Blog Tech An Introduction to Data Streaming Technologies

An Introduction to Data Streaming Technologies

Data streaming is the communication between a sender and receiver using a single or even multiple data streams. A data stream is a sequence of digitally encoded signals that make sure that data can transverse from one source to the place you need it to reach. Then it is analyzed and processed in real-time and at a very high data transfer speed.

Why Data Streaming Tools Are Key

Businesses are investing in these technologies to increase the rate at which they can conduct business while using their smartphones, laptops, and tablets. Any size business can greatly benefit from using these high-speed data streaming technologies. Using these tools can allow one to access important data about their consumer base. This technology finally allows you to haul larger amounts of customer data faster than ever before. 

These are the different types of data streaming products to know about:

Amazon Kinesis

This technology is able to collect and process streaming data into data records from its data stream all in real-time. This platform also offers flexibility by using machine learning models in order to identify patterns in existing data. Amazon Kinesis also offers Kenesis Analytics (analyzing and processing real-time streaming data using operational capabilities), Kinses Firehouse (loads streaming data to Amazin S3, Amazon Redshift, and other types of web services that Amazon provides), and Kinesis Streams (for real-time data processing that is continuous)

IBM Stream Analytics

This system is useful for those who are looking to make a customized streaming application. This can be used for analyzing, ingesting, and correlating data from multiple sources. It has a visual-programming interface that is easy for new users, data connections that can connect with any kind of data source (such as structured and unstructured), and an analytic toolkit for quick development of data streaming apps using Scala, Python, and Java.

Apache Kafla

Apache Kakla utilizes open-source distributed data streaming for taking care of real-time data feeds. It also uses data processing patterns in real-time using data from websites and smartphones. It can be deployed on Cloud or on-premise. Producer API (for publishing a stream of data on Kafka topics), Consumer API (for different applications subscribe to Kafka topics to process a stream of data records), Streams API (a stream processor useful in converting input stream data to output Kaka topics), and Connect API (to build a reusable producer/consumer that in real-time connects to Kafka topics to their data streaming application.


Confluent uses its machine learning to monitor and update large amounts of information that multiplies fast. This platform is perfect for organizations that have complex-level needs. This platform will give you marketing, sales, and business analytics, as well as log monitoring. You can also keep track of customers and their activities all in real-time. 


Striim can aggregate, analyze, and filter information while still being incredibly reliable, secure, and scalable. It pulls from various different sources, like a database or even a device that has pre-configured properties. The data streaming pipeline it has makes sure that the data is able to flow constantly to where it needs to go. It makes data processing simpler.

Dive Into the World of Data Streaming

Batch-style data pipelines just aren’t enough to keep up anymore. Making the move into learning about and investing in real-time data streams is how to keep up with the right now. Learn how to process data in real-time by building fluency in modern data engineering tools with Udacity’s Data Streaming Nanodegree program.


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