Demystifying Streaming

Demystifying Streaming

Spoiler: What does Youtube, Netflix and Facebook Live have in common?

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We live in a world that is becoming increasingly fast. Information/Data as a gold value has to be accessed as quickly as possible by the needy. Companies can not have the privilege to shut down the system in charge of analysing their customer's Data.

What are data stream examples?

As an example, if someone makes a payment in Paris with his credit card and two hours later, the bank gets notified that the same card has done a payment in New York, then the Bank must be alert that is a possible fraud. This is simply because Paris to New York can't be done in two hours. This process is called Fraud detection. Detecting it can only be possible by processing streaming Data.

These days, the slogan "Know your customers" has been democratized in e-commerce and retail companies by the analysis of customers' data as quickly as possible. When a customer goes to an e-commerce website, his movement is tracked as well as his purchased preferences. So, recognizing buying patterns, customers' preferences in real-time can be integrated into marketing. The customer could have come for a book and then also put a cup in his basket. This is called Analysing Customer Behavior.

From this, we can define Data Streaming as a technique for transferring data so that it can be processed as a steady and continuous stream. As indicated before, streaming technologies are becoming increasingly important with the growth of the Internet. We might even assume that without stream processing there is no Big Data.

Companies like those mentioned above: Netflix, Youtube, Facebook Live need sufficiently structured and solid systems to process data in real-time to serve their clients who are constantly growing in number.

So, the principal advantage of stream data processing is to constantly provide insights to clients and business users across the organization.

What are the streaming platform?

When it comes to streaming processing solutions, companies tend to work with event-driven architecture powered by stream processing technologies. There are numerous streaming platforms used by developers in organizations. Each of them has its own code complexity, programming language and fortunately or unfortunately his pros and cons. Example of streaming platform:

  • Apache Flink
  • Apache Spark
  • Amazon Kinesis Data Stream
  • Apache Storm
  • Apache Samza

Apache Spark is the most commonly used among theses streaming platform. This is due to its native language support (Python, Scala, Java and SQL), and performance Conclusion Without even being conscient, we are in contact we streaming infrastructures every day when we connect to the internet. There is a huge interest for those companies to serve us by analysing our data, even for us as clients. A good use case of data streaming can be Twitter sentiment analysis. This topic will be treated in the future article. In the meantime, stay safe.

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