Debezium vs Gluesync

Debezium captures row-level database changes and delivers them to applications as events, so they can respond to inserts, updates, and deletes. Its connectors use database logs for low-delay capture without requiring changes to the data model, and can take initial or incremental snapshots, filter schemas, tables, and columns, and mask selected values. Listed databases include MongoDB, MariaDB, MySQL, PostgreSQL, SQL Server, Oracle, Db2, and Cassandra. Connectors for Vitess, Spanner, Informix, CockroachDB, YashanDB, Ingres, and Milvus are marked incubating and may change without backward compatibility. A common deployment uses Apache Kafka Connect; Debezium can also run through Debezium Server or as a library in JVM applications. Debezium Server can send serialized events to Amazon Kinesis, Google Cloud Pub/Sub, and Apache Pulsar. Ready-to-use transformations support routing, filtering, and event flattening. Debezium is free under the Apache Software License 2.0. Its quay.io container images are for testing and evaluation, not production.

Gluesync replicates data changes in real time across relational and NoSQL databases, event streams and data lakes. Its log-based change data capture reads committed changes from transaction logs, journals or native CDC APIs instead of repeatedly polling full tables. The platform runs on customer infrastructure and supports on-premises, cloud, private-cloud and hybrid deployments. MOLO17 lists more than 31 supported platforms in four categories, with documented integrations including IBM Db2, PostgreSQL, Oracle, MySQL, Snowflake and Google BigQuery. The supported database list also includes systems such as Microsoft SQL Server, MariaDB, Cassandra, Couchbase and Amazon DynamoDB. An extensible API framework allows custom connectors. The web control plane includes visual pipeline orchestration, field functions, custom UDFs, live monitoring and SQL tools; monitoring covers throughput, latency and pipeline health, including from mobile. Listed security measures include TLS/HTTPS/WSS for data streams, AES-256 node-to-node encryption, OIDC, granular role-based access control, audit logging, and Kubernetes-native secrets and network policies. The maker says sensitive data is not persisted within the engine. Pricing is available on request, and a 30-day trial runs on the customer’s infrastructure with enterprise support during the trial. For a proof of concept, the trial page recommends a dedicated host with 8 vCPU, 8 GB RAM (16 GB recommended) and 250 GB disk.