Apache Spark SQL – Bigdata In-Memory Analytics Master Course
This course is designed for professionals from zero experience to already skilled professionals to enhance their Spark SQL Skills. The hands-on session covers on end to end setup of Spark Cluster in AWS and in local systems.
What students are saying:
At the final stage, we need to work with the structured data. SQL is a popular query language to do analysis on structured data.
Spark SQL facilitates loading and writing data from various sources like RDBMS, NoSQL databases, cloud storage like S3 and easily it can handle different format of data like Parquet, Avro, JSON and many more.
Spark Provides two types of APIs
Low-Level API – RDD
High-Level API – Dataframes and Datasets
Spark SQL amalgamates very well with various components of Spark like Spark Streaming, Spark Core and GraphX as it has good API integration between High level and low-level APIs.
Initial part of the course is on Introduction on Lambda Architecture and Big data ecosystem. The remaining section would concentrate on reading and writing data between Spark and various data sources.
Dataframe and Datasets are the basic building blocks for Spark SQL. We will learn how to work on Transformations and Actions with RDDs, Dataframes, and Datasets.
1) NHL Dataset Analysis
2) Bay Area Bike Share Dataset Analysis
Updates:
++ Apache Zeppelin notebook (Installation, configuration, Dynamic Input)
++Spark Demo with Apache Zeppelin
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