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We have come to learn that Hadoop's distributed file system was engineered to favor fewer larger files over many small files. However, we mostly would not have control over how data come. Many data ingestion to data infrastructures come in small bits and whether we are implementing a data lake on HDFS or not, we will have to deal with this data inputs.
In this online hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to resolve the small file problem in hadoop.
We will start by defining what it means, how inevitable this situation could arise, how to identify bottlenecks in a hadoop cluster owing to the small file problem and varieties of ways to solve them.
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.
Analyze clickstream data of a website using Hadoop Hive to increase sales by optimizing every aspect of the customer experience on the website from the first mouse click to the last.
In this big data project, we will continue from a previous hive project "Data engineering on Yelp Datasets using Hadoop tools" and do the entire data processing using spark.