Just a few years ago, Big Data revolutionized how businesses make decisions. With the ability to collect and analyze data from diverse sources (such as IoT sensors, click streams, and application logs), businesses could uncover more valuable insights regarding their daily operations. Most companies now use Big Data to learn more about customer behavior, market trends, and production processes.
As it currently stands, Big Data is collected, stored, and then analyzed using various tools. This process turns Big Data into historical information that’s only useful for determining future trends.
To make Big Data more effective, businesses need to leverage Fast Data. Fast Data is the information that flows into your business at high velocity. Megabytes of data per second, or Gigabytes of data per hour, typically need to be analyzed in real-time to make your business more responsive.
Why reacting to Fast Data in real-time is important
With how valuable Big Data has been over the years, you may wonder why you need to take the extra step of leveraging Fast Data. There are many benefits of considering the velocity, volume, and variety of Fast Data.
Fast Data makes you aware and reactive to real-time events, such as a customer shopping on an e-commerce website. In such cases, the website must be responsive to customers' real-time decisions (such as displaying appropriate product selections based on click-through rates).
If your business can capture and react to this Data in real-time, you can uncover valuable insights and meet customer demand more effectively.
How to capture value in Fast Data
The key to leveraging Fast Data is reducing the time between data arrival and value extraction. There are four key steps that you can follow to develop a framework for capturing the value of Fast Data. This architecture is defined by processing individual events as they arrive- often within timeframes of less than a millisecond. The step-by-step process involves:
1. Designing a data acquisition framework
Fast Data is defined by its volume, variety, and velocity. To capture the value in Fast Data, you need a data acquisition framework to deliver this Data in megabytes per second timelines.
Simply put, your framework needs to have an asynchronous data transfer method and a parallel process for data transformation. This allows you to capture only the most relevant data from diverse sources, which can be further streamlined into the correct format for analysis. Apache Storm and Kafka are two technologies you can use for data acquisition.
Storage solutions for Fast Data are very different from those used in your data center. In the context of Fast Data, storage means designing an appropriate model and temporary storage phase- where data processing platforms can retrieve data in real-time and uncover valuable insights.
Think of this storage as a holding cell in a police station, where suspects are temporarily placed before being transferred to other areas
3. Real-time processing and analysis
Perhaps the essential step when dealing with Fast Data is real-time processing. Your system needs to be a hybrid between stream and batch processing, where you can capture the accuracy, complexity, and value of each incoming event.
NewSQL systems can achieve performance and complexity for your specific needs.
4. Presenting the data in a digestible format
After analysis, Fast Data needs to be presented in an easily digestible format- otherwise, its use value will significantly decrease. Your aim should be to present visual data (such as graphs) that your target audience can easily understand. Give preference to high-level data, and have each report summarized in appropriate groupings (parallelized).
Talk to Mactores today for help leveraging your Fast Data.