Streaming ETL tools are crucial for tapping into real-time data – spotting fraud as it happens, monitoring assets remotely, and making personalized recommendations that customers love. But this utopia depends on having the perfect streaming ETL tool to make the most of all that non-stop data.
So how do you find the streaming ETL tool that fits your requirements? Should you prioritize speed over complexity? Is picking the cheapest one the best move? This guide breaks it down – offering a detailed look at the 13 best streaming ETL tools to make your decision easier.
We will thoroughly examine the capabilities, advantages, and limitations of each streaming ETL platform to provide clarity on the ideal solution for your needs. So let’s start.
13 Best Streaming ETL Tools For Efficient Data Workflows
Here are our top picks for the best streaming ETL tools:
Timeplus - Top pick
AWS Glue - Most powerful serverless data integration service
Striim - Precisely engineered streaming ETL
Let’s now discuss all 13 streaming ETL tools in more detail to find the one that best suits your business needs.
1. Timeplus - Top Pick
Timeplus is the most robust platform in data analytics that has been specifically tailored for streaming-first workflows. It stands out for its ability to process both streaming and historical data efficiently.
Primarily, it uses the open-source streaming database Proton which makes it a versatile choice for data and platform engineers across various industries. It helps explore streaming patterns using SQL and easily connects with multiple data sources like Apache Kafka, Confluent Cloud, and Redpanda.
Timeplus Key Features
Timeplus achieves ultra-low latency of 4 milliseconds and provides high throughput of over 10 million EPS on a single machine during both data ingestion and querying.
Its powerful converged multi-tier computation engine supports real-time streaming analytics, including windowing, late event handling, downsampling, and predictive analytics, all within a single SQL query framework.
The dynamic schema feature provides unparalleled flexibility in data querying and processing which facilitates quick analysis of vast data sets.
Integrating with Proton, Timeplus extends historical data storage and computing, combining streaming and OLAP data processing for advanced analytics.
It includes many developer-friendly functions like data transformation, stream joining, aggregation, and windowed stream processing that help in a variety of analytical tasks.
Timeplus supports a wide range of data sources for seamless integration and management of different data streams.
For a smooth developer experience and robust performance, Timeplus offers built-in support for complex streaming and analytical functionalities.
It offers stream processing optimization and uses timestamp predicate push-down, projection push-down, and vectorization.
You can configure Timeplus to trigger alerts and notifications based on anomalies detected in streaming data.
Timeplus Pros & Cons
Pros | Cons |
Advanced storage and processing capabilities enhance the efficiency of ETL workflows. | Advanced features may require a learning curve. |
Versatile data source integration, particularly with Kafka, facilitates diverse data operations. | |
Real-time query and analysis features provide immediate insights and support prompt actions. | |
The multi-tier computation engine empowers a range of analytics operations, enhancing its overall capability. |
2. AWS Glue - Most Powerful Serverless Data Integration Service
AWS Glue is a fully managed data integration service that simplifies the preparation and loading of data for analytics. It offers serverless architecture which makes it easy to extract data and move it into your data lake or data warehouse without the hassle of managing infrastructure.
With its recent update to version 4.0, AWS Glue introduced streaming ETL capabilities for real-time data processing scenarios. This release includes an optimized state-management store for removing duplicates in a stream and performing stream-based aggregations. It also supports IAM authentication for Amazon Managed Streaming.
AWS Glue Key Features
The platform simplifies the identification and masking of sensitive data, including PII.
With Glue, you can create, reuse, and share ETL logic with business-specific Apache Spark logic.
Serverless architecture eliminates the need for infrastructure management, simplifying setup and scaling of data integration tasks.
Glue seamlessly integrates with the AWS Data Catalog to provide a persistent metadata store for efficient management and query of data assets.
Optimized state-management store enhances the efficiency of streaming solutions across micro-batches and streamlines operations like duplicate removal and stream-based aggregations.
It integrates Python's extensive libraries with Ray.io’s scalability to expand smoothly from a single node to hundreds. This facilitates large-scale data operations without complex infrastructure management.
AWS Glue Pros & Cons
Pros | Cons |
Enhanced Apache Spark and Python support. | Potentially higher costs for continuous streaming ETL. |
Automated scaling for cost efficiency. | Complex setup and management for beginners. |
Advanced state management for streaming data. |
3. Striim - Precisely Engineered Streaming ETL
Striim is a renowned name in the on-premise segment and offers a specialized platform for streaming ETL operations that require precision and control. It is known for handling complex data flows within enterprise systems and can process and integrate large data sets with minimal latency. Striim’s infrastructure is designed for high throughput and is equipped to meet the demands of data-intensive sectors.
Striim Key Features
It performs in-flight data processing for instantaneous analytics on streaming data.
Striim maintains data security throughout the pipeline with encryption, masking, and redaction features.
It offers pre-configured templates for common data pipelines that streamline the setup process for rapid deployment.
The platform includes a powerful SQL-based transformation engine to perform complex data manipulations as data moves between sources and targets.
Striim provides interactive dashboards and visual tools for monitoring and managing data flows, offering insights into performance metrics and operational health.
It uses advanced Change Data Capture (CDC) technology to detect and capture changes in real-time from different databases without impacting the source systems.
Striim Pros & Cons
Pros | Cons |
Ideal for complex, high-volume data environments. | User interface and dashboard offerings require enhancements for better usability. |
Sophisticated CDC capabilities minimize the impact on source systems. | A comprehensive feature set might be too much for smaller-scale operations. |
Interactive dashboards offer deep visibility into system operations. | Limited community support, with insufficient technical documentation and code samples online. |
4. Confluent - 99.99% Uptime SLA
Confluent is an Apache Kafka-based streaming platform for streaming data integration and processing. It provides an advanced foundation for creating extensive streaming data pipelines while seamlessly integrating various data sources and sinks. This tool is particularly effective in hybrid and multi-cloud environments as it can manage real-time data flow between different systems.
Confluent Kafka Key Features
A no-code Stream Designer simplifies the creation, testing, and deployment of data pipelines.
Confluent offers over 120 pre-built connectors that streamline integration across multiple systems.
Confluent provides scalable and secure data flow management by effectively decoupling data producers and consumers.
For enhanced data quality and compliance, the platform offers Stream Governance that helps maintain data integrity and adhere to compliance standards.
Confluent Pros & Cons
Pros | Cons |
Wide range of connectors for different system integrations. | Optimal performance requires ongoing monitoring. |
Strong focus on data governance and quality. | Cloud service requires detailed and precise configuration settings. |
Streamlines data transformation, lowering compute costs in data lakes and warehouses. |
5. StreamSets - Ideal For Data Drift In Real-Time Data Integration
StreamSets is a cutting-edge data integration platform that specializes in constructing intelligent data pipelines. From its inception, it emerged as a leader in modern data integration and simplifying data engineering complexities. StreamSets is particularly adept at managing data operations across various cloud environments.
StreamSets Key Features
StreamSets integrates with key vaults for secure secret fetching.
It performs in-flight data processing to support instantaneous analytics on streaming data.
StreamSets outperforms traditional cron scheduling and makes job scheduling more user-friendly.
It is a versatile platform that offers a wide selection of stages like sources, processors, executors