Workshop on Data Warehouse Implementation on Azure

Simform helps enterprises minimize risks in data warehouse on Azure implementations through architectural guidance, data modeling strategies, and governance frameworks for continued performance at scale.

Modern data warehouse on Azure gives you a strong, cost-efficient foundation for all your analytics needs through its consumption-based pricing. But building it right is complex and requires deep expertise in data and cloud engineering.

You need to handle diverse data sources, optimize query performance, ensure data quality, and set up automated pipelines that actually work in production. The data migration challenges only create more risks.

Our team knows Azure's data stack inside out and has built warehouses that process terabytes of data daily. We guide you through the technical decisions around ingestion and storage layers, ETL/ELT patterns, and performance optimization so that your warehouse becomes a reliable data engine for sophisticated BI and analytics.

Workshop Plan

1. Discovery and assessment

• A collaborative session with stakeholders to analyze your data landscape, source systems, and existing pain points while identifying optimization opportunities and ROI potential. This includes assessment of data volumes, latency requirements, and BI reporting needs.

2. Architecture design and data flows

• Evaluating modern Azure technologies (Synapse, ADF, Databricks, ADLS) and designing an end-to-end architecture from data ingestion to analytics consumption. You'll get detailed data flow diagrams along with practical demos of key processing capabilities.

3. Data integration and modernization framework

• Creating an integration strategy covering ingestion patterns, data modeling, and performance optimization techniques. Also includes standardized processes for data quality, partitioning strategies, and automated pipelines to prepare data for business intelligence using Azure's native tools.

4. Security and governance planning

• Building robust data governance framework with defined roles, security controls, and DR strategies, along with integrated data lineage tracking, retention policies, and compliance measures backed by efficient monitoring capabilities.

5. Implementation roadmap and knowledge transfer

• A phased modern data warehouse implementation roadmap with resource planning and risk mitigation strategies, complemented by hands-on technical workshops that prepare your team for successful platform operations.

Key Deliverables

1. Assessment report documenting your data landscape, optimization opportunities, and estimated ROI for modern data warehouse implementation on Azure

2. Technical design document with target architecture diagram, recommended Azure data services. data flows, security controls, and infrastructure sizing recommendations

3. Data governance model with lineage tracking, security controls, and compliance recommendations

4. Implementation roadmap with prioritized workstreams, resource requirements, timeline estimates, and risk mitigation strategies specific to your organization