15 Best Data Streaming Platforms

•

January 6, 2026

Summarize this article with:

✨ AI Generated Summary

Most “data streaming platform” lists mix very different tools together. Event brokers, CDC engines, and ETL platforms all get labeled as streaming, even though they solve fundamentally different problems. That confusion makes it harder than it should be to choose the right architecture for real-time analytics, operational reporting, or AI pipelines.

This article defines data streaming, breaks platforms into clear categories, and compares the tools teams use in production today.

TL;DR: Data Streaming Platforms at a Glance

Data streaming platforms move data continuously instead of in batches.
Not all streaming tools are the same. Event streaming, CDC, and hybrid platforms solve different problems.
Most analytics and AI use cases do not need millisecond latency or Kafka-level complexity.
CDC-based streaming is often simpler, more reliable, and easier to operate at scale.
Airbyte fits teams that need near real-time data into warehouses, lakes, and AI systems with predictable costs.

What Is a Data Streaming Platform?

A data streaming platform moves data continuously from source systems to downstream consumers as changes happen, rather than in scheduled batches. Instead of waiting hours for an ETL job to run, data flows as events or change records, keeping analytics, dashboards, and applications closer to real time.

In practice, “streaming” covers a range of patterns. Some platforms focus on event streams, where applications publish messages that other systems subscribe to. Others stream database changes using change data capture, emitting inserts, updates, and deletes as they occur. Many modern platforms blend these approaches to support analytics and operational use cases without forcing teams to build everything on top of raw event infrastructure.

What defines a data streaming platform is continuity and freshness. Data moves incrementally, state is tracked so nothing is missed or duplicated, and downstream systems can rely on consistent, ordered updates. Latency can range from milliseconds to minutes, depending on the use case. The goal is not perfect immediacy, but reliable, up-to-date data that reflects what is happening in your systems right now.

What Are The Three Types of Data Streaming Platforms?

Data streaming platforms fall into three broad categories, each designed for a different way data is produced, moved, and consumed.

Type	What it does	How it streams data	Strengths	Tradeoffs	Best for
Event streaming platforms	Transport application events between producers and consumers	Publishes and subscribes to event messages in real time	Extremely low latency, high throughput, flexible fan-out	High operational overhead, requires custom consumers and schema discipline	Product events, microservices, internal event buses
CDC-based streaming platforms	Stream database changes as they happen	Captures inserts, updates, and deletes from transaction logs	Accurate, ordered data changes with minimal source impact	Typically focused on databases, less flexible for arbitrary events	Real-time analytics, replicas, operational reporting
Hybrid streaming and integration platforms	Combine CDC, APIs, and streaming sinks	Incremental syncs with state tracking and schema handling	Easier to operate, strong reliability and governance	Latency usually seconds to minutes, not milliseconds	Warehouses, data lakes, AI pipelines, cross-system analytics

What Are the 15 Best Data Streaming Platforms?

1. Airbyte

Airbyte is a CDC-based data streaming and integration platform built for analytics and AI workloads. Instead of acting as an event broker, it streams incremental changes from databases, APIs, and SaaS tools into warehouses, lakes, and downstream systems. With 600+ connectors and support for near real-time syncs, Airbyte focuses on reliability, schema evolution, and deployment flexibility across cloud, hybrid, and on-prem environments.

Pros	Cons
600+ connectors across databases, APIs, and SaaS	Not designed for millisecond-level event streaming
Strong CDC support for analytics use cases	Not a replacement for Kafka-style message buses
Flexible deployment and predictable pricing models	—

2. Apache Kafka

Kafka is the default choice for large-scale event streaming. It provides durable logs, high throughput, and a massive ecosystem of consumers and processors. Kafka excels at transporting application events but requires significant operational investment.

Pros	Cons
Extremely high throughput and low latency	Operationally complex
Mature ecosystem and tooling	Requires custom consumers and schema management
Strong durability and replay capabilities	—

3. Apache Pulsar

Pulsar is an event streaming platform designed for multi-tenancy and geo-replication. It separates compute from storage, making it attractive for very large, distributed deployments.

Pros	Cons
Built-in multi-tenancy	Smaller ecosystem than Kafka
Tiered storage and geo-replication	Steeper learning curve
Strong scalability	—

4. Amazon Kinesis

Kinesis is AWS’s fully managed streaming service for ingesting application and log events. It integrates tightly with the AWS ecosystem but ties teams closely to AWS.

Pros	Cons
Fully managed	AWS lock-in
Tight AWS integration	Pricing complexity at scale
Scales automatically	—

5. Google Pub/Sub

Google Pub/Sub is a serverless messaging service for event-driven systems. It abstracts away most operational concerns but offers less control over internals.

Pros	Cons
Simple, serverless operation	Limited tuning and customization
Strong reliability	GCP-centric
Easy integration with GCP	—

6. Debezium

Debezium is an open-source CDC engine that streams database changes into Kafka. It’s powerful but assumes Kafka expertise and infrastructure.

Pros	Cons
Accurate, log-based CDC	Requires Kafka
Open source and extensible	High operational overhead
Strong database support	—

7. Confluent

Confluent packages Kafka with managed services, connectors, and governance features. It simplifies Kafka adoption but comes at a high cost.

Pros	Cons
Managed Kafka experience	Expensive at scale
Rich connector ecosystem	Still Kafka-centric complexity
Enterprise governance features	—

8. Fivetran

Fivetran focuses on fully managed CDC pipelines into warehouses. It prioritizes simplicity but limits customization and uses volume-based pricing.

Pros	Cons
Easy setup and maintenance	Limited flexibility
Reliable CDC pipelines	Costs grow with data volume
Strong warehouse support	—

9. Estuary

Estuary combines CDC and streaming concepts with a focus on low-latency pipelines. It works well for real-time data movement but has a narrower connector catalog.

Pros	Cons
Low-latency CDC	Smaller ecosystem
Modern streaming architecture	Fewer connectors than larger platforms
Good Postgres support	—

10. Hevo Data

Hevo provides near real-time ingestion with a simple UI. It’s approachable for smaller teams but less flexible for complex streaming needs.

Pros	Cons
Easy to use	Limited deep streaming controls
Quick setup	Less extensible
Good for SMB analytics	—

11. Matillion

Matillion is warehouse-first and leans more toward ELT than true streaming. Incremental loads are supported, but real-time use cases are limited.

Pros	Cons
Strong transformation features	Mostly batch-oriented
Good Snowflake integration	Limited CDC depth
Familiar UI	—

12. Azure Data Factory

Azure Data Factory is Microsoft’s data integration service. It supports incremental pipelines but is primarily batch-focused.

Pros	Cons
Native Azure integration	Limited real-time streaming
Enterprise adoption	Complex configuration
Broad connector support	—

13. StreamSets

StreamSets offers controlled, schema-aware streaming pipelines with strong governance features, targeting regulated industries.

Pros	Cons
Strong data governance	Enterprise pricing
Schema drift handling	Heavier operational footprint
Enterprise controls	—

14. Apache Flink

Flink is a stream processing engine rather than a full ingestion platform. It excels at stateful, real-time computations.

Pros	Cons
Advanced stream processing	Not an ingestion platform by itself
Exactly-once semantics	Complex to operate
Highly flexible	—

15. Apache Spark Structured Streaming

Spark Structured Streaming brings streaming semantics to Spark’s batch engine. It’s useful for teams already invested in Spark.

Pros	Cons
Familiar Spark APIs	Higher latency than true event streaming
Good for micro-batch streaming	Resource intensive
Integrates with data lakes	—

How to Choose the Right Data Streaming Platform

When comparing data streaming platforms, the right choice depends less on feature checklists and more on how your data is produced and used.

Start with the data source. Application events, database changes, and SaaS APIs behave very differently. Event-driven systems benefit from event streaming platforms, while analytics and AI pipelines are usually better served by CDC-based streaming.
Be realistic about latency needs. Millisecond latency is essential for real-time application workflows, but most analytics, reporting, and AI use cases work well with seconds or minutes of delay. Chasing lower latency than you need adds cost and complexity.
Account for operational overhead. Platforms like Kafka offer flexibility but require ongoing infrastructure and schema management. Managed or hybrid platforms reduce that burden and free teams to focus on data usage instead of pipeline maintenance.
Evaluate schema evolution and reliability. Streaming data is only useful if downstream systems can trust it. Look for state tracking, ordering guarantees, and built-in handling for schema changes.
Consider cost predictability. Volume-based pricing can become unpredictable as data grows. Platforms with capacity-based or fixed pricing models are easier to budget for at scale.
Match deployment to compliance needs. If you operate in regulated environments or require data sovereignty, ensure the platform supports hybrid or on-prem deployments without feature compromises.

Choosing a platform that aligns with these factors helps avoid over-engineering and ensures your streaming architecture supports real business needs, not just technical ambition.

When Airbyte Makes Sense for Data Streaming

Airbyte makes sense for data streaming when you need fresh, reliable data for analytics, reporting, or AI systems without operating a full event-streaming stack. If your sources are databases, SaaS tools, or APIs, and your destination is a warehouse or lake, CDC-based streaming delivers near real-time updates with far less complexity than event brokers.

It’s also a strong fit when cost predictability, governance, and deployment flexibility matter more than millisecond latency. If you want streaming where it actually adds value, without surprise pricing or heavy infrastructure, talk to sales to see how Airbyte fits your data streaming needs.

Frequently Asked Questions

What is the difference between data streaming and batch ETL?

Batch ETL moves data on a fixed schedule, such as hourly or daily. Data streaming moves data continuously as changes happen, keeping downstream systems much closer to real time.

Do I need Kafka for real-time data pipelines?

Not always. Kafka is well suited for event-driven application workflows, but many analytics and AI pipelines work better with CDC-based streaming that avoids the overhead of running and maintaining an event broker.

Is CDC the same as event streaming?

No. CDC streams changes from databases by reading transaction logs, while event streaming relies on applications emitting events. CDC is often more reliable for analytics because it captures every change without requiring application changes.

When should I choose a hybrid data streaming platform?

Hybrid platforms make sense when you need to stream data from databases, APIs, and SaaS tools into warehouses or lakes, and you care more about reliability, schema handling, and cost predictability than ultra-low latency.

Limitless data movement with free Alpha and Beta connectors

Introducing: our Free Connector Program

The data movement infrastructure for the modern data teams.

Try a 30-day free trial

About the Author

Jim Kutz brings over 20 years of experience in data analytics to his work, helping organizations transform raw data into actionable business insights. His expertise spans predictive modeling, data engineering and data visualization, with a focus on making analytics accessible and impactful for stakeholders at all levels.