Etl (Extract, Transform, Load) Technician Course

Lesson 1 • ETL Architecture Patterns

Introduces batch, micro-batch, and streaming pipeline architectures. Students match architecture patterns to business latency requirements.

Lesson 2 • Setting Up a Learning Environment

Guides installation of tools and sample databases used throughout the course. Ensures every student has a functional local workspace before coding begins.

Lesson 3 • Roles and Responsibilities of an ETL Technician

Defines daily tasks, collaboration touchpoints, and deliverables for an ETL technician. Positions the role within data engineering teams.

Lesson 4 • What ETL Means in Practice

Defines extract, transform, and load as discrete pipeline stages. Establishes vocabulary used throughout the course.

Lesson 1 • Full vs. Incremental Extraction

Contrasts full-load extraction with delta and change-data-capture strategies. Students choose the right approach based on data volume and latency needs.

Lesson 2 • Extraction Error Handling and Logging

Builds retry logic, timeout handling, and extraction audit logs. Ensures failed extractions are detected and recoverable without data loss.

Lesson 3 • Extracting from Flat Files and Spreadsheets

Handles CSV, TSV, Excel, and fixed-width file formats. Addresses encoding issues and delimiter conflicts common in file-based sources.

Lesson 4 • Extracting from APIs and Web Services

Implements REST and SOAP API calls to retrieve JSON and XML payloads. Covers pagination, rate limiting, and authentication patterns.

Lesson 1 • Filtering and Deduplication

Removes unwanted rows and duplicate records before loading. Teaches row-level filtering logic and key-based deduplication strategies.

Lesson 2 • String Cleaning and Standardization

Applies trimming, case normalization, regex substitution, and pattern validation to text fields. Produces consistent string values across all records.

Lesson 3 • Joining and Merging Datasets

Performs inner, left, right, and full joins to combine data from multiple sources. Addresses join key mismatches and many-to-many relationship risks.

Lesson 4 • Data Type Conversion and Casting

Converts strings, dates, numbers, and booleans across incompatible source and target schemas. Prevents type mismatch errors at load time.

Lesson 1 • Data Validation and Profiling

Profiles source data for nulls, outliers, and constraint violations before transformation. Builds validation gates that halt or flag bad records.

Lesson 2 • Data Enrichment and Lookup Integration

Augments records with reference data from external tables, APIs, or static files. Improves analytical value without altering source system data.

Lesson 3 • Handling Null and Missing Values

Applies imputation, default substitution, and rejection strategies for missing data. Ensures downstream analytics are not distorted by unhandled nulls.

Lesson 4 • Slowly Changing Dimensions

Implements SCD Types 1, 2, and 3 to track historical changes in dimension tables. Connects dimension management to accurate historical reporting.

Lesson 1 • Loading into Data Warehouses

Applies star and snowflake schema loading sequences for fact and dimension tables. Enforces referential integrity during warehouse loads.

Lesson 2 • Load Error Handling and Rollback

Implements transaction management, constraint violation capture, and rollback procedures. Guarantees target data integrity when partial load failures occur.

Lesson 3 • Loading into Data Lakes

Writes partitioned Parquet, Avro, and JSON files to object storage targets. Covers partition strategy and file format selection for query performance.

Lesson 4 • Bulk Loading Techniques

Uses bulk insert utilities and staging tables to maximize load throughput. Reduces transaction overhead for large-volume datasets.

Lesson 1 • Building Pipelines with Visual Tools

Constructs end-to-end extract, transform, and load flows using drag-and-drop components. Reinforces pipeline stage concepts through hands-on tool use.

Lesson 2 • Pipeline Configuration and Parameterization

Externalizes connection strings, file paths, and date ranges into configuration files and environment variables. Enables pipeline reuse across environments.

Lesson 3 • GUI-Based ETL Tool Navigation

Introduces the workspace, component palette, and job canvas of a visual ETL tool. Builds confidence navigating tool interfaces before pipeline construction.

Lesson 4 • SQL-Based Transformation Logic

Implements transformation logic directly in SQL using CTEs, views, and stored procedures. Leverages database engine performance for heavy transformation workloads.

Lesson 1 • Retry Logic and SLA Management

Configures automatic retries, timeout thresholds, and SLA breach alerts within workflows. Reduces manual intervention for transient pipeline failures.

Lesson 2 • Workflow Dependency Management

Defines task dependencies, DAG structures, and execution order within orchestration tools. Prevents downstream tasks from running on failed upstream data.

Lesson 3 • Scheduling Fundamentals

Covers cron syntax, time-based triggers, and schedule design for ETL jobs. Establishes the baseline for all automated pipeline execution.

Lesson 4 • Orchestration Tool Configuration

Configures connections, variables, and pools in an orchestration platform. Translates pipeline designs into deployable workflow definitions.

Lesson 1 • Query and Transformation Performance Tuning

Identifies slow SQL queries, inefficient joins, and memory-heavy transformations. Applies indexing, partitioning, and query rewrites to reduce runtime.

Lesson 2 • Pipeline Monitoring and Alerting

Builds dashboards and alert rules to track job status, row counts, and latency. Enables proactive detection of pipeline degradation before business impact.

Lesson 3 • Data Reconciliation and Audit

Compares source and target record counts, checksums, and aggregates to verify load accuracy. Provides an audit trail for compliance and troubleshooting.

Lesson 4 • Pipeline Throughput Optimization

Increases data throughput using parallelism, chunking, and connection pooling. Balances resource consumption against pipeline speed requirements.