Packt

Data Engineering with Scala and Spark

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Packt

Data Engineering with Scala and Spark

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Set up a development environment for building data pipelines in Scala

  • Use Spark DataFrames, Datasets, and SQL with Scala for data processing

  • Profile and clean data using Deequ for improved data quality

Details to know

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Recently updated!

March 2026

Assessments

13 assignments

Taught in English

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There are 13 modules in this course

In this section, we explore functional programming, higher-order functions, polymorphic functions, and pattern matching in Scala for data engineering applications.

What's included

2 videos6 readings1 assignment

In this section, we explore cloud-based and local environments for data engineering pipelines, focusing on setup processes, trade-offs, and practical applications.

What's included

1 video5 readings1 assignment

In this section, we explore Apache Spark's APIs, focusing on DataFrame and Dataset for distributed data processing.

What's included

1 video3 readings1 assignment

In this section, we explore using Spark JDBC API for database access, designing database interfaces, and performing operations with configuration loading.

What's included

1 video3 readings1 assignment

In this section, we explore object stores, data lakes, and lakehouses, focusing on their roles in managing large-scale data workflows efficiently.

What's included

1 video6 readings1 assignment

In this section, we explore Spark transformations, aggregations, joins, and window functions to enhance data processing for BI and analytics. Key concepts include efficient data manipulation and pipeline development.

What's included

1 video4 readings1 assignment

In this section, we explore Deequ for implementing data quality checks, analyzing completeness and accuracy, and defining constraints to ensure reliable data pipelines.

What's included

1 video3 readings1 assignment

In this section, we explore test-driven development, static code analysis, and linting to improve code quality, maintainability, and consistency in data engineering projects.

What's included

1 video4 readings1 assignment

In this section, we explore CI/CD practices with GitHub to automate Scala data pipeline workflows, focusing on GitHub Actions, version control, and reliable deployment processes.

What's included

1 video4 readings1 assignment

In this section, we explore data pipeline orchestration using tools like Airflow, Argo, Databricks, and Azure Data Factory. We focus on workflow design, task management, and real-world implementation strategies.

What's included

1 video6 readings1 assignment

In this section, we analyze Spark UI metrics to identify performance issues, optimize data shuffling, and right-size compute resources for efficient data processing.

What's included

1 video4 readings1 assignment

In this section, we explore building batch pipelines using Spark and Scala, focusing on medallion architecture, data ingestion, transformation, and orchestration for scalable data processing.

What's included

1 video5 readings1 assignment

In this section, we explore building real-time data pipelines using Spark, Scala, and Kafka for IoT applications. Key concepts include data ingestion, transformation, and serving layer design.

What's included

1 video4 readings1 assignment

Instructor

Packt - Course Instructors
Packt
1,550 Courses 428,904 learners

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