Data processing is simply translating the raw data via a process into meaningful information. Technically, data is manipulated to produce results that lead to a problem being resolved or an existing situation improved. Stages of Big Data Processing are:
There are two stages of focus: data extraction & data mining. Data extraction is a method of extracting all data in your database from web sites. Whereas data mining is a method by which useful insights are found within the database.
For eg, you ‘re the owner of an e-commerce grocery website. You found, by using different testing methods, that about 70% of people wear jeans. That is called extracting data. Now you need to go deeper in order to understand which age, gender, and form of people use jeans from Brand 1 and Brand 2. This process is referred to as data mining. Some popular tools for data mining include RapidMiner, Teradata & Kaggle.
Data will continue to stream in as the world grows. Data needs to be continuously collected. From the example above: there will be people wearing Brand 1 who have moved to Brand 2 etc. The options are endless! The extraction of data stage in big data processing is made simpler with software such as import.io.
Google, Facebook, Apple, etc. run in computing environments of a hyper-scale. What storage type you should be using depends on the size of your business. A good data storage system provides an infrastructure that has all the latest tools and storage space for data analysis. Your data can be stored on data storage providers such as Cloudera, Hadoop (not for beginners), and Talend.
Especially if extracted from the web that needs to be cleaned, the data sets can come in all forms and degrees. All of the unwanted and incorrect data is filtered out in the cleaning stage in the big data processing. Cleaning promotes proper structuring of your files. You know, for example, the number and form of people who wear jeans all over. You may remove all duplicate entries, incorrect data, unwanted regions or details, and more while cleaning. DataCleaner or OpenRefine can be used to this end.
You’ll come across your audience trend, actions, and so on while analyzing the data in the stages of big data processing. The exploratory analysis approach is proving very useful when analyzing big data. Analytics is about asking and finding answers to a particular question. Qubole and Statwing are important instruments for data analytics. You may ask-do my audience want to wear two pocket jeans, for example? What color they like most, etc.
Identifying market retail trends using which companies can highlight their top-selling products. Public agencies use it to reach out to the right groups, geographies, and ethnicities. Marketers find big data extremely useful when it comes to determining which advertisement works for their products.
Stages in Big data processing is used in several ways depending on the particular goals that you wish to accomplish.
All you need to know about Big Data
|Introduction to Big Data||Career Options after Big Data|
|4 V’s of Big Data||Big Data for Business Growth|
|Uses of Big Data||Benefits of Big Data|
|Demerits of Big Data||Salary after Big Data Courses|
Learn Big Data
|Top 7 Big Data University/ Colleges in India||Top 7 Training Institutes of Big Data|
|Top 7 Online Big Data Programs||Top 7 Certification Courses of Big Data|
Learn Big Data with WAC
|Big Data Webinars||Big Data Workshops|
|Big Data Summer Training||Big Data One-on-One Training|
|Big Data Online Summer Training||Big Data Recorded Training|
Other Skills in Demand
|Artificial Intelligence||Data Science|
|Digital Marketing||Business Analytics|
|Big Data||Internet of Things|
|Python Programming||Robotics & Embedded System|
|Android App Development||Machine Learning|