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The systematic collection and transformation of data via the creation of tools and pipelines.
Using Amazon Redshift as your Data Warehouse
Get the most out of Redshift by performance tuning your cluster and learning how to query your data optimally.
Performing Macro Operations on PySpark DataFrames
Perform SQL-like joins and aggregations on your PySpark DataFrames.
Working with PySpark RDDs
Working with Spark's original data structure API: Resilient Distributed Datasets.
Manage Data Pipelines with Apache Airflow
Use Apache Airflow to build and monitor better data pipelines.
Structured Streaming in PySpark
Become familiar with building a structured stream in PySpark using the Databricks interface.
DataFrame Transformations in PySpark (Continued)
Continuing to apply transformations to Spark DataFrames using PySpark.
Becoming Familiar with Apache Kafka and Message Queues
An overview of how Kafka works, as well as equivalent message brokers.
Executing Basic DataFrame Transformations in PySpark
Using PySpark to apply transformations to real datasets.
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