7 ETL Tools Built for Real-Time Data Integration

In 2026 some of the top real-time ETL tools are Estuary, Confluent, Striim, Informatica IDMC, Google Cloud Dataflow, Databricks Lakeflow, AWS Glue, Fivetran and Airbyte.

The speed with which real-time data integration works depends on how fast the data needs to get to its destination; some workloads require CDC or event streaming but others suffice with the warehouse being refreshed every few minutes. The decision comes down to latency, architecture, operational complexity, and cost.

Estuary is the solution to choose in those situations where it is necessary to have CDC, streaming, and batch processing all managed within a single solution without having to set up a Kafka stack.

What Does Real-Time ETL Mean?

The idea of "real-time ETL" might seem to refer to a particular kind of architecture, but in fact the term encompasses a number of different approaches.

In practice the flow of data usually comes under four main categories.

1. Batch processing: This refers to the ETL process used in batch jobs, wherein data is extracted, transformed and loaded at fixed intervals, for example once a day. It is simple and cheap in the situation where it is not necessary for the data to be up-to-date each time.
2. Micro-batch processing: It is similar to batch processing but on a more frequent schedule. If you run it every few minutes the data won't become as stale, though it is still regarded as batch processing.
3. Change Data Capture: Rather than repeatedly scanning tables, CDC records changes to the database as they happen. Because it reads the transaction logs, log-based CDC can capture inserts, updates, and deletes, which is why it is suitable for use in high-volume database replication and integration.
4. Event streaming: Involves processing events as they are generated; for instance, an application can publish an order to Kafka, where another system can then filter it, enrich it, join it, or take some action when it arrives.

The important thing to note is that it is not necessary to have real-time processing. The level of freshness required will vary according to the specific business case and raising the frequency of batch processes will not turn it into a streaming pipeline.

How We Selected These Tools

This comparison based on each vendor’s product documentation, along with recent G2 reviews, customer case studies, and discussions among practitioners.

Community feedback does not count as benchmark data, but it may point out operational problems that product documentation doesn't address.

The comparison looks at what each platform actually supports, for example in the areas of CDC capabilities, stream support, end-to-end latency, reliability and recovery, backfills, schema evolution, ease of use, operational complexity, and cost.

The article was written by Estuary, so the section on Estuary is based on direct knowledge of the product; information regarding the other tools comes from the product documentation and user reports.

Quick Comparison: Best Real-Time ETL Tools

Compare the tools in terms of their real-time approach, CDC and streaming support, management model, the best use case for them, and their primary drawbacks.

Best Real-Time ETL tools in 2026

1. Estuary

Estuary incorporates CDC, streaming, batch, and SaaS data movement in one platform. It takes the data once and stores it in durable collections before sending it out to various destinations at different frequencies, so one source can be used to meet both real-time and scheduled workload requirements.

Its core architecture is:

Capture → Collections → Derivations → Materializations

The data is taken in by captures, stored by collections for later use and recovery, and derivations can transform it at the same time; materializations are responsible for sending the data to its destinations. Estuary provides more than 200 connectors and supports public, private, and BYOC deployments.

The tradeoff is that the extensive real-time capabilities might not be needed by teams who all they require is simple scheduled replication of warehouse data.

Recommended for: Use with the CDC, streaming, batch, and SaaS data integration when various workloads need different freshness levels, eliminating the need to manage a separate Kafka and Debezium setup.

2. Confluent

Confluent provides managed Kafka via Confluent Cloud, along with connectors and Apache Flink for use in stream processing.

Flink enables teams to perform stateful processing, joins, filtering, and enrichment without having to manage their own Flink clusters.

It makes the most sense to use Confluent when Kafka is already a key part of the architecture; if you only use it to move a few databases into a warehouse, then you'll end up with infrastructure that you don't need.

The tradeoff is greater conceptual complexity, as the teams have to understand topics, partitions, schemas, consumer groups, connectors, and stream processing.

Recommended for: Teams in which Kafka is already a key component of their architecture.

3. Striim

Striim is designed for businesses that need to connect their legacy systems to modern cloud applications; it enables real-time data streaming, which allows organizations to update their data architecture without having to interrupt their existing systems or keep separate tools for each source.

It’s suitable for use in business environments where the amount of data, its complexity, and the speed of delivery are important.

Recommended for: Use in Enterprise CDC, data replication, and real-time streaming.

4. Informatica IDMC

The Intelligent Data Management Cloud offered by Informatica extends well beyond basic ETL, including features such as batch ingestion, CDC, database replication, streaming ingestion, integration with enterprise applications, and cloud data integration.

Its larger platform also covers data governance, data quality, metadata management, master data management, hybrid integration, and access control.

It is therefore more suitable for large or closely regulated organizations since ETL is only one element of their data architecture.

The tradeoff is greater complexity; there is no reason to use the full platform if the team only needs PostgreSQL CDC to be sent to Snowflake.

Recommended for: Large enterprises that require integration, governance, CDC, and data quality capabilities.

5. Google Cloud Dataflow

Dataflow is a platform that allows Apache Beam pipelines to be executed in both batch and streaming environments. It is useful when it comes to situations in which pipelines need custom processing rather than just simple data movement.

Dataflow provides exactly-once processing by default, and it also offers at-least-once processing when it is more important to have lower latency or lower cost than to avoid duplicates.

Dataflow is the best choice in those situations where an organization is already using GCP and needs custom stream processing; in the case of simple database replication such a high degree of control is not necessary.

The downside is that in order to achieve flexibility extra engineering knowledge is required, and in more advanced pipelines one has to be familiar with Beam concepts such as event time, watermarks, windows, state, and triggers.

Recommended for: Teams that use Google Cloud Platform and need programmable stream processing.

6. Databricks Lakeflow

Lakeflow integrates the functions of data ingestion, transformation, pipelines, and orchestration within the Databricks environment, and it provides support for streaming pipelines and streaming tables; furthermore, Lakeflow Connect has managed ingestion capabilities.

The key benefit is platform integration, as teams can ingest the data, transform it, carry out quality checks, create streaming tables, and manage the downstream workloads all in Databricks.

The tradeoff is a need to rely on the platform; should the same data source have to continue supplying data to Snowflake, the operational databases, the search systems, and the other external services, then an independent data movement platform could provide more flexibility.

Recommended for: Those who use Databricks as their primary data platform.

7. AWS Glue

AWS Glue provides both batch Spark processing and streaming ETL capabilities. With Glue Streaming it is possible to read data from Amazon Kinesis, Amazon MSK, and self-managed Kafka and then write that data to S3, Redshift, relational databases, Snowflake, and various formats including Iceberg, Hudi, and Delta Lake.

Glue is a suitable choice when a team is already experienced with AWS and wants to have control over its processing architecture.

The result is that the end solution could end up making use of a number of AWS services rather than a single integrated data movement platform.

Recommended for: Teams that use AWS and need serverless Spark ETL and streaming.

Fivetran and Airbyte: Where do they fall?

It is not necessary for every pipeline to provide continuous streaming. In the case where a warehouse requires updates only a few minutes apart, near-real-time ELT would be sufficient. There's no point in introducing a streaming architecture if the workload does not demand second-level freshness.

Fivetran can be used in situations involving the ingestion of data into a managed data warehouse and offers change data capture for those database connectors that are relevant. Airbyte focuses on flexible data replication, with change data capture available for the sources it supports. Both tools are capable of performing well when frequent updates to the warehouse are required rather than when second-level event processing is needed.

A warehouse that is refreshed every few minutes is not equivalent to having a continuously processed event stream. Where a delay of a few minutes can be accepted, establishing a full streaming pipeline will result in additional infrastructure and greater operational complexity without meeting any real business need.

Real-Time ETL using Estuary in action

Consider the example of an e-commerce business that uses PostgreSQL as its database for orders.

The same data needs to reach:

• Snowflake for analytics
• An operational system that needs new orders quickly
• A slower reporting workload

Rather than having three extraction pipelines, Estuary can capture the PostgreSQL data once and then use it for all three destinations.

Step 1: Load historical data

Estuary begins by capturing the present state of the PostgreSQL tables that have been selected.

Step 2: Continue with CDC

After the initial batch, changes to PostgreSQL are continuously captured by CDC.

Step 3: Reuse the captured data

As the data collected can be sent to various destinations, there is no longer a need for each destination to read directly from PostgreSQL.

Step 4: Deliver at the required speed

The operational system can accept changes continuously, while the reporting destination can follow a more gradual delivery schedule.

Step 5: Recover from captured data

Instead of carrying out another full extraction from PostgreSQL, the data for a destination that needs to be rebuilt can be backfilled from the existing collection. Incorporating recovery and backfilling into the pipeline design rather than dealing with them at a later stage is possible.

When do you need real-time ETL?

It is reasonable to use real-time ETL when stale data causes a business problem.

• Fraud detection: To detect any suspicious activity it is essential that the fraud systems have current transaction data.
• Real-time inventory: For online shops it is necessary that important changes to the inventory are quickly communicated so as to stop customers purchasing items that are no longer available.
• Operational reporting: When it comes to operational reporting in the logistics, payments, and customer service areas, the teams might need current operational data rather than the results of an hourly batch process.
• Personalization: Recommendations can make use of things such as recent clicks, purchases, and interactions with products.
• Finance applications: With regard to finance applications, delays in the data can have an effect on decisions, since systems involved in payments, risk assessment, reconciliation and monitoring usually rely on up-to-date information.
• AI: AI systems also make use of up-to-date data; if the customer, inventory, or support data is outdated then it’ll result in outdated decisions.

It is false to say that every kind of workload demands such a high level of freshness; for instance, a daily report does not require a 100-millisecond pipeline just because the technology can offer it, and batch ETL should be used when you do not gain any advantage from processing the data every second.

Selecting the correct tool

Begin with the latency that your workload really requires.

If you need database CDC in seconds without having to use Kafka: Estuary offers continuous database capture and delivery without requiring its own Kafka, Debezium, storage, and connector stack.

If Kafka plays a central role in your architecture, then Confluent works well in cases where applications are already publishing and consuming Kafka events.

If governance is just as important as latency, then Informatica IDMC is suitable for companies that need CDC together with governance, data quality, metadata management, and hybrid integration.

If your stack is centered on cloud-native: Consider native options when they match your existing architecture:

• Dataflow for GCP and Apache Beam
• AWS Glue for AWS-based data platforms
• Databricks Lakeflow for Databricks Lakehouse workloads

If all you need is data freshness at the minute level, traditional managed ELT will be sufficient. Fivetran is a good choice when it comes to low-maintenance ingestion of warehouse data and provides a wide variety of connectors, whereas Airbyte is useful in the case where the main concern is open-source flexibility and custom connectors.

Choose the tool that meets your requirements without requiring any additional infrastructure you don’t need.

Conclusion

Real-time ETL can have a variety of meanings, such as CDC, event streaming, micro-batching, or continuous stream processing.

The decision boils down to latency, architecture, operational complexity, and cost.

For workloads that involve CDC, streaming, and batch delivery, Estuary adopts a different method: it captures the data once, stores it in a durable way, and then delivers it to each destination at the rate required.

Frequently Asked Questions

What is real-time ETL?

Real-time ETL processes and transfers data with only a small delay, as opposed to waiting until a scheduled batch job takes place. It can use CDC, event streaming, or continuously running processing pipelines.

Is real-time ETL better than batch ETL?

It doesn't have to. Real-time ETL is appropriate only in cases where outdated data influences business decisions or the behavior of an application; in all other cases, batch processing is usually simpler and cheaper.

What is the difference between CDC and streaming?

The CDC picks up changes from a source, for example from database inserts, updates, and deletes. The data streams are continuously processed as they arrive and CDC can function as a source for a streaming pipeline.

Which ETL tool is best for CDC?

It all comes down to the architecture you have and the requirements you have. Estuary and Striim place a strong emphasis on CDC, whereas Confluent, Informatica, and other platforms offer CDC as part of their more general data integration or streaming workflows.

Is Fivetran suitable for real-time data integration?

Fivetran can provide data that is extremely up to date, including one-minute syncs for connectors that are eligible. Yet its usual method is different from that of continuous event streaming and so it might not be appropriate in cases where applications need to respond to events within seconds.

How do I choose a real-time ETL tool?

Start by determining the data freshness that your workload really requires, and then look at CDC, streaming, connectors, recovery, scalability, operational complexity, and cost. Select the tool that satisfies these requirements without including any unnecessary infrastructure.