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Wikifri USA adopts metadata‑driven ingestion to speed onboarding, reduce repeat work, and improve governance
About the Client:
Wikifri USA handles ingestion for multiple insurance partners. They receive Parquet, CSV, JSON and text files, each with small differences and occasional schema drift. The team needed a standardized way to onboard their clients and also a single source of truth for ingestion rules, so work was repeatable and auditable.
Challenges:
Deployments were manual for ADF and SQL MI, which caused inconsistent releases and extra checks.
Engineers rebuilt or duplicated pipelines per client, increasing maintenance and slowing new work.
Multiple file formats and schema drift forced bespoke parsing and frequent fixes.
Onboarding a new client often took weeks, delaying outcomes and adding cost.
Monitoring was limited, so identifying and resolving failures took longer than necessary.
Solution:
A metadata‑first pattern: a central Azure SQL Managed Instance stores source definitions, schema mappings and transform rules. Parameterized Azure Data Factory pipelines read that metadata and run accordingly, while Azure DevOps automates safe deployments. The idea is simple, and practical: change configuration, not code. For full technical design, implementation steps and measured impact, please download the full case study.
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Beyond Key's Power BI finance dashboard helped Breg consolidate and analyze financial data, enabling real-time monitoring and informed decision-making.
Beyond Key implemented Power BI-based Finance Dashboards for a leading Investment Advisory company to analyze financial health and improve decision-making.
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