Azure Data Integration Services

Embrace hybrid data integration at enterprise scale to build a single source of truth for all your data.

Modernize Your Enterprise Analytics Capabilities

With modern platform-as-a-service (PaaS) offerings in the cloud, companies are no longer constrained by storage or compute power when it comes to OLAP workloads. Companies can now gather, store and analyze data from more systems than ever before. Analytical solutions are now judged by how quickly they can be created, and how easily they can be maintained as they scale. As a result, companies are no longer only looking for simple data mart or data warehouse solutions.

Azure Data Integration Benefits

Easily Extensible

By driving the data ingestion process off metadata, the rule-based pipelines are rapid to deploy and easy to learn. Metadata can be stored in any SQL engine and maintained with basic SQL statements. New tables can be mapped in minutes and added to existing jobs. This allows for a single pipeline to ingest hundreds of tables from sourceto data lake, to target tables, in a consistent manner. Pipelines can be switched to handle ETL or ELT design patterns and can be given an appropriate level of parallelism to not overwhelm source systems or run sequentially to maintain foreign key consistency in the target data warehouse layer.

Adastra’s Data Ingestion Frameworks

Adastra has ingestion frameworks to handle data from a variety of source types and ingestion patterns. Our metadata driven framework allows for hundreds of tables, files or API endpoints to be easily created and maintained. With slight modifications, the framework can drive ETL or ELT patterns with any potential target state. Common use cases include multihop data lake architecture ending in a Kimballstyle data mart, an Inmonstyle data warehouse, or a Databricksdriven lakehouse or data vault. 

Azure Data Integration Methodology

Determine the current state by walking through the enterprise architecture, and networking and cataloguing the current Azure architecture and services. Review the current reporting data architecture.

  • Map current state to a proposed future state architecture to modernize the data and BI systems, enable advanced analytics and improve BI operations
  • Determine HA/DR solutions appropriate to client needs
  • Map out raw, staging, modeled and provisioned layers for analytical reporting
  • Determine networking and security solutions
  • Settle on a citizen BI or enterprise-led solution

Adastra recommends implementing data zones for analytics in Azure.​ IT professionals/data scientists will access data from any zone, depending on the use case.​ Business analysts will access data from the provisioned zone only, using no-code Power BI.​ Provisioned zone will be fed from curated zone for integrated analytics, and from raw zone for operational analytics.

  • Foundation Build: Discovery/Design, Target Data Model, Azure Stand up, Governance Stand up, Integration of tools (Data Quality, Reference Data identification to support Data Quality processes of critical in-scope Domain data and Insights Portal)
  • Landing/Raw Zone: Automated ingestion
  • Curation / Provision Zone Iterations: Solution Design, Develop Automated Pipelines, Develop Models, Deploy
  • Network Architecture: Best of breed security and protection for sensitive data
  • Azure DevOps: Leverage Azure DevOps through all phases of application life cycle management
  • Disaster Recovery: Backup policies and Disaster Recovery solution
  • Knowledge Transfer and Transition

Frequently Asked Questions

Databricks provides a premium spark experience for data engineering and data science use cases at a competitive price per performance.

As long as we can route the network traffic, Adastra can handle multicloud environments.

Azure provides the tools and the flexibility to easily handle hot and cold data paths.

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