15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of Scala for Big Data development.
- Master Spark with Scala (Spark Scala) for distributed data processing.
- Utilize Akka for building concurrent and distributed systems.
- Implement advanced functional programming techniques in Scala.
- Design and build scalable Big Data applications with Scala.
- Optimize Scala code for performance and efficiency.
- Troubleshoot and debug Scala Big Data applications.
- Implement data security and access control in Scala data workflows.
- Integrate Scala with various Big Data platforms.
- Understand how to monitor and maintain Scala Big Data systems.
- Explore advanced Scala patterns and techniques for Big Data.
- Apply real world use cases for Scala in Big Data development.
- Leverage Scala for building real-time data processing applications.
About the Course
This advanced training provides participants with practical knowledge and skills to design, build, and optimize scalable big data architectures using Scala. The course explores Scala's role in distributed data processing, with a strong focus on building efficient, reliable, and maintainable data solutions for large-scale enterprise environments.Participants will learn how to work with Scala-based data processing frameworks, design distributed data pipelines, manage large datasets, optimize Spark workloads, and implement scalable data architectures. The training combines Scala programming concepts with big data engineering practices, enabling participants to develop solutions capable of handling high-volume, high-velocity, and complex data workloads.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of Scala for Big Data.
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Subtopic 1.2:
Overview of Scala's advantages for Big Data processing.
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Subtopic 1.3:
Setting up a Scala Big Data development environment.
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Subtopic 1.4:
Introduction to Scala concepts and syntax.
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Subtopic 1.5:
Best practices for Scala Big Data development.
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Subtopic 2.1:
Utilizing Spark with Scala for distributed data processing.
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Subtopic 2.2:
Implementing Spark DataFrames and Datasets.
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Subtopic 2.3:
Designing and building Spark applications in Scala.
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Subtopic 2.4:
Optimizing Spark applications for performance.
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Subtopic 2.5:
Best practices for Spark Scala.
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Subtopic 3.1:
Utilizing Akka for building concurrent and distributed systems.
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Subtopic 3.2:
Implementing Akka Actors and Streams.
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Subtopic 3.3:
Designing and building Akka applications for Big Data.
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Subtopic 3.4:
Optimizing Akka applications for performance.
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Subtopic 3.5:
Best practices for Akka.
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Subtopic 4.1:
Implementing advanced functional programming concepts.
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Subtopic 4.2:
Utilizing higher-order functions and pattern matching.
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Subtopic 4.3:
Implementing functional data structures.
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Subtopic 4.4:
Designing and building functional data pipelines.
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Subtopic 4.5:
Best practices for functional programming.
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Subtopic 5.1:
Designing scalable Big Data applications with Scala.
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Subtopic 5.2:
Utilizing Scala best practices for building robust systems.
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Subtopic 5.3:
Implementing microservices architecture with Scala.
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Subtopic 5.4:
Optimizing application performance and resource utilization.
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Subtopic 5.5:
Best practices for scalable application design.
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Subtopic 6.1:
Optimizing Scala code for performance and efficiency.
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Subtopic 6.2:
Utilizing profiling and benchmarking tools.
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Subtopic 6.3:
Implementing concurrency and parallelism in Scala.
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Subtopic 6.4:
Designing efficient data processing pipelines.
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Subtopic 6.5:
Best practices for performance optimization.
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Subtopic 7.1:
Debugging Scala Big Data applications.
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Subtopic 7.2:
Analyzing performance and data issues.
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Subtopic 7.3:
Utilizing debugging tools and techniques.
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Subtopic 7.4:
Resolving common Scala Big Data problems.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing data security in Scala data workflows.
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Subtopic 8.2:
Utilizing authentication and authorization.
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Subtopic 8.3:
Implementing data encryption and masking.
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Subtopic 8.4:
Managing data permissions and privileges.
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Subtopic 8.5:
Best practices for data security.
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Subtopic 9.1:
Integrating Scala with various Big Data platforms.
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Subtopic 9.2:
Utilizing data connectors and APIs.
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Subtopic 9.3:
Implementing data transfer between Scala and Big Data systems.
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Subtopic 9.4:
Best practices for integration.
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Subtopic 10.1:
Monitoring Scala Big Data systems.
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Subtopic 10.2:
Implementing alerting and notifications.
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Subtopic 10.3:
Utilizing monitoring tools and techniques.
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Subtopic 10.4:
Managing Scala Big Data applications.
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Subtopic 10.5:
Best practices for monitoring.
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Subtopic 11.1:
Implementing advanced Scala patterns for Big Data.
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Subtopic 11.2:
Utilizing Scala for building streaming applications.
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Subtopic 11.3:
Implementing advanced concurrency patterns.
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Subtopic 11.4:
Advanced techniques for Scala Big Data development.
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Subtopic 11.5:
Best practices for advanced patterns.
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Subtopic 12.1:
Implementing Scala for ETL pipelines.
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Subtopic 12.2:
Utilizing Scala for building data warehousing applications.
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Subtopic 12.3:
Implementing Scala for machine learning pipelines.
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Subtopic 12.4:
Utilizing Scala for real-time data analysis.
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Subtopic 12.5:
Best practices for real world applications.
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Subtopic 13.1:
Deploying Scala Big Data applications on cloud platforms.
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Subtopic 13.2:
Utilizing cloud-based Scala libraries and services.
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Subtopic 13.3:
Optimizing cloud resources for Scala applications.
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Subtopic 13.4:
Best practices for cloud deployment.
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Subtopic 14.1:
Implementing data governance policies in Scala data workflows.
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Subtopic 14.2:
Utilizing metadata management for Scala data.
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Subtopic 14.3:
Implementing data lineage and data dictionary.
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Subtopic 14.4:
Best practices for data governance.
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Subtopic 15.1:
Emerging trends in Scala for Big Data.
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Subtopic 15.2:
Utilizing AI and automation in Scala data pipelines.
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Subtopic 15.3:
Implementing serverless Scala data applications.
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Subtopic 15.4:
Best practices for future Scala development.
