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Data engineering is the science of acquiring, aggregating or collection, processing and storage of data either in batch or in real time as well as providing variety of means of serving these data to other users which could include a data scientist. It involves software engineering practises on big data.
In this big data project for beginners, we will continue from a previous hive project on "Data engineering on Yelp Datasets using Hadoop tools" where we applied some data engineering principles to the Yelp Dataset in the areas of processing, storage and retrieval. Like in that session, We will not include data ingestion since we are already downloading the data from the yelp challenge website. But unlike that session, we will focus on doing the entire data processing using spark.
In this big data project, we will be performing an OLAP cube design using AdventureWorks database. The deliverable for this session will be to design a cube, build and implement it using Kylin, query the cube and even connect familiar tools (like Excel) with our new cube.
In this Spark project, we are going to bring processing to the speed layer of the lambda architecture which opens up capabilities to monitor application real time performance, measure real time comfort with applications and real time alert in case of security
In this project, we will walk through all the various classes of NoSQL database and try to establish where they are the best fit.