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What is Data Streaming?

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A primer on real-time data streaming, the innovation that empowers the modern internet. Get a high-level overview of how it works and the challenges faced.

Written by
Author's avatarMatthieu
Published onDec 20, 2022
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    Apache Kafka is a data streaming technology, designed to handle the needs of modern organizations which rely extensively on real-time data streaming. But what is data streaming? And why is it so important to the functioning of the modern world?

    In this article, we will explore the basics of data streaming, looking at how it works, the emergence of real-time data streaming, the challenges it faces, and its relevance to business.

    What is Data Streaming?

    First, there is a need to clear up a point of confusion. You will see the terms “data streaming” and “streaming data” used, often interchangeably, but the two can have different meanings. We’ll use the following definitions consistently in this article:

    Streaming data is data that is generated in a continuous stream from a data source. An example of streaming data would be web analytics. Modern web applications measure almost every user activity on their site, e.g., button clicks, page views. Streaming data can also be known as unbounded data: unbounded data has no boundaries defined in terms of time. The data may have been arriving from the past, continuing today, and is expected to arrive in the future.

    Data streaming is a data processing paradigm where the input data arrives continuously, and as soon as it arrives, gets processed. The idea behind data streaming is to capture the incoming data in real time, then process it immediately. The result of this immediate analysis can be used to make decisions that affect the system itself or other systems.

    Thus, an example of data streaming would involve using a stream of analytics data - let’s say from Google Analytics - and sending it through a stream processing tool like Amazon Kinesis, which might then pass it on to a storage medium or to a business intelligence tool. 

    How does Data Streaming work?

    When it comes to describing the right way to perform data streaming, there isn’t a correct answer. There are a number of approaches to data streaming, each of which will have its place depending on the use case and the data being processed. A data pipeline designed for real-time streaming may have a huge number of inputs, outputs, and components depending on what function it is performing.

    You should also consider that real-time stream processing is not always necessary for processing data. Batch processing (which involves collecting all the information before doing any analysis) and near-real-time processing (which requires less time than real-time) will be fine in many cases.

    They key factors that separate real-time processing from batch or near-real-time are storage and the processing itself.

    Batch processing vs. Stream processing

    Batch processing refers to the method of collecting data from various sources and storing it in database management systems (DBMS). Once all the data has been collected, batch processes are run on them to produce results. The results are then stored in another database management system. If there is any change in the original data set, then all of these processes need to be repeated again. In other words, batch processing is an offline process that doesn't require any user interaction.

    Stream processing refers to running queries on real-time data streams as soon as they arrive at a DBMS. A query may contain multiple steps that process events one after another until there are no more events left for processing. This means that when one query is completed, another query can start processing new events coming into your system from various sources

    When it comes to choosing between real-time and non-real-time options for data streaming, there are a few key factors to consider:

    • Time:

    Obviously, the most important factor is whether the data you are dealing with is time-sensitive or not. For example, prices for ride-sharing services like Uber or Lyft need to be determined in real-time based on demand, whereas other forms of travel like air or rail would not need real-time pricing for tickets.

    • Continuous vs Discrete:

    There is also the question of the stream itself. If data is being transferred in discrete chunks as opposed to a continuous stream, there is obviously no need for a real-time data pipeline.

    • Fault Tolerance:

    How sensitive is the data to errors in the pipeline? Can it be sent again? Real-time data streams can rarely be retransmitted, and even if they can the data may not be the same as it was originally. If the data is more tolerant to errors and to retransmission, real-time processing may be unnecessary.

    Challenges of working with streaming data

    There are several challenges that come with working with streaming data, including the need for real-time processing, the need for scalable and fault-tolerant systems, and the need to deal with potentially infinite amounts of data. 

    The sheer volume of data that must be processed in real-time puts a lot of strain on computational resources. This leads to requirements for the use of powerful and scalable infrastructure to handle the data.

    A related challenge is the need to process data as it arrives, in a timely and efficient manner. This requires the use of specialized algorithms and techniques that can quickly analyze and act on the data, without sacrificing accuracy or reliability.

    Additionally, streaming data is often incomplete, noisy, and can arrive in a variety of formats, making it difficult to clean and process effectively. This can require the use of advanced data preprocessing techniques to ensure the data is ready for analysis.

    As mentioned above, the systems that handle real-time data streams need to be fault tolerant since the data itself is not. If a stream of data fails to reach its target, the data may be lost permanently or any analysis may be corrupted.

    Why is data streaming important for business?

    Data streaming is important for businesses because it allows them to process and analyze large amounts of data in real-time, which can be crucial for making timely and informed decisions. Data streaming is used to analyze and make decisions based on data as it arrives, rather than waiting until all the data has been collected before starting any analysis. As more and more applications become connected to the internet, there's more pressure on companies to process data streams quickly and accurately.

    This is especially important for businesses that need to process large amounts of data from a variety of sources, such as those in the finance, retail, and e-commerce industries. Data streaming also enables businesses to create personalized and engaging experiences for their customers, which can help to improve customer satisfaction and loyalty. Overall, data streaming can help businesses to stay competitive and improve their operations by providing them with the timely and actionable insights they need to make better decisions.

    Data streaming was first introduced in the late 1990s as a way to process information from sensors, like those used in industrial plants or weather stations. But today, it's being used by many companies as a way to analyze large amounts of data coming from social media, emails, and other sources that provide valuable insight into their customers' needs and wants.

    Why should a company use Apache Kafka?

    As soon as a company has real-time data streaming needs, a streaming platform must be put in place.

    Apache Kafka is one of the most popular data streaming processing platforms in the industry today, being used by more than 80% of the Fortune 100 companies. Kafka provides a simple message queue interface on top of its append-only log-structured storage medium. It stores a log of events. Data is distributed to multiple nodes. Kafka is highly scalable and fault-tolerant to node loss.

    Kafka has been deployed in sizes ranging from just one node to thousands of nodes. It is used extensively in production workloads in companies such as Netflix, Apple, Uber, Airbnb, in addition to LinkedIn.

    The use cases of Apache Kafka are many. These include stream processing for different business applications. Apache Kafka makes up the storage mechanism for some of the prominent stream processing frameworks, e.g., Apache Flink or Samza.

    • Messaging systems

    • Activity Tracking

    • Gather metrics from many different locations, for example, IoT devices

    • Application logs analysis

    • De-coupling of system dependencies

    • Integration with Big Data technologies like Spark, Flink, Storm, and Hadoop.

    • Event-sourcing store

    Nonetheless, Kafka is not designed for every possible real-time data streaming use case, and there are situations where other technologies would work better, such as an embedded real-time technology for IoT, or where a work queue is necessary.

    How to make Kafka pipelines simple

    Apache Kafka is one of the most widely used technologies for data streaming, but many companies continue to struggle with its complexity and a lack of expertise. That’s why we created Conduktor Platform, the best way to handle everything Apache Kafka-related. Conduktor make it simple to take control of every aspect of Kafka development, providing a simple user interface to manage, test, monitor, and optimize your Kafka data streaming pipelines. There’s even a pre-built demo environment to enable you to jump straight in and see how easy Kafka can be: https://www.conduktor.io/get-started/platform. Give it a try today.

    Summary

    This article has scratched the surface of data streaming, providing a glimpse into one of the most important revolutions for modern software. Much of the world around you now depends on real-time data, whether you are transferring money, taking a taxi, streaming a movie, or just browsing the internet. For organizations, developers, and aspiring developers, data streaming is likely to continue to be a focus for growth, with Apache Kafka remaining at the forefront.