Data Processing: Making Information Ready to Use

After data is collected, it is processed to prepare it for storage and use. Intel® technologies accelerate data processing with improvements that go silicon deep.

Data Processing Takeaways:

  • Data processing is necessary to ensure that raw data is of good quality and ready to be used. Data can be processed as it is generated and/or before it is used in an analytics application.

  • Data processing can be divided into multiple types, including batch processing, stream processing, distributed processing, and multiprocessing.

  • With hardware designed for big data, Intel® technologies enhance data processing capabilities starting with the silicon. Intel® processors, memory, and storage work together to accelerate data processing workloads.



What Is Data Processing?

Data processing occurs after the data collection stage of the data pipeline. In the processing stage, data is prepared for use, then stored in a system that can be accessed by applications and users.

In order to be analyzed, data must first be processed to ensure that it is clean and high quality. Processing can verify and format data, making it easier to access, query, and store.

To achieve insights without delay, organizations must maximize data processing performance and throughput while staying cost effective. Intel® hardware and software technologies work together to accelerate data processing from edge to cloud.

Types of Data Processing

There is no one-size-fits-all method for processing data. Different types of workloads and applications require different approaches to make processing performant and cost-effective.

Methods for processing data may include:

  • Batch processing: Batch processing consists of dividing data into groups, or batches, that can be processed as resources become available. During batch processing, batches of data are processed serially, one after another. While batch processing can efficiently process large volumes of data, it typically is best for data that does not require immediate use.
  • Stream processing: Stream processing occurs when data is processed continuously as it enters the data pipeline. This type of processing yields faster analysis of smaller amounts of data than batch processing. It typically is used to process data that must be acted upon quickly.
  • Distributed data processing: As network technologies have evolved, data processing tasks no longer need to be completed on the same node. With distributed data processing, multiple nodes running in the same cluster work in parallel to process data workloads across a network. Using distributed data processing allows advanced analytics workloads to be processed using lower-cost, lower-power-consumption hardware.

Depending on the type of data being processed, and its intended use, several of these strategies may be used in a single data pipeline with a unified eventual output.

Data Processing Technology

As one of the most resource-intensive stages of the data pipeline, data processing efficiency can be significantly impacted by hardware and software optimization.

Today, many leading software vendors optimize their products for Intel® hardware. The Intel® ecosystem of solution and technology partners ensures that many software solutions run best on Intel® hardware and helps customers get the best return on their technology investments.

Intel brings a wide-ranging portfolio of hardware and software technologies to accelerate today’s data processing workloads, including:

  • Intel® Xeon® processors: Offering flexibility that can tackle diverse workloads from many sources, Intel® Xeon® processors include features like Intel® Deep Learning Boost that are optimized for tasks like data normalization and noise reduction for AI processing.
  • Intel® Optane™ SSDs: Designed for longevity and built to optimize storage and data caching performance, Intel® Optane™ SSDs can help to accelerate streaming and real-time data processing while maintaining high system reliability.
  • Open source technologies: Intel offers a range of open source libraries and platforms that accelerate data processing and analysis, including Intel® oneAPI toolkits, Intel® oneAPI Math Kernel Library (Intel® oneMKL), and Intel® oneAPI Data Analytics Library (Intel® oneDAL).
  • Security enhancements: With Intel® QuickAssist Technology (Intel® QAT), data teams can accelerate encryption and decryption performance to enhance security for data processing applications.

Intel® technologies are designed to let each organization create its own flexible, unique data processing pipelines for new data sources and applications. With software- and hardware-based acceleration by Intel, data can be processed with the speed and efficiency demanded by today’s most advanced analytics use cases.