Skip to main content

Get the OLake Advantage

View all Performance Benchmarks
3 - 100X
Faster than traditional tools
90%
Cost Savings with OSS
O.Lake Data Pipeline

Fastest way to replicate your data from

Database→Data Lakehouse

Talk to usLive on Github. Contribute

The Fundamental

Experience the most seamless workflow

Workflow timeline
Workflow image 1
Workflow image 2
Workflow image 3
Workflow image 4
IcebergExclusively for Apache Iceberg

Built on Iceberg.
Born for Scale.

Schema evolution

Schema evolution

Apache Iceberg enables seamless schema evolution by supporting column additions, deletions, renames, and reordering ensuring reliable analytics on evolving datasets without rewriting historical data.

Schema datatype changes

Schema datatype changes

Apache Iceberg enables safe and forward-compatible data type evolutions. This guarantees robust schema evolution without the need to rewrite existing data or disrupt downstream queries.

Partitioning and partition evolution

Partitioning and partition evolution

Apache Iceberg supports flexible partitioning without requiring data to be physically rewritten. Partition evolution allows you to safely change partition strategies over time without impacting existing data.

Benchmarks

Get the bestwith OLake

Rows synced
OLake
4.01 Billion
Airbyte12.7 Million
Fivetran4.01 Billion
Debezium1.28 Billion
Estuary0.34 Billion
Elapsed time
OLake
22.5 minutes
Airbyte23 hours
Fivetran31 minutes
Debezium60 minutes
Estuary4.5 hours
Speed (Rows/Sec)
OLake
46,262 RPS
Airbyte457 RPS
Fivetran46,395 RPS
Debezium14,839 RPS
Estuary3,982 RPS
Comparison
OLake
-
Airbyte101× slower
Fivetransame
Debezium3.1× slower
Estuary11.6× slower
Cost
OLake
$ 75
Airbyte$ 5,560
Fivetran$ 7,446
Debezium$ 75
Estuary$ 4,462
Why OLake?

We know how to stand out

Faster Parallel & Full Load

Faster Resumable Full Load

Full load performance is improved by splitting large collections into smaller virtual chunks, processed in parallel.

Stay updated with ingestion logs

Schema-Aware Logs and Alerts for Integrity

Actively monitors sync failures, schema changes, and data type modifications, ensuring that issues like incompatible updates or ingestion errors are swiftly detected, clearly logged, and immediately surfaced through real-time alerts

CDC Cursor Preservation

CDC Cursor Preservation

When you add new big tables after a long time of setting up the ETL, we do full load for it, in parallel to already running incremental sync. So CDC cursors are never lost. We manage overhead of data ingestion order and deduplication.

Fast & Stable Connectors

Achieve near real-time latency

Using Databases change stream logs (binglogs for MySQL, oplogs for mongoDB, WAL logs for Postgres), OLake enables parallel updates for each collection. This method facilitates rapid synchronization and ensures that data is consistently updated with near real-time updates.

The OLake Experience

Fast & Efficient
That is OLake

Step I
Source
Step II
Destination
Step III
Schema
Step IV
Job Config
Capture information

Click to select a database connector

Connect Source
Lake background

OLake

Register for Pilot Program

Set up your account to get started

OLake makes data replication faster by parallelising full loads, leveraging change streams for real-time sync, and pulling data in a lake house

OLake

Discover more
Join us now

Iceberg Native
Instead of directly transforming data from Databases during extraction, we first pull it in its native format.
Faster & More Efficient
Engineered for high-throughput EL with adaptive chunking, parallel execution for historical loads, and CDC for optimized data pipeline.
Common Challenges Using Debezium and Kafka Connect for CDC

Common Challenges Using Debezium and Kafka Connect for CDC

Uncover the key challenges of extracting, transforming, and loading data from MongoDB into a data lakehouse. Learn be...

14 Min Read
Blogs

Stay ahead,
with our latest reads

View all Blogs
Four Critical MongoDB ETL Challenges and Ho...

Four Critical MongoDB ETL Challenges and Ho...

Uncover the key challenges of extracting, transforming, and loading data fr...

14 Min Read
Troubleshooting Common Issues and Solutions to Mo...

Troubleshooting Common Issues and Solutions to Mo...

Uncover the key challenges of extracting, transforming, and loading data fr...

08 Min Read
Problems with Debezium and How we (OLake, Open...

Problems with Debezium and How we (OLake, Open...

Uncover the key challenges of extracting, transforming, and loading data fr...

18 Min Read
Frequently Asked Questions
What is OLake, and how does it handle MongoDB data?
↑
OLake is a data engineering tool designed to simplify and automate the real-time ingestion & normalization of complex MongoDB data. It handles the entire process — from parsing and extraction to flattening/extrapolating and transforming raw, semi-structured data into relational streams — without the need for coding.
How does OLake ensure data accuracy and prevent data loss during transformation?
↓
What data platforms and tools does OLake integrate with?
↓
How does OLake handle large data volumes and maintain performance?
↓
Can OLake be customized to fit my specific data pipeline needs?
↓