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   OLake
   One StackFull feature data platform
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Fastest way to replicate MongoDB/JSON

data in Apache Iceberg
Join Waitlist
Achieve fast data replication for MongoDB, helping with flattening, normalized
or extrapolated arrays and schema evolution management, with near real-time
transfer to data lakehouse (in Iceberg format).
Unlock the full potential of MongoDB replication with OLake
Iceberg as a Lakehouse format
Avoid vendor lock-in and query from any warehouse/query engine
Monitoring alerts & error handling
Monitoring alert for schema changes, backup tables and columns for error
handling in strict schema 
Real-time replication
CDC based approach for data ingestion
Schema discovery and selection
Automatic identification of object keys and arrays, generating schema
representations 
Parallel Initial Load
Define parallelism for initial sync times from days to minutes
Auto flattened table population
To convert semi structured objects into relational flat tables and separate
exploded tables for array type objects.
Data Quality at scale
Manage changing data types (polymorphic data) and schema drift without any
manual effort.
OLake
Interested?
Get Early Access.

Read more from our blogs
New Release
Four Critical Challenges in MongoDB ETL and How to tackle them for your Data
Lake
Uncover the key challenges of extracting, transforming, and loading data from
MongoDB into a data lakehouse. Learn best practices and common pitfalls to
ensure seamless data integration and unlock valuable insights.
Read more
New Release
Troubleshooting Common Issues and Solutions to MongoDB ETL Errors
Explore practical solutions to common MongoDB ETL errors in our troubleshooting
guide. Learn how to address issues like schema mismatches, data type conflicts,
and performance bottlenecks to streamline your ETL processes and ensure smooth
data integration.
Read more
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?


Olake provides monitoring and alerts for schema evolution, helping you detect
changes and prevent data loss and inaccuracies caused by transformation logic
errors. Custom alerts can be set up to notify you of schema changes, ensuring
continuous data accuracy.

What data platforms and tools does Olake integrate with?


As of now, we are integrating with Apache Iceberg as a destination. You can
query this from most of the big data platform like Snowflake, Databricks,
Redshift and BigQuery

How does Olake handle large data volumes and maintain performance?


Olake is designed to process millions of rows in minutes using a
configuration-based approach, which reduces processing time from months to
minutes. It supports efficient data pipelines by connecting to streaming
platforms like Kafka and dynamically generating SQL code to optimize data
handling.

Can Olake be customized to fit my specific data pipeline needs?


Olake provides a highly customizable, code-free interface for tailoring data
extraction, transformation, and normalization processes to your specific data
pipeline requirements. It allows you to adjust settings and automate tasks to
match your unique use cases.

Composable Lakehouse Platform
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