Big data processing, storage, and analytics is a bit like the magic hat of a magician. You take some data and put it in one end, and out comes something magical. In reality the process isn’t quite so simple. There are three main parts to big data processing:
Data storage is the foundation for big data processing and analytics. The more data you store, the more you can process and analyze. Data storage is also a critical component of big data solutions that must be addressed early in any project.
Big data projects are often complex and require many different components to work together seamlessly as one system. This includes all aspects of your environment including hardware, software, people (IT staff), systems architecture design (including network architecture) and business rules/policies/standards.
Data processing is the transformation of data from one state to another. It includes a number of distinct stages, including:
- Data cleaning and validation – Removing duplicates, correcting errors, adding missing values and transforming data into useful formats for analysis
- Data integration – Combining multiple datasets with overlapping information into a single dataset that can be used for analysis or visualization purposes (e.g., combining customer billing information with sales records)
- Data transformation – Converting raw text into structured fields that are easier to work with in other parts of the pipeline (e.g., performing sentiment analysis on tweets)
Data processing can also include transformations such as aggregating or averaging values in order to improve performance during later steps in your pipeline.
Data analytics is the process of examining data to discover patterns, trends and other useful information. It has been used in business and government for a variety of purposes, such as making decisions and predictions. It can also be used to automate decision-making processes.
Data analytics relies on data from multiple sources being combined into one place so that it can be analyzed together rather than separately. For example, it might combine information about customers’ purchases with their demographics or geographical location so that you can see how different groups react differently to product promotions or advertising campaigns – this type of analysis is known as “predictive modeling.”
The three main parts of big data processing and analytics
Big data processing and analytics can be broken down into three main parts:
- Data storage, which is the process of storing data in a database. Data storage can be performed using relational databases, NoSQL databases or even flat files on disk.
- Data processing, which is the process of extracting value from raw data by integrating it with existing enterprise information sources such as ERP systems or CRM applications. This step involves transforming unstructured or semi-structured data into structured formats that are easier to analyze and query later on (for example by running SQL queries).
- Data analytics, which is analyzing large amounts of historical information (e.g., sales figures), current conditions (e.g., customer feedback) and forecasts for future trends so you can make better decisions about how best to use your resources (people/capital/time).
In this article, we explored the three main parts of big data processing and analytics: storage, processing, and analytics. We learned about the different types of storage available for big data, including relational databases and NoSQL stores. Next we looked at how these systems work together to process data before turning our attention toward analytics–how they can be used to extract insights from large amounts of information across an organization.