This Hive tutorial gives in-depth knowledge on Apache Hive. Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive structures data into well-understood database concepts such as tables, rows, columns and partitions. ================================
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It will shortout this issues:
The video talks about the following points:
1. What is Hive?
2. Why to use Hive?
3. Where to use hive and not Pig?
4. Hive Architecture
5. Hive Components
6. How Facebook Uses hive?
7. Hive vs RDBMS
8. Limitations of hive
9. Hive Types
10. Hive commands and Hive queries
Why Learn Hadoop?
Big Data! A Worldwide Problem?
According to Wikipedia, "Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, it is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!
The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing, handling, evaluating and retrieving hundreds of terabytes, and even petabytes of data.
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Opportunities for Hadoopers!
Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more
Who should go for this course?
If you belong to any of the following groups, knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career:
1. Analytics professionals
2. BI /ETL/DW professionals
3. Project managers
4. Testing professionals
5. Mainframe professionals
6. Software developers and architects
7. Recent graduates passionate about building a successful career in Big Data
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