Dremio is a data-as-a-service platform that provides a modern data architecture, allowing organizations to easily query, analyze, and explore data from various sources. It is particularly popular for its ability to combine data from different systems in a self-service way, providing a unified view and streamlining data operations.

Module 1: Introduction to Dremio

1. What is Dremio?

   • Overview of Dremio as a Data-as-a-Service platform.
   • The evolution of data architectures: from traditional data warehouses to Data Lakehouses.
   • Benefits of using Dremio.
   • Key concepts: Data Lakehouse, Self-Service Data, and Query Acceleration.

2. Dremio’s Role in Modern Data Ecosystem

   • How Dremio integrates with various data sources.
   • Benefits for data analysts, data engineers, and data scientists.
   • Dremio’s competitive advantages in comparison to traditional data platforms.

Module 2: Dremio Architecture

1. Architecture Overview

   • Understanding the Dremio platform architecture.
   • The distributed nature of Dremio’s query execution engine.
   • Overview of components:
     • Dremio Engine
     • Dremio Catalog
     • Dremio Console

2. Dremio Data Layers

   • Dremio’s Logical Layer (Virtual Datasets).
   • Physical Layer (Datasets, Sources, and Reflections).
   • Key concepts: Reflections and Acceleration.

3. Metadata and Data Management

   • The Dremio Catalog for managing metadata.
   • Data source configuration and management.
   • How Dremio organizes datasets, folders, and queries.

Module 3: Setting Up Dremio

1. Installation & Deployment

   • How to install Dremio (Cloud and On-Premises).
   • Required hardware and software configurations.
   • Using Dremio Cloud and Dremio on Kubernetes.
   • Connecting Dremio to your cloud storage (AWS S3, Azure Blob, etc.).

2. Configuring Data Sources

   • Connecting Dremio to relational databases (e.g., PostgreSQL, MySQL, SQL Server).
   • Connecting to NoSQL data sources (e.g., MongoDB, Elasticsearch).
   • Connecting to cloud storage (AWS, Azure, GCP).
   • Connecting to Hadoop-based systems (Hive, HDFS).

3. User and Role Management

   • Creating and managing users and groups.
   • Assigning roles and permissions for access control.
   • Configuring security settings (authentication and authorization).

Module 4: Data Exploration and Querying

1. Dremio UI Overview

   • Exploring the Dremio Web Console.
   • Navigating the Data Catalog and Data Explorer.
   • Basic operations: Creating, browsing, and managing datasets.

2. Querying Data with SQL

   • Writing SQL queries in Dremio.
   • Working with structured, semi-structured, and unstructured data.
   • Filtering, joining, and aggregating data.
   • Introduction to SQL functions in Dremio.

3. Using Virtual Datasets

   • What are virtual datasets in Dremio?
   • How to create virtual datasets using SQL queries.
   • Benefits of using virtual datasets for data exploration and reporting.

Module 5: Data Transformation and ETL

1. Transforming Data in Dremio

   • Data transformation basics using Dremio’s UI.
   • Writing and executing complex SQL transformations.
   • Using the SQL Editor for data manipulation.

2. Creating and Managing Data Pipelines

   • Setting up pipelines for data processing.
   • Reusing queries with Views and Virtual Datasets.
   • Integration with other tools for ETL/ELT processes.

3. Working with Reflections

   • What are Dremio Reflections and how they accelerate queries?
   • Best practices for configuring and using reflections.
   • Managing and monitoring reflections for performance tuning.

Module 6: Data Governance and Security

1. Data Access and Security

   • Setting up data access controls in Dremio.
   • Role-based access control (RBAC) and how to manage user permissions.
   • Encryption and data protection methods.
   • Managing datasets securely in the Data Lakehouse.

2. Audit and Logging

   • Enabling and interpreting audit logs in Dremio.
   • Monitoring user activity and system performance.
   • Security best practices for Dremio deployments.

Module 7: Performance Optimization

1. Query Optimization Techniques

   • Understanding the execution plan of Dremio queries.
   • How Dremio optimizes queries automatically.
   • Tips for optimizing performance (e.g., partitioning, query rewriting).

2. Using Reflections to Improve Performance

   • Leveraging Dremio Reflections to accelerate data access.
   • Managing reflection strategies for best performance.
   • Best practices for scaling Dremio in high-traffic environments.

3. Caching and Materialization

   • Caching data for faster query execution.
   • Managing materialized views and their refresh strategies.

Module 8: Advanced Features and Use Cases

1. Federated Querying

   • What is federated querying in Dremio?
   • How to query data across multiple data sources in a single SQL query.
   • Combining data from cloud, on-prem, and big data systems.

2. Working with Semi-Structured Data

   • Handling JSON, Parquet, Avro, and other semi-structured formats in Dremio.
   • Best practices for querying and transforming semi-structured data.

3. Dremio and Machine Learning

   • Using Dremio for data exploration and preparation in ML workflows.
   • Connecting Dremio with machine learning tools (e.g., Python, Jupyter Notebooks).
   • Integrating Dremio with popular ML platforms for model training.

Module 9: Integrating Dremio with Other Tools

1. Dremio and BI Tools Integration

   • How to connect Dremio to business intelligence tools (Tableau, Power BI, etc.).
   • Best practices for visualizing data from Dremio.
   • Performance considerations when using BI tools with Dremio.

2. Dremio REST API

   • Introduction to the Dremio REST API.
   • Using the API for automation and integration with other systems.
   • Creating custom integrations and workflows.

Module 10: Cloud and Big Data Integration

1. Dremio on Cloud Platforms

   • How to deploy Dremio on AWS, Azure, and Google Cloud.
   • Connecting Dremio to cloud-native storage and compute resources.
   • Managing cost and performance in cloud environments.

2. Integrating Dremio with Hadoop and Spark

   • Using Dremio with Hadoop-based systems (HDFS, Hive, etc.).
   • Integrating with Spark for big data processing.

Module 11: Best Practices and Troubleshooting

1. Dremio Best Practices

   • Optimizing query performance.
   • Organizing and managing datasets efficiently.
   • Best practices for collaboration with teams using Dremio.

2. Troubleshooting Dremio

   • Common issues and their solutions.
   • Diagnosing performance bottlenecks.
   • Analyzing and interpreting logs for debugging.