What you'll learn:
- Setup Environment to learn SQL and Python essentials for Data Engineering
- Database Essentials for Data Engineering using Postgres such as creating tables, indexes, running SQL Queries, using important pre-defined functions, etc.
- Data Engineering Programming Essentials using Python such as basic programming constructs, collections, Pandas, Database Programming, etc.
- Data Engineering using Spark Dataframe APIs (PySpark) using Databricks. Learn all important Spark Data Frame APIs such as select, filter, groupBy, orderBy, etc.
- Data Engineering using Spark SQL (PySpark and Spark SQL). Learn how to write high quality Spark SQL queries using SELECT, WHERE, GROUP BY, ORDER BY, ETC.
- Relevance of Spark Metastore and integration of Dataframes and Spark SQL
- Ability to build Data Engineering Pipelines using Spark leveraging Python as Programming Language
- Use of different file formats such as Parquet, JSON, CSV etc in building Data Engineering Pipelines
- Setup Hadoop and Spark Cluster on GCP using Dataproc
- Understanding Complete Spark Application Development Life Cycle to build Spark Applications using Pyspark. Review the applications using Spark UI.
As part of this course, you will learn all the Data Engineering Essentials related to building Data Pipelines using SQL, Python as Hadoop, Hive, or Spark SQL as well as PySpark Data Frame APIs. You will also understand the development and deployment lifecycle of Python applications using Docker as well as PySpark on multinode clusters. You will also gain basic knowledge about reviewing Spark Jobs using Spark UI.
About Data Engineering
Data Engineering is nothing but processing the data depending on our downstream needs. We need to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETLDevelopment, Data Warehouse Development, etc.
Here are some of the challenges the learners have to face to learn key Data Engineering Skills such as Python, SQL, PySpark, etc.
Having an appropriate environment with Apache Hadoop, Apache Spark, Apache Hive, etc working together.
Good quality content with proper support.
Enough tasks and exercises for practice
This course is designed to address these key challenges for professionals at all levels to acquire the required Data Engineering Skills (Python, SQL, and Apache Spark).
Setup Environment to learn Data Engineering Essentials such as SQL (using Postgres), Python, etc.
Setup required tables in Postgres to practice SQL
Writing basic SQLQueries with practical examples using WHERE, JOIN, GROUPBY, HAVING, ORDERBY, etc
Advanced SQLQueries with practical examples such as cumulative aggregations, ranking, etc
Scenarios covering troubleshooting and debugging related to Databases.
Performance Tuning of SQLQueries
Exercises and Solutions for SQLQueries.
Basics of Programming using Python as Programming Language
Python Collections for Data Engineering
Data Processing or Data Engineering using Pandas
2 Real Time Python Projects with explanations (File Format Converter and Database Loader)
Scenarios covering troubleshooting and debugging in Python Applications
Performance Tuning Scenarios related to Data Engineering Applications using Python
Getting Started with Google Cloud Platform to setup Spark Environment using Databricks
Writing Basic Spark SQLQueries with practical examples using WHERE, JOIN, GROUPBY, HAVING, ORDER BY, etc
Creating Delta Tables in Spark SQL along with CRUDOperations such as INSERT, UPDATE, DELETE, MERGE, etc
Advanced Spark SQLQueries with practical examples such as ranking
Integration of Spark SQL and Pyspark
In-depth coverage of Apache Spark Catalyst Optimizer for Performance Tuning
Reading Explain Plans of Spark SQLQueries or Pyspark Data Frame APIs
In-depth coverage of columnar file formats and Performance tuning using Partitioning
