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I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More
Recently I became interested in Hadoop as I think its a great platform for storing and analyzing large structured and unstructured data sets. The experts did a great job not only explaining the... Read More
The hype around SQL-on-Hadoop had died down and now people want more from these SQL-on-Hadoop engines. More requirements like real-time queries, support from various file formats, support from user-defined functions and support from various client connectivities.
In this Hackerday, we will take a look at three different SQL-on-Hadoop engines - Hive, Phoenix, Impala, and Presto. While our expectations for hive should be relatively expected, we want to to see what it will take to get to adopt other SQL-on-Hadoop engines in our big data infrastructure.
After this Hackerday session, you should be able to make a choice about these engines, make the choice with a real informed decision and be able to extend these to your data processing infrastructure.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
In this big data project, we will discover songs for those artists that are associated with the different cultures across the globe.
In this hadoop project, learn about the features in Hive that allow us to perform analytical queries over large datasets.