Featured post

Best Machine Learning Book for Beginners

You need a mixof different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of any project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short.
A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project.
Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort is Machine learning.  Machine Learning best book to start.

Related Posts How to write multiple IF-conditions in Python Simplified

15 Awesome Features Should Present in Big Data System

Really good post. I have given useful points on the features of big data system. If there are no right features, you will miss the benefits that you get from big data.

What does traditional BI tools....

Read next step...

Traditional tools quickly can become overwhelmed by the large volume of big data. Latency—the time it takes to access the data—is as an important a consideration as volume.

A little difference is there...

Suppose you might need to run an ad hoc query against the large data set or a predefined report.

A large data storage system is not a data warehouse, however, and it may not respond to queries in a few seconds. It is, rather, the organization-wide repository that stores all of its data and is the system that feeds into the data warehouses for management reporting.
Big data top components
Image courtesy|Stockphotos.io
Big data needs to be considered in terms of how the data will be manipulated. The size of the data set will impact data capture, movement, storage, processing, presentation, analytics, reporting, and latency.

Key features of Big data system
  1. A method of collecting and categorizing data
  2. A method of moving data into the system safely and without data loss
  3. A storage system that is distributed across many servers
  4. Is scalable to thousands of servers
  5. Will offer data redundancy and backup
  6. Will offer redundancy in case of hardware failure
  7. Will be cost-effective
  8. A rich tool set and community support
  9. A method of distributed system configuration
  10. Parallel data processing
  11. System-monitoring tools
  12. Reporting tools: ETL-like tools (preferably with a graphic interface) that can be used to build tasks that process the data and monitor their progress
  13. Scheduling tools to determine when tasks will run and show task status
  14. The ability to monitor data trends in real time
  15. Local processing where the data is stored to reduce network bandwidth usage 
Related Content: 13 Must Read Blogs in Data and Analytics

Comments

Popular posts from this blog

Hyperledger Fabric: 20 Real Interview Questions

Best Machine Learning Book for Beginners

Python Assigning Multiple Values at Once