Introduction:
In a world where data is the new oil, the importance of big data analytics tools has increased like nothing else. Big data is a new concept that is gaining interest in everyone’s mind. Now the main question which rises to everyone’s mind is what is big data? Big data is complex data that is very huge in size and cannot be analysed using any traditional management tools. Big data analytics is used for the purpose.
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Understanding big data analytics:
Now you must be wondering as to what is big data analytics is. Big data analytics meaning is the analysis of huge data sets with different variables to find out the hidden patterns into the data and convert that raw data into useful information. The big data analytics definition as given by IBM is “Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes”.
What are the different types of big data analytics?
The different types of big data analytics are:
Predictive analysis: It is used to predict the future by using techniques like data mining, statistical modelling, etc. From the company’s situation, it tries to predict the solution for the root problems.
Prescriptive analytics: Prescriptive analysis is the main type of analytics used by companies to find a solution to their problems. Some describe it as if it is a part of predictive analysis though it isn’t. It has a little different vision than predictive analysis. What prescriptive analysis does is analyse the large available data set and find the best recommendation, and suggest it. The core thing is that prescriptive analysis works for the present, predictive analysis works for the future, and descriptive analysis is working for the past.
Diagnostic analytics: Diagnostic analysis uses the data from the past and makes an analysis as to why the particular thing happened. This means it answers the why of every situation. This is best to find the reasoning behind something.
Descriptive analysis: As we know, the diagnostic analysis uses past data, and in the same way, the descriptive analysis takes data from the past. The main differentiating factor in it is that in the diagnostic analysis, we answered why the particular event happened, whereas, in the descriptive analysis, we answer what happened. So, it answers the factor behind every event. So what is given by descriptive, whereas why is given by diagnostic.
Cyber analytics: The latest technology which is invented to maintain the balance between knowledge and cybersecurity. This analytics has increased in many folds now because the number of devices with the internet has increased.
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Top 10 Big Data analytics tools:
One should pursue data science course and use these big data analytics tools:
- Hadoop: The main strength of Hadoop is its HDMS which is its distributed file system. It is useful for R&D and is highly scalable. It provides access to large data.
- MongoDB: This tool is best for using on the data, which is unstructured and that changes again and again. You should learn it perfectly and should know working queries.
- Cassandra: The most reliable tool as used by many big entities like Twitter, Netflix, which is founded by Facebook.
- Drill: Drill is an open-source programme that will permit experts to work on large datasets.
- Elastisearch: Elastisearch is used for application search, website search, logging and log analytics, security analytics and business analytics.
- HCatalog: HCatalog is a table and storage management layer for Hadoop that enables users with different data processing tools — Pig, MapReduce — to more easily read and write data on the grid.
7.Oozie: Oozie is a server-based workflow scheduling system to manage Hadoop jobs. Workflows in Oozie are defined as a collection of control flow and action nodes in a directed acyclic graph.
- Storm: Storm is a free and open-source distributed realtime computation system. Apache Storm makes it easy to reliably process unbounded streams of data.
- Knime: Knime is used for simple ETL processing, has rich algorithm sets, and there are no stability issues in it.
- Datawrapper: It is device friendly, fast and fully responsive.
So, these were the top 10 big data analytics tools that one can learn to use and make a future in it.
Conclusion:
Pursuing a data scientist certificate course will give you data scientist certification and will increase your value in the market in terms of employment. It is a bright field to work in. So, as big data analytics tools is a new buzzword today, one should go and set a career in it to have a bright future with knowledge and a good return from that knowledge. So, this was all about big data analytics tools and data science course.
Reference:
https://www.jigsawacademy.com/8-big-data-tools-need-know/
https://www.jigsawacademy.com/post-graduate-diploma-in-data-science-pgds-certification-training/
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