When should I use db2lake instead of Apache Gobblin?

Automated data extraction from relational databases to data lake formats (Parquet, Delta Lake). Teams building lakehouse architectures from legacy database sources with minimal custom code. Low-code database-to-lake ingestion without writing custom Spark or SQL extraction jobs

When should I use Apache Gobblin instead of db2lake?

Large-scale data ingestion from diverse sources at LinkedIn scale with built-in quality checks. Unified ingestion framework with encryption, compaction, and quality-aware pipeline features. Hadoop and cloud-based data lake ingestion where data quality enforcement is a first-class requirement

What are the main weaknesses of db2lake?

Small project with limited community documentation and few production references. Connector support is narrower than Airbyte or dlt for diverse or exotic source systems. Limited transformation capabilities — focused on ingestion only with no transform layer

What are the main weaknesses of Apache Gobblin?

Java-centric — Python integration is not a first-class experience. Complex to configure and deploy; significant infrastructure and engineering investment required. Smaller community than Airbyte or dlt for modern ingestion projects

db2lake vs Apache Gobblin: Key Differences for Python Data Engineering

Data Ingestion

db2lake

Database to Data Lake ETL

★ 3.5

MIT

pip install db2lake

Apache Gobblin

Universal Data Ingestion Framework

★ 3.9

Apache-2.0

N/A — Java-based

Side-by-Side Comparison

db2lake

Apache Gobblin

db2lake

Apache Gobblin

Best For

✓Automated data extraction from relational databases to data lake formats (Parquet, Delta Lake)
✓Teams building lakehouse architectures from legacy database sources with minimal custom code
✓Low-code database-to-lake ingestion without writing custom Spark or SQL extraction jobs

✓Large-scale data ingestion from diverse sources at LinkedIn scale with built-in quality checks
✓Unified ingestion framework with encryption, compaction, and quality-aware pipeline features
✓Hadoop and cloud-based data lake ingestion where data quality enforcement is a first-class requirement

Best For

✓Automated data extraction from relational databases to data lake formats (Parquet, Delta Lake)
✓Teams building lakehouse architectures from legacy database sources with minimal custom code
✓Low-code database-to-lake ingestion without writing custom Spark or SQL extraction jobs

✓Large-scale data ingestion from diverse sources at LinkedIn scale with built-in quality checks
✓Unified ingestion framework with encryption, compaction, and quality-aware pipeline features
✓Hadoop and cloud-based data lake ingestion where data quality enforcement is a first-class requirement

Weaknesses

•Small project with limited community documentation and few production references
•Connector support is narrower than Airbyte or dlt for diverse or exotic source systems
•Limited transformation capabilities — focused on ingestion only with no transform layer

•Java-centric — Python integration is not a first-class experience
•Complex to configure and deploy; significant infrastructure and engineering investment required
•Smaller community than Airbyte or dlt for modern ingestion projects

Weaknesses

•Small project with limited community documentation and few production references
•Connector support is narrower than Airbyte or dlt for diverse or exotic source systems
•Limited transformation capabilities — focused on ingestion only with no transform layer

•Java-centric — Python integration is not a first-class experience
•Complex to configure and deploy; significant infrastructure and engineering investment required
•Smaller community than Airbyte or dlt for modern ingestion projects

License

MIT

Apache-2.0

License

MIT

Apache-2.0

Install

pip install db2lake

N/A — Java-based

Install

pip install db2lake

N/A — Java-based

Rating

★ 3.5

★ 3.9

Rating

★ 3.5

★ 3.9

Key Features

db2lake

1Tool for migrating relational database data to data lake formats (Parquet, Delta)
2Reads from PostgreSQL, MySQL, Oracle, and SQL Server
3Writes Parquet files with correct schema mapping and partitioning
4Supports full and incremental extraction modes
5Configurable via YAML for repeatable, version-controlled migrations

Apache Gobblin

1Distributed data ingestion framework originally developed at LinkedIn
2Source and writer plugin model for custom connectors
3Compaction and deduplication of ingested data built in
4Throttling and rate limiting for polite API consumption
5Gobblin-as-a-Service for cloud-native execution on Kubernetes

How Python Data Engineers Use These Tools

db2lake

Python data engineers use db2lake to bootstrap data lake migration projects — extracting historical data from relational databases and writing it as partitioned Parquet files to S3 or HDFS. Once the initial migration is done, incremental extractions keep the lake in sync, and Python-based PySpark or DuckDB pipelines take over for ongoing processing.

Apache Gobblin

Python data engineers interact with Gobblin by defining configuration files that specify source, extractor, converter, and writer plugins — executed as a Hadoop or standalone Java job. Python orchestration scripts manage Gobblin execution via REST API, monitor job completion, and process ingested output files with PySpark for downstream transformation and loading.

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Individual Tool Pages

View db2lake details →View Apache Gobblin details →

Side-by-Side Comparison

db2lake

Apache Gobblin

db2lake

Apache Gobblin

Best For

✓Automated data extraction from relational databases to data lake formats (Parquet, Delta Lake)
✓Teams building lakehouse architectures from legacy database sources with minimal custom code
✓Low-code database-to-lake ingestion without writing custom Spark or SQL extraction jobs

✓Large-scale data ingestion from diverse sources at LinkedIn scale with built-in quality checks
✓Unified ingestion framework with encryption, compaction, and quality-aware pipeline features
✓Hadoop and cloud-based data lake ingestion where data quality enforcement is a first-class requirement

Best For

✓Automated data extraction from relational databases to data lake formats (Parquet, Delta Lake)
✓Teams building lakehouse architectures from legacy database sources with minimal custom code
✓Low-code database-to-lake ingestion without writing custom Spark or SQL extraction jobs

✓Large-scale data ingestion from diverse sources at LinkedIn scale with built-in quality checks
✓Unified ingestion framework with encryption, compaction, and quality-aware pipeline features
✓Hadoop and cloud-based data lake ingestion where data quality enforcement is a first-class requirement

Weaknesses

•Small project with limited community documentation and few production references
•Connector support is narrower than Airbyte or dlt for diverse or exotic source systems
•Limited transformation capabilities — focused on ingestion only with no transform layer

•Java-centric — Python integration is not a first-class experience
•Complex to configure and deploy; significant infrastructure and engineering investment required
•Smaller community than Airbyte or dlt for modern ingestion projects

Weaknesses

•Small project with limited community documentation and few production references
•Connector support is narrower than Airbyte or dlt for diverse or exotic source systems
•Limited transformation capabilities — focused on ingestion only with no transform layer

•Java-centric — Python integration is not a first-class experience
•Complex to configure and deploy; significant infrastructure and engineering investment required
•Smaller community than Airbyte or dlt for modern ingestion projects

License

MIT

Apache-2.0

License

MIT

Apache-2.0

Install

pip install db2lake

N/A — Java-based

Install

pip install db2lake

N/A — Java-based

Rating

★ 3.5

★ 3.9

Rating

★ 3.5

★ 3.9

Key Features

db2lake

1Tool for migrating relational database data to data lake formats (Parquet, Delta)
2Reads from PostgreSQL, MySQL, Oracle, and SQL Server
3Writes Parquet files with correct schema mapping and partitioning
4Supports full and incremental extraction modes
5Configurable via YAML for repeatable, version-controlled migrations

Apache Gobblin

1Distributed data ingestion framework originally developed at LinkedIn
2Source and writer plugin model for custom connectors
3Compaction and deduplication of ingested data built in
4Throttling and rate limiting for polite API consumption
5Gobblin-as-a-Service for cloud-native execution on Kubernetes

How Python Data Engineers Use These Tools