Skip to main content
Data Analytics in Cloud Migration

Data Analytics in Cloud Migration

$298.00
How you learn:
Self-paced • Lifetime updates
Who trusts this:
Trusted by professionals in 160+ countries
Your guarantee:
30-day money-back guarantee — no questions asked
When you get access:
Course access is prepared after purchase and delivered via email
Toolkit Included:
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.
Adding to cart… The item has been added

What does the Data Analytics in Cloud Migration course cover?

Data Analytics in Cloud Migration is covered here in 9 modules: Assessing Data Readiness for Cloud Migration, Designing Cloud-Native Data Architectures, Data Governance in Hybrid and Multi-Cloud Environments and 6 more. The outline lists 63 specific topics, opening with conducting data lineage audits to identify dependencies between on-premises data sources and downstream analytics applications.

How do you approach Data Analytics in Cloud Migration step by step?

The work is sequenced in 9 stages. It starts with Assessing Data Readiness for Cloud Migration, moves through Designing Cloud-Native Data Architectures and Data Governance in Hybrid and Multi-Cloud Environments, and ends at Change Management and Organizational Enablement. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Data Analytics in Cloud Migration course?

Module 1 is Assessing Data Readiness for Cloud Migration. It works through conducting data lineage audits to identify dependencies between on-premises data sources and downstream analytics applications., determining data quality thresholds for migration based on historical accuracy, completeness, and consistency metrics., classifying data assets by sensitivity and regulatory scope to align with cloud provider data residency requirements. and 4 more.

How is the Data Analytics in Cloud Migration course delivered?

The Data Analytics in Cloud Migration course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Data Analytics in Cloud Migration course cost?

The Data Analytics in Cloud Migration course is $296 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

Closely related courses: Cloud Analytics in Cloud Migration, Migration Project in Project Analytics Kit, Unified Analytics Platform Migration for Insurance, SharePoint Data Migration and Integration for Real Time.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the technical, governance, and operational dimensions of cloud data migration with a scope and granularity comparable to a multi-workshop technical advisory engagement for enterprise teams modernizing analytics infrastructure across hybrid environments.

Module 1: Assessing Data Readiness for Cloud Migration

  • Conducting data lineage audits to identify dependencies between on-premises data sources and downstream analytics applications.
  • Determining data quality thresholds for migration based on historical accuracy, completeness, and consistency metrics.
  • Classifying data assets by sensitivity and regulatory scope to align with cloud provider data residency requirements.
  • Deciding which legacy data systems will be decommissioned post-migration and establishing archival protocols.
  • Mapping existing ETL pipelines to assess rehosting versus refactoring needs in the cloud environment.
  • Validating metadata completeness across source systems to ensure discoverability in cloud data catalogs.
  • Establishing data ownership workflows to assign accountability for migrated datasets.

Module 2: Designing Cloud-Native Data Architectures

  • Selecting between data lakehouse, data warehouse, and federated query models based on query performance and governance needs.
  • Defining partitioning and clustering strategies in cloud storage to optimize query cost and latency.
  • Implementing medallion architecture (bronze, silver, gold layers) with versioned datasets for auditability.
  • Choosing between batch and streaming ingestion based on SLA requirements and source system capabilities.
  • Designing cross-account data sharing mechanisms in multi-tenant cloud environments.
  • Integrating data mesh principles for decentralized domain ownership in large enterprises.
  • Configuring lifecycle policies for object storage to manage cost and retention compliance.

Module 3: Data Governance in Hybrid and Multi-Cloud Environments

  • Implementing centralized policy enforcement using cloud-native IAM and attribute-based access control (ABAC).
  • Deploying data classification engines to automatically tag sensitive fields in cloud data stores.
  • Establishing cross-cloud data provenance tracking using metadata registries and audit logs.
  • Integrating on-premises identity providers with cloud directories for seamless authentication.
  • Defining data retention and deletion workflows aligned with GDPR, CCPA, and industry-specific mandates.
  • Creating governance playbooks for handling data access requests and breach notifications across regions.
  • Enforcing data quality rules at ingestion points using schema validation and anomaly detection.

Module 4: Migrating and Modernizing Data Pipelines

  • Re-architecting monolithic ETL jobs into serverless workflows using cloud functions and orchestration tools.
  • Validating data consistency between source and target systems using checksums and row-count reconciliation.
  • Implementing idempotent data loads to support retry logic in unreliable network conditions.
  • Optimizing pipeline concurrency and resource allocation to avoid throttling in cloud APIs.
  • Refactoring SQL-based transformations to leverage cloud data warehouse capabilities like materialized views.
  • Monitoring pipeline latency and failure rates to establish performance baselines post-migration.
  • Automating rollback procedures for failed data migrations using snapshot and backup mechanisms.

Module 5: Securing Data in Transit and at Rest

  • Enabling end-to-end encryption using customer-managed keys (CMK) in cloud key management services.
  • Configuring private service endpoints to prevent data exfiltration via public internet routes.
  • Implementing data masking and tokenization for non-production environments accessing live datasets.
  • Enforcing TLS 1.2+ for all data transfer operations between on-premises and cloud systems.
  • Conducting periodic access key rotation and auditing for cloud storage and database credentials.
  • Deploying data loss prevention (DLP) tools to detect and block unauthorized data exports.
  • Validating encryption settings across backup copies and snapshot repositories.

Module 6: Optimizing Performance and Cost of Analytics Workloads

  • Right-sizing compute clusters based on historical query patterns and peak concurrency demands.
  • Implementing auto-scaling policies for data processing jobs to balance cost and performance.
  • Using query cost estimation tools to evaluate impact of SQL changes before deployment.
  • Applying data compression and columnar storage formats to reduce I/O and storage expenses.
  • Setting up budget alerts and cost allocation tags for department-level cloud spend tracking.
  • Archiving cold data to lower-cost storage tiers with automated retrieval triggers.
  • Benchmarking query performance before and after migration to quantify optimization gains.

Module 7: Enabling Real-Time Analytics and Streaming

  • Selecting streaming platforms (e.g., Kafka, Kinesis, Pub/Sub) based on throughput and durability requirements.
  • Designing event schema evolution strategies to support backward and forward compatibility.
  • Implementing exactly-once processing semantics in stream pipelines to prevent data duplication.
  • Integrating stream processing with batch layers for unified analytics views (lambda architecture).
  • Monitoring lag and backpressure in real-time pipelines to detect processing bottlenecks.
  • Securing streaming endpoints using mutual TLS and role-based access controls.
  • Validating data ordering and timestamp consistency across distributed sources.

Module 8: Monitoring, Observability, and Incident Response

  • Deploying distributed tracing for end-to-end visibility across data pipelines and services.
  • Creating alerting rules for data freshness, pipeline failures, and SLA breaches.
  • Centralizing logs from cloud data services into a secured SIEM for forensic analysis.
  • Establishing incident runbooks for common data platform outages and data corruption events.
  • Conducting chaos engineering tests to evaluate resilience of data workflows under failure conditions.
  • Generating data health dashboards with metrics on latency, volume, and error rates.
  • Performing root cause analysis on data quality incidents using audit trail and log correlation.

Module 9: Change Management and Organizational Enablement

  • Redesigning data analyst workflows to align with new cloud tooling and access procedures.
  • Conducting role-based training for data stewards, engineers, and business users on cloud capabilities.
  • Updating data dictionary and documentation to reflect cloud schema and naming conventions.
  • Establishing feedback loops with business units to refine analytics deliverables post-migration.
  • Integrating cloud analytics tools into existing IT service management (ITSM) platforms.
  • Managing resistance to change by demonstrating performance and usability improvements with pilot datasets.
  • Defining support escalation paths for data access, performance, and governance issues.
!