Big Data Analytics (SF)
About This Course
Upon the completion of the course, the learner would be able to:
1. Describe the Differences Between Relational Databases and Hadoop.
2. Describe The internals of Apache Hadoop, HDFS, Map reduce, Namenode, Datanode, Data reading/writing in HDFS, Distributed computing etc.
3. Describe the features that Pig has to offer for data acquisition, storage, and analysis.
4. Use Pig to improve productivity for typical analysis tasks.
5. Analyse the Structured Big Data using Hive and HCatalogue for typical analysis tasks that happen in Business Intelligence.
6. Gain valuable business insight by joining diverse datasets.
7. Perform real-time, complex queries on datasets.
8. Capture real-time data from various unstructured data sources.
9. Develop Custom Hive User Defined function.
10. Describe the Purpose of YARN, list the components of YARN, and explain the Lifecycle of a YARN Application.
11. Orchestrate Data Processing using 3 different tools in Hadoop Platform using HCatalogue for the usual analytical task.
12. Describe the Differences Between Apache Spark and MapReduce.
13. Analyse structured and unstructured in-Memory data using the Spark programming.
14. Identify the emerging technologies in Big Data domain and their significance and benefit compared to existing technologies.
What You'll Learn
2) Module 2: Hadoop Distributed File System (HDFS) and Ingestion Tools
3) Module 3: Pig Programming
4) Module 4: Hive Programming
5) Module 5: Advanced Hive Programming
6) Module 6: Hadoop 2 and YARN
7) Module 7: HCatalogue
8) Module 8: Introduction to Spark Core
9) Module 9: Emerging technologies in Big Data and Ecosystem
Entry Requirements
Demographics:
• Age: Minimum 18 years old.
Education:
• Minimum Diploma Level.
• Language: Workplace Literacy and Numeracy Level 5 (WPLN: Speaking, Writing, Listening, Reading, and Numeracy at ESS level 5 or equivalent to Upper Secondary Level of English and Mathematics).
Computer Literacy
• The learner can use the computer and gadgets on the day today basis.
• The learner has a basic understanding of programming language.
Working Experience
• Recommended to have at least 1-year working experience related to data processing or data warehousing.