Enhance Your Analytics Capabilities
Modern cloud-based platform-as-a-service (PaaS) offerings have removed traditional limitations around storage and compute power for OLAP workloads. Businesses can now collect, store, and analyze data from a wider range of systems than ever before.
The focus has shifted to how quickly analytical solutions can be deployed and how easily they can be maintained at scale. As a result, organizations are moving beyond basic data mart or data warehouse solutions in search of more agile and scalable analytics platforms.
Azure Data Integration Benefits
Migrating workloads to Azure can provide businesses with a more flexible, scalable, and cost-effective infrastructure, along with enhanced security and integration capabilities.
Easily Extensible
Auditable History
Scalability
Rule-based pipelines enable rapid deployment and easy learning by leveraging metadata stored in any SQL engine. They efficiently ingest hundreds of tables while adapting to ETL or ELT patterns with controlled parallelism to ensure system stability.
Lower cloud storage costs have made fully auditable historical data lakes accessible for any use case. Azure enhances operational visibility with log analytics, native alerts, and SIEM integration for a 360-degree view of analytics operations.
The system scales up or down in minutes with simple adjustments to Synapse SQL pools, Databricks clusters, or integration runtimes. Its columnar MPP architecture enables seamless handling of terabyte- to petabyte-scale solutions while optimizing costs.
Scope, Architecture and Sizing
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Define the current architecture and design a modern future-state to upgrade data and BI systems, support advanced analytics, and enhance BI operations.
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Identify high availability (HA) and disaster recovery (DR) solutions that align with client requirements.
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Outline the data layers—raw, staging, modeled, and provisioned—for effective analytical reporting.
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Plan appropriate networking and security configurations.
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Choose between a citizen-led BI approach or an enterprise-driven model.
Define the Implementation Roadmap
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Foundation Build: Includes discovery and design, creation of the target data model, Azure environment setup, governance framework, and integration of tools (such as data quality and reference data tools) to support critical domain data processes and the Insights Portal.
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Landing/Raw Zone:Automated data ingestion setup.
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Curation/Provision Zone Iterations: Design the solution, build automated pipelines, develop data models, and deploy iteratively.
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Network Architecture: Implement industry-leading security practices to safeguard sensitive data.
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Azure DevOps: Utilize Azure DevOps across all phases of the application lifecycle.
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Disaster Recovery: Define backup policies and implement a comprehensive disaster recovery strategy.
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Knowledge Transfer and Transition: Ensure smooth handover and enablement for ongoing operations.
Data Integration Methodology
Discovery
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Assess your existing servers/virtual machines, discover workloads with dependencies and determine wave groups.
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Prepare the migration plan, timeline, and cloud expenses.