The world generates an unfathomable amount of data every minute of every day, and it continues to multiply at a staggering rate. Companies in every industry are quickly shifting from batch processing to real-time data streams to keep up with modern business requirements. In this article, we’ll cover what streaming data is, how it works, benefits and use cases, differences from batch processing, and how to choose a streaming data platform.
Also known as event stream processing, streaming data is the continuous flow of data generated by various sources. By using stream processing technology, data streams can be processed, stored, analyzed, and acted upon as it's generated in real-time.
The term "streaming" is used to describe continuous, never-ending data streams with no beginning or end, that provide a constant feed of data that can be utilized/acted upon without needing to be downloaded first.
A simple analogy is how water flows through a river or creek. The streams come from various sources, in varying speed and volumes and flow into a single, continuous, combined stream.
Similarly, data streams are generated by all types of sources, in various formats and volumes. From applications, networking devices, and server log files, to website activity, banking transactions, and location data, they can all be aggregated to seamlessly gather real-time information and analytics from one source of truth.
Data processing is not new. In previous years, legacy infrastructure was much more structured because it only had a handful of sources that generated data and the entire system could be architected in a way to specify and unify the data and data structures.
Modern data is generated by an infinite amount of sources whether it’s from hardware sensors, servers, mobile devices, applications, web browsers, internal and external and it’s almost impossible to regulate or enforce the data structure or control the volume and frequency of the data generated.
Applications that analyze and process data streams need to process one data packet at a time, in sequential order. Each data packet generated will include the source and timestamp to enable applications to work with data streams.
Applications working with data streams will always require two main functions: storage and processing. Storage must be able to record large streams of data in a way that is sequential and consistent. Processing must be able to interact with storage, consume, analyze and run computation on the data.
This also brings up additional challenges and considerations when working with data streams. Many platforms and tools are now available to help companies build streaming data applications.
Some common examples of streaming data are real-time stock trades, retail inventory management, and ride-sharing apps.
For example, when a passenger calls Lyft, not only does the application know which driver to match them to, they know how long it will take based on real-time location data and historical traffic data, and how much it should cost based on both real-time and past data.
Data streams play a key part in the world of big data, providing real-time analyses, data integration, and data ingestion.
Legacy batch data processing methods required data to be collected in batch form before it could be processed, stored, or analyzed whereas streaming data flows in continuously, allowing that data to be processed in real time without waiting for it to arrive in batch form.
Today, data arrives naturally as never ending streams of events. This data comes in all volumes, formats, from various locations and cloud, on-premises, or hybrid cloud.
With the complexity of today's modern requirements, legacy data processing methods have become obsolete for most use cases, as it can only process data as groups of transactions collected over time. Modern organizations actively use real-time data streams, acting on up-to-the-millisecond data. This continuous data offers numerous advantages that are transforming the way businesses run.
Scalability: When system failures happen, log data coming from each device could increase from being sent a rate of kilobits per second to megabits per second and aggregated to be gigabits per second. Adding more capacity, resources and servers as applications scale happens instantly, exponentially increasing the amount of raw data generated. Designing applications to scale is crucial in working with streaming data.
Ordering: It is not trivial to determine the sequence of data in the data stream and very important in many applications. A chat or conversation wouldn’t make sense out of order. When developers debug an issue by looking an aggregated log view, it’s crucial that each line is in order. There are often discrepancies between the order of the generated data packet to the order in which it reaches the destination. There are also often discrepancies in timestamps and clocks of the devices generating data. When analyzing data streams, applications must be aware of its assumptions on ACID transactions.
Consistency and Durability: Data consistency and data access is always a hard problem in data stream processing. The data read at any given time could already be modified and stale in another data centre in another part of the world. Data durability is also a challenge when working with data streams on the cloud.
Fault Tolerance & Data Guarantees: these are important considerations when working with data, stream processing, or any distributed systems. With data coming from numerous sources, locations, and in varying formats and volumes, can your system prevent disruptions from a single point of failure? Can it store streams of data with high availability and durability?
Data collection is only one piece of the puzzle. Today’s enterprise businesses simply cannot wait for data to be processed in batch form. Instead, everything from fraud detection and stock market platforms, to ride share apps and e-commerce websites rely on real-time data streams.
Paired with streaming data, applications evolve to not only integrate data, but process, filter, analyze, and react to that data in real-time, as it's received. This opens a new plethora of use cases such as real-time fraud detection, Netflix recommendations, or a seamless shopping experience across multiple devices that updates as you shop.
In short, any industry that deals with big data, can benefit from continuous, real-time data will benefit from this technology.
Stream processing systems like Apache Kafka and Confluent bring real-time data and analytics to life. While there are use cases for data streaming in every industry, this ability to integrate, analyze, troubleshoot, and/or predict data in real-time, at massive scale, opens up new use cases. Not only can organizations use past data or batch data in storage, but gain valuable insights on data in motion.
Typical uses cases include:
As long as there is any type of data to be processed, stored, or analyzed, a stream processing system like Apache Kafka can help leverage your data to produce numerous use cases. It's open source software that anyone can use for free.
If you don't have the manpower or expertise to build your own stream processing applications, Confluent makes it easy to get started with virtually any type of data without the hassle of building, configuring, or managing your own applications.
By integrating data from disparate IT systems into a single stream data platform, your business can organize, manage, and act on the massive amounts of data that arrive every second.
From retail, logistics, manufacturing, and financial services, to online social networking, Confluent lets you focus on deriving business value from your data rather than worrying about the underlying mechanics of how data is shuttled, shuffled, switched, and sorted between various systems.
Confluent is the only complete data streaming platform that works with 100+ data sources for real-time data streaming and analytics. Deploy on your own infrastructure, multi-cloud, or serverless in minutes with platinum support.
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