Azure Data Integration Services

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

Adastra Named 2024 Data & AI Americas Partner of the Year

Adastra is proud to have won our 11th Impact Award this year—the Azure Data & AI 2024 Microsoft Americas Partner of the Year Award! We were also recognized as a finalist for three awards: Analytics Partner of the Year (Global), Business Applications (Americas), and Azure Infrastructure and App Innovation (Americas).

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.

Auditable History

With the drop in storage costs in the cloud, it has put a fully auditable, historical data lake within reach of any use case. From an operational perspective, all audit history can be funneled to a log analytics workspace. Azure provides native alerting capabilities as well as integration into common Security Information and Event Management (SIEM) tools. This allows a single 360-degree view of the analytics operations from storage to pipeline to analytics engine.

Scalability

The system can be scaled up or down in minutes. Adjusting the size of a Synapse dedicated SQL pool, Databricks cluster or integration runtime cluster is often as simple as swiping a slider or a single line of PowerShell. Different environments can be set to different levels of compute and some environments can be paused all together when not in use to how keep costs down. By using columnar Massively Parallel Processing (MPP) architecture, the Synapse dedicated SQL pool can handle terabyte, or even petabyte-scale solutions with ease.

Security

The entire solution can be delivered as PaaS, removing the constant update cycles from the calculation. Azure provides multiple levels of security baked right into the services. Starting with advanced threat detection to identify and react seamlessly to distributed denial-of-service (DDoS) attacks. Microsoft provides advanced network security with flexible and secure routing and firewall solutions. With private endpoint we can remove public access. Access and authorization based off Active Directory groups, with role-based access controls providing a method of applying the principal of least access. All data is encrypted at rest and in transit at all times in Azure. All secrets are safely stored away in an Azure Key Vault.

CI/CD Ready

All code can be stored in source control and deployed via CI/CD Pipelines. With native integration to Azure DevOps, the deployments can be set up with ease, even when protected with private endpoints. Code reviews, pull requests, multiple branches and flexible project settings allow for extensive customization. When combined with Azure Boards, projects can be managed from epics down to tasks and managed as waterfall or agile. With integration into CI/CD pipelines deployments can be automatically triggered, gated and audited.

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 multi–hop data lake architecture ending in a Kimball–style data mart, an Inmon–style data warehouse, or a Databricks–driven 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

Success Stories

Build a Single Source of Truth on Azure

Let’s map your cloud and on-premises data sources and design an Azure data integration approach that scales with the enterprise.

Azure Data Integration FAQ

How does Adastra approach Azure data integration?

Adastra uses metadata-driven ingestion frameworks that handle data from many source types and ingestion patterns. Rule-based pipelines are quick to deploy, new tables can be mapped in minutes and a single pipeline can ingest hundreds of tables, files or API endpoints consistently. With small changes, the same framework drives ETL or ELT patterns for any target.

Common targets include a multi-hop data lake architecture ending in a Kimball-style data mart, an Inmon-style data warehouse, a Databricks-driven lakehouse or a data vault. Adastra recommends data zones in Azure: raw, curated and provisioned. Data scientists access any zone they need, while business analysts use the provisioned zone with no-code Power BI.

Databricks provides a premium Spark experience for data engineering and data science at a competitive price per performance. In Azure, you also control cost directly: Databricks clusters and Synapse dedicated SQL pools scale up or down in minutes, and environments that are not in use can be paused altogether. You pay for the compute you actually need.

No. As long as network traffic can be routed between environments, Adastra can handle multicloud data integration. Our metadata-driven pipelines ingest data from many source types, files and API endpoints, and Azure networking with private endpoints and secure routing keeps that traffic protected while your data comes together in a single source of truth.

Yes. Azure provides the tools and flexibility to handle both hot and cold data paths, so you can follow Lambda or Kappa architecture designs. Adastra’s frameworks can run pipelines in parallel to keep throughput high, or sequentially to maintain foreign key consistency in the target data warehouse layer, depending on what each use case needs.

The solution can be delivered fully as platform as a service, which removes constant update cycles. Azure adds threat detection against DDoS attacks, secure routing, firewalls and private endpoints that remove public access. Role-based access through Active Directory groups applies least privilege, data is encrypted at rest and in transit, and secrets are kept in Azure Key Vault.

Adastra starts with discovery of your enterprise architecture, networking, Azure services and reporting. We then propose a future state architecture, including HA/DR, security and data layers, and define the implementation roadmap: foundation build, automated ingestion, curated and provisioned zone iterations, Azure DevOps, disaster recovery and knowledge transfer, with all code deployed through CI/CD.