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Business Intelligence Migration in Cloud Migration

Business Intelligence Migration in Cloud Migration

$199.00
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Course access is prepared after purchase and delivered via email
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Self-paced • Lifetime updates
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Includes a practical, ready-to-use toolkit containing implementation templates, worksheets, checklists, and decision-support materials used to accelerate real-world application and reduce setup time.
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This curriculum spans the technical and operational rigor of a multi-workshop cloud migration advisory engagement, addressing inventory assessment, platform alignment, governance transition, pipeline modernization, semantic layer replatforming, performance tuning, and operational handover as performed in enterprise-scale BI migrations.

Module 1: Assessing On-Premises BI Landscape and Dependency Mapping

  • Inventory existing BI tools, data sources, and ETL pipelines to identify integration points and version dependencies.
  • Document data lineage for critical reports to determine upstream data quality and transformation logic.
  • Classify workloads by usage frequency, user base, and business impact to prioritize migration sequence.
  • Identify embedded SQL queries and custom scripts that may not be compatible with cloud SQL dialects.
  • Engage data stewards to validate ownership and sensitivity of datasets subject to regulatory constraints.
  • Assess performance benchmarks of current report execution times to establish cloud migration success criteria.

Module 2: Cloud Platform Selection and Architecture Alignment

  • Compare native analytics services (e.g., BigQuery, Redshift, Synapse) based on query concurrency and storage elasticity.
  • Determine whether to adopt a single-cloud or multi-cloud analytics strategy based on enterprise agreements and redundancy needs.
  • Align data warehouse schema design with cloud platform strengths (e.g., columnar vs. row-based storage).
  • Decide between managed vs. self-hosted BI tools based on internal skill sets and operational overhead tolerance.
  • Evaluate network egress costs and data transfer latency between on-prem systems and target cloud regions.
  • Define naming conventions and tagging standards for cloud resources to support cost allocation and access control.

Module 3: Data Governance and Security Framework Transition

  • Map existing role-based access controls (RBAC) to cloud identity providers (e.g., Azure AD, IAM) with least-privilege enforcement.
  • Implement data classification labels in cloud catalog tools to automate policy enforcement for PII and financial data.
  • Configure audit logging for query access and dataset modifications across cloud data platforms.
  • Establish data retention policies aligned with legal holds and archival requirements in cloud storage tiers.
  • Integrate data masking rules into query layers to protect sensitive fields in non-production environments.
  • Validate encryption at rest and in transit configurations across data pipelines and BI endpoints.

Module 4: Data Pipeline Modernization and Integration

  • Replace legacy ETL jobs with cloud-native orchestration (e.g., Airflow, Data Factory) using declarative configuration.
  • Refactor batch windows to leverage incremental data loading and change data capture (CDC) mechanisms.
  • Migrate flat-file data sources to cloud storage (e.g., S3, ADLS) with structured folder hierarchies and metadata files.
  • Implement idempotent data ingestion patterns to support retry without duplication in unreliable networks.
  • Standardize data type mappings between source systems and cloud data warehouse schemas to prevent truncation.
  • Set up monitoring for pipeline failure alerts and data freshness SLAs using cloud observability tools.

Module 5: BI Semantic Layer and Reporting Replatforming

  • Rebuild semantic models (e.g., LookML, DAX, BISM) to align with cloud data warehouse performance characteristics.
  • Validate calculated fields and aggregations against on-premises outputs to ensure numerical consistency.
  • Migrate user dashboards incrementally, preserving filter states and visualization configurations.
  • Optimize query patterns by replacing broad SELECTs with column-specific requests to reduce cloud compute costs.
  • Implement caching strategies at the BI tool level for high-frequency reports with static data requirements.
  • Test cross-report drill-through functionality after migration to confirm context preservation.

Module 6: Performance Optimization and Cost Management

  • Right-size compute clusters based on query workload patterns and peak concurrency demands.
  • Partition large fact tables by time and region to improve query filter performance and reduce scan volume.
  • Apply materialized views or aggregates for recurring complex queries to minimize on-the-fly computation.
  • Monitor and enforce query timeout policies to prevent runaway jobs consuming excessive resources.
  • Implement budget alerts and cost allocation tags to track BI spending by department or project.
  • Conduct regular query plan reviews to identify inefficient joins or full table scans in production reports.

Module 7: Change Management and Operational Handover

  • Develop runbooks for common troubleshooting scenarios, including failed refreshes and authentication errors.
  • Train support teams on cloud monitoring dashboards and log navigation for incident triage.
  • Transition ownership of data refresh schedules from individual analysts to centralized operations.
  • Establish a regression testing protocol for validating reports after schema or data model updates.
  • Implement version control for BI code artifacts (e.g., DDL, dashboard definitions) using Git workflows.
  • Define escalation paths and SLAs for report accuracy, availability, and performance issues post-migration.
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