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Spark 2 offers a huge but yet backward-compatible break from the Spark 1.x, not only in terms of high-level API but also in performance. And spark the module with the most significant new features is Spark SQL.
In this apache spark project, we will explore a number of this features in practice.
We will discuss using various dataset, the new unified spark API as well as the optimization features that makes Spark SQL the first way to explore in processing structured data.
However, there are times when it is inevitable to resort to Spark Core - RDD in Spark 2. We will explore that as well alongside the newest and cool structured streaming API that enables fault-tolerant stream processing engine built on the Spark SQL engine.
In this project, we will look at two database platforms - MongoDB and Cassandra and look at the philosophical difference in how these databases work and perform analytical queries.
In this Apache Spark SQL project, we will go through provisioning data for retrieval using Spark SQL.
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.