In Simple words, Analytics means, examining data and based on certain set of ideas about what we want to analyse, how we analyse, how we want to present the data and what tools we are choosing for analysis. The data Analytical project team choose between existing algorithm and predefined methods and based on research they will develop tools to present the data as per requirement. There are many numbers of tools in the market for different requirements.
Core Requirement of Big data Analytical Tools and Frameworks
A Lot of Study and Hard work need to do before bring Big Data analytics tools to help deliver that value and bring that data to life. To extracting and transforming data into a usable format, but once that’s done, Analysed data can provide greater insights into their owners, business, customers, and industry.
Different Languages and Frameworks for Big Data Analytics
- R Programming
- Tableau Public
- Apache Spark
Different Segments of Analytical Tools
- Big Data Cleaning Tools and Predefined Programs
- Big Data Tools for Transferring data between different source and destinations
- With Core concepts predefined Tools for analytics
- ETL tools and Frame works
- Business Intelligence and Visualization Tools
- Analysis tools for Specific Category like Health domain, Sales Domain etc.,
- Open Source tools for Analytics
- Enterprise Data Warehouse (EDW) Tools
- Vertical Big Data Platforms, specialized for a specific industry vertical
- Horizontal Big Data Platforms - on top of Hadoop
- Scale-Out Database Tools: Includes both NoSQL and NewSQL databases
Key Technical Areas and Challenges of Learning and Applying Analytical Tools and Big Data Frameworks
In a discussion of the history of analytics, highlights a number of communities from which learning analytics draws techniques, including:
1) Statistics: :
Statistics is Essentials for Analytics, knowledge in Statistics is mandatory for Analytical tools, Stats nothing but study of the collection, analysis, interpretation, presentation, and organization of data.
The major topics cover under Statistics as a profile for Bigdata Developer or Analyst :
In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied.
2.Few terminologies on Statistics
Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments
The topics related to Statistics & Probability have extensively been covered in our course ‘Statistics Essentials for Analytics’.
2) Business Intelligence
The wide range of the Business Intelligence is nothing but Big data Analysis, which has similarities with Dash boards, MIS Reports, Decisions making analytics, Process flow optimization etc., are new form of the Business Intelligence as Big data Analytics. Although it has historically been targeted at making the production of reports more efficient through enabling data access and summarizing performance indicators.
3) Web Analytics
Tools such as Facebook Pixel, Google Analytics, Digital Marketing Analysis, Online Survey etc are the most popular web analytics. Report on web page visits and references to websites, brands and other key terms across the internet.
4) Operational research
Every Big data project needs some research and build the model based on the requirements. Analyst or Data Scientist will do special Research about design optimisation for maximising objectives through the use of mathematical models and statistical methods.
5) Artificial Intelligence and Data Mining
machine learning techniques built on data mining and AI methods are capable of detecting patterns in data. In learning analytics such techniques can be used for intelligent tutoring systems, classification of students in more dynamic ways than simple demographic factors, and resources such as “suggested course” systems modeled on collaborative filtering techniques.
6) Social Network Analysis (SNA)
The Big data’s 80 % of huge data is generated by the most visited domains on the Internet related social media networks like Facebook, Twitter, and Google+, Instagram, Youtube etc. They contain huge data about the users and the relationships among them. By using analyse and mine , special graph-based mining tools are required that can easily model the structure of the social networks and dig useful information from these huge social network data. By choosing appropriate analysis tools for a particular task is difficult to decide few of the most popular tools use for Social media analysis example Gephi, Pajek, IGraph. We need to select the proper tools based on platform, execution time, Graph types, algorithms complexity, input file format and graph features. A number of such analysis tools are available with their own features and benefits.
7) Information visualization
The most important final Step in many Analytics, is Data Visualization or Information Visualization, around the data provided, and is used across most techniques to Provide best expectations form Analyst by Data owners (including those above)
List of the Popular tools used in Analytical Science
8. Google Analytics
11. Little Bird
15. Rival IQ
16. Salesforce Marketing Cloud
17. Simply Measured
19. Social Mention
21. Followerwonk (Platform – Twitter)
22. Iconosquare (Platform – Instagram)
23. SocialBro (Platform – Twitter)
24. Tailwind (Platform – Pinterest)
25. TweetReach (Platform – Twitter)
26. IBM Solution
31. Talend Open Studio
35. HPCC Systems
38. Google BigQuery
40. Amazon Web Service
41. Cloudera Enterprise
48. Mu Sigma
50. Opera Solutions
78. HPCC Systems
93. InfoBright Community Edition
111. Hadoop Distributed File System