In yesterday’s blog post we learned how to become a Data Scientist for Big Data. In this article we will go over various learning resources related to Big Data.
In this series we have covered many of the most essential details about Big Data. At the beginning of this series, I have encouraged readers to send me questions. One of the most popular questions is –
“I want to learn more about Big Data. Where can I learn it?”
This is indeed a great question as there are plenty of resources out to learn about Big Data and it is indeed difficult to select on one resource to learn Big Data. Hence, I decided to write here a few of the very important resources which are related to Big Data.
Learn from Pluralsight
Pluralsight is a global leader in high-quality online training for hardcore developers. It has fantastic Big Data Courses and I started to learn about Big Data with the help of Pluralsight. Here are few of the courses which are directly related to Big Data.
- Big Data: The Big Picture
- Big Data Analytics with Tableau
- NoSQL: The Big Picture
- Understanding NoSQL
- Data Analysis Fundamentals with Tableau
I encourage all of you start with this video course as they are fantastic fundamentals to learn Big Data.
Learn from Apache
Resources at Apache are single point the most authentic learning resources. If you want to learn fundamentals and go deep about every aspect of the Big Data, I believe you must understand various concepts in Apache’s library. I am pretty impressed with the documentation and I am personally referencing it every single day when I work with Big Data. I strongly encourage all of you to bookmark following all the links for authentic big data learning.
- Haddop – The Apache Hadoop® project develops open-source software for reliable, scalable, distributed computing.
- Ambari: A web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters which include support for Hadoop HDFS, Hadoop, MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. Ambari also provides a dashboard for viewing cluster health such as heat maps and ability to view MapReduce, Pig and Hive applications visually along with features to diagnose their performance characteristics in a user-friendly manner.
- Avro: A data serialization system.
- Cassandra: A scalable multi-master database with no single points of failure.
- Chukwa: A data collection system for managing large distributed systems.
- HBase: A scalable, distributed database that supports structured data storage for large tables.
- Hive: A data warehouse infrastructure that provides data summarization and ad hoc querying.
- Mahout: A Scalable machine learning and data mining library.
- Pig: A high-level data-flow language and execution framework for parallel computation.
- ZooKeeper: A high-performance coordination service for distributed applications.
Learn from Vendors
One of the biggest issues with about learning Big Data is setting up the environment. Every Big Data vendor has different environment request and there are lots of things require to set up Big Data framework. Many of the users do not start with Big Data as they are afraid about the resources required to set up framework as well as a time commitment. Here Hortonworks have created fantastic learning environment. They have created Sandbox with everything one person needs to learn Big Data and also have provided excellent tutoring along with it. Sandbox comes with a dozen hands-on tutorial that will guide you through the basics of Hadoop as well it contains the Hortonworks Data Platform.
Learn from Books
There are indeed few good books out there which one can refer to learn Big Data. Here are few good books which I have read. I will update the list as I will learn more.
- Ethics of Big Data Balancing Risk and Innovation
- Big Data for Dummies
- Head First Data Analysis: A Learner’s Guide to Big Numbers, Statistics, and Good Decisions
In tomorrow’s blog post we will wrap up this series of Big Data.
Reference: Pinal Dave (https://blog.sqlauthority.com)
There is a typo in the “Learn from Apache” section, Haddop was written instead of Hadoop.
Excellent post, Pinal. PluralSight is a great resource, but I’d also include , BigDataUniversity and HortonWorks.
They’re all excellent resources to learn about Hadoop. BTW, the poster above is right, it’s misspelled “Haddop” above.