15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of Scala for data engineering.
- Master functional programming principles and Scala's type system.
- Utilize Scala's collections and data manipulation libraries.
- Implement distributed data processing with Apache Spark and Scala.
- Design and build scalable data pipelines using Scala.
- Optimize Scala applications for performance and efficiency.
- Troubleshoot and address common issues in Scala data engineering.
- Implement data serialization and schema management in Scala.
- Integrate Scala with various data storage and processing systems.
- Understand how to handle large-scale data processing with Scala.
- Explore advanced Scala features for data engineering (e.g., Akka, Cats).
- Apply real world use cases for Scala in data engineering.
- Leverage Scala tools and frameworks for efficient data application development.
About the Course
This advanced training equips data engineers and technology professionals with the skills to apply functional programming principles in Scala to build scalable, reliable, and maintainable data engineering solutions. The course combines Scala programming with modern data engineering practices, focusing on immutability, higher-order functions, type safety, concurrency, distributed processing, and efficient data pipeline development.Participants will learn how functional programming concepts can improve the design and performance of data-intensive applications. Through practical exercises and real-world scenarios, the training explores building robust ETL/ELT pipelines, processing large datasets, working with distributed computing frameworks, handling errors, optimizing workloads, and developing production-ready data solutions using Scala.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of Scala for data engineering.
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Workshop 1.2:
Overview of Scala's features and ecosystem.
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Workshop 1.3:
Setting up a Scala development environment.
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Workshop 1.4:
Introduction to Scala's functional programming concepts.
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Workshop 1.5:
Best practices for Scala data engineering.
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Workshop 2.1:
Mastering functional programming principles and Scala's type system.
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Workshop 2.2:
Utilizing Scala’s immutable data structures.
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Workshop 2.3:
Implementing higher-order functions and pattern matching.
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Workshop 2.4:
Designing and building functional Scala applications.
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Workshop 2.5:
Best practices for Scala functional programming.
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Workshop 3.1:
Utilizing Scala's collections and data manipulation libraries.
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Workshop 3.2:
Implementing data transformations and aggregations.
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Workshop 3.3:
Designing and building data processing pipelines.
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Workshop 3.4:
Optimizing data manipulation with Scala.
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Workshop 3.5:
Best practices for Scala collections.
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Workshop 4.1:
Implementing distributed data processing with Apache Spark and Scala.
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Workshop 4.2:
Utilizing Spark's RDDs and DataFrames.
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Workshop 4.3:
Designing and building Spark applications.
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Workshop 4.4:
Optimizing Spark jobs for performance.
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Workshop 4.5:
Best practices for Spark with Scala.
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Workshop 5.1:
Designing and building scalable data pipelines using Scala.
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Workshop 5.2:
Utilizing Scala for ETL and data integration.
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Workshop 5.3:
Implementing data streaming applications.
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Workshop 5.4:
Optimizing pipelines for data processing.
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Workshop 5.5:
Best practices for pipeline development.
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Workshop 6.1:
Optimizing Scala applications for performance and efficiency.
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Workshop 6.2:
Utilizing performance tuning techniques.
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Workshop 6.3:
Implementing memory management and garbage collection.
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Workshop 6.4:
Designing efficient Scala code.
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Workshop 6.5:
Best practices for performance optimization.
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Workshop 7.1:
Troubleshooting and addressing common issues in Scala data engineering.
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Workshop 7.2:
Analyzing Scala logs and error messages.
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Workshop 7.3:
Utilizing debugging techniques for problem resolution.
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Workshop 7.4:
Resolving common Scala application errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data serialization and schema management in Scala.
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Workshop 8.2:
Utilizing Avro and Protobuf for data serialization.
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Workshop 8.3:
Designing and building schema registries.
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Workshop 8.4:
Optimizing serialization for data transfer.
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Workshop 8.5:
Best practices for data serialization.
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Workshop 9.1:
Integrating Scala with various data storage and processing systems.
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Workshop 9.2:
Utilizing Scala for database integration.
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Workshop 9.3:
Implementing Scala for data warehousing tasks.
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Workshop 9.4:
Optimizing integration for data retrieval and processing.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle large-scale data processing with Scala.
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Workshop 10.2:
Utilizing Scala for distributed computing.
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Workshop 10.3:
Implementing data sharding and parallel processing.
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Workshop 10.4:
Designing scalable data processing applications.
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Workshop 10.5:
Best practices for large data.
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Workshop 11.1:
Exploring advanced Scala features for data engineering (Akka, Cats).
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Workshop 11.2:
Utilizing Akka for concurrent and distributed systems.
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Workshop 11.3:
Implementing functional programming with Cats.
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Workshop 11.4:
Designing and building advanced Scala applications.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced features.
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Workshop 12.1:
Implementing Scala for real-time data processing.
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Workshop 12.2:
Utilizing Scala for building data analytics applications.
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Workshop 12.3:
Implementing Scala for machine learning pipelines.
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Workshop 12.4:
Utilizing Scala for data warehousing and ETL.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing Scala tools and frameworks (sbt, IntelliJ IDEA).
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Workshop 13.2:
Implementing Scala applications with specific tools.
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Workshop 13.3:
Designing and building automated workflows.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing application monitoring and logging.
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Workshop 14.2:
Utilizing Scala logging frameworks.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in Scala for data engineering.
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Workshop 15.2:
Utilizing Scala in cloud-native environments.
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Workshop 15.3:
Implementing Scala for data mesh architectures.
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Workshop 15.4:
Best practices for future applications.
