databricks/learning-spark

Features

• How-To Structured Data - Provides practical code samples and functional examples demonstrating distributed data processing patterns.
• Distributed Computing Curricula - Offers a structured educational course for mastering scalable data workflows and machine learning pipelines using Apache Spark.
• Big Data Processing - Provides frameworks and methodologies for transforming massive volumes of data across distributed systems.
• Data Processing Workflows - Guides the definition and execution of complex sequences of data analysis and transformation tasks.
• Distributed Data Processing Frameworks - Implements systems for partitioning, transforming, and processing large-scale datasets across compute clusters.
• Distributed Task Schedulers - Provides implementation patterns for orchestrating and distributing data processing workflows across computing clusters.
• External Data Connectors - Demonstrates how to integrate and host external data streams using specific connectors for distributed processing.
• External Storage Integrations - Implements support for connecting diverse external storage drivers to distributed processing engines.
• Distributed Job Execution - Demonstrates how to execute computational jobs across multiple worker nodes using submission scripts.
• Big Data Learning Paths - Provides a comprehensive set of educational resources and practical examples for mastering distributed data processing.
• Code Examples - Offers practical source code and project layouts demonstrating distributed data and streaming processing.
• Distributed Training - Provides implementation examples for scaling machine learning algorithms across clusters to handle massive training sets.
• Scalable Distributed Pipelines - Demonstrates the development of high-scale data processing sequences across distributed compute resources.
• Lazy Evaluation Frameworks - Illustrates the use of lazy evaluation frameworks to defer computation and enable global query optimization.
• Machine Learning Pipelines - Implements scalable machine learning pipelines for distributed data transformation and model execution.
• Orchestrator-Worker Models - Explains the architectural separation between central coordination logic and remote execution nodes in a cluster.
• Polyglot Application Development - Shows how to implement processing functions across multiple languages through a shared core engine.