How data gets from source to report without surprises: the ETL and ELT choice, a first pipeline, cleaning, quality checks, safe re-runs and scheduling.
Outcomes
Explain the difference between ETL and ELT in one sentence
Define idempotence and say why pipelines need it
Name what made ELT practical
Turn an append-only load into a safe upsert
List the trade-offs: cost, flexibility, governance and skills
Choose between upsert, replace-a-partition and deduplicate-on-read
Covered
Learning plan
Each lesson is short enough for one sitting and ends by pointing to the next.
ExplainerETL / ELTDoneStarted
Both move data from where it is made to where it is analysed. The difference is where the transformation happens, and that decides cost, flexibility and who can change it.
ComparisonData PlatformsDoneStarted
Three ways to store analytical data, compared on the things that decide a real project: what is stored, who queries it, how it is governed and what it costs to run.
TutorialPython & SQLDoneStarted
Load a CSV export into SQLite, reject the rows that do not fit, and answer a business question with SQL. Nothing but Python's standard library.
TutorialPython & SQLDoneStarted
Work through the five problems almost every export has: stray spaces and case, numbers stored as text, bad dates, missing values and repeated rows.
ExplainerData PipelinesDoneStarted
A job that fails halfway will be run again. Here is what it takes for the second run to leave the data exactly as a clean first run would have.
Questions
Open the first lesson now: nothing to install, nothing to sign up for.