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Tailored Training Agenda for Your Unique Needs

Our Databricks training program goes beyond standard coursework, offering a personalized approach that aligns with your company’s specific industry needs and team structure. We don’t just provide training; we deliver a consulting experience, guiding your team in adopting best practices that are most relevant to your business.

Each module in this program is adaptable, ensuring that the content resonates with your team’s responsibilities and addresses your unique challenges in the data landscape. Below is an outline of the general topics we cover, which will be fine-tuned to meet the specific objectives and requirements of your organization.

The programme is delivered by Dan Williams – Databricks Partner Solutions Architect Champion, and it’s available both on-site (UK) and remote.
  • Introduction to Lakehouse

    • Concept and Evolution: Understanding the evolution from Data Lakes and Data Warehouses to Lakehouse.
    • Lakehouse vs. Traditional Systems: Comparing features and capabilities with traditional data systems.
  • Architecture of Databricks Lakehouse

    • Core Components: Detailed overview of the architecture, including Delta Lake, Photon, and Unity Catalog.
    • Lakehouse Platform Workloads: Exploring different workloads like data warehousing, data engineering, data streaming, etc.
  • Security in Lakehouse

    • Security Fundamentals: Discussing the security features within the Databricks Lakehouse platform.
    • Best Practices: Strategies for maintaining secure and compliant data environments.
  • Lakehouse in Action

    • Real-world Applications: Case studies demonstrating the practical use of the Lakehouse platform.
    • Workload Management: Techniques for effectively managing diverse workloads on the platform.
  • Understanding Unity Catalog

    • Key Features: Exploration of Unity Catalog’s capabilities and how it acts as a central hub for data.
    • Integration with Databricks: How Unity Catalog integrates and enhances the Databricks environment.
  • Administration and Security

    • Managing Access: Setting up and managing user and group access.
    • Auditing and Compliance: Tools and strategies for auditing and ensuring compliance.
  • Hands-on With Unity Catalog

    • Practical Exercises: Working with the Unity Catalog for data governance and management.
    • Best Practices: Tips for efficient use of Unity Catalog in various scenarios.
  • Fundamentals of Data Engineering

    • ETL Processes: Deep dive into Extract, Transform, Load processes in Databricks.
    • Data Modeling and Design: Principles of effective data modeling and design in a Lakehouse environment.
  • Advanced Data Engineering Techniques

    • Data Transformation and Storage: Advanced methods for data transformation and optimal storage.
    • Pipeline Orchestration: Strategies for creating and managing robust data pipelines.
  • Data Engineering Projects

    • Hands-On Projects: Real-life project scenarios for practical experience.
    • Optimization and Troubleshooting: Techniques for optimizing data pipelines and troubleshooting common issues.
  • Introduction to Data Science and ML in Databricks

    • Key Concepts: Overview of data science and machine learning principles in the context of Databricks.
    • Toolsets and Languages: In-depth look at the tools (e.g., Spark ML, MLflow) and languages (Python, Scala) used.
  • Building and Deploying ML Models

    • Model Development: Techniques for developing, training, and tuning machine learning models.
    • Deployment Strategies: Approaches for deploying models in various environments including batch and real-time.
  • Advanced ML Techniques

    • Deep Learning and AI: Exploring advanced techniques in machine learning and artificial intelligence.
    • Real-World ML Applications: Case studies and practical examples of ML applications in industry.
  • MLOps and Lifecycle Management

    • MLflow for Experimentation: Using MLflow for tracking experiments, model versioning.
    • MLOps Best Practices: Strategies for implementing machine learning operations effectively.
Labs and Hands-on Sessions
Throughout the course, there will be practical labs and hands-on sessions to reinforce the learning from each module. These sessions will include setting up environments, working with data, building and deploying models, and troubleshooting.

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DecisionForest is a Data & AI Consultancy dedicated to elevating businesses through tailored corporate training, cloud architecture and data & AI solutions.

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