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Fastest way to replicate MongoDB/JSON

data in Apache Iceberg
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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
Iceberg as a Lakehouse format
Avoid vendor lock-in and query from any warehouse/query engine
Monitoring alerts & error handling
Monitoring alerts & error handling
Monitoring alert for schema changes, backup tables and columns for error handling in strict schema 
Real-time replication
Real-time replication
CDC based approach for data ingestion
Schema discovery and selection
Schema discovery and selection
Automatic identification of object keys and arrays, generating schema representations 
Parallel Initial Load
Parallel Initial Load
Define parallelism for initial sync times from days to minutes
Auto flattened table population
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
Data Quality at scale
Manage changing data types (polymorphic data) and schema drift without any manual effort.
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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.
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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.
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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.
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.
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
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.
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.