Big Data refers to a collection of data that is vast in size and growing at ever-increasing rates. Now a question arises, what is data? It is the quantity, symbol on which any operation is performed by a computer, which may be transmitted in the form of electrical signals and recorded on magnetic, photosensitive, or automatic recording media.
In other words, as the name implies, Big Data is so big that none of the traditional data management tools can store or process it efficiently. Big data generally arrives in multiple formats and comes from multiple sources. These are the answers to the question of what is Big Data.
Importance of Big Data
Big Data is important because one can take data from any source, and analyze it to find answers that allow several things such as cost reductions, time reductions, new product development & optimized offerings, and smart decision making.
Several businesses use the Big Data technologies collected in their systems to improve operations, provide better customer service, increase profitability, and create modified marketing campaigns based on specific customer preferences. And, when combining big data analytics with high-powered analytics, you can achieve business-related tasks, which are given below:
- Defining root causes of failures, disputes, and flaws in near-real-time.
- Creating tickets or vouchers at the point of sale based on the customers’ buying habits.
- Evaluating the entire risk portfolios in minutes.
- Identifying fake performance before it affects your organization.
Benefits of Using Big Data Concept
- Better decision making
- Greater innovations
- Improvement in the education sector
- Using big data applications cuts your costs, and increases your efficiency
- Product price optimization
- Allows you to focus on local preferences
- Helps you increase sales and loyalty
- Ensures you hire the right employees
- Recommendation engines
- Life-Saving application in the healthcare industry
- You can compete with big businesses
Classifications of Big Data
Big Data is classified into three forms that include-
1. Structured: – It refers to any data which can store, access and process in the form of fixed-format. The ability in computer science has achieved bigger success in developing some methods or techniques for working with such kind of data and also deriving value out of it.
Have a look at the example of structured data.
2. Unstructured: – Any data with unknown formula or the structure is defined as ‘unstructured’ data. An unstructured data being huge poses various challenges in terms of its handling for developing value out of it. For example, unstructured data is a mixed data source containing a combination of simple text files, images, videos, etc.
Have a look at the example of structured data.
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3. Semi-structured: – The data is a combination of structured data and unstructured data, called semi-structured. We can see semi-structured data as structured in form, however, it is not defined with.
Have a look at the semi-structured data.
In addition to these, let’s discuss the top 5 Big Data technologies to be used to store and Querying/Analysis of the data!
- Apache Hadoop: This technology is a highly effective yet java-based free software framework and used to store a large amount of data in a cluster. It can allow us to process data across all nodes.
- Microsoft HDInsight: This technology is available as a service in the cloud, powered by Apache Hadoop. Microsoft HDInsight provides high availability with low cost and also uses Windows Azure Blob storage as the default file system.
- NoSQL: NoSQL gives better performance especially when in storing massive amounts of data. And there are numerous open-source NoSQL DataBases available to analyses big Data.
- Sqoop: it is used in connecting Hadoop with various relational databases to transfer data and transferring structured data to Hadoop or Hive.
- Hive: This technology is a distributed data management for Hadoop, which supports SQL-like query option HiveSQL (HSQL) to access big data.
Final Say
We hope you had a happy reading to this Blog. You must have earned some knowledge upon analysis of Big Data that researchers and business users to make better yet faster decisions using data. Even several businesses can use advanced analytics techniques such as text analytics, predictive analytics, machine learning, data mining, statistics, and natural language processing to gain new insights into the industry.