Data Quality and Governance Fundamentals — Data Quality, Governance &…

The business cost of untrustworthy data

Steps in Data Quality and Governance Fundamentals

What ETL and ELT Actually Mean — beginner · Extract-transform-load versus extract-load-transform, and why the order shifted
Why Data Quality and Governance Matter — beginner · The business cost of untrustworthy or ungoverned data
What Data Platform Engineering Covers — beginner · Building the infrastructure other data engineers and analysts build on top of
What Makes Data 'Big' — beginner · Volume, velocity and variety as the classic framing, and why it still matters
Batch vs Streaming: A Fundamental Trade-off — beginner · Why some problems genuinely need real-time processing
What Analytics Engineering Actually Is — beginner · The discipline bridging data engineering and data analysis
The Modern Data Pipeline Lifecycle — beginner · From source system to analytics-ready table
Data Quality vs Data Governance vs Data Observability — beginner · Clarifying three related but distinct disciplines
Data Platform Engineer vs Data Engineer — beginner · Where infrastructure-focused work differs from pipeline-focused work
What Counts as 'Real-Time' — beginner · Latency expectations across different real-time use cases
Analytics Engineer vs Data Engineer vs Data Analyst — beginner · Where each role's responsibilities start and stop
Distributed Computing Fundamentals — beginner · Why processing splits across many machines instead of one bigger one
Why ELT Became the Default Pattern — beginner · How cheap cloud warehouse compute changed where transformation happens
Why dbt Became the Standard — beginner · How dbt brought software engineering practices to SQL transformation
The Cost of Bad Data — beginner · How data quality problems compound as they move downstream
The Modern Data Platform Stack — beginner · Compute, storage, orchestration and governance layers that make up a platform
MapReduce and the Origins of Big Data Processing — beginner · The paradigm that started the modern big data era
Common Streaming Use Cases — beginner · Fraud detection, real-time dashboards and event-driven microservices
The Streaming Data Stack Overview — beginner · Where Kafka, Flink and related tools each fit
Sources, Sinks and Transformations — beginner · The three core concepts every pipeline is built from
Platform Thinking for Data Teams — beginner · Building reusable infrastructure instead of one-off solutions per team
Where This Work Sits in a Data Organization — beginner · How quality and governance responsibilities are typically distributed
The Modern Analytics Engineering Workflow — beginner · From raw warehouse tables to trusted, documented business metrics
The Modern Big Data Stack — beginner · Where Spark, table formats and lakehouses fit relative to older Hadoop tools
Trade-offs of Adopting Streaming — beginner · The real operational cost of moving from batch to streaming
The Modern Data Quality and Governance Tool Landscape — beginner · Where tools like Great Expectations, Monte Carlo and catalogs fit
Data Engineering Tools Landscape Overview — beginner · Where Fivetran, Airbyte, dbt, Airflow and warehouses each fit
ELT and the Analytics Engineer's Role in It — beginner · Where analytics engineering fits in the extract-load-transform pattern
Where Platform Engineering Fits in a Data Org — beginner · How this role interacts with data engineers, analysts and security teams
When You Actually Need Big Data Tools — beginner · Recognizing when a dataset genuinely requires distributed processing
Setting Up Basic Data Quality Checks — beginner · Adding your first automated checks to an existing pipeline
Setting Up a Local Spark Environment — beginner · Running Spark locally to learn before touching a cluster
Setting Up a Minimal Data Platform — beginner · The smallest reasonable infrastructure setup for a new data team
Building Your First Simple Pipeline — beginner · Moving data from a source file into a warehouse table end to end
Setting Up a Local Kafka Environment — beginner · Running Kafka locally to learn before touching a cluster
Setting Up Your First dbt Project — beginner · Connecting dbt to a warehouse and running your first model

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Data Quality, Governance & Observability roadmap — the full learning path

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