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In this big data project we build a live workflow for a real project using Apache Airflow which is the new edge workflow management platform. We will go through the use cases of workflow, different tools available to manage workflow, important features of workflow like CLI and UI and how Airflow is differnt. We will install Airflow and run some simple workflows.
In this big data hadoop project, we will download the raw page counts data from wikipedia archieve and we will process them via Hadoop. Then map that processed data to raw SQL data to identify the most lived up pages of a given day. Then we will visualize the proecessed data via Zeppelin Notebooks to identify the daily trends. We will use Qubole to power up Hadoop and Notebooks.
All steps like downloading, copying data to S3, creating tables and processing them via Hadoop would be task in Airflow and we will learn how to craft scheduled workflow in Airflow.
The goal of this hadoop project is to apply some data engineering principles to Yelp Dataset in the areas of processing, storage, and retrieval.
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.
In this NoSQL project, we will use two NoSQL databases(HBase and MongoDB) to store Yelp business attributes and learn how to retrieve this data for processing or query.