Data ingestion and transport are crucial processes in the field of data management. They involve collecting, moving, and integrating data from various sources into a centralized location, typically a data warehouse or data lake. These processes are essential to ensure that data is available, up-to-date, and ready for analysis.
They can be broadly categorized into four main areas:
- Data Replication: Process of copying and synchronizing data between different systems or locations. It maintains consistent data copies across multiple servers or sites, improving availability, reliability, and performance of dependent applications.
- Event/Stream Processing: Handles real-time data flows as events occur, enabling continuous capture, processing, and analysis of data streams. Ideal for scenarios requiring immediate insights and real-time analytics, supporting high-throughput processing of live data.
- Log Collection and Processing: Gathers, aggregates, and analyzes log data from various systems and applications. Provides capabilities for log parsing, filtering, and routing, essential for system monitoring, troubleshooting, and security analysis.
- Change Data Capture (CDC): Identifies and captures data changes at the source, transferring them to targets in real-time or near real-time. Enables efficient data replication and synchronization without full data transfers.
Here is a summary table of the main open-source data ingestion and transport tools we have identified, organized by their primary function:
Data Replication
Tools focused on comprehensive data integration, ETL/ELT operations, and workflow management:
| Tool |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Airbyte |
27/07/2020 |
19551 |
4789 |
388 |
14/08/2025 |
17/09/2025 |
Yes |
https://github.com/airbytehq/airbyte |
| Apache Camel |
21/05/2009 |
5955 |
5071 |
329 |
N/A |
17/09/2025 |
Yes |
https://github.com/apache/camel |
| Apache Gobblin |
01/12/2014 |
2246 |
751 |
118 |
20/07/2017 |
08/09/2025 |
No |
https://github.com/apache/gobblin |
| Apache NiFi |
12/12/2014 |
5678 |
2866 |
311 |
N/A |
17/09/2025 |
Yes |
https://github.com/apache/nifi |
| Apache NiFi |
12/12/2014 |
5678 |
2866 |
311 |
N/A |
17/09/2025 |
Yes |
https://github.com/apache/nifi |
| data load tool (dlt) |
26/01/2022 |
4161 |
329 |
120 |
10/09/2025 |
17/09/2025 |
Yes |
https://github.com/dlt-hub/dlt |
| Meltano |
21/06/2021 |
2201 |
182 |
127 |
08/08/2025 |
15/09/2025 |
Yes |
https://github.com/meltano/meltano |
| Singer |
28/10/2016 |
572 |
132 |
24 |
N/A |
24/03/2025 |
Yes (all tap) |
https://github.com/singer-io/singer-python |
Event/Stream Processing
Tools specialized in handling real-time data streams and event processing:
| Tool |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Apache Kafka |
15/08/2011 |
30921 |
14636 |
345 |
N/A |
17/09/2025 |
Yes |
https://github.com/apache/kafka |
| Rudderstack |
19/07/2019 |
4264 |
4 |
102 |
17/09/2025 |
17/09/2025 |
Yes |
https://github.com/rudderlabs/rudder-server |
| Snowplow |
01/03/2012 |
6957 |
1192 |
77 |
31/01/2022 |
28/05/2025 |
Yes |
https://github.com/snowplow/snowplow |
Log Collection and Processing
Tools focused on collecting, processing, and routing log data:
| Tool |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Fluentd |
19/06/2011 |
13313 |
1364 |
231 |
12/09/2025 |
16/09/2025 |
Yes |
https://github.com/fluent/fluentd |
| Logstash |
18/11/2010 |
14636 |
3520 |
347 |
16/09/2025 |
17/09/2025 |
Yes |
https://github.com/elastic/logstash |
Change Data Capture
| Tool |
Creation Date |
Stars |
Forks |
Contributors |
Last Release |
Latest Commit |
Meets Criteria* |
Link |
| Debezium |
22/01/2016 |
11844 |
2744 |
364 |
N/A |
17/09/2025 |
Yes |
https://github.com/debezium/debezium |
| Databus |
17/12/2012 |
3667 |
738 |
13 |
N/A |
07/05/2020 |
No |
https://github.com/linkedin/databus |
*Criteria: >40 contributors, >500 stars, and recent releases/commit
Data Replication
- Airbyte: An open-source data integration platform focusing on ELT (Extract, Load, Transform). It offers a wide range of connectors and is designed for easy customization.
- Apache Camel: A versatile open-source integration framework based on known Enterprise Integration Patterns. It supports a vast array of protocols and data formats.
- Apache Gobblin: A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management.
- Apache NiFi: A software project for automating and managing the flow of data between systems. It provides a web-based interface for designing, controlling, and monitoring data flows.
- Embulk: An open-source bulk data loader that helps data transfer between various databases, storages, file formats, and cloud services.
- Meltano: An open source ELT platform built by GitLab. It integrates with Singer taps and targets, making it versatile for various data sources and destinations.
- Singer: An open-source standard for writing scripts that move data. It defines a JSON-based data exchange format that works with various sources and destinations.
Event/Stream Processing
- Apache Kafka: A distributed event streaming platform known for its high-throughput, fault-tolerant architecture, widely used for data ingestion and real-time stream processing.
- Rudderstack: An open-source customer data platform that enables collecting, routing, and transforming data from various sources to multiple destinations.
- Snowplow: An open-source event data collection platform that enables collection, enrichment, and tracking of event data from multiple sources.
Log Collection and Processing
- Fluentd: An open source data collector for unified logging layer. It allows you to unify data collection and consumption for better use and understanding of data.
- Logstash: Part of the Elastic Stack, Logstash is a server-side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to your favorite “stash.”
Change Data Capture
- Debezium: An open-source distributed platform for change data capture. Built on top of Apache Kafka, it provides a set of Kafka Connect compatible connectors that monitor specific database management systems, capturing row-level changes in real-time.
- Databus: Developed by LinkedIn, Databus is a source-agnostic distributed change data capture system. It’s designed for online low-latency consumption of high-volume database changes.
Selection Criteria
When choosing a data ingestion and transport tool, consider these key factors:
- Data Sources and Destinations: Ensure the tool supports your required data sources and destinations.
- Volume and Velocity: Consider the tool’s ability to handle your data volume and speed requirements.
- Technical Expertise: Evaluate whether your team has the necessary skills to implement and maintain the tool.
- Integration Capabilities: Check compatibility with your existing data stack.
- Community and Support: Look for active development, good documentation, and community support.
- Scalability: Ensure the tool can grow with your needs.
- Performance: Consider throughput, latency, and resource requirements.
For CDC tools specifically, additional considerations include:
- Source database system compatibility
- Target system requirements
- Latency requirements
- Scalability needs
It’s recommended to test multiple solutions to find the best fit for your specific use case and requirements. The open-source nature of these tools allows for extensive customization and community support, which can be crucial for addressing unique data ingestion challenges.
The Challenge of Choice
The open-source community has developed numerous solutions for various aspects of data handling, including: