Data Streaming Technology for High Volume Data Feeds

Data streaming has become the core of enterprise data architecture amid all the data generated from non-traditional sources like security logs, web applications, and IoT sensors. The digital universe was estimated to comprise 44 zettabytes of data in 2020 and is likely to reach approximately 463 exabytes daily by 2025 worldwide.

To keep up with this explosive growth of data, companies are pivoting towards real-time data streams. In this article, we’ll take a closer look at data streaming, its benefits, how it works, examples, and ways to create a data stream.

What Is Data Streaming?

Streaming data is defined as data that is continuously generated by multiple data sources such as server log files, real-time advertisements, e-commerce purchases, geospatial services, and other instrumentation in data centers.

This data is simultaneously sent in the data records in small sizes, usually in kilobytes, and sequentially and incrementally processed on a record-by-record basis.

The processed data is used for various insights and analytics, such as correlations, filtering, and sampling. The insights from data streaming provide real-time visibility into different aspects of business and client activity, allowing companies to respond promptly to emerging situations.

How Data Streaming Works?

Data streaming architecture comprises a framework of various software components that process large volumes of information from multiple sources. Since streaming is a challenge rarely solved with a single ETL tool or database, there has to be an architecture that comprises several building blocks. Here are the four key components of streaming architecture:

1. Stream Processor

The first component is the software solution that fetches data from various sources and converts it into a standard message format. Some examples of this big data technology include RabbitMQ, Apache ActiveMQ, and Apache Kafka data.

2. Real-time ETL tools

Data streams from a stream processor need to be aggregated, transformed, and structured before it is analyzed with SQL-based analytics tools. That is why ETL tools are required to receive user questions, fetch events and apply queries to provide results, such as an API call, a visualization, an action or alert, and even a new data stream.

3. Data Analytics

The third component is analyzing data or streaming data analytics for prompt actions.

4. Streaming Data Storage

The last stage is storing streaming event data. There are several low-cost storage technologies, including a data lake.

Examples of Data Streaming

The top three examples of streaming data are:

  1. Vehicles with sensors as well as industrial equipment and other machinery are data sources that send vital information to a streaming app. The app is used to monitor performance, identify potential defects, and automatically order spare parts to avoid downtime.
  2. Financial institutions use big data streaming tools to monitor changes in stock prices in real-time, calculate value-at-risk, and automatically rebalance portfolios depending upon price change.
  3. Another streaming data example is media publishers, who use it to deliver a relevant and better user experience. These companies stream clickstream records from their online properties, aggregate and enrich user data, and optimize content placement on its website.

The Benefits of Data Streaming

1. Detects Patterns

With real-time data streaming, you can detect patterns over time with continuous data processing and analysis. This is difficult to achieve in batch processing since it breaks data or events into different batches.

2. Scalable

Exploding data volumes can break a batch processing system, forcing companies to allocate more resources or modify its architecture. Modern stream architecture is hyper-scalable and can handle gigabytes of data per second with a single stream processor.

Therefore, stream technology helps to efficiently deal with growing data size without making infrastructural changes.

3. Visualize Data

Streaming analytics makes it easy to display updates in real-time and see what is happening in each passing second. It also provides valuable business insights and sends alerts about any serious issue.

4. Increases Competitiveness

Streaming architecture gives a competitive edge over companies still based on batch processing analysis since it provides real-time analytics.

5. Boosts Security

Stream technology is the best way to detect and prevent fraud since it immediately detects aberrations and notifies users to restrict the damage.

Batch Processing vs. Real-Time Streaming

Modern organizations are using real-time data streams due to the complexity and flow of data. Today, data is sent continuously in different volumes and formats from multiple locations such as cloud, hybrid cloud, or on-premises. Thus, it is important to know what type of architecture is best suited for your organization.

There are some critical differences between batch data processing and real-time data processing. In the legacy batch processing methods, data is collected in batches before it is processed, stored, and analyzed. On the other hand, streaming data flows continuously and processes it in real-time.

Batch processing is lengthy and is meant for the volume of data that is not time-sensitive. Stream processing is quick and is used for information that’s required immediately.

Turning Batch Into Data Streaming 

Since the nature of your data sources plays a big role in deciding whether you’d like to use batch or streaming processing, you should know how to convert your batch data into real-time data streaming.

If you are currently working on legacy data sources, such as mainframes, you can use various tools to automate data access and integration processes to convert your batch data into streaming data.

As the volume of data increases, many platforms have also emerged to provide the right infrastructure required to create streaming data applications. Some big data streaming tools include Amazon Kinesis Streams, Apache Kafka, Apache Flume, Apache Storm, Amazon Kinesis Firehose, and Apache Spark Streaming.

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RecoSense offers AI-based Customer Data Platform with data intelligent solutions for unified customer view, product discovery, advance search, and user engagement solutions. RecoSense has built expertise in technology areas of Data Mining, Text Analytics, NLP, and Knowledge Graph to build deep context definition, User Personalization & Recommendation, Actionable Insights, Analytics, Marketing Automation Integration, and Data Visualization.

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