An energy distributor needed clean, daily pricing data to train and run a machine-learning pricing model.

Oracle extracts land in S3 as Parquet, are crawled into the Data Catalog and exposed to Redshift. Bronze, silver and gold layers refresh on a fixed schedule, with public weather data joined to each delivery.

Medallion layers on materialized views, Step Functions orchestration from crawl to model run, and partitioned daily extracts for the data science team.

A software vendor ran its data transformations in an in-house legacy tool that was hard to maintain and to change.

Client databases and files are loaded into Snowflake with Airbyte, Fivetran and staged S3 loads. dbt Cloud rebuilds the logic in three tested SQL layers and produces the pivot model.

The pivot tables are unloaded back to S3 and into the existing application database, so the product kept working while the engine underneath was replaced.

An ad-tech team received its revenue data only as daily emailed reports, copied by hand into spreadsheets.

A scheduled Cloud Function reads the inbox and saves each report to Cloud Storage. Airbyte loads it into BigQuery, and a scheduled query merges it incrementally into the serving tables.

Append-then-merge with a staging truncate keeps reruns safe, and a separate backfill path reloads history without touching the daily flow.

A fast-growing subscription brand needed one trusted view of sales, retention, delivery and marketing across a dozen tools.

Managed and open-source connectors plus event webhooks land everything in BigQuery. dbt turns it into four tested layers, with snapshots for customer and subscription history.

KPIs defined with the head of data, one shared Power BI model for finance and operations, and reverse ETL to feed segments back to marketing.

A growing services company was moving to a new operational platform and needed one reliable view across operations, recruiting and CRM data.

API sources land in BigQuery through custom Python connectors and a managed EL tool. dbt models, versioned in Git, turn staging data into tested, documented marts.

Dashboards rebuilt for operations teams in a short time, and modelled data synced back into the CRM and the ops workspace through reverse ETL.

An energy distributor wanted to see its fuel prices against competitors, station by station, and react faster to price gaps.

SSIS packages scheduled by SQL Agent pull sales and delivery data from the AS400 ERP and the public daily fuel-price feed into SQL Server staging, then into incremental reporting views.

Power BI price maps and competitor-gap reports went into production and replaced manual spreadsheet comparisons.

A customer-journey analytics product had to turn large client event logs into journeys and cycles that its web app could query quickly.

An insertion API stores uploaded logs in S3. Spark jobs rebuild events, journeys, cycles and graph edges and bulk-load them into Vertica, while MongoDB keeps the app metadata.

A columnar analytical store for aggregation queries, and a clear split between batch processing and the API that serves the app.