5 CDC Tools and the Tradeoffs You Should Know
Not every data pipeline or process needs to be real-time. In fact, in my experience, often times when clients ask for real-time they mean daily or hourly loads. But, every so often there are use cases where real-time is required.
Beyond how frequently a data pipeline needs to be processed, some also need to capture every change.
This is where change data capture (CDC) comes in. Instead of extracting entire tables, CDC only pulls in the changes to the source data, and streams those changes to another system. Whether it’s real-time or not is up to the developer and how you design your system.
But it is a great way to get near real-time replication.
However, choosing among the CDC tools involves more than comparing the number of connectors a vendor offers. There are several factors you should consider when picking a CDC solution. This includes things such as how the tool handls, recovery, schema changes, delivery guarantees, monitoring, deployment, and cost.
This article compares five CDC platforms: Estuary, Debezium, Fivetran, Airbyte, and Qlik Replicate. The aim of this article is to provide an objective framework to assess their capabilities and to understand where each approach may fit.
What To Consider When Picking A CDC Platform
Before diving too deep into the various change data capture tools that do exist. Let’s talk about what you should consider as you’re picking tools.
CDC Capture Method
The most common approach is log-based CDC. Instead of repeatedly querying tables to determine what changed, the CDC tool reads the database’s transaction log. Depending on the database this might be PostgreSQL’s WAL, MySQL’s binlog, or SQL Server’s transaction log.
This generally has several advantages. It reduces the amount of work placed on the database, captures inserts, updates, and deletes more reliably, and can support much lower latency than repeatedly polling tables.
However, not every connector or platform uses log-based CDC for every source. Some rely on timestamp columns, incremental queries or triggers.
Initial Snapshot and Backfill Support
Truth be told there are plenty of use-cases that you might be considering if you are looking into CDC. Not all of them are data analytics. But you are on the Seattle Data Guy website. So I am biased. Most of the problems I am trying to answer are around data analytics. Meaning that the CDC I usually need to do is meant to capture data for data warehouses and lakehouses. With that, I need to make sure there is a reliable way to both ingest all the initial data as well as backfill tables after corrections have been made to the source data.
Schema Evolution
Production databases change over time. Software teams love adding and removing columns. If you’re a data engineering team and you want to limit the disruption that occurs, then your platform should have a method for handling new columns, dropped columns, changed data types, and other schema changes. Otherwise you will be woken up at 2 AM by a failing pipeline.
Delivery Guarantees
Organizations should know if events are delivered at least once, exactly once, or some other delivery model. They should also decide how they want to handle duplicates, ordering, and failed deliveries.
Transformation Capabilities
Some platforms do transformation as part of moving data. Others are focused on replication and leave transformation for downstream systems. Meaning you need to ask yourself, what do you want this tool to do?
Do you just want it to ingest data or do you also want it to do some pre-transforms.
One example I’ve had in the past of this was a client trying to remove PII prior to the data being ingested. In this case, having a tool with the ability to also transform and remove the data from JSON was very helpful.
Monitoring and Recovery
Change data capture pipelines should be monitored for things like latency and failures. Recovery procedures are also important when pipelines encounter interruptions. Some tools don’t provide the ability to track errors. Especially if you are just buying a low-code solution. This can make it very difficult to understand why a pipeline fails. If you’ve ever tried to debug a pipeline without error codes, good luck.
Deployment Model
Platforms may be offered as managed services or as self-hosted software products, or both. There are a few reasons this is important.
Some companies need to keep their infrastructure and data inside of their network. So they might want to purchase a managed solution that supports the ability to be internally hosted. Whether this be via a local installation or often many cloud providers such as AWS have partnerships with change data capture solutions to run internally.
Of course, you can also go the open-source route which is self-hosted generally by default.
Operational Ownership
Managed services can handle upgrades, infrastructure, and some recovery duties, while self-managed platforms usually require internal teams to own more of the operational environment.
Pricing Approach
Pricing can vary depending on factors such as compute usage, data volume, the number of records changed, or the connectors in use. Additional costs may also apply when performing backfills, resyncs, or other large-scale data reloads.
Estuary
Estuary is a data integration platform built to enable real-time streaming and batch data movement. Its change data capture capabilities allow teams to switch between real-time and batch easily. It’s also a solution I work with a lot and am an advisor for.
Architecture and CDC Approach
Estuary uses several methods to provide near real-time data pipelines. For example, they use log-based CDC for several key databases so that changes can be captured without having to repeatedly query entire tables on the source.
As another fun fact. Estuary recently improved their architecture so it now loads initial data 8x faster. Meaning the first initial backfill will be considerably faster.
Supported Sources and Destinations
Estuary supports over 200+ databases and sources. This ranges from APIs to SFTP data sources. In addition, if you’re looking to load your data into a modern cloud data platform, it’ll make it easy. Think Snowflake, Databricks, BigQuery, etc.
Real-Time and Batch Integration
Estuary is designed to support real-time and batch data pipelines. This allows data teams to quickly swap between the two without having to redevelop entire sections of their code-bases. You want data loaded sooner, click, click, done.
Streaming Transformations
Data can be transformed while it moves through the platform. This can eliminate the need for a separate processing stage for some filtering, reshaping, or routing needs.
Backfill and Schema Management
Backfills and historical data movement are important when establishing a new pipeline. Estuary also provides ways of managing schema changes in data transfer between systems, which can help reduce manual work in adapting to changes in source schemas.
Deployment and Management Model
Estuary is mainly seen as a managed platform. This can lower the infrastructure and maintenance overhead of running CDC systems in a siloed environment.
Debezium
Look, you can’t give a CDC list without mentioning Debezium. It just is not done. Debezium is an open source CDC platform that captures changes in databases and publishes them as events. It is mainly used with Apache Kafka Connect, and it is more of a CDC layer than a full managed data integration service.
Log-Based CDC Architecture
Debezium captures changes by reading a database’s transaction logs. For example, with PostgreSQL, Debezium can monitor the Write Ahead Log (WAL) to keep track of the various CRUD events that occur.
Because it reads from the transaction log, Debezium doesn’t require a timestamp column on every table. If a new row is added to a customers table, Debezium can pick up that insert from the WAL and send the change downstream in near real-time.
Kafka Connect Integration
Debezium is often run with Kafka Connect, which publishes database changes into Kafka topics for other systems to consume. Brex, for example, has used Debezium and Kafka to stream changes from PostgreSQL into systems supporting search, analytics, and security.
This makes Debezium a natural fit for teams that are already running Kafka.
Supported Databases
Debezium supports databases including PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, and Db2.
How it captures changes depends on the database. As mentioned earlier, PostgreSQL uses the WAL, while MySQL uses the binary log. Because of that, teams should check both whether their database is supported and what is required to enable CDC for their specific setup.
Snapshot and Schema-History Handling
Debezium can take an initial snapshot of a database before it starts streaming new changes.
This is useful when you are adding CDC to a table that already contains data. Debezium can first capture the current state of the table and then continue tracking inserts, updates, and deletes from that point forward.
It can also keep track of schema changes so that it can continue reading events correctly as tables change over time.
Deployment Requirements
Debezium gives teams a lot of control, but that also means there is more to manage.
When running Debezium with Kafka Connect, teams may be responsible for Kafka, connector configuration, monitoring, security, upgrades, scaling, and recovery. Yotpo has written about some of the challenges they ran into while operating Debezium and Kafka in production, which is a good example of the tradeoff: you get a lot of flexibility, but you also own more of the infrastructure.
Fivetran
Fivetran is a managed data integration platform with connectors to move data from operational systems into analytics and other destinations. It uses CDC, among other approaches, for supported database sources.
Managed CDC
Fivetran sits on the other end of the spectrum from something like Debezium. Instead of you having to run Kafka Connect, managing connectors, and monitoring the CDC infrastructure yourself, Fivetran handles most of that for you.
You connect a source and destination, select the tables you want, and Fivetran handles the initial load and ongoing replication. For teams that don’t want to operate their own CDC platform, that’s a big part of the appeal.
Supported Sources and Destinations
Fivetran supports databases like PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB, along with a large number of SaaS applications.
But I wouldn’t choose it because it has a huge connector count. What matters is whether it supports the systems you actually use and how those specific connectors work.
Even among database connectors, the CDC method can be different. Some use the database’s native change logs, while others use Fivetran’s own methods for finding changed records.
Automated Schema Handling
One of the nicer parts of using Fivetran is that you don’t have to manually update a pipeline every time someone adds a column.
For example, if someone adds customer_status to a source table, Fivetran can detect the new column and add it to the destination, depending on how you have schema changes configured.
There are limits, though. Some schema changes can trigger a full table resync, so changing a source table isn’t always completely painless.
Historical Syncs and Resyncs
When you first create a database connector, Fivetran loads the existing rows before moving on to incremental changes.
That’s important because most CDC projects aren’t starting with an empty database. You might have five years of orders that need to land in Snowflake before you care about the order that was created five seconds ago.
Fivetran can also resync a table or an entire source when needed. This can happen intentionally, but it can also happen when something like a database log expires or certain schema changes occur.
Pricing Considerations
Pricing is probably the part of Fivetran that gets the most attention.
Fivetran uses what it calls Monthly Active Rows, or MAR. In the normal sync modes, if the same row gets updated several times during a month, it generally still counts as one active row.
But Fivetran changed some of those rules in 2026 in ways that can increase MAR. Deletes now count toward paid MAR, where they previously did not under the newer pricing model. Fivetran also began charging for repeated updates when History Mode is enabled. For pay-as-you-go customers those changes started January 1, 2026, while many annual customers pick them up when their contracts renew.
That makes the shape of your workload pretty important. A massive database where only 100,000 rows change each month could be cheaper than a much smaller database where millions of different rows are constantly inserted, updated, and deleted.
And if you’re using History Mode on a table that changes constantly, pay even closer attention. Since repeated changes can now add to MAR, a high-churn table can look very different from the same table under Fivetran’s normal sync behavior.
Striim
Striim is more focused on real-time data movement than a typical ELT tool. It can capture database changes and continuously send them into warehouses, databases, Kafka, or other downstream systems.
CDC and Initial Loads
Striim can handle the initial load and CDC at the same time.
For example, while it is copying an existing database into a new system, it can also start capturing inserts, updates, and deletes happening on the source. Once the initial copy finishes, Striim applies the changes that happened during the migration and then continues syncing new changes.
That makes it useful for migrations where you can’t simply stop writes to the source database for several hours.
Supported Systems
Striim supports databases including Oracle, SQL Server, PostgreSQL, MySQL, and Db2, along with systems like Kafka, Snowflake, Teradata, and several cloud services.
This makes it particularly interesting when the pipeline isn’t simply database-to-warehouse. For example, you might capture changes from Oracle, send some of those events into Kafka for an application, and send another copy into Snowflake for analytics.
Streaming and Processing
Striim can also do work on the data while it is moving.
You can filter records, transform fields, or route events to different destinations before they reach the target. That’s useful when CDC is feeding operational systems rather than simply creating a copy of a database somewhere else.
Operational Considerations
Striim handles more of the CDC plumbing than building directly on something like Debezium, but it is still a platform that needs to be designed and operated.
The tradeoff is that you get more control over how data moves and what happens to it in flight. For teams that just want to replicate a few SaaS sources into a warehouse, that may be more than they need. For teams building real-time pipelines across several systems, that flexibility can be the reason to use it.
Qlik Replicate
Qlik Replicate comes from a different part of the CDC market.
It is much more common in large enterprise environments where you might be moving data out of Oracle, SQL Server, SAP, or other older systems and into Snowflake, Databricks, or another newer data platform. It’s not to say that they only support these solutions. But it’s where we frequently run into it.
Enterprise CDC
For many sources, Qlik Replicate reads the database transaction logs and sends those changes to the destination without continually querying the source tables.
A replication job can first perform a full load and then continue applying changes as they happen. Qlik also keeps changes that belong to the same transaction together until that transaction commits.
There are exceptions depending on the source. For example, Qlik can capture SQL Server changes through Microsoft’s CDC change tables instead of reading the transaction log directly.
Large Replication Workloads
Qlik is also built for moving a lot of operational data continuously.
United Federal Credit Union, for example, moved from nightly batch loads to using Qlik Replicate across its on-prem systems, core banking platform, and Snowflake environment. Processes that had taken hours were reduced to minutes, with some reporting data available in near real-time.
BNL BNP Paribas provides an even larger example. Qlik reports that the bank uses its replication tooling to move around 40 million records per day, with data reaching its digital archive in roughly five seconds.
Monitoring and Administration
Qlik Replicate gives administrators a central place to create replication jobs, see whether they’re running, and track full loads and CDC.
That matters more when you have dozens or hundreds of replication jobs than when you’re maintaining two Postgres pipelines.
It is also more GUI-driven than something like Debezium. Qlik’s own getting-started example walks through configuring an Oracle-to-SQL Server replication job from the Replicate console rather than building the pipeline in code.
Deployment Model
I wouldn’t reach for Qlik Replicate because I needed a quick connector for Stripe.
It makes more sense when CDC is part of a larger enterprise data architecture: SAP and Oracle sources, hybrid environments, large migration projects, or systems where you need replication running continuously with centralized administration.
That also means evaluating it differently from Airbyte or Debezium. Licensing, infrastructure, source-database requirements, and the people who will administer it all matter before you commit.
Questions to Ask a CDC Vendor
Buyers should structure their evaluation of one of the best CDC tools around several areas.
Capture and Delivery
Is the connector actually log-based? What are the delivery guarantees? How are duplicate events handled? How are deletes represented?
Snapshots and Recovery
Find out if snapshots can be restarted after failures, what happens when the transaction logs expire, whether historical data can be replayed, and who is responsible for pipeline recovery.
Schema and Operations
Ask about handling of schema changes, monitoring metrics, source-system permissions needed, and how the platform responds to destination outages.
Pricing
Specify if pricing is based on rows, events, data volume, compute, or otherwise. Also, specify if backfills, resynchronizations, and multiple updates to the same row are priced differently.
Common CDC Implementation Mistakes
Even a technically capable CDC platform can lead to problems if implementation decisions are not carefully considered.
Typical errors include choosing CDC when a simpler batch or incremental method would suffice for the required latency, treating each database change as a full business event, ignoring delete events, and missing or unstable primary keys being used.
Mistakes may also arise from misjudging the length of a snapshot, failure to monitor transaction log growth, ignoring destination merge and calculating costs, failing to test changes to the schema, and launching without reconciliation controls.
Regardless of which vendor you choose, these problems can affect data quality and operational reliability.
The Hidden Requirements of Production CDC
A production CDC implementation has a couple of requirements that may not be obvious during an initial proof of concept.
Initial Snapshots and Backfills
Teams should think about snapshot consistency, how changes are captured while a snapshot is running, and whether failed backfills can be restarted or resumed.
The baseline from which the present CDC operates is the first snapshot. If that baseline is incomplete or inconsistent, downstream systems can still be inaccurate even if subsequent changes are captured correctly.
Delivery and Ordering
Organizations need to understand the semantics of ordering and delivery of transactions. At-least-once delivery means that duplicate events can occur. Therefore, destination systems must be designed to process duplicate events safely.
Deletes and Schema Changes
CDC implementations must have a defined strategy for hard and soft deletes. Some systems use tombstone events, and some represent deletions differently.
Schema changes also need to be tested. Downstream systems can be affected by adding, removing, or modifying columns, and primary key changes can add further complications in update and delete processing.
Data-Type Fidelity
Never assume data-type compatibility. Decimal precision, timestamps, time zones, JSON and semi-structured fields, and database-specific types may all behave differently between source and target systems.
Testing representative data before deployment into production can uncover such issues early.
Conclusion
No one CDC tool is best for everyone. Each platform has a different way of data capture, deployment, management, transformation, and pricing.
Therefore, the number of connectors should be just one factor in a purchasing decision. Recovery capabilities, schema handling, impact on the source system, delivery guarantees, pricing, monitoring, and operational ownership can be just as important.
Ultimately, the best platform for CIOs and CTOs considering change data capture is a function of the organization’s architecture, data volumes, latency requirements, operational capacity, and budget.
A good evaluation should not only test whether a tool can capture changes, but also how it behaves when the pipeline fails, the database changes, the destination is unavailable, or the organization needs to recover historical data.
If you’d like to read more about data engineering and data science, check out the articles below!
The Data Engineer’s Guide to ETL Alternatives
Does ELT vs. ETL Even Still Matter?
The Data Engineering Job No One Wants To Do – Backfilling
Why Data Pipelines Exist – Beyond Moving Data From Point A To B
What Leading a Data Team Actually Looks Like Right Now
Schema Drift in Snowflake Pipelines and How to Handle It
How To Set-up Your Data Stack For 2026 – Data Infrastructure For AI