Big Data refers to the massive amount of data collected over time that are difficult to analyze and handle using common database management tools. Big Data can be identified if they fulfill 3 V’s i.e. Velocity (large amount of data generated, which is taking more time in processing than its incoming rate), Volume (data collected over the time becomes huge) and Variety (unstructured, Semi-structured and Structured).
- Unstructured Data- Emails, Blogs, Tweets, Social Networks, mobile data, Web pages and so on.
- Semi-Structured Data- text files, system log files, XML files, etc.
- Structured Data –Transaction data, RDBMS (Relational Database Management Systems), OLTP, etc.
Big Data deals with large scale data which cannot be effectively processed.
Data Analytics is the field that analyzes large amounts of data sets and predicts useful information which is essential in business decision making using different statistical techniques and tools. Data Analytics deals with raw data that needs to be interpreted to find meaningful information out of them. Most people think that data science and data analytics are similar, which is not correct. Data analytics is actually the fundamental level of data science. Data Analytics is mostly used in business, computer science and in commercial industries to increase business efficiency.
Click here to find the differences between Data Scientists, Data Analysts and Data Engineers.