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On-prem installers
Sign in| Feature | Etlworks | Debezium |
|---|---|---|
| Category | Managed integration and CDC platform with an embedded Debezium based engine | Open source distributed platform for change data capture |
| Primary Audience | Engineering and data teams that want production ready CDC and integration with minimal infrastructure work | Teams comfortable running Kafka or other streaming platforms and building their own sinks and transformations |
| Focus | End to end pipelines: log based CDC, ETL and ELT, transformations, orchestration, APIs, files, queues, and automation | Streaming row level changes from databases into event streams so downstream systems can consume them |
| CDC Engine | Built in, customized Debezium engine managed entirely inside Etlworks flows, no separate Connect cluster required | Standalone CDC platform that runs as Kafka Connect connectors or Debezium Server processes |
| Supported Sources | Same major databases supported by Debezium (MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, and others) plus additional systems exposed through Etlworks connectors | Broad set of relational and NoSQL databases with dedicated connectors for MySQL, PostgreSQL, SQL Server, Oracle, MongoDB, Db2, Cassandra, Vitess, and more |
| Targets and Sinks | Any Etlworks destination: relational and cloud warehouses, data lakes, message queues, REST and SOAP APIs, files, SaaS platforms, and more | Typically writes change events to Kafka topics or other messaging systems; separate consumers or stream processors are required to load targets |
| Transformations and Mapping | Visual and script based mappings, filtering, enrichment, joins, normalizing and denormalizing, validation, and routing rules | Focuses on producing raw change events; transformations are handled in Kafka Streams, Flink, consumers, or other external tools |
| Setup and Infrastructure | Runs as a managed cloud service or a single lightweight on prem instance with optional agents; CDC is configured as flows inside the UI | Requires operating Kafka or another event platform plus Kafka Connect or Debezium Server, including topics, storage, and retention tuning |
| Operations and Monitoring | Single UI for flows, offsets, error handling, retries, alerts, and data previews across CDC and non CDC pipelines | Relies on Kafka and Connect metrics, logs, and third party tooling; teams must wire up dashboards and alerting on their own |
| Orchestration and Automation | Schedules, events, webhooks, file and queue triggers, dependency chains, error pipelines, and a full CLI for automation | Provides CDC streams only; orchestration and scheduling are handled in external platforms or custom code |
| Developer Experience | Low code flows with optional scripting in SQL, JavaScript, Python, and Shell; same tooling is used for CDC and all other integrations | Configuration driven connectors with JSON and properties files; business logic lives in separate applications or stream processors |
| Typical Architecture | Single platform that reads from logs, applies transformations, and lands data in one or more targets with monitoring included | CDC layer that feeds one or more downstream systems such as Kafka Streams, Flink, Spark, or custom microservices |
| Deployment | Cloud, on prem, and hybrid with agents close to the databases | Self managed services deployed on Kubernetes, VMs, or bare metal alongside Kafka or other brokers |
| Total Cost of Ownership | Subscription pricing with no separate CDC or Kafka infrastructure to run; one platform to operate | Open source with no license fee, but requires engineering and DevOps time to deploy, scale, secure, and monitor |
Etlworks vs Debezium
Compare a complete data integration platform with the leading open source CDC engine.
Side by side Comparison
Difference
Why Teams Use Etlworks With or Instead of Debezium
Option A - Etlworks as a Complete Debezium Replacement
Etlworks includes a built in, fully managed Debezium based CDC engine. You configure CDC flows in the UI, choose your source and targets, and Etlworks handles snapshots, streaming, offsets, and recovery inside the platform. There is no need to install Kafka, Kafka Connect, or separate Debezium services unless you want to.
Option B - Etlworks Working Alongside Debezium
If you already run Debezium in production, Etlworks can consume its topics or Debezium Server outputs and use them as sources for downstream flows. This lets you keep your existing CDC layer while adding transformations, multi target replication, scheduling, and monitoring without rewriting what you have.
From Raw Change Events to Complete Pipelines
Debezium focuses on producing reliable change events. Etlworks goes further by turning those events into ready to use datasets in warehouses, lakes, search indexes, APIs, and operational databases. You can join CDC streams with batch loads, apply business rules, and route data to many systems in a single place.
Lower Operational Overhead
Running Debezium directly usually means operating Kafka clusters, Connect workers, storage for offsets and logs, and separate observability stacks. With Etlworks, CDC is just another flow type in the same platform that already runs your integrations, which reduces the number of moving parts and the time spent on operations.
Modernize Your CDC Without Losing Flexibility
Debezium is an excellent CDC engine. Etlworks lets you use that engine either inside the platform or as part of a larger architecture, so you can keep reliable log based replication while gaining orchestration, transformations, and observability in one place.