
π Data Engineering & Analytics Β· Gantt
A 5-month plan to move reporting from an on-premise warehouse to a cloud warehouse, running both in parallel before switching off the old one.
Drawing diagramβ¦
Gantt chart for migrating from an on-premise Oracle warehouse to Snowflake over 5 months: assessment and inventory, setting up Snowflake and loading, rebuilding pipelines in dbt, migrating reports in two waves, a parallel run with reconciliation, business sign-off, cut-over and decommissioning.
gantt title Warehouse Migration to Snowflake dateFormat YYYY-MM-DD axisFormat %b %d section Assess Inventory reports and jobs :a1, 2026-10-05, 14d Target design :a2, after a1, 10d section Build Snowflake setup and access :b1, after a2, 10d Historical data load :b2, after b1, 15d Rebuild pipelines in dbt :b3, after b1, 40d section Reports Wave 1 finance reports :c1, after b3, 15d Wave 2 sales and ops :c2, after c1, 15d section Go-live Parallel run and reconcile :d1, after c1, 30d Business sign-off :milestone, d2, after d1, 0d Cut-over :d3, after d2, 3d Decommission old warehouse :d4, after d3, 14d
A typical modern data stack: data is loaded from apps and SaaS tools into a cloud warehouse, modelled with dbt, orchestrated with Airflow and served to BI dashboards.
How every insert, update and delete in an operational database is streamed to the data lake and search index in near real time using change data capture.
A classic star schema for sales analytics: one fact table of order lines surrounded by date, customer, product, store and promotion dimensions.
The steps of a nightly batch pipeline with data quality gates: extract, validate, transform, load and publish, stopping safely when checks fail.
Where a real-time analytics stack runs: Kafka for events, Flink for stream processing, a real-time OLAP database and live dashboards.
The states of a single pipeline run in an orchestrator like Airflow, including retries, upstream failures and manual reruns.