Collect

This section is key in a Big Data life cycle; it defines which type of data is captured at the source. Some examples are gathering logs from the server, fetching user profiles, crawling reviews of organizations for sentiment analysis, and order information. Examples that we have mentioned might involve dealing with local language, text, unstructured data, and images, which will be taken care of as we move forward in the Big Data life cycle.

With an increased level of automating data collection streams, organizations that have been classically spending a lot of effort on gathering structured data to analyze and estimate key success data points for business are changing. Mature organizations now use data that was generally ignored because of either its size or format, which, in Big Data terminology, is often referred to as unstructured data. These organizations always try to use the maximum amount of information whether it is structured or unstructured, as for them, data is value.

You can use data to be transferred and consolidated into Big Data platform like HDFS (Hadoop Distributed File System). Once data is processed with the help of tools like Apache Spark, you can load it back to the MySQL database, which can help you populate relevant data to show which MySQL consists.

With the amount of data volume and velocity increasing, Oracle now has a NoSQL interface for the InnoDB storage engine and MySQL cluster. A MySQL cluster additionally bypasses the SQL layer entirely. Without SQL parsing and optimization, Key-value data can be directly inserted nine times faster into MySQL tables.