10 Best Debezium Alternatives & Competitors in 2026

Apache Kafka is used by more than 80% of Fortune 100 companies, and Debezium runs on top of it. That made Debezium a natural starting point for CDC.

But the realistic production setup is not just Debezium. It’s Postgres or MySQL feeding Debezium, which feeds Kafka, which feeds a stream processor, which feeds the warehouse. Every component needs to be provisioned, monitored, and recovered when something breaks.

That works if you have the engineering capacity to own it. For teams that don’t, the operational burden competes with the actual goal: getting clean, current data into your analytical systems.

The 10 tools in this guide take different approaches. Some eliminate Kafka entirely. Some are fully managed. Some are open-source with more flexibility but still require engineering lift. By the end, you’ll know which one fits your stack, your downtime tolerance, and your team’s capacity to manage it.

Quick Tabular Comparison of the Top 10 Debezium Alternatives in 2026

Before diving into each tool in detail, here is a quick comparison across deployment type, best fit, core differentiator, and key limitation to help you shortlist faster.

Tool	Type	Best For	Core Differentiator	Key Limitation
Hevo Data	Fully managed, no-code ELT	Teams wanting real-time CDC without managing Kafka or infrastructure	Auto-healing pipelines, transparent pricing, end-to-end visibility, no Kafka required	Cloud-only
Fivetran	Managed ELT	Teams needing broad connector coverage with minimal setup	Automated schema drift handling and a large connector library	Costs scale quickly with data volume
Stitch Data	Cloud ETL	Small to mid-sized teams with simpler CDC needs	Fast setup, incremental loading, and a straightforward pricing model	Limited transformation capabilities out of the box
Dataddo	No-code data integration	Teams moving data from cloud apps and APIs into BI tools	No-code connector builder with flexible scheduling and destination support	Not designed for high-volume database CDC use cases
Keboola	All-in-one data workspace	Teams that need orchestration, CDC, and AI-assisted pipeline management in one place	Visual orchestration with real-time streams and built-in AI agents	Steeper learning curve compared to simpler ELT tools
Integrate.io	Low-code ETL and CDC	Teams needing sub-minute replication with on-premise agent support	Sub-60-second CDC with a visual pipeline builder and on-prem agent option	Pricing can be opaque for high-volume workloads
Airbyte	Open-source ELT	Engineering teams that want open-source flexibility and custom connectors	Modular connector architecture with self-hosted or cloud deployment options	Requires engineering effort to maintain and scale
Informatica PowerExchange	Enterprise CDC	Large enterprises with mainframe sources or complex compliance requirements	Mainframe and legacy system support with deep Informatica ecosystem integration	High cost and implementation complexity
Qlik Replicate	GUI-driven replication	DBAs and data teams who prefer a visual interface over CLI-based configuration	High-performance heterogeneous replication with a no-code GUI	Expensive licensing, particularly for smaller teams
Striim	Real-time streaming CDC	Teams needing in-flight transformation alongside CDC with built-in AI agents	Streaming CDC with real-time transformation and AI-powered pipeline agents	Complex setup and high cost for smaller deployments

What is Debezium?

Debezium is an open-source platform designed for change data capture (CDC), tracking real-time changes in databases and streaming them to other systems. 

It enables you to move data continuously and reliably between sources and destinations for analytics, integration, or backup purposes.

It’s widely used by small to mid-sized businesses (SMBs) and data teams that need to sync data from traditional databases to SaaS tools, data warehouses, or cloud storage systems without running full ETL jobs every time for the above features and more.

Why Are People Moving Away from Debezium?

1. Running Debezium means owning and managing the entire Kafka stack

Debezium doesn’t run on its own. It requires Kafka brokers, Kafka Connect workers, Zookeeper (or KRaft), topic partition configurations, retention policies, and consumer lag monitoring. Every one of those components needs to be provisioned, configured, scaled, and recovered independently — because Debezium is typically deployed via Apache Kafka Connect.

The result is that a simple CDC requirement quickly becomes a distributed systems problem. Configuration errors, network issues, or broker failures don’t surface cleanly. They often appear as silent ingestion failures or connector crashes that require manual diagnosis across multiple systems.

User review:

2. Snapshots stall and lag compounds as data volumes grow

Debezium’s initial snapshot runs sequentially with no parallelism by default. On large tables, this can take hours. At scale, it can take days. During that window, metadata locks can block the source database, causing downstream replicas to fall behind.

The snapshot problem gets worse as connector versions change. Teams running high table counts have reported that version upgrades introduced performance regressions that weren’t caught until the connector hit production.

User review:

3. Schema changes require manual intervention and can break pipelines silently

Production schemas change constantly. Columns get added, types evolve, tables get renamed. Debezium captures these events, but what happens downstream is entirely your team’s responsibility.

For PostgreSQL specifically, logical decoding does not natively support DDL events. Schema modifications appear as part of data change events rather than as dedicated schema change events, making them harder to detect and handle automatically. When a schema change is incompatible with what the schema registry expects, the connector throws errors and stops ingesting, often without an obvious signal to the team.

User review:

4. There is no built-in recovery, monitoring, or alerting; it all falls on your team

Debezium has no native dashboard, no alerting system, and no recovery automation. Teams need to wire up Prometheus, build Grafana dashboards, define their own lag thresholds, and write custom failure recovery logic. When a connector crashes or a replication slot gets invalidated, diagnosis requires tracing events across Kafka logs, connector logs, and the destination system separately.

For self-managed deployments, this means the operational burden never ends. Even minor configuration errors or database changes can cause silent ingestion failures, making it risky for teams to install Debezium without dedicated data engineers. 

User review:

5. Complex setup and maintenance

Debezium’s setup and maintenance process is often complex and time-consuming. Even minor configuration errors or database changes can cause silent ingestion failures, making it risky for teams to install Debezium without dedicated data engineers.

User review:

Detailed Comparison of the Best 10 Debezium Alternatives in 2026

1. Hevo Data

Hevo is a fully managed, no-code ELT platform that makes data pipeline movement simple, reliable, and transparent. Unlike Debezium, it requires no Kafka infrastructure, no connector management, and no custom recovery logic. You connect your source, configure your destination, and Hevo handles everything in between, including schema changes, retries, and pipeline health, automatically.

For teams that want real-time CDC without owning the operational complexity of a Debezium-based stack, Hevo is the most direct alternative.

Best for: Teams that need reliable, production-ready CDC pipelines without engineering overhead or infrastructure management.

Key features:

• Auto-healing pipelines with intelligent retries and fault-tolerant architecture that keep data flowing even when sources fail, with no manual intervention required
• Full pipeline visibility through unified dashboards, detailed logs, data lineage views, and batch-level anomaly detection, with no black boxes
• No-code setup with automatic schema mapping and support for dbt integration and Python-based scripting when you need more control
• 150+ pre-built connectors across databases, SaaS apps, and cloud sources, all fully managed

Pros:

• No Kafka dependency. Pipelines run without any streaming infrastructure to provision or maintain
• Automatic schema handling adjusts to source changes without breaking downstream workflows
• SOC 2 Type II, HIPAA, and GDPR compliant out of the box
• 24/7 support from real engineers, not community forums

Cons:

• Not suited for teams that need custom connector development or self-hosted infrastructure control
• Cloud-only. No on-premise deployment option

We had numerous files that needed rapid ingestion and a pipeline established without being hindered by server infrastructure or schema complexities. Hevo enabled us to achieve this efficiently.

Mike C.

SVP of Technology

View Review

2. Fivetran

Fivetran is a fully managed ELT platform designed to automate data integration across cloud and on-premises systems. It centralizes data from multiple SaaS applications, databases, and event streams into a cloud data warehouse.

Fivetran continuously monitors source changes and replicates them in near real time using pre-built connectors. The platform is widely used by data engineering and analytics teams that want reliable, hands-free data ingestion without maintenance overhead.

Fivetran offers a fully managed, plug-and-play CDC solution that scales automatically. It handles schema drift, automates connector updates, and provides built-in monitoring and transformations.

Key features:

• Incremental data sync: Fivetran supports incremental data replication across many connectors, tracking changes via logs or snapshots so new or updated records move quickly to your destination.
• Schema change handling: Fivetran supports schema migration detection, table/column changes, and allows re-sync or full overwrite to repair mismatches. This means when your source evolves, your destination remains consistent with less manual effort.
• Private networking: Fivetran offers private-network syncs and column-level hashing for PII. For companies with strict compliance/security needs, this is a major plus.

Pros:

• The “set and forget” design needs minimal infrastructure management.
• Wide variety of pre-built connectors across SaaS & databases.
• Reliable data syncing and robust alerting features.

Cons:

• MAR-based pricing can become unpredictable with large datasets.
• Some users report sync delays or limited control over scheduling.
• Limited flexibility for niche sources.

Pricing:

Pricing is determined by MAR, based on the number of unique rows inserted, updated, or deleted each month. You can try the platform with a 14-day free trial.

3. Stitch Data

Stitch Data is a cloud-based ETL platform designed to streamline the movement of data from a variety of sources into a single destination, such as a data warehouse or lake.

Stitch enables users to define a destination and then connect one or more integrations (such as MySQL, MongoDB, or Salesforce) to extract data. The workflow helps BI teams and data engineers to centralize disparate data without spending excessive effort on pipeline infrastructure or custom coding.

Stitch stands out as a fully managed service, eliminating the need to set up or maintain Kafka clusters and connector infrastructure themselves. It also offers an Import API to ingest arbitrary data when native connectors don’t exist.

Key features:

• Advanced orchestration: Stitch provides advanced orchestration features, including granular scheduling, error handling, and detailed logging. Users can monitor replication progress and receive notifications for any issues.
• Managed cloud service: Stitch is a fully managed cloud service that handles infrastructure, scaling, and maintenance. You can set up data pipelines quickly without worrying about underlying infrastructure.
• Built-in data quality: Offers built-in data quality checks, including data validation and schema evolution handling. It also provides features to support compliance requirements, such as data masking and auditing capabilities.

Pros:

• Quick onboarding with minimal infrastructure overhead.
• Large library of built-in source & destination connectors.
• Reliable data replication for standard workloads.

Cons:

• Data transformation capabilities are limited.
• Some users report inconsistent data quality.
• Some connectors dependent on open-source Singer taps are less stable.

Pricing:

• Standard: $100 monthly
• Advanced: $1,250 monthly
• Premium: $2,500 monthly

4. Dataddo

Dataddo is a cloud-based, no-code data integration platform designed to connect disparate sources, transform datasets, and load them to target destinations such as warehouses, dashboards, or operational applications.

Dataddo handles data flows by letting users select a source, designate a destination, and configure the synchronization frequency. It helps BI teams to unify data from multiple services or dashboards without building a full CDC infrastructure themselves.

What sets Dataddo apart is its broad connector library, transparent pricing, and a focus on relieving engineering burden. It is ideal for teams that need more than CDC in relational databases and want an “all-in” integration platform.

Key features:

• Real-time data synchronization: Dataddo supports real-time data synchronization, ensuring that data is continuously updated across systems. Crucial for businesses that require up-to-date information for decision-making and analytics.
• Transformation capabilities: Dataddo offers robust pre-processing capabilities, including sensitive data detection and hashing, flattening, type harmonization, unions, and joins.
• Headless data integration: With Dataddo’s Headless Data Integration, users can build their data products on top of the unified Dataddo API.

Pros:

• Seamless integration with BI tools like Tableau.
• Automated handling of API changes for many connectors.
• Free tier available to test core features.

Cons:

• Customer service response times are occasionally slower.
• Limited control over advanced transformations.
• Custom connector development can be slower than expected.

Pricing:

• Free up to 3 data flows.
• Data to Dashboards: $99.00 per month for hourly sync to any visualization tool.
• Data Anywhere: $99.00 per month for data sync from any source to any destination.
• Enterprise: Customizable for flexible deployment in hybrid environments.

5. Keboola

Keboola is a cloud-based data operations platform designed to streamline data integration, transformation, and orchestration. It caters to data engineers seeking a unified solution to manage and automate their data workflows.

The platform supports data transformation using SQL, Python, and dbt, allowing for comprehensive data manipulation within a single environment. Its intuitive visual interface and automation capabilities make it accessible to both technical and non-technical users.

Keboola’s support for AI-powered data pipelines and its modular architecture provide flexibility and scalability, making it a compelling alternative for organizations seeking a more user-friendly and integrated solution.

Key features:

• Visual orchestration: Keboola’s Orchestrator component allows users to design and automate complex data workflows with visual dependencies.
• Real-Time data streams: Keboola’s Data Streams feature enables the ingestion of real-time event data directly into the platform without the need for additional middleware. Supports scenarios like IoT data collection or real-time analytics.
• AI-powered data operations: Keboola integrates artificial intelligence to enhance data operations, offering features like AI component suggestions, automated flow creation, and intelligent error explanations.

Pros:

• Supports multiple transformation languages (SQL/Python/R).
• Strong customer support and responsive service.
• Cloud-based platform eliminates infrastructure overhead.

Cons:

• UI and UX have some usability issues.
• Limited version control or branching capabilities in pipelines.
• Documentation sometimes lags behind platform changes.

Pricing:

Keboola provides a free plan with basic functionality, while access to advanced features and enterprise capabilities requires contacting their sales team for a custom plan.

6. Integrate.io

Integrate.io is a cloud-based, low-code data integration platform designed to streamline ETL, ELT, CDC, and Reverse ETL workflows. The platform’s user-friendly interface and pre-built connectors help businesses aiming to accelerate data-driven decision-making.

Users can design data workflows using a drag-and-drop interface, enabling both technical and non-technical teams to manage data pipelines efficiently. The platform supports real-time data replication with sub-60-second latency, catering to multiple use cases.

Integrate.io’s ability to handle both batch and real-time data workflows, along with its support for bidirectional data synchronization, makes it a versatile choice for organizations looking to modernize their data integration processes without any complexity.

Key features:

• On-premise data access: Integrate.io offers on-premise agents that enable secure access to local data sources, facilitating hybrid deployments.
• API ingestion capabilities: With its REST API connector, Integrate.io allows users to connect to virtually any RESTful endpoint without custom code.
• Error handling: The platform offers advanced error-handling mechanisms, including the capability to capture and route failed records for detailed analysis.

Pros:

• Visual, low-code drag-and-drop pipeline builder.
• Built-in scheduling and orchestration for workflows.
• Ability to transform nested JSON and other complex data types.

Cons:

• The learning curve increases with complex pipelines.
• Limited debug and retry capabilities for complicated workflows.
• Fewer specialized connectors for niche sources.

Pricing:

Priced at $1,999 per month, this plan provides full platform access, 60-second pipeline updates, and unlimited connectors, with options to customize and add extra features.

7. Airbyte

Airbyte is an open-source ELT platform designed to simplify data integration. The tool allows users to create custom integrations, making it ideal for teams with diverse data sources and integration needs.

Its modular architecture enables users to configure and manage data pipelines through a user-friendly interface, API, or Terraform. This flexibility caters to data engineers, analysts, and teams requiring a customizable and scalable data integration solution.

Airbyte provides a comprehensive ELT framework that encompasses CDC capabilities, reverse ETL, and data activation features. Its AI-powered connector builder and low-code SDK facilitate rapid development of custom connectors, reducing the time and complexity associated with data integration.

Key features:

• Modular connector architecture: Airbyte’s open-source platform offers a modular connector architecture, enabling users to build and customize connectors easily.
• User-Friendly UI: Airbyte provides a browser-based UI that simplifies the setup and monitoring of data pipelines. The UI streamlines the process of configuring and managing data integrations, making it accessible for users with varying technical expertise.
• Open-source community: Airbyte benefits from an active community that contributes to its advancements. A collaborative environment fosters innovation and ensures that the platform meets growing user needs.

Pros:

• Flexible deployment: self-hosted or cloud-based options.
• Rapid updates and a growing ecosystem of integrations.
• Custom connector builder allows niche source support.

Cons:

• Requires technical expertise for setup and troubleshooting.
• Performance issues with very large data sets are reported.
• Requires external tools like dbt for complex transformations.

Pricing:

Airbyte offers a free self-hosted version, a 14-day free trial for its Cloud offering, and flexible Team and Enterprise plans tailored to different business needs.

8. Informatica PowerExchange

Informatica PowerExchange is an enterprise-grade data integration and CDC platform that enables both bulk data movement and near-real-time change replication. Well-suited for organizations requiring comprehensive support across heterogeneous environments.

PowerExchange allows enterprises to define data sources, map those sources to targets, and then replicate either full data loads or incremental changes. It is ideal for enterprises that need to integrate data from legacy systems, mainframes, relational databases, and file systems.

PowerExchange stands out for its broad source compatibility and native support for mainframes and legacy systems. It offers enterprise-grade reliability, vendor support, and built-in integrations within the Informatica ecosystem.

Key features:

• Built-in metadata: PowerExchange automatically captures relevant metadata and allows users to define data maps and extraction maps via GUI tools.
• Data processing: PowerExchange allows for both initial bulk loads (batch) and continuous CDC (real-time) within the same framework, offering flexibility in how ingestion is handled. 
• Enterprise-grade performance: Designed to handle high-volume data movement at scale into both on-premises and cloud platforms, including UOW-based and grid-based job execution.

Pros:

• Broad connectivity to legacy systems and mainframes.
• Strong real-time data capture and replication engine.
• Strong metadata and transformation capabilities.

Cons:

• Performance issues at large data volumes or heavy workloads.
• High total cost of ownership and expensive licensing.
• Support and responsiveness are reported as inconsistent.

Pricing:

Informatica runs on a consumption-based pricing model, billing you for what you use.

9. Qlik Replicate

Qlik Replicate is a high-performance data replication and CDC solution designed to move, stream, and synchronize data across heterogeneous and homogeneous endpoints.

Qlik Replicate enables users to define a replication task by establishing source and target endpoints, selecting tables, and then configuring either a full load or CDC mode. The tool helps database administrators migrate legacy databases, integrate multiple data sources, or maintain up-to-date data in analytics platforms.

Qlik Replicate offers a turnkey, enterprise-ready platform with a web-based console, multi-server and multi-threaded architecture, and broad endpoint support. Qlik Replicate is a compelling alternative for teams looking to maintain CDC pipelines.

Key features:

• Target endpoint support: Qlik Replicate supports a vast range of source and target systems, including relational databases, big data platforms, cloud warehouses, mainframes, streaming brokers like Kafka, and file stores.
• GUI-driven monitoring: Qlik Replicate offers a browser-based interface to define endpoints, tasks, schemas, and transformations, and monitor performance via a central console.
• Advanced transformation: Users can filter, transform, and enrich data in-flight, and deliver change events into streaming brokers (Kafka) or analytics platforms with transactional order.

Pros:

• Broad support for heterogeneous source and target systems.
• Real-time or near-real-time replication enables timely analytics.
• Automated schema change handling keeps targets in sync.

Cons:

• UI issues and error messages are reported as unclear. 
• Community support and documentation gaps for niche use-cases.
• Setting up and advanced configuration involve a steep learning curve.

Pricing:

Qlik offers customizable plans divided into four levels: Starter, Standard, Premium, and Enterprise. The pricing depends on the features you require.

10. Striim

Striim is a real-time data integration and streaming platform designed to handle both initial loads and continuous CDC from various sources. The platform is best suited for organizations that need to replicate and move data with minimal latency.

Striim ingests data from a wide range of sources using its Database Reader and CDC readers and delivers this data to target systems via built-in writers, all while allowing transformations, filtering, and enrichment along the way.

Striim stands out with a unified platform that combines initial load, CDC, streaming analytics, and delivery in a single system. Striim offers wizards, auto schema conversion, and broad connector coverage (databases, cloud targets, log sources) out of the box.

Key features:

• No-code pipeline construction: Striim’s Flow Designer allows creation of streaming pipelines and inclusion of AI agents without requiring heavy custom code.
• Pre-built AI agents: Striim includes AI agent components such as Sentinel, Euclid, and Foreseer that can be embedded into streaming flows.
• Sensitive data detection: With features like Sentinel for PII detection and governance built into streaming pipelines, Striim offers governance controls natively.

Pros:

• Low-latency streaming ingestion across sources.
• Unified monitoring and visual pipeline designer.
• Support for real-time transformation and enrichment.

Cons:

• Vendor lock-in risk due to proprietary platform.
• Requires advanced skills for configuration and tuning.
• The pricing model is complex and difficult to estimate.

Pricing:

Striim’s pricing has three levels:

• Striim Developer: Free
• Striim Cloud: Contact sales
• Striim Platform: Contact sales

Factors to Consider When Choosing a Debezium Alternative

1. Can you go from setup to live data without dedicated infrastructure or engineering time?

Debezium requires provisioning Kafka brokers, configuring Kafka Connect workers, and managing topic partitions before a single row of data moves. When evaluating an alternative, look at how long it realistically takes to get from a blank configuration to live data in your destination. The ideal alternative should have a guided setup and a path to production that does not require Kafka expertise.

2. Does it capture and deliver changes in real time, not just in scheduled batches?

Some tools marketed as CDC alternatives are batch-based under the hood, polling the source on a schedule rather than reading from the transaction log continuously. That distinction matters for use cases like fraud detection, inventory management, and real-time analytics where stale data has a direct operational cost. Verify whether the tool uses log-based CDC or polling-based replication before committing.

3. Will it hold up as table counts, data volume, and throughput grow?

Debezium’s scaling challenges appear when a single table receives high-volume updates, when snapshot sizes grow, or when the number of tables being monitored increases. Ask specifically how the tool handles initial snapshots on large tables, what happens to lag under sustained high write volume, and whether it can add new sources without impacting existing pipelines.

4. Does it connect to the databases and destinations your stack actually uses?

Alternatives vary significantly in connector coverage, and not all connectors are equal in quality or maintenance. Before committing to a tool, verify that the specific database versions you run are fully supported, that destination connectors are actively maintained, and that the connector has been tested against your data volumes and schema complexity.

5. When something breaks, does the tool recover on its own or does your team have to?

When a Debezium connector crashes or a replication slot gets invalidated, diagnosing and recovering is a manual exercise across multiple systems. The alternative you choose should have fault-tolerant pipelines that detect failures, retry automatically, and surface clear alerts. If the answer to failure recovery is “check the logs and restart the connector,” the operational overhead is similar to what you’re moving away from.

6. Does it meet your compliance requirements without additional configuration?

With Debezium, compliance is your responsibility. Encryption, access controls, audit logging, and certifications like SOC 2, HIPAA, and GDPR require additional tooling on top of the base setup. If your organization operates in a regulated industry, verify that the alternative comes with these controls built in and can provide documentation to satisfy auditors.

7. Can you forecast your monthly cost without modeling infrastructure and engineering overhead?

Debezium is free to download, but the real cost includes Kafka cluster compute, broker memory, disk throughput, egress fees, and the engineering time to keep everything running. Look for an alternative with transparent, event-based pricing where the pricing page tells you what you’ll pay at your current data volume without requiring separate infrastructure cost calculations.

8. Does the tool make it easy to migrate away from Debezium without rebuilding your pipelines?

Switching from Debezium means accounting for existing source configurations, offset histories, schema mappings, and downstream consumers. A tool with no migration support means rebuilding everything from scratch. Look for support for historical data backfills, automatic schema mapping, and clear onboarding documentation for teams moving off self-managed Kafka setups.

Why is Hevo the Best Debezium Alternative?

Debezium is a capable CDC tool, but it solves only one part of the problem. It captures changes. Everything else, delivering them reliably, handling schema evolution, monitoring pipeline health, recovering from failures, is your team’s responsibility.

Hevo handles the full pipeline. Here is how it directly addresses the gaps that push teams away from Debezium.

1. Hevo requires no Kafka infrastructure, Debezium requires all of it

No brokers, no workers, no topic partitioning, no consumer lag to manage.

With Debezium, the realistic production setup is Kafka brokers, Kafka Connect workers, Zookeeper or KRaft, topic partition configurations, and retention policies. Every component needs to be provisioned, monitored, and recovered independently. Hevo eliminates this entirely. You connect your source and destination, and data flows. The infrastructure overhead that defines a Debezium production setup does not exist.

2. Hevo handles schema changes automatically, Debezium does not propagate them downstream

When a column is added or a type changes, Hevo adapts without breaking your pipeline.

With Debezium, schema changes are captured at the source but propagating them downstream is your responsibility. For PostgreSQL specifically, DDL changes are not emitted as separate events, making them harder to detect and handle. Incompatible changes can cause connectors to stop ingesting silently. Hevo detects and maps schema changes automatically at every stage, with no manual intervention required.

3. Hevo gives end-to-end pipeline visibility, Debezium spreads it across multiple systems

Unified dashboards, row-level logs, lineage views, and anomaly detection in one place.

With Debezium, observability requires wiring up Prometheus, building Grafana dashboards, and tracing events across Kafka logs, connector logs, and destination systems separately. Hevo provides all of this out of the box, with a single view covering every pipeline, including batch-level anomaly detection to catch data quality issues before they reach downstream systems.

4. Hevo recovers from failures automatically, Debezium leaves recovery to your team

Auto-healing pipelines with intelligent retries and no custom recovery logic to write.

When a Debezium connector crashes or a replication slot gets invalidated, diagnosing and recovering from it is a manual exercise. There is no built-in alerting, no automated restart logic, and no native recovery workflow. Hevo’s auto-healing pipelines detect failures, retry intelligently, and keep data flowing without requiring engineering intervention.

Frequently Asked Questions