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Creating Systems for Real-Time Processing and Analysis of Streaming Data Featured

Creating Systems for Real-Time Processing and Analysis of Streaming Data Luke Chesser

With the number of devices increasing each day, the amount of data being generated is equally increasing at an unprecedented rate. The data from various sources come in various forms, such as social media posts, sensor data, and the internet. It is important to have systems that can process and analyze data in real-time if you are to make sense of this data. This is where we realize the importance of real-time stream processing. Here is the explanation of real-time processing and some strategies to create real-time processing and analysis of streaming data.  

What is Real-Time Stream Processing?

Real-time stream processing is the entire process of analyzing data as it is generated (real-time) instead of analyzing data in batches. This approach to data processing increases the speed of generating insights while enhancing accurate decision-making. There are two types of data processing: batch processing and stream processing.

  1. Batch Processing

Batch processing is the traditional method of processing data. It entails the process where data is collected over a period of time and then processed at once. It is a suitable method for large data sets that can be processed in batches. However, it is unsuitable for real-time data processing as it takes too much time to process the data. Furthermore, the insights generated may be outdated when they are made available.

  1. Stream Processing

Unlike batch processing, where data is gathered first and then processed, stream processing is a continuous process where data is processed as soon as it is generated. This approach is suitable for real-time data processing because it allows for faster insights and more accurate decision-making. With this processing approach, data is processed in small chunks allowing the streaming of data and insights in near real-time insights.

Real-Time Stream Processing Best Practices

  1. Embrace a streaming-first approach to data integration

The first and most critical step that must be taken is to ensure a streaming-first approach. This means that data streaming requires a different approach to data integration instead of just batch data. The streaming-first approach can be achieved by adopting technologies such as file tailing and change data capture (CDC).

  1. Analyze data in real-time with Streaming SQL

Streaming SQL is a powerful tool for analyzing data in real-time. Together with real-time views, Streaming SQL is a powerful tool that allows you to run the same SQL queries as on batch data. This means that you can analyze data within milliseconds of collecting it. Furthermore, data can be processed before being loaded into a warehouse.

  1. Scale horizontally

Real-time stream processing requires a large amount of processing power. It can only be attained by scaling systems horizontally. With horizontal scaling, the workload is distributed across multiple machines. This allows for a greater amount of data to be processed in real-time.

  1. Use a distributed storage system

Streaming data is generated at a fast rate and can be vast in volume. With this in mind, you should use distributed storage system such as HDFS or S3 to store the data for processing. Such a storage system allows data to be stored and processed in parallel, increasing the speed and efficiency of the system.

  1. Data processing should be continuous

Real-time data processing needs to be continuous. Therefore, data needs to be processed as soon as it is generated instead of waiting for a batch of data to be collected. This increases the speed of the generation of insights and improves decision-making.

  1. Monitor and manage the system

Real-time stream processing systems should always be monitored and managed closely. You can use tools like Grafana or Prometheus to monitor the system. Other tools, such as Kubernetes or Apache Mesos, can be used to manage systems. Proper and close management of the system ensures optimization for performance and efficiency.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

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