Hey,
It’s Sarvar Nadaf again, a senior developer at Luxoft. I worked on several technologies like Cloud Ops (Azure and AWS), Data Ops, Serverless Analytics, and Dev Ops for various clients across the globe.
What is Big Data?
Big data is defined as data that is either challenging or impossible to process using commodity hardware well known as traditional methods because it is large in value, quick, or complex. Large-scale data access and storage for analytics has been performed for a very long time.
Big Data makes it far simpler for your business to understand what your clients desire from you, the particular goods or services they’re hunting for, and even how they prefer to communicate with your brand. Knowing more about your clients will allow you to modify every aspect of your company to better suit their requirements. By analyzing and forecasting consumer behavior using data from social media, GPS-enabled gadgets, and CCTV footage, big data has been used in the business to deliver customer insights for transparent and simpler goods. Big Data also enables insurance businesses to better retain their customers.
Characteristics of Big Data -
Big data was only ever discussed in terms of the three Vs, or volume, velocity, and variety, in the early years of this century. Value and veracity, are two more Vs that have been added throughout time to improve businesses’ ability to get big data features.
The five primary and fundamental characteristics of big data are the five V’s: Velocity, Volume, Value, Variety, and Veracity.
1. Volume -
The term “Big Data” simply implies a huge size. Volume refers to the quantity of data available. Data is gathered from IoT, video streaming, sensor data, and social media, among other sources. This is an example of how we can obtain vast amounts of data.
2. Velocity -
A big data company will have a significant and continuous flow of data being produced and sent to its final destination. Data may be produced by devices, networks, smartphones, or social media. This information must be quickly processed and analyzed, sometimes in close to real-time.
3. Value -
This is a reference to the advantages that big data can give, and it has a direct impact on what businesses can do with the information they collect. It is essential to be able to extract value from big data because the value of big data greatly depends on the insights that can be obtained from them.
4. Veracity -
Data accuracy and quality are reviewed. The gathered information can be incomplete, incorrect, or unable to offer any useful, insightful information. Veracity, in general, refers to the degree of confidence in the data that has been gathered.
5. Variety -
There are three types of data that are typically present in big data, and they come from a variety of sources, including social media, sensors, and internet of things devices.
let’s examine the specific categories of data that big data handles.
Unstructured, semi-structured, and structured data that has been gathered by businesses are combined to form big data. This data can be analyzed to learn new things, and used in fraud deduction, live streaming, data analytics, click steaming, business insight, predictive modeling tasks, and other advanced analytics applications.
Structure Data -
Structured data has features that can be handled for efficient analysis. It has been put together into a database-like style repository. It relates to any information that may be put into a table with rows and columns in a SQL database. They can be quickly mapped into pre-designed fields and have relational keys. These data are now processed in the most efficient and modern manner possible. Relational data is the best example.
Semi Structure Data -
Semi-structured data has a better level of organization than unstructured data but is less organized than structured data since only a portion of the data is organized and the rest is not. While semi-structured data uses XML/RDF to partially arrange its content.
Unstructured data -
Last but not least, unstructured data has the lowest level of organization because the data is completely unorganized. Unstructured data organizes data in a data store in a logical, preset way using information from a variety of sources, such as social media postings, conversations, satellite imagery, IoT sensor data, emails, and presentations.
Why Big Data -
In the past, developers would perform business logic on the data that had been processed by ETL systems in order to gain insight from it. Data was generated and collected from various sources, including systems, machines, transactional data, and exchanges. Once the data had been processed by ETL systems, it was stored in databases.
But managing and processing this data using this traditional method becomes difficult as it continues to grow bay by bay. Big data, which has greater advantages in terms of trading approaches, was at that time introduced to the world. To process our data and scale up and down according to our needs, we can set up big data on commodity hardware.
Big data helps customers run big data on commodity hardware and process enormous amounts of data on big data clusters because it is more reliable, secure, and open source.
Here is my Favourite Quote -
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” (Geoffrey Moore, author, and consultant)
I sincerely hope you enjoy the article I shared with you here. I’ve come here to describe my entire experience and my main skill set.
Happy learning!
