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A notebook is a code execution environment that allows for creating, sharing code and its execution, visualization and other text information (like markups). It enables an interactive computing in the area of data exploration or analysis. It is logical to a sharable Grunt shell for Pig, or scala shell and PySpark shell for Spark, or beeline for Hive but with visualization, discovery and collaboration.
In this big data Project, we will talk about one of this notebook - Apache Zeppelin. With Zeppelin, we will do a number of data analysis by answering some questions on the crime dataset using Hive, Spark and Pig. We will prepare some chart to better represent our results and finally share our results with the collaborative or sharing feature of the notebook.
On completing this big data project using zeppelin, participants will have known what Zeppelin is, gained the ability to install new interpreters, use Zeppelin for performing data analysis, sharing results with their friends or colleagues. Also, the participant will be informed of other notebooks in the data ecosystem like Jupyter or the databricks cloud notebooks.
In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
This is in continuation of the previous Hive project "Tough engineering choices with large datasets in Hive Part - 1", where we will work on processing big data sets using Hive.
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.