra, and Airbyte Open Source self-hosts on your own infrastructure.
How the pieces of an open lake fit together
A data lake is not one product but a stack of layers, and the table format is the layer that decides everything above it. Object storage holds the files, but analytics depends on how those files are grouped into tables, how schemas evolve, and whether writes are atomic. Apache Iceberg, Delta Lake, and Apache Hudi all add ACID transactions, snapshots, and time travel over Parquet so engines like Spark, Trino, and Flink can safely share the same tables. Apache Paimon leans toward streaming updates through an LSM structure, and DuckLake keeps table metadata in a plain SQL database rather than scattered metadata files.
Above the format sit the catalog and the compute. Project Nessie versions table metadata as Git-style commits, so you can branch, tag, and roll back an entire dataset as one unit, while lakeFS brings that same branch-and-commit model down to the storage layer with zero-copy branches. For queries, the pattern you need matters more than raw speed: Apache Pinot and Apache Druid target low-latency, high-concurrency analytics on fresh data, and Citus scales PostgreSQL across a cluster when you would rather keep familiar SQL. Getting data in is its own layer, handled by ELT tools such as Airbyte, dlt, and Sling.
Before committing, test the exit. The point of an open lake is that metadata, manifests, and data files stay understandable without the original service, so confirm your table format is one that multiple engines can read and that backups capture catalogs and transaction logs, not only the object store. If the only way to read your tables is one vendor's control plane, the lock-in you set out to avoid has simply moved up a layer.
Related categories
Frequently asked questions
How is a data lake different from a data warehouse?+
A warehouse usually owns storage, compute, and access through one managed system. A data lake separates storage from compute and is more flexible about data shape, ingestion timing, and which engines read it. That flexibility has a cost: you make deliberate choices about table formats, catalogs, compaction, and permissions that a warehouse hides. The payoff is that the same open files can serve SQL analytics, batch jobs, streaming, and machine learning without copying data into separate systems.
What does a table format like Iceberg or Delta Lake actually give me?+
It turns a pile of Parquet files into something that behaves like a real SQL table. Apache Iceberg and Delta Lake add ACID transactions, snapshots, schema evolution, and time travel over object storage, so inserts, updates, and deletes are reliable even with concurrent writers. Crucially, Iceberg lets Spark, Trino, Flink, Presto, Hive, and Impala read and write the same tables at once, which is what keeps your data engine-agnostic instead of tied to one tool.
Do I need a separate catalog?+
You need something to track table metadata, and a dedicated catalog adds capabilities a bare file layout cannot. Project Nessie versions that metadata as Git-like commits, so you can branch tables for an experiment, validate, then merge, and roll back a bad write atomically across Spark, Flink, and Trino via Iceberg. DuckLake takes a different route, storing all table metadata in a transactional SQL database. Either way, the catalog is what coordinates concurrent readers and writers safely.
Can the same lake serve streaming and batch data?+
Yes, if the table format and ingestion handle frequent commits cleanly. Apache Hudi and Apache Paimon are built for this, absorbing high-frequency updates and deletes and merging streaming changes directly into lake tables, and Apache Pinot ingests both batch and Kafka or Pulsar streams. The common pitfall is small files: streaming creates many of them and hurts query speed unless compaction is planned. Also check exactly-once behavior, late events, and checkpoint recovery before relying on it.
How do I get data into an open data lake?+
Through a dedicated ingestion or ELT layer rather than the storage itself. Airbyte offers 600+ connectors plus a no-code builder for moving data from APIs, databases, and files into lakes and warehouses. dlt is a Python library that extracts from APIs, databases, and cloud storage while inferring schemas and normalizing nested data. Sling is a single-binary CLI for database, file, and data-lake movement, including Iceberg. Match the tool to how much code your team wants to write.
Which tools give low-latency queries for dashboards?+
For interactive, high-concurrency workloads, a purpose-built OLAP engine beats a general query layer. Apache Pinot filters and aggregates petabyte data sets in milliseconds and supports upserts during real-time ingestion, which is why it is used for user-facing analytics. Apache Druid targets the same interactive UIs and ad hoc operational queries, with streaming and batch ingestion and a built-in query console. Test both against your real dashboards, since cold-query latency and file skipping vary with table layout.
What happens if the project behind my data lake is abandoned?+
Your exposure depends on how much lives in open files versus proprietary services. Because formats like Iceberg, Delta Lake, and DuckLake store data as plain Parquet with documented metadata, another engine can usually read your tables even if one tool stops moving, which is the core reason to prefer them. Keep regular exports of catalog metadata, schemas, and transaction logs. If the control plane disappears but the data and table metadata stay portable, replacement is painful but realistic.