--- title: "Change Data Capture Tools: 11 Best Options Compared" excerpt: "Compare the 11 best Change Data Capture tools in 2026 and see how Tinybird simplifies real-time analytics without added complexity." authors: "Tinybird" categories: "AI Resources" createdOn: "2025-12-11 00:00:00" publishedOn: "2025-12-11 00:00:00" updatedOn: "2026-03-27 00:00:00" status: "published" --- # **Change Data Capture Tools: 11 Best Options Compared** These are the best **change data capture tools** for real-time data pipelines: 1. [Tinybird](https://www.tinybird.co/) 2. Debezium 3. AWS Database Migration Service (DMS) 4. Google Cloud Datastream 5. Fivetran 6. Airbyte 7. Confluent CDC Connectors 8. Oracle GoldenGate 9. Qlik Replicate 10. Maxwell's Daemon 11. Estuary When you need to **capture database changes** and propagate them to analytical systems, **Change Data Capture (CDC)** has become the industry standard. Instead of **re-extracting entire tables** in expensive batch jobs, CDC captures only the deltas—**inserts, updates, and deletes**—and streams them with minimal latency. **CDC's core appeal is efficiency**: lower impact on source databases, **fresher data in analytical systems**, and the foundation for **event-driven architectures** that respond to changes in real-time. But choosing the right CDC tool involves **more than just capturing changes**. You need to consider **delivery guarantees**, **snapshot and backfill strategies**, **schema evolution handling**, **delete representation**, and **operational complexity**. The difference between a successful CDC pipeline and a **production nightmare** often comes down to these details. Teams evaluating CDC tools typically fall into **three categories**: those building **real-time analytics pipelines**, those implementing **data replication for disaster recovery**, and those creating **event-driven microservices architectures**. We evaluate each tool based on **capture mechanisms**, **delivery semantics**, **operational complexity**, and **integration capabilities** to help you choose the right solution for your specific needs. ## **Need to turn CDC streams into real-time analytics APIs?** If you're implementing CDC to power **real-time dashboards**, **user-facing analytics**, or **operational intelligence**, consider Tinybird. It's a **real-time data platform built on ClickHouse®** that can ingest CDC streams from **Kafka, webhooks, or direct connections** and transform them into **instant HTTP APIs**. No complex ETL pipelines, just **SQL queries that become production-ready endpoints** in seconds. ## **1\. Tinybird: Real-Time Analytics Platform for CDC Destinations** Before diving into CDC capture tools, let's address **where those captured changes should go**—and how to turn them into **actionable analytics**. **Tinybird isn't a CDC capture tool**—it's the **ideal destination** for CDC streams. As a **real-time data platform** built on ClickHouse®, Tinybird handles the **ingestion, transformation, and API publication** of change events in one integrated service. If your goal is **real-time analytics from database changes**, Tinybird **completes the CDC pipeline** that capture tools start. ### **The Missing Piece in Most CDC Architectures** Most CDC discussions focus on **capturing changes**—but capturing is only half the problem. Once you have a stream of **inserts, updates, and deletes**, you need to: - **Materialize current state** from the change stream - **Handle deduplication** for at-least-once delivery - **Process deletes** appropriately for analytical queries - **Serve queries** with sub-second latency at scale Traditional data warehouses **weren't designed for these patterns**. They expect **batch loads**, not **continuous streams**. They struggle with **high-frequency updates**. And they **can't serve user-facing applications** with the latency requirements modern products demand. This limitation highlights the architectural differences between batch-oriented [data warehouses](https://www.tinybird.co/blog/why-data-warehouses) and streaming-first analytical systems. ### **Purpose-Built for Streaming Ingestion** Tinybird connects directly to **Kafka topics** where CDC tools like Debezium publish changes. Data flows continuously into **ClickHouse®-powered storage** and becomes **immediately queryable**. This ingestion approach aligns closely with modern [real-time data processing](https://www.tinybird.co/blog/real-time-data-processing) practices, ensuring minimal latency and consistent freshness across analytical endpoints. The platform handles **CDC event semantics** naturally: - **Upserts** via ReplacingMergeTree engines - **Delete handling** through soft deletes or filtered views - **Schema evolution** with managed migrations - **Exactly-once semantics** through idempotent writes You don't build custom consumers or manage Kafka Connect sinks. **Tinybird is the sink**—optimized specifically for analytical queries on streaming data. ### **Instant APIs from CDC Data** One of Tinybird's most powerful features is the **instant API layer**. Write a SQL query over your CDC-derived data, **publish it as a secure HTTP endpoint with one click**. No backend service to build, no API framework to maintain, no infrastructure to scale. For teams building **operational dashboards**, **customer-facing analytics**, or **real-time monitoring** from database changes, this capability **saves months of development time**. ### **Fully Managed Infrastructure** With most CDC architectures, you manage capture tools, message brokers, stream processors, and analytical databases—each requiring separate expertise and monitoring. This operational burden often increases with hybrid or [cloud computing](https://www.ibm.com/think/topics/cloud-computing) environments, where scalability and cost-efficiency must be carefully balanced. **Tinybird collapses this complexity.** Connect your CDC stream, write SQL transformations, publish APIs. Automatic scaling, built-in high availability, **SOC 2 Type II compliance**, and expert support come standard. ### **When Tinybird Makes Sense** Tinybird is ideal when: - Your CDC goal is **real-time analytics**, not just replication - You need **sub-100ms query latency** on fresh data - You want **instant APIs** from database changes - **Operational simplicity** matters more than maximum flexibility - You're building **user-facing features** powered by CDC ## **2\. Debezium: The Open-Source CDC Standard** Debezium has become the **de facto standard** for open-source CDC, providing **log-based capture** from major databases through **Kafka Connect**. ### **Log-Based Capture Architecture** Debezium reads **transaction logs directly**—PostgreSQL's WAL, MySQL's binlog, SQL Server's transaction log, Oracle's redo logs. This **log-based approach** has **minimal impact on source databases** compared to trigger-based or polling alternatives. Each change becomes a structured event with rich metadata: operation type, before and after states, source information, and transaction context. This event structure enables sophisticated downstream processing across both upstream and [downstream systems](https://medium.com/@ogunodabas/downstream-upstream-system-c1dc6cf4b59e). ### **Kafka Connect Integration** Debezium runs as **Kafka Connect source connectors**, publishing changes to **Kafka topics**. This integration provides: - **Distributed, fault-tolerant execution** - **Offset management** for exactly-once source semantics - **Schema Registry integration** for typed events - **Extensive sink ecosystem** for destinations For teams already running Kafka, Debezium **fits naturally** into existing infrastructure. ### **Critical Production Considerations** Debezium's **event format** requires understanding for proper consumption. Events include **before** and **after** states, with **tombstones** (null values) for deletes that enable **log compaction** in Kafka. **Snapshot handling** is crucial: Debezium performs an **initial consistent snapshot** before streaming, and supports **incremental snapshots** for adding tables or backfilling without restarting connectors. **Schema evolution** flows through the **schema history topic**, tracking DDL changes so events can be properly deserialized. If history becomes inconsistent, **connectors can fail** on new tables. ### **When Debezium Fits** Consider Debezium when: - You want **open-source CDC** with community support - **Kafka is your event backbone** - Your team has **Kafka Connect operational expertise** - You need **maximum flexibility** in event processing - **Multi-database capture** is required ## **3\. AWS Database Migration Service: Managed CDC for AWS** AWS DMS provides **managed CDC capabilities** for database migration and ongoing replication, deeply integrated with the AWS ecosystem. ### **Full Load Plus Ongoing Replication** DMS supports **full load** (initial migration) combined with **ongoing replication** (CDC) to keep targets synchronized. This pattern enables **zero-downtime migrations** and **continuous data pipelines** to analytical systems. The service handles **heterogeneous migrations**—different database engines between source and target—making it valuable for **modernization projects**. ### **AWS Ecosystem Integration** DMS integrates natively with **RDS, Aurora, Redshift, S3, and Kinesis**. For AWS-centric architectures, this **simplifies connectivity** and **security configuration** compared to self-managed solutions. **IAM-based access control** and **VPC networking** follow standard AWS patterns, reducing **operational learning curve** for teams already on AWS. ### **Operational Simplicity vs. Flexibility** DMS abstracts **replication instance management**, **task configuration**, and **monitoring**. You don't operate Kafka clusters or manage connector deployments. The trade-off: **less control** over event format, delivery semantics, and transformation logic. DMS is **opinionated** about how CDC works, which simplifies operations but **limits customization**. ### **When AWS DMS Fits** Consider AWS DMS when: - **AWS is your primary cloud platform** - You're doing **database migrations** with CDC - **Managed operations** are preferred over flexibility - Targets are **AWS services** like Redshift or S3 - You don't need **fine-grained event control** **While AWS DMS simplifies CDC management, it still depends on the structure and performance of the underlying [database](https://www.oracle.com/database/what-is-database/). Understanding database behavior remains essential to achieving stable, low-latency replication pipelines.** ## **4\. Google Cloud Datastream: Serverless CDC** Google Cloud Datastream provides **serverless CDC** with automatic scaling and tight GCP integration. ### **Serverless Architecture** Datastream **scales automatically** based on change volume—no capacity planning or instance sizing required. You pay for **data processed**, not provisioned infrastructure. For teams wanting to **avoid CDC operations** entirely, this **removes significant burden**. ### **GCP Ecosystem Focus** Datastream targets GCP services: **BigQuery**, **Cloud SQL**, **Cloud Storage**, and **Pub/Sub**. For GCP-native architectures, the integration is **seamless**. The service handles **backfills**, **schema changes**, and **ongoing synchronization** with minimal configuration. ### **Limitations to Consider** Datastream's **source support** is narrower than Debezium—primarily **MySQL, PostgreSQL, Oracle, and AlloyDB**. If you have diverse database estates, you may need **multiple CDC solutions**. **Event transformation** capabilities are limited compared to Kafka Connect's SMT ecosystem. Complex routing or enrichment requires **additional processing layers**. ### **When Datastream Fits** Consider Google Cloud Datastream when: - **GCP is your primary cloud platform** - **Serverless operations** are a priority - Targets are **GCP services** like BigQuery - Source databases are **MySQL, PostgreSQL, or Oracle** - You want **minimal CDC infrastructure** ## **5\. Fivetran: SaaS CDC for Data Teams** Fivetran provides **managed CDC** as part of its broader ELT platform, targeting teams who want **connect-and-go simplicity**. ### **Log-Based Replication** Fivetran's CDC uses **log-based capture** where supported—reading transaction logs asynchronously with **minimal source impact**. Changes flow to data warehouses with **configurable sync frequencies**. The service abstracts **connector configuration**, **schema mapping**, and **incremental loading**—data teams define sources and destinations, Fivetran handles the mechanics. ### **Warehouse-Centric Model** Fivetran targets **analytical warehouses**: Snowflake, BigQuery, Redshift, Databricks. The model assumes you're **building analytics**, not **event-driven systems**. **History mode** options let you choose between **current state** (upsert) and **append-only** (full history) tables—important for analytics that need **point-in-time queries**. ### **Operational Simplicity vs. Control** Fivetran **eliminates CDC operations** almost entirely. No Kafka clusters, no connector management, no schema registry maintenance. The cost: **less flexibility** in event routing, transformation, and delivery semantics. **Pricing scales** with data volume, which can become **expensive at scale**. ### **When Fivetran Fits** Consider Fivetran when: - **Data warehouse** is your analytical target - **Operational simplicity** is the top priority - You're building **analytics**, not event systems - Budget allows for **SaaS pricing** at your scale - **Speed to value** matters more than customization ## **6\. Airbyte: Open-Source ELT with CDC** Airbyte provides **open-source ELT** with CDC capabilities, often using **Debezium under the hood** for log-based capture. ### **Open-Source Foundation** Airbyte's **open-source core** gives you **full visibility** into connector implementation and the option to **self-host** for cost control or compliance requirements. The project has **rapid connector development**, with community contributions expanding source and destination coverage. ### **CDC Implementation** Many Airbyte CDC connectors use **Debezium internally**, providing **log-based capture** with familiar semantics. The platform handles **connector orchestration**, **state management**, and **destination loading**. **Delete handling** is explicit: Airbyte typically marks deleted rows with **metadata columns** rather than removing them, preserving **audit history** in destinations. ### **Cloud and Self-Hosted Options** Airbyte offers both **Airbyte Cloud** (managed) and **self-hosted deployment**. Self-hosting reduces costs but requires **operational investment** in Kubernetes, databases, and monitoring. The **connector ecosystem** is large but **quality varies**—production-critical connectors may need **testing and validation**. ### **When Airbyte Fits** Consider Airbyte when: - **Open-source** is important for your organization - You want **self-hosting options** for cost or compliance - **Connector coverage** meets your source needs - You're building **ELT pipelines**, not event systems - **Budget constraints** favor open-source over SaaS ## **7\. Confluent CDC Connectors: Managed Kafka CDC** Confluent provides **managed Debezium connectors** as part of Confluent Cloud, combining **Debezium's capabilities** with **managed Kafka infrastructure**. ### **Debezium on Managed Infrastructure** Confluent's CDC connectors are **Debezium-based**, providing the same **log-based capture**, **event format**, and **source support**. The difference: **Confluent manages everything**—Kafka brokers, Connect workers, Schema Registry. This **eliminates Kafka operations** while preserving Debezium's **flexibility and ecosystem**. ### **Enterprise Features** Confluent adds **enterprise capabilities**: enhanced security, **audit logging**, **RBAC**, and **support SLAs**. For organizations with **compliance requirements**, these features matter. **Schema Registry** is fully managed, with **compatibility enforcement** and **schema evolution** handling built in. ### **Cost Considerations** Confluent Cloud pricing combines **compute, storage, and networking costs**. At scale, costs can **exceed self-managed Kafka significantly**—evaluate carefully for **high-volume CDC**. The **operational savings** may justify premium pricing for teams **without Kafka expertise**. ### **When Confluent Fits** Consider Confluent CDC when: - You want **Debezium capabilities** without operations - **Managed Kafka** aligns with your strategy - **Enterprise features** (security, support) matter - Budget accommodates **premium managed pricing** - **Kafka ecosystem** is your event backbone ## **8\. Oracle GoldenGate: Enterprise CDC Standard** Oracle GoldenGate is the **enterprise standard** for CDC in Oracle environments, with decades of **production proven** deployment. ### **Log-Based Real-Time Capture** GoldenGate provides **log-based CDC** with **real-time delivery** and **minimal source impact**. The architecture supports **high-availability configurations** with **conflict detection and resolution**. For **Oracle-to-Oracle** replication, GoldenGate is **unmatched in capabilities** and **vendor support**. ### **Heterogeneous Support** Beyond Oracle, GoldenGate supports **heterogeneous replication**: Oracle to Kafka, PostgreSQL, MySQL, SQL Server, and various targets. This makes it viable for **mixed-database estates**. **Transformation capabilities** during replication enable **data filtering**, **column mapping**, and **format conversion**. ### **Enterprise Complexity** GoldenGate requires **significant expertise** to deploy and operate. The licensing model is **complex and expensive**. Configuration involves **multiple components** with intricate dependencies. For **Oracle shops with dedicated DBA teams**, this investment makes sense. For others, **simpler alternatives** may be more practical. ### **When GoldenGate Fits** Consider Oracle GoldenGate when: - **Oracle databases** are primary sources - **Enterprise support** and SLAs are required - **High-availability replication** is critical - Your organization has **Oracle licensing agreements** - **Dedicated database teams** can manage complexity ## **9\. Qlik Replicate: Enterprise CDC Platform** Qlik Replicate (formerly Attunity) provides **enterprise CDC** with broad source support and **high-performance capture**. ### **High-Performance Architecture** Qlik Replicate emphasizes **low-latency, high-throughput** CDC with **minimal source impact**. The architecture handles **high change volumes** that stress simpler tools. **Parallel processing** and **optimized capture** make it suitable for **enterprise-scale workloads**. ### **Broad Source Coverage** Qlik Replicate supports **extensive source databases**: mainframes, Oracle, SQL Server, MySQL, PostgreSQL, SAP, and more. For **heterogeneous enterprise environments**, this breadth is valuable. The platform handles **both log-based and trigger-based** CDC, choosing the **optimal method** per source. ### **Enterprise Deployment Model** Qlik Replicate requires **on-premises or cloud infrastructure**, with **license-based pricing**. The deployment model suits **large organizations** with **dedicated data integration teams**. **Professional services** are often needed for **complex implementations**. ### **When Qlik Replicate Fits** Consider Qlik Replicate when: - **Enterprise-scale CDC** is required - Source databases include **mainframes or SAP** - **High-performance requirements** exceed simpler tools - Budget and team support **enterprise platform** investment - **Vendor support** for complex scenarios matters ## **10\. Maxwell's Daemon: MySQL-Specific CDC** Maxwell's Daemon provides **lightweight CDC** specifically for MySQL, offering a **simpler alternative** to Debezium for MySQL-only environments. ### **MySQL Binlog Focus** Maxwell reads **MySQL binlog** directly and emits changes as **JSON events** to Kafka, Kinesis, or other destinations. The **single-purpose design** results in **simpler operation** than general-purpose CDC tools. **Row-based binlog** (with `binlog_row_image=FULL`) is required for **complete change capture**. ### **Lightweight Deployment** Maxwell runs as a **single Java process**—no Kafka Connect cluster, no distributed coordination. For **smaller deployments**, this **reduces operational complexity** significantly. Configuration is **straightforward**, with sensible defaults for common use cases. ### **MySQL-Only Limitation** Maxwell **only supports MySQL**. Organizations with **multiple database platforms** need additional tools for **non-MySQL sources**. The project has **smaller community** than Debezium, which may affect **long-term maintenance** and **feature development**. ### **When Maxwell Fits** Consider Maxwell's Daemon when: - **MySQL is your only CDC source** - **Simplicity** is more important than features - **Lightweight deployment** suits your scale - You want **JSON events** without complex configuration - **Kafka Connect overhead** isn't justified ## **11\. Estuary: Streaming-First CDC Without Running Kafka** Estuary is a **streaming-first CDC and data movement platform** that goes beyond capture alone. It reads changes from databases, SaaS applications, and streaming systems, stores them as durable collections in cloud storage, and materializes them to downstream systems with low latency. ### **Capture, Backfill, and Delivery in One System** Estuary uses **log-based CDC** where available, reading WAL, binlog, or oplog streams with minimal impact on the source. It performs an **initial backfill** and then moves into **continuous change capture** without downtime. It also supports **incremental backfills** alongside live pipelines, which is useful when adding tables or recovering history without stopping ingestion. ### **Exactly-Once Semantics and Schema Evolution** Estuary guarantees **exactly-once delivery** across capture, transformation, and materialization. It also includes **automated schema evolution**, so compatible field changes can be applied without forcing a reload or manual intervention. That makes it a good fit for teams dealing with evolving application schemas or mixed source systems. ### **Dekaf and Flexible Deployment** One of Estuary's more practical features is **Dekaf**, which exposes Estuary collections as **Kafka-compatible topics**. Downstream systems such as Tinybird can connect using standard Kafka protocols without running a Kafka cluster. If deployment constraints matter, Estuary also supports [public, private, and BYOC deployment options](https://estuary.dev/deployment-options/). For the Kafka-compatible interface itself, see the [Dekaf reference](https://docs.estuary.dev/reference/Connectors/dekaf/). ### **When Estuary Fits** Consider Estuary when: - You want **CDC without operating Kafka** - You need **one pipeline across databases, SaaS tools, and streams** - **Schema changes** keep breaking existing pipelines - You need **live pipelines with concurrent backfills** - **Exactly-once delivery** is important - **Private deployment** or **BYOC** is a requirement ## **Why Tinybird Is the Best CDC Destination** After evaluating CDC capture tools, the destination matters as much as the capture. Tinybird emerges as the strongest choice for teams whose CDC goal is real-time analytics and APIs rather than simple replication. Its design enables seamless [real-time analytics](https://www.tinybird.co/blog/real-time-analytics-a-definitive-guide) built directly on top of CDC streams. ### **The Right Architecture for CDC Analytics** Most CDC tools focus on **capture and delivery**—getting changes from source to Kafka or a warehouse. But for **real-time analytics**, you need more: - **Sub-second query latency** on fresh data - **High concurrency** for user-facing dashboards - **Proper handling** of updates and deletes - **API serving** without additional infrastructure Traditional warehouses **struggle with these requirements**. They're designed for **batch analytics**, not **streaming workloads**. Tinybird solves this by providing a **purpose-built analytical layer** that consumes CDC streams and serves **real-time APIs**. Each component does what it was designed for. ### **Native Kafka Integration** Tinybird connects **directly to Kafka topics** where CDC tools publish changes, including Kafka-compatible CDC platforms like [Estuary](https://estuary.dev). Data flows continuously into **ClickHouse®-powered storage** with **materialization strategies** that handle CDC semantics: upserts, deletes, and late-arriving data. ### **From CDC Stream to Production API in Seconds** No other platform offers Tinybird's **instant API publication** for CDC data. Write a SQL query over your change stream, **click publish**, get a **production-ready HTTP endpoint**. For teams building **operational dashboards** or **customer-facing analytics** from database changes, this capability **replaces weeks of backend development**. ### **Zero Pipeline Complexity** With traditional CDC architectures, you manage **capture tools, Kafka clusters, stream processors, and analytical databases**—each requiring separate expertise. **Tinybird collapses this stack.** Connect your CDC stream, write SQL, publish APIs. The platform handles **scaling, availability, and performance optimization** automatically. ### **Predictable Economics** CDC architectures can have **unpredictable costs**: Kafka pricing, compute for stream processing, warehouse costs that scale with data and queries. Tinybird offers **fixed monthly plans** with included compute and storage. **You know costs upfront**, regardless of CDC volume or query patterns. ## **Conclusion** Choosing CDC tools depends on understanding **your complete data pipeline**, not just the capture layer. **For open-source CDC capture**, Debezium provides the **most complete solution** with broad database support and Kafka integration. Maxwell's Daemon offers **simpler MySQL-specific** capture. **For managed CDC capture**, AWS DMS and Google Cloud Datastream provide **cloud-native options** with minimal operations. Fivetran and Airbyte offer **ELT-focused CDC** for warehouse destinations. Estuary is a strong option when you want **streaming-first CDC**, **concurrent backfills**, and **Kafka-compatible delivery** without running Kafka yourself. **For enterprise CDC**, Oracle GoldenGate and Qlik Replicate provide **proven platforms** for complex, high-volume environments. **For real-time analytics from CDC**—the goal that drives many CDC implementations—**Tinybird offers the most compelling destination**. Purpose-built columnar architecture, **native Kafka integration**, **instant API publication**, and **fully managed infrastructure** let teams focus on **building analytics products** rather than managing CDC pipelines. The right choice depends on your **sources, destinations, scale**, and **team capabilities**. But if your goal is **real-time analytics and APIs from database changes**, starting with a platform **designed for that workload** will serve you far better than assembling components that weren't built to work together. ## **Frequently Asked Questions (FAQs)** ### **What is Change Data Capture (CDC) and why does it matter?** CDC captures **database changes** (inserts, updates, deletes) and propagates them to other systems with **low latency**. Instead of re-extracting entire tables in batch, CDC streams **only the changes**, enabling **real-time data pipelines** with **minimal source impact**. ### **What's the difference between log-based and trigger-based CDC?** **Log-based CDC** reads database transaction logs directly, with **minimal impact** on source performance. **Trigger-based CDC** adds database triggers that write to shadow tables, creating **overhead on every transaction**. Log-based is generally preferred for **production workloads**. ### **Is Tinybird a CDC tool?** No. Tinybird is a **real-time analytics platform** that serves as an **ideal destination** for CDC streams. It ingests changes from Kafka or other sources and transforms them into **instant APIs** for real-time dashboards and applications. CDC tools capture; **Tinybird serves analytics**. ### **How do I handle deletes in CDC pipelines?** Most CDC tools emit **delete events** that downstream systems must process appropriately. Strategies include **soft deletes** (marking records as deleted), **tombstones** for Kafka log compaction, or **filtering** in analytical queries. The right approach depends on your **analytical requirements**. ### **What's the best CDC tool for PostgreSQL?** **Debezium** is the most common choice, using PostgreSQL's **logical decoding** to capture changes. Managed options include **Fivetran, Airbyte**, and cloud services like **AWS DMS**. For real-time analytics destinations, **Tinybird** can ingest directly from Kafka topics populated by any capture tool. ### **How does schema evolution work in CDC?** CDC tools must handle **schema changes** (new columns, type changes) gracefully. Debezium maintains a **schema history topic** tracking DDL changes. Destinations must **evolve schemas** correspondingly. This requires **coordination** between capture and consumption—a common source of **production issues**. ### **Can I use CDC without Kafka?** Yes. **Debezium Server** sends changes to Kinesis, Pub/Sub, or other destinations without Kafka. **Maxwell's Daemon** supports multiple outputs. Managed services like **Fivetran**, **AWS DMS**, and **Estuary** don't require you to manage message brokers directly. In Estuary's case, **Dekaf** provides a Kafka-compatible interface for downstream consumers when you need Kafka semantics without the brokers. The right architecture depends on your **existing infrastructure**.