Ins and Outs of Data Streaming

Continuous data generation from several sources is referred to as streaming data. Real-time data streams may be processed, stored, analyzed, and acted upon using stream processing technologies. Continual, never-ending data streams with no beginning or finish are referred to as "streaming" because they offer a steady stream of data that may be used or acted upon without having to be downloaded first. Similarly, data streams are produced from a wide range of sources and come in various forms and quantities. Applications, networking tools, server log files, website activity, financial transactions, and location data may all be combined to glean real-time data and analytics from a single source of truth.

What is Data Streaming?

There are countless data sources nowadays, including servers, apps, security logs, internal/external systems, and log files. Controlling the structure, data integrity, or the amount or velocity of the data created is essentially impossible.

Streaming data architecture offers the capacity to consume, persist to storage, enrich, and analyze data in motion. In contrast, conventional systems are designed to ingest, process, and organize data before it can be used.

As a result, processing and storage will always be the two fundamental tasks needed by programs that operate with data streams. Storage must be able to sequentially and consistently record big streams of data. Processing has to be able to communicate with storage, ingest data, analyze it, and do calculations on it.

Applications of Data Streaming

Use cases for streaming data exist in every industry. They include real-time market exchanges, current retail inventory management, social media feeds, multiplayer gaming interactions, and ride-sharing apps, to name a few.

For instance, real-time data streams come together when a passenger phones Lyft to produce a seamless user experience. The program uses this information to combine real-time position monitoring, traffic statistics, pricing, and traffic data to choose the best driver for the passenger, the cost, and the estimated travel time based on real-time and historical data.

To avoid fines, a solar power business must keep up with its customers' electricity flow. It created a streaming data application that keeps track of every panel in the field, plans service in real-time, and reduces the time each panel experiences low throughput and the resulting penalty payouts.

A media publisher streams billions of clickstream records from its online domains, combines and enhances the data with user demographic data, and optimizes content placement on its site to provide a more relevant and enjoyable experience for its audience.

Streaming data regarding player-game interactions are gathered by an online gaming corporation and sent to the gaming platform. Then, to keep gamers interested, it does real-time data analysis and provides rewards and exciting experiences.

Real-Time Analytics

Real-time analytics may be used by businesses to track their operations thanks to data streams. To report on what is happening, the produced data may be analyzed using time-series data analytics techniques. The type and volume of data that may be broadcast have increased dramatically due to the Internet of Things (IoT). The data streams are heavily used for real-time analytics.

Thus, we acknowledge the three V's of data analytics and data streams-Variety, Volume, and Velocity-generally acknowledged. When IoT is used in conjunction, a business may get data streams from several sensors and monitors, enhancing its capacity to micro-manage numerous dynamic variables in real-time.

Real-time analytics are beneficial because they enable businesses to keep tabs on their operations better. As a result, if the equipment failed or the readings returned information requiring immediate action, the firm has the information to act.

In this way, streaming data serves as the foundation for every data-driven company, enabling the intake of large amounts of data, their integration, and real-time analytics.

Reasons to Use Data Streaming

The collecting of data is just one aspect of the challenge. Enterprise organizations of today cannot wait for batch data processing. Instead, real-time data streams are used by everything from e-commerce websites to ride-sharing applications and stock market platforms.

When used with streaming data, applications develop to not only integrate data but also process, filter, analyze, and respond to that data as it is received. This enables a wide range of new use cases, including real-time fraud detection, Netflix suggestions, and an updated, seamless shopping experience across various devices. In other words, this technology will benefit every sector that uses big data and can profit from continuous, real-time data.

Challenges for Data Streaming

A processing layer and a storage layer are needed for streaming data processing. To provide record ordering and reliable consistency, the storage layer must be able to handle reads and writes of massive data streams that are quick, affordable, and replayable. Consuming data from the storage layer, processing it, and finally telling the storage layer to delete material that is no longer required are the responsibilities of the processing layer. Scalability, data longevity, and fault tolerance must be considered while designing the storage and processing layers. One may build streaming data applications using a variety of platforms, including Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon Managed Streaming for Apache Kafka (Amazon MSK), Apache Flume, and Apache Spark.


Until recently, only a limited number of extremely niche industries-like media streaming and stock market valuations-used data streaming. Today, it is utilized across many industries. Organizations may manage data in real-time using data streams, allowing them to monitor every aspect of their business.