ETL Pipeline

Data Pipeline

An automated set of processes that moves and transforms data from source systems to target destinations.

Data Quality

The measure of data's fitness for its intended purpose based on accuracy, completeness, consistency, and timeliness.

Data Lineage

The documentation of data's origins, movements, and transformations throughout its lifecycle.

Streaming Analytics

Processing and analysing continuous data streams in real time to detect patterns and trigger responses.

Data Mart

A subset of a data warehouse focused on a particular business area, department, or subject.

Data Observability

The ability to understand, diagnose, and resolve data quality issues across the data stack by monitoring freshness, distribution, volume, schema, and lineage of data assets.

Reverse ETL

The process of moving transformed data from a central warehouse back into operational tools such as CRM, marketing platforms, and customer support systems to activate insights.

The analysis of geographic and spatial data to discover patterns, relationships, and trends tied to location.

Identifying data points that differ significantly from other observations in a dataset.

The process of applying statistical analysis to a dataset, identifying relationships and patterns within the data.

The process of labelling data with informative tags to make it usable for training supervised machine learning models.

A visual interface displaying key metrics and data points for monitoring performance and making informed decisions.

The analysis of historical data to understand what has happened in the past and identify patterns.

Artificially generated datasets that preserve the statistical properties of real data while protecting privacy, used for testing, development, and sharing across organisational boundaries.

The discipline of analysing data as soon as it becomes available to support immediate decision-making.