Azure Data Factory Automation Generate 100s Of Azure Data Factory Pipelines, Check Into Azure DevOps, Test, Deploy and Execute Within Hours
The Azure Data Factory Service
The Azure Data Factory Service is a fully integrated cloud-based ETL and data integration service to create, execute and orchestrate data-driven workflows (pipelines) for directing data movement and data transformation between disparate data stores. However, authoring Data Factory Pipelines requires significant technical expertise. Issues can be encountered with performance, supportability, and maintainability if consistent best practices are not diligently applied. Where repeatable patterns are necessary, work can be laborious and time-consuming.
The implementation covers:
- Blob Storage to Azure Synapse Analytics
- Blob Storage to Azure DB
- Azure DB to Blob Storage
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Automate and Accelerate
Most organisations struggle to prepare and use their valuable data for the intended purpose, and non-optimal Data Warehouse practices inflate TCO. Automated creation of Data Factory Pipelines expedites project delivery, increases productivity, applies industry best practices, and reduces TCO. Bulk package generation allows 100s of Data Factory Pipelines to be generated, deployed, and executed within an hour.
Elait has created the Azure Cloud Elevait application to metadata drive the generation of single or multiple Azure Data Factory Pipelines exposing comprehensive functionality and leveraging industry best practices without handcrafting Pipelines.
Combined with Automated Testing and CI/CD frameworks, pipelines are created, checked into Azure DevOps, tested, and executed. The framework raises failures as bugs and deploys successful runs to a higher environment and guarantees reliable adherence to best practices and consistency of build. Additionally, End to End Data Lineage is available with a full audit of data sources, data destinations, and transformations throughout the data journey. Since there is no manual creation of Pipelines, developers are free for more productive and rewarding activities.
Cloud Elev - Data Factory Automation - Overview
- Creates the Data Factory instance if does not exist.
- Creates Pipelines that match a predefined processing template.
- Processes all blobs that match a simple wildcard pattern.
- Truncates the destination table before loading.
- Creates the destination table if does not exist.
- Applies generic transformation rules.
- Logs rejected records
- Moves the files after completion to avoid reprocessing.
- Creates an Azure DevOps Repository and checks in the pipelines to support CI/CD.
Engagement Model
Case Study
Cloud Elevait
Migrating Legacy Data And Enabling Cloud Based Analytics
A non-profit organisation having a worldwide presence and with 100 centers spread throughout India, does a lot of charitable work like manage shelter homes for street children and HIV-infected kids, rescue victims of child trafficking and child labour, etc.
Trusted By
"Elait brought many solutions and new ways of working to our company. Elait's understanding of our business needs and their talented technical teams led to a high quality on time delivery for the programme."
- Programme Director