ETL
ETL (Extract, Transform, Load) is a data integration process that pulls data from source systems, transforms it into a clean, structured format outside the target system, and then loads it into a destination such as a data warehouse.
16 resources across 3 libraries
Glossary Terms(7)
OLAP
OLAP (Online Analytical Processing) refers to database systems and workloads optimized for complex, read-heavy analytical queries — aggregations, trends, and m…
Data Warehouse
A data warehouse is a centralized repository that stores structured, cleaned, and integrated data from multiple source systems, organized specifically to suppo…
Data Lake
A data lake is a centralized storage repository that holds raw data in its native format — structured, semi-structured, or unstructured — without requiring a p…
ETL
ETL (Extract, Transform, Load) is a data integration process that pulls data from source systems, transforms it into a clean, structured format outside the tar…
ELT
ELT (Extract, Load, Transform) is a data integration pattern that loads raw data into a destination system first and performs transformation afterward, using t…
Change Data Capture (CDC)
Change Data Capture (CDC) is a set of techniques for detecting and capturing row-level inserts, updates, and deletes in a source database as they happen, so th…
Data Lineage
Data lineage is the tracked record of where a piece of data originated, how it has moved and transformed across systems, and where it is ultimately consumed —…
Study Notes(3)
Building an ETL Pipeline with Spark
A practical guide to designing extract, transform, and load stages in Spark, from schema-safe ingestion to atomic loads.
Building an ETL DAG
A hands-on walkthrough of designing and implementing a complete Extract-Transform-Load pipeline as an Airflow DAG, including failure handling and local testing.
dbt vs Traditional ETL
How dbt's ELT, SQL-in-Git approach differs from traditional GUI-based ETL tools like Informatica or SSIS in compute cost, collaboration, and testing.
Interview Questions(6)
What Are Fact and Dimension Tables in a Data Warehouse?
A fact table stores the measurable, numeric events of a business process — like sales amount or quantity sold — along with foreign keys to related dimensions,…
ETL vs ELT Pipelines: What is the Difference?
ETL (Extract, Transform, Load) transforms data in a separate processing engine before loading it into the target system, while ELT (Extract, Load, Transform) l…
What is Data Lineage Tracking and Why Does It Matter?
Data lineage tracking is the practice of recording where each piece of data originated, every transformation it passed through, and every downstream table, rep…
How Do You Implement Data Quality Validation in Pipelines?
Data quality validation in pipelines means running automated checks — for null rates, uniqueness, referential integrity, value ranges, and row-count anomalies…
Data Lake vs Data Warehouse: What Is the Difference?
A data lake stores raw data of any format — structured, semi-structured, or unstructured — cheaply at scale and applies schema only when the data is read, whil…
How Would You Design an ETL Pipeline?
An ETL pipeline extracts data from source systems, transforms it into a clean, validated, and correctly shaped form, and loads it into a target store like a wa…