Massive data processing

Leverage Big Data Analysis and Get Improved Insights with Massive Data Processing

Staying one step ahead of the game at all times is the key to success in modern-day business environment. As data generation via online platforms reaches an all-time high, the need to put it to good use is growing. Massive data processing solutions have become the need of the hour and the need for data stream processing is at an all-time high.

What is Data Stream Processing?

Data stream processing is a technology specially devised for addressing the requirements of Big Data tech. It is utilized to query constantly generating data streams. The biggest difference it has with other data processing technologies is that it processes data within a very short time limit after the data is received by the system.

Why You Need Data Stream Processing?

Data stream processing is extremely useful for businesses especially the ones that rely on Big Data analysis, and for good reasons too. Have a look at some prominent ones:

  1. Data that comes from a source in the form of streams requires massive storage for its execution. Data stream processing employs in-memory processing hardware and makes use of MemSQL for computation of data right after ingestion.
  2. When data steam processing is used as part of massive data processing solutions, it allows for inspection of results, detection of patterns and provides multiple levels of focus, all at the same time.
  3. For cases where it is essential to collect time-series data in order to detect patterns, data stream processing can come to your aid. For example, in a long browsing session that spans over two batches, it is hard to detect its length. But, with data stream processing, it becomes a whole lot easier.
  4. When you require approximate results, data stream processing lets you accomplish the tasks with much less hardware. It is one of the best massive data processing techniques for several business use cases.
  5. It is an exceptional natural platform for technologies like IoT that require massive data processing solutions in real-time. As IoT is expected to generate multiple zettabytes of data in the future, data stream processing is essential for fulfilling future requirements.

Essential Requirements for Massive Data Processing

Massive data processing provides a great way to compute large streams of data. However, there are some critical requirements that need to be fulfilled by such a platform for being effective in a real-world scenario:

  1. Fault Tolerance – A superfast processing solution should have high fault tolerance and self-diagnosis capability for fixing any problem that arises during day-to-day operations.
  2. Horizontal Scalability – At any time, you should be able to expand your resources horizontally i.e. by putting in additional servers to accommodate for an ever-increasing data stream processing
  3. Load Balancers – When a server is stretched to the limit, it should be able to shift the load to another server. Load balancers are extremely useful in such cases and can work wonders for a massive data processing

At Superfastprocessing, you get the best platform to handle your present and emerging data stream processing requirements.


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