The uninterrupted streaming of media content, the real-time analysis of capital markets, or instant notifications about traffic updates on your daily commute – all these experiences are made possible by something you might not see but is at the core of our connected world: data streaming tools.
These tools are the backbone behind numerous applications we rely on daily. Without them, the streaming experience would stutter, businesses would lag behind, and the promise of interconnected devices would remain a dream. Data streaming tools keep the wheels turning and ensure that the pulse of our digital world stays strong.
But the question remains: among all the available data streaming tools, which one should you pick? That is exactly the purpose of our article. We will analyze each tool for its key features, strengths, and weaknesses. We will also give you a guide on major factors that you should consider before choosing one.
11 Best Data Streaming Tools For Transforming Information Delivery
1. Timeplus - Fast time-to-value
Timeplus is a comprehensive streaming-first data analytics platform that integrates both historical and real-time data processing. The platform is designed to be accessible for organizations of all sizes and industries, driven by its core functionality in handling streams.
These streams, central to Timeplus, represent continuous and evolving sequences of data, treated as append-only and unbounded datasets. This approach to data handling allows for dynamic and efficient management of real-time data flows.
Timeplus Key Features
Timeplus employs TDF, a column-based data format that supports quick serialization and deserialization. This format allows data to be vectorized for high-performance analytic computation, making it an essential tool for speedy data processing.
Combined with TDF, the Timeplus Native Log stream storage is designed for high-performance data ingestion. It can rapidly prune data on disk, filter out unnecessary data for streaming processing, and support timestamp-based seek operations and optimized time series data analytics scenarios.
Timeplus has a powerful streaming SQL engine that uses vectorized data computing capability. Utilizing modern SIMD parallel processing technology, it processes streaming data with high efficiency.
It achieves ultra-low latency, with an end-to-end latency of just 4 milliseconds, and can handle over 10 million events per second (EPS) even on a single commodity machine.
Timeplus offers comprehensive real-time streaming analytics capabilities, like windowing/non-windowing, late event handling, downsampling, and streaming predictive analytics, all executable via a single SQL query.
It provides an intuitive console for easily connecting to a variety of data sources, including Apache Kafka, Confluent Cloud, Redpanda, and CSV file uploads. This allows you to explore streaming patterns via SQL queries and send real-time insights and alerts to other systems or individuals.
You can create dashboards and visualizations for real-time data analysis and decision-making based on the insights derived from streaming data.
Timeplus Pros & Cons
Pros | Cons |
High throughput of 10 million EPS and low latency of 4 milliseconds | Advanced features might require additional expertise |
Timeplus supports more than 50 sources and 60 destinations | |
Open core architecture for customization | |
High-performance and feature-rich streaming SQL engine |
2. Apache Kafka - Used By 80% Of Fortune 100 Companies
Apache Kafka is a distributed streaming platform designed to handle high volumes of data streams in real-time. Used by over 80% of Fortune 100 companies, it was initially developed by LinkedIn and later adopted by the Apache Software Foundation. The tool operates on a publish-subscribe model to manage data streams efficiently across multiple nodes or servers.
Kafka uses topics to organize data streams where producers publish messages and consumers subscribe to these topics to process the messages.
Apache Kafka Key Features
Kafka partitions data across multiple servers for parallel processing and scalability.
It offers easy integration with various systems and tools, acting as a central data hub.
Kafka operates in a distributed setup where data can be spread across multiple nodes or brokers.
Data in Kafka is persisted on disk to ensure durability even if a consumer temporarily goes offline.
It can handle a massive volume of messages per second which makes it suitable for high-throughput applications.
Kafka's architecture allows for easy scalability by adding more brokers and extending functionalities through APIs.
Kafka replicates data across brokers to provide fault tolerance and ensure data availability even if a broker fails.
With Kafka Streams API, developers can build stream processing applications to perform transformations and analytics on data streams in real-time.
Apache Kafka Pros & Cons
Pros | Cons |
Offers reliable data storage and replication | Disk I/O can become a bottleneck under heavy loads |
Supports various data integration scenarios | |
Data persists and can be retrieved/replayed |
3. Amazon Kinesis - Most Scalable
Amazon Kinesis is a cloud-based platform by Amazon Web Services (AWS) that offers a suite of services designed for real-time data streaming and processing. Its primary goal is to handle large amounts of streaming data efficiently and let users collect, process, and analyze data in real-time.
Kinesis integrates with other AWS services like Lambda, S3, Redshift, etc., for streamlined data processing workflows. It can scale automatically to accommodate different data ingestion rates. Kinesis can also load streaming data into data stores or analytics tools without manual intervention.
Amazon Kinesis Key Features
Kinesis provides real-time insights by automatically delivering data to destinations like S3, Redshift, or Elasticsearch.
It provides aggregation and transformation of time series data before storing it in data lakes or analytics services.
Kinesis offers real-time analytics on streaming data. It integrates with SQL queries for data transformations, aggregations, and filtering on the fly.
Kinesis allows the creation of scalable, durable, and real-time streams of data. These streams can handle terabytes of data per hour from various sources.
AWS CloudWatch integrates with Kinesis to monitor stream health, throughput, and performance metrics. You can also set alarms and respond to issues promptly.
It offers encryption at rest and in transit for sensitive data which ensures durability and availability of data streams through replication across multiple availability zones.
Kinesis Client Library (KCL) simplifies building applications that process Kinesis streams. It provides functions to handle checkpoints, load balancing, and error handling.
Amazon Kinesis Pros & Cons
Pros | Cons |
Automatically scales to handle loads | Can be costly for high data volumes |
Advanced security and access control features | |
Integration with AWS services simplifies development and deployment processes |
4. Google Cloud Dataflow - Most Streamlined
Google Cloud Dataflow is a fully managed service provided by Google Cloud Platform for streaming and batch data processing. It is designed to handle the complexities of parallel processing and managing data pipelines efficiently.
Dataflow offers a unified programming model where developers can write a single pipeline to process both batch and streaming data. This simplifies the development and maintenance of data pipelines.
Google Cloud Dataflow Key Features
Dataflow provides native streaming data analytics for processing continuously flowing data in real-time with low latency.
It automatically optimizes resources based on workload demands, dynamically scaling resources up or down to handle peak data loads efficiently.
Dataflow provides monitoring capabilities through Stackdriver so you can monitor pipeline health, track data flow, and troubleshoot issues in real-time.
Integrates with various Google Cloud services, like BigQuery, Pub/Sub, and Datastore, for smooth data ingestion, storage, and analysis workflows.
It offers flexibility in deployment by supporting both managed service and portable execution modes, allowing pipelines to run on different execution engines.
It provides sophisticated windowing operations and triggers for handling time-based aggregations and analysis on streaming data, facilitating real-time analytics.
Dataflow is based on the Apache Beam SDK which provides a portable and unified programming model for defining both batch and streaming data processing pipelines.
Google Cloud Dataflow Pros & Cons
Pros | Cons |
Auto-scaling and resource optimization | Might have limited visibility and control over the underlying optimizations |
Simplified pipeline development for efficiency | |
Language flexibility for different developer teams. |
5. Azure Stream Analytics - Easiest To Use
Azure Stream Analytics is a fully managed, real-time event processing engine by Microsoft Azure. It ingests, processes, and analyzes data streams from various sources. It can handle high-volume data streams and scales automatically to manage peak data ingestion loads efficiently. This ensures consistent performance even during spikes in data.
Azure Stream Analytics Key Features
Stream Analytics processes incoming data in real-time for immediate analysis and action upon data as it arrives.
It ensures low latency in data processing and offers high throughput for handling continuous data streams efficiently.
Stream Analytics provides support for temporal analytics, enabling analysis of time-based patterns and trends within the streaming data.
It integrates with various Azure services like Event Hubs, IoT Hub, Azure Blob Storage, etc., for data ingestion, storage, and further analysis.
It has a user-friendly interface for configuring and deploying jobs. Azure Monitor enables real-time monitoring and diagnostics for Stream Analytics jobs.
With its Complex Event Processing (CEP) capabilities, Stream Analytics can detect complex patterns and relationships across multiple data streams in real-time for deeper insights.
Stream Analytics uses a SQL-like query language for defining transformations, aggregations, and filtering on streaming data. This simplifies development for users familiar with SQL.
Azure Stream Analytics Pros & Cons
Pros | Cons |
Efficient time-based windowing functions | While SQL-like language simplifies development, it might limit the complexity of operations |
Low-latency processing for immediate insights | |
Integration with Azure Machine Learning for anomaly detection and predictive analytics |
6. Talend - Most Flexible
Talend is an open-source data integration platform that provides both batch and real-time data integration capabilities. It allows you to connect, access, and manage various data types in real-time for easy data movement and transformation.
Talend provides components and connectors specifically designed for stream processing, enabling real-time analytics, transformations, and actions on streaming data. The platform is built to scale – it can handle large volumes of data and adapt to varying processing loads efficiently.
Talend Key Features
Talend supports the scheduling and automation of data integration jobs for operational efficiency.
It includes monitoring and management features to track data flow, performance, and health of the data integration processes.
Talend provides data quality checking tools to ensure the integrity and quality of data during movement and transformation processes.
With Talend, you can design, deploy, and manage both batch and streaming data processing workflows within a single unified environment.
It connects to a vast ecosystem of data sources, databases, cloud services, and applications, providing flexibility in data integration and processing.
Talend employs a visual interface that makes it easy for developers and data engineers to design data pipelines using drag-and-drop components.
To ensure seamless integration across systems, Talend has a wide range of connectors and components for easy data movement between various sources and targets.
Talend Pros & Cons
Pros | Cons |
Comprehensive job scheduling capabilities | Might require significant computational resources for complex data processing tasks or large-scale operations |
Effective monitoring for streamlined operations | |
Strong metadata management for organization |
7. Spark Streaming - Ideal For Complex Tasks
Spark Streaming is a powerful tool that can handle continuous data streams for real-time data processing and analysis. It is part of the Apache Spark project that has been specifically designed to handle and process large amounts of data in real-time.
Spark Streaming Key Features
Spark Streaming divides incoming data streams into small, manageable chunks and processes them in mini-batches to provide resilience and fault tolerance.
Spark Streaming allows for stateful computations for complex analysis that depends on historical data. The tool maintains and updates the state as new data arrives.
It ensures fault tolerance through RDDs (Resilient Distributed Datasets), enabling recovery from failures and providing reliable processing of data streams.
The tool integrates with the broader Spark ecosystem. This lets you combine batch processing, machine learning, and SQL queries with real-time streaming data.
Spark Streaming offers a high-level abstraction that makes it easier for developers to write applications for stream processing using familiar batch processing techniques.
With Spark Streaming, you can perform operations on data streams over sliding windows of time which is crucial for analyzing patterns and trends within specific time frames.
Spark Streaming Pros & Cons
Pros | Cons |
Fault-tolerant and reliable | Setting up and configuring Spark Streaming might be complex |
Interactive analytics and SQL support | |
Processes large volumes of data with minimal latency |
8. Apache Flink - For All Common Cluster Environments
Apache Flink stands as a robust framework and distributed processing engine specifically designed for handling stateful computations over both unbounded and bounded data streams. Its architecture helps it operate across various cluster environments and executes computations at remarkable in-memory speeds, irrespective of the scale.
Apache Flink Key Features
Flink processes events based on their actual occurrence time rather than their arrival time.
It handles delayed data effectively, enabling sophisticated handling of late-arriving events in the stream.
Flink maintains state consistency and guarantees that each event is considered exactly once in the computation process.
With scale-out architecture, Flink can process large amounts of data, including very large states, and implement incremental checkpoints.
It provides flexible deployment options, including high-availability setups and savepoints, for reliable and resilient operation in various cluster environments.
Flink offers a layered approach to APIs which helps developers work with stream and batch data using specialized APIs like DataStream and DataSet, or use SQL on both data types.
Apache Flink Pros & Cons
Pros | Cons |
Impressive scalability across large data sets | Maintenance complexity in setting up high-availability |
Optimized for high-performance in-memory computing | |
Excellent fault tolerance and exactly-once semantics |
9. Apache NiFi - Most Extensible Design
Apache NiFi is an advanced data streaming tool designed to efficiently handle real-time data streams. It comes equipped with a range of features for streaming analytics. With it, you can manage and process data as it moves through systems. Being open-source, NiFi has an active community that contributes to its development and provides continuous updates and improvements.
Apache NiFi Key Features
NiFi ensures complete lineage tracking so you can trace information from its origin to its destination.
You can extensively configure NiFi and fine-tune its behavior to suit specific requirements.
It provides guaranteed delivery mechanisms that ensure the data is reliably transmitted even in the event of disruptions or failures.
NiFi has a user-friendly interface accessible via a browser which helps in seamless design, control, feedback, and monitoring of data flows.
NiFi prioritizes efficiency with its low-latency processing capabilities while ensuring high throughput to handle large volumes of data seamlessly.
You can dynamically modify flow configurations during runtime. This allows for on-the-fly adjustments without interrupting ongoing processes.
With its component architecture, you can create custom Processors and Services. This promotes rapid development and iterative testing for tailored data processing needs.
NiFi ensures secure communication through HTTPS with configurable authentication strategies, supports multi-tenant authorization, and implements standard protocols for encrypted communication like TLS and SSH.
Apache NiFi Pros & Cons
Pros | Cons |
Strong fault tolerance mechanisms | Limited support for some niche data sources |
Detailed data provenance for tracking purposes | |
Efficient data routing and transformation capabilities |
10. SQLstream - Best For IoT Stream Processing
SQLstream is a data streaming powerhouse. It is sought after by major players like Verizon, Walmart, Cisco, and Amazon for its ability to manage data flowing in from various sources across different formats.
SQLstream processes Kafka and Kinesis streams at incredibly high speeds – up to 1.8M messages per second per core. This allows for on-the-spot insights and actions based on continuous SQL queries.
SQLstream Key Features
SQLstream uses StreamLab, an interactive graphical interface, to create data pipelines swiftly without code, thereby expediting deployment.
Deployment becomes more flexible with Kubernetes which provides a scalable and manageable environment for your streaming applications.
The platform offers real-time, continuous machine-learning capabilities. You can build predictive analytics models to score new data against historical patterns.
With SQLstream, you can clean, filter, and enrich data in real-time to ensure that streams are prepared for immediate analysis.
You can perform continuous SQL queries on the streaming data and trigger actions based on the analysis.
A community edition is available that gives full functionality for processing up to 1GB of messages each day.
SQLstream Pros & Cons
Pros | Cons |
Extensive SQL standards support | Requires familiarity with SQL for use |
Sub-millisecond latency for real-time insights | |
Analyst-ready, allowing easy exploration for insights |
11. Striim Cloud - Most Reliable
Striim Cloud is an advanced and versatile tool for continuous ingestion, processing, and delivery of high-velocity data. It is a fully managed service across AWS, Google Cloud, and Microsoft Azure. Its core capabilities revolve around stream data processing for agile operations and real-time analytics.
Striim Cloud Key Features
With a user-friendly interface, it takes mere minutes to build complex data streaming pipelines.
Striim offers over 100 connectors that link various enterprise sources and targets for seamless integration across different systems.
Striim guarantees data freshness through its 'ReadOnceWriteMany' methodology that provides flexibility without impacting production databases.
Offering the freedom to add or remove new targets seamlessly, Striim simplifies data management and provides dynamic adjustments without disruptions.
Striim can handle hundreds of data pipelines and stream billions of events daily to ensure consistent performance during peak business hours or high-traffic periods.
Striim Cloud Mission Critical is an all-in-one platform designed specifically for data and AI applications. It guarantees faultless deployment, scalability, and resilience for uninterrupted operations even during the most demanding scenarios.
Striim Cloud Pros & Cons
Pros | Cons |
99.9% uptime guarantee | Continuous monitoring is required for optimal performance |
High-speed real-time data processing | |
Usage-based pricing for cost-effective scalability |
How To Choose The Best Data Streaming Tool? 6 Key Factors
Now that we have the 11 best data streaming tools, it is time to simplify your decision-making process. Here are 6 factors to consider when getting a data streaming tool.
I. Scalability
Evaluate how well the tool scales with your data needs. Consider factors like volume, velocity, and variety of data. Make sure the tool can handle your current data load and can grow with your business.
II. Latency & Performance
Look for tools that offer low latency for real-time or near-real-time data processing. Assess the tool's performance metrics, like throughput, processing speed, and handling of concurrent streams.
III. Ease Of Integration
Check if the tool easily integrates with your existing tech stack. Compatibility with databases, cloud platforms, programming languages, and APIs is crucial for seamless data flow and processing.
IV. Reliability & Fault Tolerance
Make sure the tool provides fault tolerance mechanisms to handle failures gracefully without data loss. Look for features like data replication, backup, and recovery options to maintain data integrity.
V. Cost & Support
Consider the tool's pricing structure, including licensing fees, subscription models, or additional costs for scaling. Also, assess the level of support the tool's vendor provides, including documentation, community forums, and customer service.
VI. Operational Complexity & Maintenance
Consider the ease of use and maintenance. A tool that offers good documentation, user-friendly interfaces, and adequate support can reduce operational complexities and troubleshooting efforts.
Conclusion
Each of the data streaming tools we talked about has its own strengths and weaknesses. Some prioritize speed but might struggle with handling bigger tasks, while others are proficient in real-time processing but might not be very customizable. The crux here is understanding the trade-offs inherent in each choice.
The perfect data streaming tool is the one that matches what you need the most. Try testing them out, read user reviews, and consult with experts to understand the ins and outs before you pick one.
Out of all the data streaming tools we talked about, Timeplus is one tool that covers all the bases and gets the nod of approval from almost everyone in the industry. Considering the trade-offs we discussed earlier, Timeplus found a way to minimize those compromises.
It is flexible enough to match different needs which makes it a strong contender if you are looking for a reliable and versatile data streaming tool. Request a live demo today or try it for free to see the difference.