Big Data refers to large volumes of structured and unstructured data, which is hard to manage. Big Data is so large, that it is beyond the traditional data management tools’ capabilities to process it efficiently. The challenge can be caused by the data’s size, complexity, or speed. All of these factors make it impossible to process the data with the traditional methods.
What is big data used for?
Big data became a necessity for data analytics in recent years. Big data is used for gathering the information needed allowing organizations to act according. When looking at big data, data analysts aim to determine if there is a correlation exists between different types of data. Every department in every company can benefit from big data. For example, the biggest social media companies are using big data to be able to generate ad revenue. Big data allows those companies to place targeted ads to users. Big streaming services are also making recommendations according to the analyzes made from big data.
What are the 7 V’s of big data?
7 V’s of big data refers to:
Volume: The term volume refers to the amount of data. The data is stored in terms of Zettabytes, Exabytes, and Yottabytes.
Variety: Variety refers to the type of data sources, such as structured, semi-structured, and unstructured. The data being created doesn’t have a set of rules, which makes it harder to process and analyze.
Velocity: Velocity refers to the speed of processing and accessing the data. In some use cases, it is crucial for the system to work properly, such as stock exchanges.
Variability: Variability refers to the data that is changing constantly. It focuses on understanding and interpreting the raw data correctly.
Veracity: Veracity refers to the accuracy of the gathered data. It focuses on the accuracy of the data and tries to keep the bad data away from the system.
Visualization: Visualization refers to the presentation of the gathered data. Similar to business analytics, it is important for decision-makers to be able to see the data and meaningful information.
Value: Value refers to the value provided by the big data after it is processed correctly. It is the main purpose to get a value after all efforts to gather actionable information.
What are examples of big data?
The most obvious example of big data is social media. Hundreds of terabytes of new data are uploaded to social media every day. This includes messages, comments, audio files, photos, and videos. Processing all of this data is impossible with old methods. Another example of big data is stock exchanges. A single stock exchange can create terabytes of new trade data every day and it is crucial to process that data quickly and without loss. There are various industries that completely rely on big data to be able to make business decisions.
What are the big data trends in 2022?
Just like our internet usage habits, big data is evolving to be able to meet the organizations’ needs to gather the information required to make the right decisions. While the amount of data is getting bigger every day, new methods are also being implemented to be able to process this data. One of those is artificial intelligence, which is gaining popularity in other fields too.
Another new term being popular recently is composable data analytics, which allows organizations to combine and consume analytics capabilities from different data sources. It provides better agility by enabling swappable modules. Another new term is AnalyticsOps, which is basically DevOps for analytics. Data analytics are driving businesses’ decision-making process, and organizations want to provide all employees with analytics, which is called data democratization.
What is big data analytics?
Big data analytics is the main purpose of gathering zettabytes of data. Without analytics, the data gathered means nothing. Big data analytics focuses on analyzing the gathered data to provide actionable information. The decision-makers are relying on these analytics while making important decisions. If uses advanced analytic techniques while analyzing the large, diverse data sets. As we mentioned before, big data includes structured, semi-structured, and unstructured data from various sources, which makes the process more difficult. It allows organizations to make faster and better decisions. It also empowers an organization to be data-driven.
What are big data services?
There are various big data services that help organizations with big data consulting, big data implementation, big data support, storage, and big data managed analytics services. All of these services aim to make it easier for organizations to gather, store, process, and analyze big data.
Is big data analytics a good career?
The world’s biggest organizations already adopted big data and trying to find new methods to benefit from big data. Thus we can easily say that big data won’t lose its popularity anytime soon. Also, it is considered a new field and there are thousands of companies struggling to find the professionals they need. Considering its popularity, big data analytics can be a good career and it can also lead the way to c-level positions after the required experience is acquired.
Which certifications are best for big data?
Some of the most popular certifications for big data are:
- Microsoft Certified: Azure Data Scientist Associate
- Microsoft Certified: Data Analyst Associate
- SAS Certified Data Scientist
- Oracle Business Intelligence Certification
- IBM Data Science Professional Certificate
- IBM Data Analyst Professional Certificate
- Dell EMC Proven Professional – Data Scientist
What is the future of big data?
The future of big data is looking bright. Billions of devices are generating huge amounts of data every day and being able to analyze this data is crucial to improve how we manage them. Thus, big data will be more important in the near future. While organizations are creating new methods and looking for professionals, big data and big data analytics will be an important part of the organizations’ decision-making process.