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I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More
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The use of Hive or the hive meta-store is so ubiquitous in big data engineering that achieving efficient use of the tool is a factor in the success of many big data projects. Whether in integrating with Spark or using hive as an ETL tool, many big data projects either fail or succeed as they grow in scale and complexity because of decisions made in the early lifecycle of the analytics project.
In this hive project, we will explore using hive efficiently and this big data project format will take an exploratory pattern rather than a project building pattern. The goal of these sessions will be to explore Hive in uncommon ways towards mastery.
We will be using different sample dataset for hive in the series of these hive real time projects, exploring different Hadoop file formats like text, CSV, JSON, ORC, parquet, AVRO and sequence file, will look at compression and different codecs and take a look at the performance of each when you try integration with either spark or impala. The idea of this hadoop hive project is to explore enough so that we can be made a reasonable argument about what to do or not in any given scenario.
In this big data project, we will discover songs for those artists that are associated with the different cultures across the globe.
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.
This is in continuation of the previous Hive project "Tough engineering choices with large datasets in Hive Part - 1", where we will work on processing big data sets using Hive.