Stream processing big data

The Importance of Stream Processing Big Data for Modern Businesses

Big Data is an important part of modern-day business operations.  If analyzed correctly, Big Data can help a company improve its work. It can also help them form better business strategies by providing insights relevant to customer behavior.  But, to process Big Data, it is important to have relevant processing solutions. Stream processing big data offers the best platform for the analysis of large amounts of data as it employs real-time stream processing technology.

What Is Real-Time Stream Processing?

Real-time stream processing is a technology that is used for querying continuous streams of data in order to detect patterns. Its USP is that it allows the detection of any change in condition or new development in a process within a small amount of time. As the time between an important event and its detection is very low, real-time stream processing becomes advantageous in several different operations.

Below are some operations/scenarios in which this type of processing can be extremely useful:

  1. Collation of data from a variety of social media data sources like Facebook and Twitter.
  2. Monitoring of user activity in a lengthy web session, which cannot be done in batch processing
  3. Cases where the volume of data is too much and you do not have the necessary storage

The Requirements for Real-time Stream Processing Big Data

For real-time stream processing Big Data to be effective, it is essential to have the following requirements fulfilled:

  1. In-memory based processing platform: Fetching data from standard storage devices like HDDs and SSDs takes a lot of time, which is why; it cannot be implemented in real-time stream processing Big Data. You require an in-memory based processing platform that stores the data on volatile memory i.e. RAM. As RAM facilitates quick fetching of data, it allows a system to process continuously flowing information conveniently.
  2. MemSQL: Commonly used RDMS for database management and querying the data is not good enough for stream processing Big Data. There is a need to employ cutting-edge memSQL platform for extracting real value out of real-time stream processing. It is a contemporary relational database that is designed for cloud and in-house operations. It delivers quick insights for modern-day applications and analytical systems and works extremely well with in-memory solutions.
  3. Load balancers, fault tolerance, and high availability: A real-time stream processing system should have complete horizontal scalability i.e. new servers can be added to manage an increase in the load of work. The system should come with load balancers that automatically switch the operations from one server to another depending on the load. It should also tolerate minor faults, self-diagnose the problem and ensure high availability for applications to run flawlessly.

At Superfastprocessing, you get the best stream processing Big Data platform equipped with all the modern functionalities. It has a team of experienced DBAs and server experts who always ensure the operations are performed consistently at clients’ end. We are always reachable on our customer support number and do our very best to fix any issue or answer clients’ queries.

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