Last time we saw how messy data creates business chaos. Now let's explore the solution: the modern analytics stack.
Think of it as a data assembly line with five key stages, each handling a specific job.
Engagement Message
What's one trait of an assembly line that also fits a data pipeline?
The analytics stack has five main layers: Sources → Ingestion → Storage → Processing → Consumption. Data flows through each layer, getting cleaner and more valuable.
Each layer has specialized tools designed for its specific challenges.
Engagement Message
Which layer deals with the rawest, least-refined data?
Sources are where your data originates: databases, APIs, files, event streams, third-party services. This is your raw material—unprocessed and inconsistent.
For example, a CSV file from a partner might have missing values, different date formats, or extra columns you don't need.
Engagement Message
What is one data source your organization uses?
The ingestion layer moves data from sources into your stack. Tools extract, validate, and initially format data.
"Formatting" here means making sure the data is in a consistent structure—like making sure all dates look the same, or all numbers use the same decimal separator.
Engagement Message
What might go wrong when copying data from one system to another?
Storage is where cleaned data lives long-term. Think of it as a big digital warehouse.
Modern options include data warehouses (structured), data lakes (flexible), and lakehouses (hybrid approach).
Engagement Message
What's one advantage of keeping all company data in one place?
