Big data is not enough to collect – it needs to be used somehow, for example, to make forecasts of business development or test marketing hypotheses. And to use the data, you need to structure and analyze it. We will tell you what methods and technologies of big data exist and how they help process big data.
Crowdsourcing
What is it?
Usually, computers are involved in Big Data analysis, but sometimes people are also entrusted with it. For these purposes, Crowd-sourcing is attracting a large group of people to the solution of any problem.
How Does It Work?
Let’s say you have a lot of raw data—for example, records of store sales, where products are often recorded with errors and abbreviations. For example, a Dexter drill with a ten mAh battery is recorded as “Dexter Drill 10 mAh”, “Dexter 10 Drill”, “Dexter Acc 10 Drill,” and a dozen other ways. You find a group of people willing to manually look through tables for money and bring such names to one form.
Why And Where They Are Used: Crowdsourcing
Is good if the task is one-time and there is no point in developing a complex artificial intelligence system to solve it. If you need to analyze big data regularly, a system based on Data Mining or machine learning is likely to be cheaper than Crowdsourcing. In addition, machines can handle complex analyses based on mathematical methods, such as statistics or simulation.
Mixing And Integrating Data
What Is It?
Working with big data often involves collecting heterogeneous data from different sources. To work with this data, you need to put it together. You cannot simply load them into one database – different sources can provide data in different formats and with different parameters. This is where mixing and integrating data will help bring heterogeneous information to a single form.
How It Works
To use data from different sources, the following methods are used:
- They bring data to a single format: they recognize text from photographs, convert documents, convert text into numbers.
- Complement the data. If there are two sources of data about one object, information from the first source is supplemented with data from the second to get a complete picture.
- They filter out redundant data: if some source collects unnecessary information that is not available for analysis, it is deleted.
Why And Where They Are Used
Mixing and integrating data is necessary if there are several different data sources, and you need to analyze this data in a complex.
For example, your store sells offline, through marketplaces, and simply over the Internet. To get complete information about sales and demand, you need to collect a lot of data: cash receipts, inventory balances, online orders, orders through the marketplace, and so on. All of this data comes from different places and usually has a different format. To work with them, they need to be brought to a single form.
Traditional data integration methods are mainly based on the ETL process – extraction, transformation, and loading. Data is obtained from sources, cleaned, and loaded into storage. The dedicated tools of the extensive data ecosystem from Hadoop to NoSQL databases also have their approach for extracting, transforming, and loading data.
After integration, big data is subjected to further manipulations: analysis and so on.
Also Read: What Is Apache Spark, And How Is It Used In Big Data
