Live Online Firebolt Course for Data Engineering

Live Course Module: Firebolt Course for Data Engineering

Total Duration: 40 Hours (4 Weeks)

WEEK 1: Introduction & Firebolt Fundamentals

Duration: 8 hours (4 sessions × 2 hrs)

Topics:

1. Overview of Firebolt & Cloud Data Warehousing (2 hrs)

   • What is Firebolt, its vision, and position vs other warehouses (Snowflake, Redshift, BigQuery) Wikipedia+2firebolt.io+2

   • Firebolt architecture: decoupled storage & compute, engines, indexing firebolt.io+2firebolt.io+2

   • Use cases: interactive analytics, high concurrency, mixed workloads

2. Account setup, Engines, and Basic SQL (2 hrs)

   • Creating Firebolt database and engines (compute units) Hevo Data+2firebolt.io+2

   • Understanding engine lifecycle, sizing, scaling, starting/stopping engines YouTube+1

   • Running simple SELECT/INSERT queries

3. Data Ingestion & External Tables (2 hrs)

   • Creating external tables from S3 / cloud storage (Parquet, JSON) Hevo Data+2firebolt.io+2

   • Loading data into Firebolt tables (COPY, INSERT)

   • Best practices for ingestion and batch load

4. Hands-on Lab / Mini Project & Q&A (2 hrs)

   • Setup Firebolt environment, create engine, ingest sample data

   • Perform SQL queries to validate data

Learning Outcomes (Week 1):

• Understand Firebolt’s architecture, features, and advantages

• Be able to provision engines, start/stop, and run basic queries

• Load data from cloud storage into Firebolt and verify correctness

WEEK 2: Schema Design, Indexing & Query Performance

Duration: 10 hours (5 sessions × 2 hrs)

Topics:

1. Data Modeling & Schema Design (2 hrs)

   • Designing fact/dimension tables

   • Choosing primary indexes, distribution strategies

   • Partitioning and segmentation logic

2. Indexes, Aggregating Indexes & Performance Structures (2 hrs)

   • Primary index usage and maintenance

   • Aggregating indexes (materialized summaries) and how they speed queries

   • When and how to use these indexes

3. Query Optimization & Tuning (2 hrs)

   • Understanding query plans, explain statements

   • Pruning, vectorized execution, caching strategies

   • Dealing with large data scans, selective filters

4. Semi-structured & JSON Data Handling (2 hrs)

   • Working with variant / JSON fields

   • Querying nested JSON, flattening, extraction

   • Performance considerations for semi-structured data

5. Mini Project & Q&A (2 hrs)

   • Build optimized schemas + aggregated indexes for a dataset

   • Compare query performance before vs after tuning

Learning Outcomes (Week 2):

• Model relational and dimensional schemas tuned for Firebolt

• Use indexes and aggregating indexes to accelerate queries

• Tune queries, inspect execution plans, and optimize performance

• Handle semi-structured data within Firebolt

WEEK 3: Pipelines, Streaming & Integration

Duration: 10 hours (5 sessions × 2 hrs)

Topics:

1. ETL / ELT Patterns & Firebolt (2 hrs)

   • ELT: load raw data then transform inside Firebolt

   • ELT vs ETL tradeoffs in Firebolt context

   • Use of external transformations, staging, and materialization

2. Incremental Loads, Change Data Capture (CDC) (2 hrs)

   • Strategies for incremental updates

   • Handling inserts, updates, deletes

   • Efficient upserts and merging logic

3. Orchestration & Workflow Integration (2 hrs)

   • Integrating with Apache Airflow, dbt, or managed orchestration

   • Scheduling loads, dependencies, and pipeline monitoring

4. Connecting BI / Analytics Tools (2 hrs)

   • Connecting Firebolt with dashboard / BI tools (e.g. Tableau, Looker)

   • Real-time or near-real-time dashboards

   • Best practices for concurrency & consistency

5. Mini Project & Q&A (2 hrs)

   • Build a pipeline: ingest raw → transform → load optimized tables → query via BI

   • Automate via orchestration tool

Learning Outcomes (Week 3):

• Implement ETL/ELT pipelines suited for Firebolt

• Perform incremental updates, CDC, and merging logic

• Integrate with orchestration tools and BI layers

• Build end-to-end data flows using Firebolt as the core serving layer

WEEK 4: Administration, Monitoring, Security & Capstone Project

Duration: 12 hours (6 sessions × 2 hrs)

Topics:

1. Cluster / Engine Management & Scaling (2 hrs)

   • Scaling compute up/down, auto-scaling strategies

   • Monitoring engine health, resource usage

   • Concurrency considerations

2. Security, Access Controls & Governance (2 hrs)

   • User roles, privileges, row/column-level security

   • Data encryption, secure network configuration

   • Audit logs and compliance

3. Monitoring, Logging & Observability (2 hrs)

   • Metrics, query performance dashboards, alerts

   • Integration with observability tools

   • Data quality monitoring and anomaly detection

4. Cost Optimization & Best Practices (2 hrs)

   • Managing compute costs, auto-suspend, cost per query

   • Compression, storage vs compute trade-offs

   • Lifecycle management of tables and snapshots

5. Capstone Project Implementation (2 hrs)

   • Build a full production-grade data warehouse pipeline with Firebolt

   • Include ingestion, transformation, indexing, BI integration, and monitoring

6. Capstone Presentation & Feedback (2 hrs)

   • Present architecture, results, performance metrics

   • Peer/instructor review and discussion of real-world best practices

Learning Outcomes (Week 4):

• Manage and scale Firebolt engines in production

• Secure your Firebolt deployment with roles and governance

• Monitor performance, detect anomalies, and optimize cost

• Complete and present a robust Firebolt-based data engineering project

🧩 Capstone Project Example

Project Title: Real-Time Analytics Data Warehouse on Firebolt
Goals:

• Ingest event / transactional data continuously from a source (e.g. streaming or periodic batch)

• Use Firebolt to store raw & transformed layers

• Build optimized tables with indexes for analytics

• Serve dashboards or analytical queries via BI tool

• Monitor performance, cost, and data quality

Deliverables:

• Ingestion & transformation pipeline

• Schema with indexing and optimized queries

• Dashboard / reporting interface

• Monitoring & alerting setup

• Documentation & presentation