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This is one of the best of investments you can make with regards to career progression and growth in technological knowledge. I was pointed in this direction by a mentor in the IT world who I highly... Read More
I have worked for more than 15 years in Java and J2EE and have recently developed an interest in Big Data technologies and Machine learning due to a big need at my workspace. I was referred here by a... Read More
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 hive project, you will design a data warehouse for e-commerce environments.
In this hadoop project, we are going to be continuing the series on data engineering by discussing and implementing various ways to solve the hadoop small file problem.
In this Apache Spark SQL project, we will go through provisioning data for retrieval using Spark SQL.