Guide to data tools landscape for developers
Summary
This developer-focused guide maps the data tooling landscape across the data lifecycle, from ingestion and storage to processing, orchestration, and consumption. It explains where each tool fits (lake, lakehouse, warehouse) and compares approaches (ETL vs ELT, batch vs realtime) with concrete examples like Fivetran, Airbyte, dbt, Spark, Airflow, and BI/embedded analytics. A practical primer for SMB teams seeking to understand data workflows and tool choices.