Key Takeaways
• Artie is the top pick for teams replacing AWS DMS for ongoing replication, offering fully managed log-based CDC with sub-minute latency, automatic schema evolution, free backfills, and destinations across major warehouses and lakehouses.
• AWS DMS fits one-time migrations well, but continuous real-time replication exposes limits in tuning, schema change handling, and operational overhead.
• Log-based CDC reads changes from database logs rather than querying tables, keeping load on production databases low.
• Self-managed open source tools offer flexibility but require teams to run Kafka and connectors themselves.
• Cloud-native services work well inside their own ecosystems, while independent platforms suit multi-cloud and multi-warehouse stacks.
AWS Database Migration Service was built to move databases into AWS, and for one-time migrations it does that job well. Many teams then keep it running for ongoing change data capture, feeding Redshift, S3, or other targets from production databases. That is where friction tends to appear: replication instances need sizing and tuning, large objects and schema changes require careful handling, tasks fail and need restarts, and monitoring a fleet of long-running tasks becomes a job of its own.
As analytics, operational dashboards, and AI applications come to depend on fresh production data, replication stops being a migration step and becomes permanent infrastructure. Teams start looking for tools designed for continuous CDC into modern destinations such as Snowflake, BigQuery, Databricks, ClickHouse, and Apache Iceberg, with less operational overhead than DMS requires.
Why Teams Move Off AWS DMS for Ongoing Replication
Most teams do not leave AWS DMS because it fails outright. They leave because the effort needed to keep continuous replication healthy grows over time. Three factors come up most often:
• Operational overhead: replication instances, task settings, and memory limits need ongoing tuning as data volumes grow, and long-running tasks require monitoring and restarts when they stall or fail.
• Schema change handling: production schemas evolve constantly. When columns are added, renamed, or retyped, DMS tasks may need manual intervention, and downstream tables can drift from the source.
• Destination fit: DMS is centered on AWS targets. Teams replicating into Snowflake, BigQuery, Databricks, or Iceberg-based lakehouses often need extra pipelines or transformation steps to get data where analysts and applications actually use it.
When replication becomes a permanent part of the data platform, these factors translate into engineering time, stale data, and the risk of silent inconsistencies, which is why teams begin evaluating dedicated CDC platforms.
The 7 Best AWS DMS Alternatives for Real-Time Data Replication
1. Artie
Most teams that outgrow AWS DMS face a choice between building their own CDC stack or adopting another tool that still needs constant attention. Artie removes that trade-off by handling the entire replication layer as a fully managed service. It captures every row-level insert, update, and delete from source databases using log-based CDC and streams those changes continuously to the destination in under 60 seconds, with no Kafka, Debezium, or custom merge code for the team to operate.
The operational problems that push teams off DMS are the ones Artie was built to solve. Schema changes are detected and applied downstream automatically, so tables stay aligned with production without manual task edits. Historical backfills run without locking tables and can read from replicas, and they do not interrupt ongoing replication. Artie guarantees exactly-once delivery and never skips ingestion errors, so data teams can trust that the warehouse matches the source. An internal buffer absorbs spikes in change volume, and fan-in lets teams consolidate many sources, including thousands of single-tenant Oracle databases, without an explosion of topics or infrastructure.
Artie’s connectors cover the databases teams actually run, including PostgreSQL (with Aurora, RDS, Cloud SQL, and Supabase), MySQL, Microsoft SQL Server, Oracle, MongoDB and DocumentDB, DynamoDB, CockroachDB, and Amazon Keyspaces. Destinations include Snowflake, BigQuery, Redshift, Databricks, ClickHouse, PostgreSQL, MotherDuck, S3, GCS, Iceberg REST Catalog, and Amazon S3 Tables, which lets teams replicate straight into open lakehouse formats. One source can feed several destinations at once, reducing the load of multiple replication slots.
Security and deployment options fit regulated environments. Column-level inclusion, exclusion, encryption, and hashing protect sensitive fields, PrivateLink and SSH connectivity keep traffic private, and pipelines can run in Artie’s cloud or in the customer’s own cloud. Artie is SOC 2 Type II compliant and supports HIPAA workloads, and teams already committed to AWS can purchase it through AWS Marketplace. Most teams run their first sync in under an hour, and a 14-day free trial is available.
Key features:
• Fully managed log-based CDC with sub-minute latency
• Automatic schema evolution across destinations
• Backfills without table locks, including from read replicas
• Exactly-once delivery with no skipped ingestion errors
• Sources including Postgres, MySQL, SQL Server, Oracle, MongoDB, and DynamoDB
• Destinations including Snowflake, BigQuery, Databricks, Redshift, ClickHouse, and Iceberg
• Column-level encryption, hashing, and inclusion rules
• Deployment in Artie’s cloud or your own, with SOC 2 Type II and HIPAA support
For teams that want real-time replication to run as dependable infrastructure rather than a pipeline they have to maintain, Artie offers the most direct path beyond AWS DMS.
2. Qlik Replicate
Qlik Replicate, formerly Attunity, is one of the longest-established enterprise replication tools. It uses log-based CDC across a very wide range of sources, including mainframe and SAP systems that many other tools do not support, and offers a graphical interface for configuring and monitoring tasks.
That breadth makes it a common choice for large enterprises with heterogeneous legacy estates. The trade-off is that it is typically deployed and managed as enterprise software, with licensing and infrastructure planning that smaller data teams may find heavy compared with fully managed cloud platforms.
Key features:
• Log-based CDC across broad source coverage, including mainframe and SAP
• Graphical task configuration and monitoring
• Support for many warehouse and streaming targets
• Enterprise-grade deployment options
Qlik Replicate makes sense for large enterprises replicating from diverse and legacy source systems.
3. Debezium
Debezium is the leading open source CDC project, capturing row-level changes from databases such as PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB and publishing them as events, usually through Kafka Connect. Debezium Server also allows streaming to other messaging systems.
It gives engineering teams complete control and no license cost, and it underpins many commercial products. In exchange, teams must run and scale Kafka, manage connectors, handle schema changes, and build the logic that merges change events into destination tables, which is often the work teams were hoping to avoid when leaving DMS.
Key features:
• Open source log-based CDC for major databases
• Event streaming through Kafka Connect or Debezium Server
• Large community and connector ecosystem
• Full control over deployment and configuration
Debezium works best for teams with strong streaming expertise that want to own their CDC infrastructure.
4. Oracle GoldenGate
Oracle GoldenGate has long been the standard for replicating Oracle databases, and it supports heterogeneous replication to and from other systems. It is available as software and as a managed service, OCI GoldenGate, within Oracle Cloud.
For organizations with significant Oracle estates, GoldenGate offers deep compatibility and mature features for high availability and replication. It is generally more complex and costly than lighter cloud CDC tools, and its strongest value appears in Oracle-centric environments.
Key features:
• Mature, log-based replication for Oracle databases
• Heterogeneous replication across databases
• Managed option through OCI GoldenGate
• High availability and disaster recovery use cases
Oracle GoldenGate makes sense for enterprises with large Oracle footprints and demanding replication requirements.
5. Striim
Striim combines CDC with stream processing, allowing teams to filter, transform, and enrich data in flight before it reaches the destination. It supports a range of databases and targets and is available as a managed cloud service.
That in-flight processing is useful when data needs shaping before delivery or when the same stream feeds several systems. For teams that simply want a warehouse copy of production tables, the additional streaming capabilities may add more complexity than needed.
Key features:
• Real-time CDC with in-flight stream processing
• Filtering, transformation, and enrichment before delivery
• Managed cloud deployment option
• Support for multiple databases and targets
Striim works best for teams that need to transform data in motion as part of replication.
6. Streamkap
Streamkap offers managed streaming CDC built on proven open source streaming technology, giving teams the benefits of Kafka-based CDC without running the infrastructure themselves. It streams changes from common databases into destinations such as Snowflake, ClickHouse, and other analytics platforms.
For teams that like the Debezium and Kafka model but do not want to operate it, Streamkap is a practical middle ground. Buyers should confirm support for their specific sources, destinations, and deployment requirements.
Key features:
• Managed streaming CDC without self-hosted Kafka
• Support for common databases and analytics destinations
• Stream transformations
• Usage-based pricing
Streamkap makes sense for teams wanting a managed version of a Kafka-based CDC architecture.
7. Google Cloud Datastream
Google Cloud Datastream is a serverless CDC and replication service that captures changes from databases such as MySQL, PostgreSQL, Oracle, and SQL Server and delivers them to BigQuery and Cloud Storage. Its tight integration with BigQuery makes it straightforward for teams building analytics on Google Cloud.
Like AWS DMS, Datastream is designed around its own cloud. It suits organizations consolidating analytics in BigQuery, while teams replicating to several warehouses or across clouds typically need a more independent platform.
Key features:
• Serverless CDC with no infrastructure to manage
• Native delivery to BigQuery and Cloud Storage
• Support for MySQL, PostgreSQL, Oracle, and SQL Server
• Integration with Google Cloud services
Google Cloud Datastream works best for teams centering their analytics on BigQuery.
How to Choose the Right AWS DMS Alternative
The right replacement depends on why DMS no longer fits and what the replication layer needs to do next.
Teams that want to stop maintaining pipelines should prioritize fully managed platforms that handle schema evolution, backfills, retries, and merges automatically. This is the most common reason teams leave DMS, and it is where a platform like Artie, which runs the whole replication layer, delivers the clearest benefit.
Enterprises with legacy and highly varied sources may need the broad connector coverage of established replication suites, especially when mainframe, SAP, or large Oracle estates are involved, accepting more infrastructure and licensing complexity in return.
Engineering teams with streaming expertise can build on open source CDC and keep full control, provided they have the capacity to run Kafka, manage connectors, and handle schema changes over time.
FAQ
Organizations committed to one cloud’s analytics stack may prefer that provider’s native CDC service, as long as they do not expect to replicate across clouds or into several warehouses later.
What is AWS DMS used for?
AWS Database Migration Service moves data between databases, most often to migrate on-premises or cloud databases into AWS. It supports full loads and ongoing change data capture, which some teams use for continuous replication into targets such as Redshift and S3.
Why do teams look for AWS DMS alternatives?
Teams typically look elsewhere when replication becomes long-running infrastructure rather than a one-time migration. Common reasons include the effort of tuning and monitoring tasks, manual handling of schema changes, and the need to replicate into destinations such as Snowflake, BigQuery, Databricks, or Iceberg with less custom work.
What is log-based change data capture?
Log-based CDC reads inserts, updates, and deletes from a database’s transaction log, such as the Postgres write-ahead log or the MySQL binlog, rather than repeatedly querying tables. It captures every change with minimal load on the source, which makes it the preferred approach for real-time replication.
Can I replicate from AWS databases to Snowflake or BigQuery in real time?
Yes. Managed CDC platforms such as Artie capture changes from Aurora, RDS, DynamoDB, and other AWS databases and stream them into Snowflake, BigQuery, Databricks, and other destinations with sub-minute latency, without requiring custom Kafka or merge pipelines.
How do DMS alternatives handle schema changes?
It varies. Some tools require manual updates when source schemas change, while others detect and apply changes automatically. Artie, for example, applies schema evolution downstream automatically, which keeps destination tables aligned with production and prevents pipelines from breaking when columns are added or modified.
Is open source CDC cheaper than a managed platform?
Open source tools have no license cost, but teams pay in engineering time to run Kafka, manage connectors, handle schema changes, and monitor pipelines. For many teams, the total cost of ownership of a managed platform is lower once staffing and maintenance are included.
