Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
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 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 Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
In this big data project, we will talk about Apache Zeppelin. We will write code, write notes, build charts and share all in one single data analytics environment using Hive, Spark and Pig.