Key Takeaways
• Streaming readiness depends on measurable freshness, not simply whether a platform supports ongoing replication.
• Artie combines continuous CDC, automated schema handling, and destination-aware delivery for operational warehouse and lakehouse pipelines.
• Recovery design should protect checkpoints, source logs, and destination correctness during interruptions or backfills.
• Schema changes need explicit policies for automatic propagation, alerts, pauses, and downstream validation.
• The strongest replacement matches the required latency, deployment model, source coverage, and operational ownership.
AWS Database Migration Service can handle initial loads and ongoing change replication, but a pipeline built for migration does not always satisfy the operating demands of continuous streaming. Once dashboards, product features, fraud models, or customer-facing applications depend on fresh database changes, teams need predictable latency, reliable delete handling, controlled schema evolution, and recovery that does not require a complete reload.
What Makes an AWS DMS Replacement Ready for Streaming Workloads?
AWS DMS can continue reading changes after an initial load, but support for CDC alone does not make a pipeline streaming-ready. A permanent data product must keep working through traffic spikes, connector restarts, destination slowdowns, source failovers, and schema changes. It also needs enough operational visibility for teams to know whether data is merely moving or actually arriving within the promised freshness window.
Before comparing platforms, define the production contract the new pipeline must satisfy. The most important criteria are:
• End-to-end freshness: Measure time from source commit to destination visibility, including capture, transport, and apply latency.
• Change correctness: Confirm how inserts, updates, deletes, transaction order, and duplicate events are represented at the destination.
• Checkpoint recovery: Verify how the platform stores source positions, resumes after failure, and behaves when retained logs expire.
• Online backfills: Determine whether historical loads can run without blocking or destabilizing the live change stream.
• Schema evolution: Separate safe automatic changes from destructive changes that should pause, alert, or require approval.
• Operational ownership: Match managed, hybrid, or self-hosted deployment to the team that will monitor and maintain the pipeline.
8 Leading AWS DMS Replacements for Streaming Workloads
1. Artie: Managed CDC for Warehouse and Lakehouse Operations
Artie is built around continuous change data capture rather than treating CDC as an extension of a one-time database move. It reads database change logs and streams committed inserts, updates, and deletes into analytical destinations with sub-minute delivery as its standard operating model. The platform is designed for teams that want warehouse or lakehouse tables to remain current without assembling and operating a separate capture framework, message transport, consumer fleet, and merge layer.
That narrow focus is important in a DMS replacement project. Instead of moving complexity from a managed migration task into several self-managed services, Artie takes responsibility for checkpointing, failure recovery, destination-aware writes, and schema evolution across the replication path. Exactly-once delivery is intended to keep retries from creating duplicate destination state, while online backfill patterns allow historical data to be loaded without abandoning the live change stream.
Artie also supports deployment models in which the data plane can run in a customer-controlled cloud account or on premises. Its architecture is designed so the service does not persist customer data, and it provides controls for column selection, exclusion, hashing, and encryption. Those properties make it relevant when a streaming replacement must improve operational simplicity without weakening data residency or sensitive-field controls.
Key Features:
• Sub-minute CDC for continuously changing operational datasets
• Exactly-once delivery for inserts, updates, and deletes
• Automated schema evolution, checkpointing, recovery, and backfills
• Managed operation without separate streaming infrastructure
• Customer-controlled deployment with column-level data protection
2. Oracle GoldenGate: Heterogeneous Replication for Distributed Estates
Oracle GoldenGate is an established real-time replication platform for environments where the source landscape is broader than a set of cloud-managed relational databases. It supports heterogeneous, hybrid, and multicloud data movement, with deployment options that include a managed cloud service and on-premises software. This makes it relevant when a DMS replacement must span traditional enterprise databases, distributed applications, cloud analytics platforms, and streaming consumers.
The platform’s value is its replication depth. It can preserve transaction ordering, support bidirectional and active-active patterns, and feed both operational and analytical use cases. GoldenGate can also participate in data fabric architectures that publish database changes into event streams, rather than writing only to a single reporting destination.
Key Features:
• Real-time replication across on-premises, cloud, and multicloud systems
• Transaction-aware capture and apply for operational workloads
• Unidirectional, bidirectional, and active-active replication patterns
• Database, analytics platform, and event-stream connectivity
• Managed-cloud and self-managed deployment choices
3. Striim: Streaming Integration With In-Flight Processing
Striim combines change data capture, streaming transport, continuous processing, and delivery in one environment. It can run as a fully managed cloud service across major cloud platforms or as a self-managed platform, making it useful for organizations that want to replace DMS while also consolidating custom stream-processing code. Database changes can be filtered, transformed, enriched, joined with cached reference data, and routed before they reach an analytical or operational target.
Its streaming SQL layer is the distinguishing architectural element. A team can express continuous queries over live data instead of limiting the replication path to direct source-to-target copying. That can reduce the number of separate services needed for masking, routing, light transformation, and event derivation.
Key Features:
• Log-based CDC with continuous streaming delivery
• Streaming SQL for filtering, enrichment, joins, and routing
• Configurable schema propagation, pauses, and alerts
• Managed and self-managed cloud deployment models
• Central monitoring across analytical and operational pipelines
4. Fivetran HVR: High-Volume Replication With a Self-Hosted Control Plane
Fivetran HVR, short for High-Volume Replicator, is the self-hosted side of the Fivetran portfolio. It is designed for enterprise database and file-system replication where organizations want direct control over the hub, agents, network placement, and capture topology. The platform is particularly relevant when replacing DMS tasks that serve large databases, constrained networks, or systems that cannot expose change data through a purely SaaS-operated connector.
HVR organizes replication around source capture, a central hub, and target integration. Administrators can configure and monitor channels through a web interface, command-line tools, or a REST API. Support for data-type mapping, compare and repair workflows, and database-specific capabilities helps teams manage the less visible parts of heterogeneous replication.
Key Features:
• Self-hosted replication for databases and file systems
• Log-based capture for sustained high-volume change streams
• Web, command-line, and API administration surfaces
• Data mapping with compare-and-repair validation workflows
• Flexible agent placement across private and hybrid networks
5. Qlik Replicate: Low-Impact CDC Across Legacy and Cloud Systems
Qlik Replicate provides graphical configuration for log-based change data capture across a wide range of enterprise sources and targets. It is suited to organizations replacing DMS across a mixed estate that may include relational databases, enterprise applications, mainframe data, cloud warehouses, and streaming platforms. Automatic target-schema generation reduces initial setup work, while parallel processing helps with large historical loads.
The platform offers two useful apply models. Transactional application preserves the source commit order when referential integrity and low latency are important. Batch-optimized application groups transactions to improve ingestion and merge efficiency for analytical destinations. That distinction lets architects tune the last mile instead of forcing every target through the same write pattern.
Key Features:
• Low-impact CDC across relational and mainframe sources
• Automatic target-schema generation from source metadata
• Transactional and batch-optimized destination apply modes
• Delivery to warehouses, data platforms, and event streams
• Graphical setup and monitoring across heterogeneous environments
6. Airbyte: Extensible Data Movement for Connector-Heavy Portfolios
Airbyte approaches the replacement problem as an extensible data integration platform. Its open-source foundation, large connector catalog, and connector development kits are useful when an organization has many application and database sources that need to share one ingestion operating model. CDC is available for supported databases, while incremental extraction and scheduled synchronization cover sources that do not expose transaction logs.
This breadth can help teams retire a patchwork of DMS tasks, scripts, and SaaS extractors under a common control plane. Airbyte can be deployed in managed, hybrid, or self-managed forms, and custom connectors can be created with no-code, low-code, or language-specific tooling.
Key Features:
• Broad connectivity across databases, applications, files, and warehouses
• CDC plus incremental synchronization for supported sources
• Managed, hybrid, and self-managed deployment options
• Multiple development paths for custom connectors
• Unified operations across heterogeneous ingestion portfolios
7. Informatica Cloud Mass Ingestion: CDC Inside an Enterprise Data Platform
Informatica Cloud Mass Ingestion places database replication and change data capture inside a wider cloud data integration and governance environment. It is appropriate when replacing DMS is part of a larger consolidation involving batch ingestion, ELT, ETL, application integration, data quality, cataloging, observability, and policy controls. Rather than treating the CDC path as an isolated utility, the platform can connect it to enterprise metadata and operational oversight.
That context is useful in large programs where ownership is distributed. Platform teams can provide standardized connections and policies while domain teams build pipelines within governed boundaries. High-performance ingestion can feed cloud warehouses and lakes, and monitoring can expose pipeline health alongside related integration workloads.
Key Features:
• CDC within a broader cloud integration environment
• High-volume ingestion into warehouses and data lakes
• Shared monitoring, metadata, quality, and governance
• Low-code development for standardized enterprise pipelines
• Unified CDC, batch, ETL, and ELT operations
8. Matillion: Cloud Data Pipelines With Embedded CDC
Matillion’s Data Productivity Cloud, now presented as Maia Foundation, combines data connectivity, transformation, orchestration, and pipeline management. Its connectivity layer supports log-based change data capture for replicating database changes as they occur, alongside batch loading and connectors for application, file, and database sources. This makes it a candidate when the replacement objective includes both continuous ingestion and the downstream work required to make data usable.
The platform’s center of gravity is the cloud data pipeline rather than a standalone event backbone. Teams can bring extraction, loading, transformation, scheduling, and monitoring into a shared workflow, using no-code components or higher-code approaches where needed. .
Key Features:
• Log-based CDC alongside batch ingestion workflows
• Integrated extraction, transformation, scheduling, and monitoring
• No-code and higher-code pipeline development options
• Lineage and development-workflow integration for governed DataOps
• Destination-oriented operations for analytics and AI pipelines
How to Replace AWS DMS Without Losing Replication State
The riskiest migration plan is to create a new connector, wait until records appear, and switch off the old task. Two pipelines can produce similar row counts while disagreeing about deletes, null handling, time zones, large objects, transaction order, or rows changed during the handoff. A dependable cutover treats capture position and destination state as assets that must be transferred deliberately.
1. Freeze the contract before changing the transport
Write down the current source tables, filters, transformations, target mappings, primary keys, and DDL behavior. Record the required freshness and the maximum tolerable recovery time. This contract becomes the acceptance test for the new path. Without it, teams tend to compare interfaces while overlooking behavior that was embedded in task settings or downstream repair jobs.
Also inventory every consumer of the replicated data. A pipeline that feeds only a reporting model can tolerate a different transition from one that powers alerts, APIs, or machine-learning features. Consumer owners should identify which fields, delete semantics, and freshness windows are truly operationally significant.
2. Establish a common snapshot boundary
The initial load and the change stream must meet at a known source position. Depending on the database, that position may be represented by a log sequence number, a system change number, a binary-log coordinate, or a logical replication checkpoint. Capture it before the historical load starts and preserve the logs long enough for the new pipeline to catch up.
For very large tables, use chunked or online backfills that do not block normal writes. The objective is not merely speed. It is to ensure that rows changed during the snapshot are reconciled correctly when the continuous stream reaches the same time boundary.
3. Run a shadow stream and compare outcomes
Operate the old and new paths in parallel into separate schemas or tables. Compare row counts, checksums, primary-key sets, aggregate values, and samples of update and delete behavior. Reconciliation should be repeated over time, not performed once, because lag and retry defects often appear only during bursts or restarts.
Inject controlled tests when possible: add a nullable column, update a row several times in one transaction, delete it, interrupt the destination briefly, and restart the connector. These tests reveal whether the new pipeline satisfies the recovery and schema contract under conditions closer to production reality.
4. Cut consumers over without discarding rollback state
Move downstream reads only after the new stream has reached the agreed freshness window and reconciliation thresholds. Preserve the former capture task, checkpoints, and source logs during a rollback period. Stopping a pipeline can be reversible; deleting its task or replication state often is not.
The cutover should have an owner, a decision time, objective abort criteria, and a documented route back. If lag exceeds the threshold, checksums diverge, or schema handling becomes ambiguous, the team should know whether to pause consumers, return to the former tables, or replay from a preserved position.
5. Retire migration-era assumptions
After the cutover, remove temporary mappings, duplicate infrastructure, and reconciliation jobs only when the rollback window closes. Replace migration dashboards with operational service measures: source-log retention, capture lag, transport backlog, destination apply latency, failed-record counts, schema-change events, and recovery drills.
Ownership must also move from the project team to a durable production team. A streaming pipeline without an on-call policy, incident runbook, and capacity review is still operating like a migration task, even if the underlying technology has changed.
I’m Rajesh Kumar, a DevOps, SRE, DevSecOps, Cloud, and Platform Engineering expert passionate about sharing practical knowledge, real-world experiences, and industry best practices. I have worked at Cotocus and regularly write about technology, travel, investing, health, product reviews, and digital marketing through my various platforms.
I publish technical articles at DevOps School, travel stories at Holiday Landmark, stock market insights at Stocks Mantra, health and fitness guidance at My Medic Plus, product reviews at TrueReviewNow, and SEO and digital marketing strategies at Wizbrand.
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