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Top 10 Streaming Analytics Tools In 2024

Streaming analytics tools enable real-time decision-making from data, significantly impacting a company's business processes, customer experiences, and revenues.

Elisa Mueller
Jan 30, 202460 Shares6043 Views
Streaming analytics toolsenable real-time decision-making from data, significantly impacting a company's business processes, customer experiences, and revenues.
These platforms complement stream processing tools, combining and processing raw data into insights quickly and at scale.
The following 10 streaming analytics tools are selected based on modern features, market strength, and the ecosystem of supporting tools and capabilities.

Apache Kafka

For real-time data stream management, businesses rely on Apache Kafka, an open-source distributed data streaming platform. As a backend tool for microservice integration, Kafka often makes use of real-time data streaming channels provided by libraries like Spark and Flink.
It may come as a surprise, but the majority of streaming services for real-time data can work in tandem with Kafka to make analytics and processing of streams much easier.
When it comes time for numerical analysis, Kafka can also send incoming data to other portals. A compelling boost to Kafka's record and the prestige of all other data streaming tools came from the features of fault tolerance and redundant data.

IBM Stream Analytics

When it comes to analytics-based cloud streaming platforms, IBM is right up there. There isn't a streaming analytics tool on the market with more features than this one.
A number of languages, including those often linked with big data, such as Java, Scala, and Python, are supported by its eclipse-based integrated development environment.
Watson IoT, Cloud Object Storage, and Data Science Experience are some of the other IBM services that it can integrate with for data ingestion, storage, and analysis. Additionally, IBM Stream Analytics is built on top of development, so users can make educated decisions with ease of monitoring.

Apache Spark

Apache Spark is just one of many open-source software projects created by the US-based Apache Software Foundation. In order to make these tools even more powerful, the developer community often adds new ideas to them. The original intention behind the 2012 release of Spark, a unified data analytics platform, was to analyze massive datasets through clustered computing.
The machine learning-powered data analytics modules it uses are capable of handling both batch processing and stream data. Additionally, you have the option to work in your preferred developer environment with multiple choices, including Python, R, Java, SQL, and Scala APIs. The open-source nature and built-in capabilities of Spark make it a good fit for almost any industry that makes use of data science.
If you need a platform for streaming data analytics that is both free and open-source and can manage batch and stream processing and help calculate bounded and unbounded data streams, look no further than Apache Flink.
You can analyze streaming data from multiple sources, distribute it over multiple nodes, and ingest it all with Flink. You won't need much training to use Flink because of its user-friendly interface.
Additionally, you have the option to integrate with popular cluster resource management systems like YARN, Hadoop, and Kubernetes. Furthermore, Flink is capable of handling millions of events in milliseconds. To manage complicated event processing, it also makes use of graph processing and machine learning.

Apache Hadoop

While Apache Spark was specifically named as a top performer, it is important to note that Apache Hadoop is also highly regarded. Hadoop is an open-source platform similar to Spark; it is designed to store and process massive amounts of data using a distributed file system and a MapReduce engine.
Hadoop may be slower than Spark and has been around for a while (first released in 2006), but many companies that have adopted it will stick with it even if a better option comes along.
Hadoop also offers additional advantages. No supercomputers are required to run it because it is compatible with many different types of commodity hardware.
While it might lack in user-friendliness, it more than makes up for it in reliability and robustness. Not only is it an inexpensive solution, but it also partitions storage and workload. Not only that, but many business cloud providers continue to support Hadoop.


You can explore data locally and on the cloud with Memgraph, a real-time graph streaming platform. Whether they're big data analysts, business users, or engineers, its streaming analytics system makes it easy to import data from different platforms and run analyses without having to build bespoke solutions.
Whether you prefer to work in CLI or with a graphical user interface, Memgraph has you covered with their CLI mgconsole, Memgraph Lab GUI, and drivers. A few examples of the languages that can be connected are Python, Java, C#, PHP, Golang, Ruby, and JavaScript.
With the help of streaming analytics provided by the platform's advanced artificial intelligence and machine learning algorithms, businesses can make better decisions. You can build a lot of different kinds of systems with Memgraph, including ones that track user events, model permissions, and create recommendation systems.

Amazon Kinesis Data Analytics

One streaming analytics tool that is native to the cloud is Amazon Kinesis Data Analytics, or KDA. It works well for businesses that want to combine streaming data from all over their AWS cloud.
This solution enables the automatic scalability and integration with other Amazon applications and third-party services through the AWS ecosystem, as well as the provisioning of serverless instances of Apache Flink for stream and batch processing.
Data analytics development tools like Kinesis Data Analytics Studio and Apache Zeppelin notebooks are part of it. The Apache Beam programming model also allows developers to build analytics apps by establishing data processing pipelines.
If a company needs to handle streaming data outside of Amazon's cloud, they might want to look into alternatives to KDA.
Amazon Kinesis logo
Amazon Kinesis logo

Cloudera DataFlow

Cloudera was born out of the need to process large amounts of data. Cloudera DataFlow (CDF) is a scalable, real-time data streaming processing and analytics platform that it has integrated with its core competency in building large-scale data lakes.
Businesses now have the ability to develop streaming analytics apps that integrate both private and public cloud services. The open-source data routing and transformation layer known as Apache NiFi allows developers to connect to any data source.
Serverless microservices are compatible with AWS Lambda, Azure Functions, and Google Cloud Functions, allowing businesses to scale their applications across various cloud platforms.

Confluent KsqlDB

The creators of the Apache Kafka framework for data processing also founded Confluent. You can't rely on Kafka as a standalone streaming analytics platform. For developers looking to build streaming analytics apps on top of Kafka, Confluent has created ksqlDB.
Use of the most recent improvements to the open source platform is a hallmark of Confluent's offerings. Companies have the option to deploy ksqlDB internally as a standalone application or as a managed service with integrated security and management tools.
Businesses looking to tailor Kafka tools to their unique streaming analytics needs while still having access to platform-wide improvements and security patches will find Confluent ksqlDB to be an ideal solution.


A streaming analytics tool that allows real-time processing of streaming data using SQL queries, stream SQL is hosted in the cloud, as the name suggests. The simplicity of it is its strength. You don't need to be a developer to use it.
Training machine learning models with real-time data is the main use case for this tool. The ease with which it can be used to build data manipulation, data surveillance, and real-time compliance monitoring applications makes it a valuable tool for Data Science projects.

FAQs -

How Do Streaming Analytics Tools Enhance Real-time Data Processing?

Streaming analytics tools enable organizations to process and analyze data in real-time, providing immediate insights into dynamic datasets. These tools use continuous data streams, allowing for quick decision-making and proactive responses.

What Are The Key Features To Consider When Evaluating Streaming Analytics Tools?

When evaluating streaming analytics tools, consider factors such as scalability, ease of integration, support for various data sources, real-time processing capabilities, and the tool's ability to handle complex event processing (CEP) for meaningful insights.

Can Streaming Analytics Tools Be Applied To Various Industries Beyond IT And Data Analytics?

Yes, streaming analytics tools have diverse applications and can be employed across various industries, including finance, healthcare, telecommunications, and manufacturing. They offer real-time insights and decision support in different domains.

How Do Streaming Analytics Tools Handle Large Volumes Of Data In Motion?

Streaming analytics tools employ distributed processing and parallel computing techniques to handle large volumes of data in motion. They often use technologies like Apache Kafka, Apache Flink, and Apache Storm to ensure efficient and scalable processing.

What Role Do Machine Learning Algorithms Play In Streaming Analytics Tools?

Machine learning algorithms play a crucial role in streaming analytics tools by enabling predictive and prescriptive analytics. These algorithms can analyze streaming data patterns, detect anomalies, and make predictions, enhancing the tool's ability to derive actionable insights in real-time.

Final Words

In the market, you can find an extensive range of Streaming Analytics tools. There are specific uses and capabilities for every tool. The needs will dictate the choice made by the person or the group.
While some open-source tools may lack full functionality on their own, they work wonders when combined with others to meet specific needs. Although they provide a one-stop solution, the larger cloud platforms, of which some tools are a part, can be quite pricey.
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