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, and destinations.
StreamSets supports both batch processing and streaming pipelines to cater to various data processing needs.
StreamSets Pros & Cons
Pros | Cons |
Support both batch and streaming data processing. | Limited auto-scalability for large-scale data transfers. |
Simplified scheduling over traditional methods. | Logging systems can be complex for new users. |
Secure secret management with key vault integration. |
6. Snaplogic - Most Flexible
Snaplogic is a powerful data integration platform known for its efficient handling of both ETL processes. This platform enables seamless data mobilization to cloud environments and supports reverse ETL for comprehensive data application.
It is designed for businesses looking to eliminate data silos and streamline their data integration across different environments, whether on-premises or in the cloud.
Snaplogic Key Features
Snaplogic supports both ETL and ELT data load patterns for flexibility in how data is managed and integrated.
It streamlines data flow and automates data extraction from various applications into leading cloud data warehouses.
Snaplogic features a user-friendly “drag-and-snap” interface which is enhanced by AI and makes the design and execution of data pipelines both simple and efficient.
Snaplogic offers an integrated platform for API development and management to promote API-driven data services and enhance collaboration across data systems.
The platform includes AutoSuggest, an AI-powered engine, which offers recommendations based on extensive metadata analysis, thus optimizing pipeline development.
Snaplogic Pros & Cons
Pros | Cons |
Simplifies complex data tasks with its advanced graphical interface. | Lacks support for standard Git repositories, using its own version system instead. |
Accelerates and enhances data processing with AI-driven pipeline recommendations. | Provides XML support but does not handle XML mixed content effectively. |
Offers robust support for various data sources and cloud data warehouses. |
7. Estuary Flow - Easiest To Use
Estuary Flow is a robust streaming ETL tool for handling complex data integration challenges with efficiency and precision. It excels in real-time change data capture (CDC) from databases and offers seamless integrations with various SaaS applications.
Flow can handle large volumes of streaming data which makes it a great option if you are looking for a tool with agile and dynamic data processing capabilities.
Estuary Flow Key Features
It efficiently converts unstructured data into structured formats for enhancing data usability and analysis.
Flow can capture data from different sources in real-time to ensure timely and accurate data streams.
It can handle vast data volumes, scaling up to 7 GB/s which makes it suitable for high-demand scenarios.
With Flow’s extensive connectors and real-time capabilities, you can integrate different data systems easily.
It offers advanced streaming SQL and Javascript transformations for complex data manipulation and enrichment.
Flow comes with unit tests and a fault-tolerant architecture which maintains data accuracy and consistent performance.
Estuary Flow Pros & Cons
Pros | Cons |
Scalable architecture to manage increasing data volumes. | Complex to create transformations, requiring a learning curve. |
Advanced data transformation capabilities using SQL and Javascript. | Complexity in managing large-scale data replication, particularly when dealing with diverse data sources. |
High-speed, real-time data processing and integration. |
8. Meroxa - Most Developer-Focused Streaming ETL Platform
Meroxa is a streaming ETL tool with a developer-centric approach. Developers can efficiently build stream-processing data applications using popular coding languages like JavaScript, Go, Python, and Ruby.
Meroxa’s platform focuses on real-time data processing and does not need complex, specialized data infrastructure and multiple user interfaces. It can manage high-volume data workflows and real-time event-driven applications, services, and products, all through regular code.
Meroxa Key Features
Designed for handling data stream processing at massive scales, Meroxa simplifies scaling efforts.
Meroxa automates stream processing data at scale while integrating seamlessly with current data stacks.
The tool provides a code-first platform that allows developers to work in familiar programming languages.
Meroxa combines data from various sources into a unified view and supports functions like data visualization and analytics.
It offers end-to-end encryption and complies with strict security standards, including those set by the U.S. Department of Defense.
Meroxa Pros & Cons
Pros | Cons |
Designed for developers’ productivity. | Requires familiarity with coding languages for optimal use. |
Cost-effective through real-time data processing and infrastructure reduction. | May demand continuous adaptation for integration with evolving data stacks. |
Maintains high data security standards with comprehensive encryption and compliance with top-tier security protocols. |
9. Decodable - Fastest Time To Production
Decodable’s streaming ETL tool is known for its user-friendliness and robust capabilities. It is powered by Apache Flink and Debezium and offers a fast, easy, yet powerful stream processing platform. It makes stream processing easier and more cost-effective by removing the usual complications and costs of assembling the necessary components.
Decodable Key Features
It adheres to SOC2 Type II and GDPR which guarantees data safety and compliance with regulatory standards
Decodable provides seamless data transfer to multiple destinations like operational databases, data warehouses, and data lakes.
You can write standard SQL for data transformation or leverage the Apache Flink APIs for advanced stateful stream processing.
Using Apache Flink’s low-latency stream processing capabilities, Decodable can handle business-critical applications in various settings.
Decodable efficiently scales to manage demanding workloads and ensures that performance is maintained regardless of the data volume.
With its pre-built connectors, Decodable simplifies acquiring data from various sources like applications, databases, and APIs and facilitates real-time data integration.
Decodable Pros & Cons
Pros | Cons |
Flexible and scalable, ideal for handling large-scale data streams. | Only one SQL connection per pipeline which limits parallel SQL integrations. |
Secure and compliant with critical data protection standards. | Watermark configuration can be complex and needs clearer guidance. |
Streamlines the ETL process with pre-built connectors and SQL support |
10. Talend - Most Versatile
Talend is a comprehensive solution for streaming ETL that offers a mix of traditional data integration and cutting-edge stream processing capabilities. Renowned for its flexibility, it caters to both batch ETL and real-time data processing needs, making it suitable for diverse data management scenarios.
Talend Key Features
Talend has an intuitive interface that simplifies the creation and management of data pipelines.
It has a scalable architectural framework that makes handling large volumes of streaming data easy.
It integrates with various data sources and data lakes to enhance data accessibility and streamline the ETL process.
Talend offers a comprehensive set of tools for data quality that guarantee the integrity and reliability of transformed data.
Talend supports complex data integration scenarios with ease and accommodates both batch and streaming ETL processes.
Talend Pros & Cons
Pros | Cons |
Strong data integration and quality features. | Time-consuming data set curation for visualization. |
Efficient in both batch and real-time data processing. | Limitations in joining tables from different schemas. |
Supports a variety of data sources. | Error messages could be more user-friendly for troubleshooting. |
11. Microsoft Azure Stream Analytics - Enterprise-Grade reliability
Azure Stream Analytics is widely recognized as a powerful real-time analytics service, tailored for handling large business workloads. It allows you to efficiently develop serverless streaming pipelines. The tool is known for its smooth integration with other Microsoft Azure services which enhances the effectiveness of stream processing and data management.
Microsoft Azure Stream Analytics Key Features
You can easily scale up or down your streaming data pipelines for efficient handling of changing data volumes.
It guarantees high reliability with built-in recovery and checkpoints which keeps operations smooth and data integrity intact.
The platform offers seamless integration with various data sources and sinks, including Azure IoT Hub and Event Hub, allowing for versatile data flow management.
To enhance streaming data utility, Azure Stream Analytics provides embedded machine learning capabilities for advanced data analysis, including anomaly detection.
With Azure Stream Analytics, you can quickly construct end-to-end streaming pipelines using either a no-code editor or familiar SQL language, which can be extended with JavaScript and C# for complex scenarios.
Microsoft Azure Stream Analytics Pros & Cons
Pros | Cons |
User-friendly interface and straightforward setup and management of streaming data pipelines. | Limited customization options for advanced users looking for granular control over their data processing and ETL workflows. |
Strong integration with Azure IoT to optimize IoT scenario handling. | The integration with non-Azure services and third-party tools can be complex. |
Economical with a pay-for-what-you-use model. |
12. DeltaStream - Most Compliant
DeltaStream’s powerful stream processing platform is renowned for its sophisticated use of Apache Flink. It unifies, processes, and governs data streams, enabling organizations to fully harness the potential of their streaming data.
DeltaStream is designed to manage and process event streams effectively. It offers a serverless stream processing experience that scales automatically, making it ideal for streaming ETL requirements.
DeltaStream Key Features
It offers workload isolation and query-level scaling for independent operation of data queries.
DeltaStream provides a streaming catalog to organize streaming data into a unified and accessible format.
It uses serverless stream processing powered by Apache Flink which balances efficiency and scalability.
DeltaStream seamlessly integrates with major streaming data stores like Kafka and Kinesis to provide comprehensive data connectivity.
It guarantees data security with measures like federated data governance, role-based access control (RBAC), and encrypted data storage.
DeltaStream combines streaming database capabilities with streaming analytics to support both materialized views and streaming pipelines.
DeltaStream Pros & Cons
Pros | Cons |
Streamlines real-time data processing with a user-friendly interface. | As the storage layer expands, managing access control becomes increasingly challenging. |
Offers high scalability with serverless architecture. | The Apache Flink framework, which Deltastream heavily relies on, lacks its own security model. |
Prioritizes data security and compliance, including GDPR and CCPA readiness. |
13. Popsink - Most Effortless & Efficient
Popsink distinguishes itself in the streaming ETL market with its innovative implementation of Change Data Capture (CDC) technology. This tool streamlines real-time data processing by efficiently synchronizing changes across various data sources. Designed with a focus on simplicity and efficiency, it caters to different data management needs.
Popsink Key Features
CDC in Popsink ensures efficient and timely data ingestion from multiple sources.
It offers a user-friendly interface that simplifies the setup and management of data streams.
Popsink integrates with existing data warehouses for a more unified data management strategy.
Dynamic SQL-based transformations provide flexible and efficient streaming data processing for different business needs.
Popsink is designed to scale with business growth and can accommodate increasing data volumes and complexity with ease.
Advanced monitoring and alerting feature offers real-time insights into data flow health for proactive management of data streams.
With real-time data replication, Popsink maintains data consistency across various platforms which is essential for accurate real-time analytics.
Popsink Pros & Cons
Pros | Cons |
Advanced monitoring capabilities ensure data flow reliability. | You might need time to adapt to its specific streaming data management approach. |
Provides robust integration options with popular data sources and warehouses. | |
Offers enhanced operational responsiveness with a strong focus on real-time streaming ETL. |
Conclusion
Picking the right streaming ETL tool depends on your needs. Take a good look at specific requirements, scalability, integration capabilities, and budget constraints before you settle on one.
While all 13 streaming ETL tools we discussed offer many advantages, getting them to work well depends on how you implement them. Make sure you provide the right training, dedicated support, and a strategic approach for using them to get the most out of what they offer.
An emerging leader among streaming ETL tools is Timeplus which strikes an optimal balance between ease of use and enterprise-grade functionality. Its versatility allows seamless deployment across cloud, on-premise, or hybrid environments.
The dynamic schema and patented Time-Stream technology ensure smooth handling of structured, semi-structured, and unstructured data streams. To experience the difference Timeplus delivers as a streaming ETL platform, request a live demo today or try it out for free.