Big Data Migration in Cloud Migration

Description

What does the Big Data Migration in Cloud Migration course cover?

Big Data Migration in Cloud Migration is covered here in 9 modules: Assessing On-Premises Data Ecosystems, Cloud Platform Selection and Sizing, Data Migration Strategy Design and 6 more. The outline lists 63 specific topics, opening with inventory legacy data sources by type, volume, update frequency, and ownership to determine migration priority and complexity.

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

The work is sequenced in 9 stages. It starts with Assessing On-Premises Data Ecosystems, moves through Cloud Platform Selection and Sizing and Data Migration Strategy Design, and ends at Organizational Change and Capability Transfer. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Assessing On-Premises Data Ecosystems. It works through inventory legacy data sources by type, volume, update frequency, and ownership to determine migration priority and complexity., map existing ETL pipelines to identify dependencies, bottlenecks, and components requiring re-architecture in cloud environments., classify data sensitivity levels using regulatory frameworks (e.g., GDPR, HIPAA) to define handling protocols during migration. and 4 more.

How is the Big Data Migration in Cloud Migration course delivered?

The Big Data Migration 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 Big Data Migration in Cloud Migration course cost?

The Big Data Migration in Cloud Migration course is $298 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.

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

This curriculum spans the technical, governance, and operational rigor of a multi-phase cloud migration engagement, comparable to an enterprise advisory program that integrates platform re-architecture, compliance alignment, and organizational upskilling.

Module 1: Assessing On-Premises Data Ecosystems

Inventory legacy data sources by type, volume, update frequency, and ownership to determine migration priority and complexity.
Map existing ETL pipelines to identify dependencies, bottlenecks, and components requiring re-architecture in cloud environments.
Classify data sensitivity levels using regulatory frameworks (e.g., GDPR, HIPAA) to define handling protocols during migration.
Engage data stewards and business unit leads to validate data lineage and resolve discrepancies in metadata documentation.
Assess performance SLAs of existing systems to establish baseline metrics for post-migration validation.
Document technical debt in current data models, including denormalized schemas and undocumented transformations.
Identify redundant, obsolete, or trivial (ROT) data for archival or deletion prior to migration.

Module 2: Cloud Platform Selection and Sizing

Compare managed data warehouse offerings (e.g., BigQuery, Redshift, Synapse) based on query concurrency, pricing models, and regional availability.
Size cloud storage tiers according to access patterns, retention policies, and compliance requirements.
Model data egress costs across hybrid and multi-cloud architectures to avoid unexpected operational expenses.
Select ingestion services (e.g., Dataflow, Glue, Azure Data Factory) based on transformation needs and integration with source systems.
Define network topology requirements, including VPC peering, private endpoints, and bandwidth provisioning for data transfer.
Evaluate encryption-at-rest and encryption-in-transit capabilities across target platforms for regulatory alignment.
Plan for cross-region replication needs based on disaster recovery objectives and data sovereignty laws.

Module 3: Data Migration Strategy Design

Choose between lift-and-shift, refactor, or rebuild approaches based on application coupling and future scalability goals.
Design phased migration waves using business-criticality and data interdependence as prioritization criteria.
Implement dual-write mechanisms during cutover to maintain data consistency across source and target systems.
Select batch vs. streaming ingestion based on source system capabilities and downstream latency requirements.
Define rollback procedures, including data restoration timelines and validation checkpoints for failed migrations.
Establish data freeze windows for transactional systems to ensure referential integrity during final sync.
Integrate change data capture (CDC) tools with source databases to minimize downtime during transition.

Module 4: Data Governance and Compliance in Transit

Implement dynamic data masking in staging environments to limit exposure of PII during migration testing.
Configure audit logging for all data access and movement activities across cloud and on-premises systems.
Enforce role-based access control (RBAC) in cloud storage with least-privilege principles during migration phases.
Integrate data classification tools to automatically tag sensitive fields during ingestion into cloud data lakes.
Validate data residency compliance by configuring storage locations and transfer routes within legal jurisdictions.
Document data provenance for audit trails, including timestamps, actors, and transformation steps applied.
Conduct third-party security assessments on migration tooling and pipelines prior to production use.

Module 5: Data Quality and Validation Frameworks

Develop automated reconciliation scripts to compare row counts, checksums, and aggregate metrics pre- and post-migration.
Implement schema validation rules to detect structural drift between source and target datasets.
Use statistical sampling to verify referential integrity across related tables in large-scale databases.
Instrument data profiling jobs to detect anomalies such as null spikes, value truncation, or encoding issues.
Integrate data quality rules into CI/CD pipelines for migration scripts to catch defects early.
Define thresholds for acceptable data variance and escalation paths for validation failures.
Validate timestamp normalization across time zones and daylight saving transitions in historical data.

Module 6: Scalable Data Ingestion and Transformation

Partition large datasets by time or key ranges to enable parallel loading and reduce transfer duration.
Optimize file formats (e.g., Parquet, ORC) for columnar storage and compression efficiency in cloud data lakes.
Configure auto-scaling policies for ingestion workers to handle variable data volumes without overprovisioning.
Implement idempotent data loading patterns to support retry safety in case of partial failures.
Refactor legacy SQL transformations for compatibility with cloud-native query engines and cost models.
Cache frequently accessed reference data in managed memory layers to accelerate transformation jobs.
Monitor data skew in distributed processing frameworks to prevent job stragglers and timeouts.

Module 7: Performance Optimization and Cost Management

Index and cluster cloud data warehouse tables based on query patterns to reduce scan volume and cost.
Implement data lifecycle policies to transition older partitions from hot to cold storage tiers automatically.
Right-size compute clusters for ETL jobs using historical utilization metrics and peak load analysis.
Use materialized views selectively to balance query performance gains against storage and refresh overhead.
Monitor and optimize query patterns to eliminate full table scans and inefficient joins.
Set budget alerts and quota enforcement at project and service levels to prevent cost overruns.
Conduct workload isolation by separating reporting, ML, and operational queries into dedicated resources.

Module 8: Post-Migration Operations and Monitoring

Deploy observability dashboards to track data freshness, pipeline latency, and error rates in real time.
Establish automated alerts for data drift, schema changes, and SLA violations in production pipelines.
Conduct root cause analysis for data discrepancies using logs, traces, and version-controlled pipeline code.
Rotate credentials and decommission legacy access paths after confirming full operational cutover.
Update data catalog entries with lineage, ownership, and usage metadata post-migration.
Schedule routine data consistency checks between cloud and remaining on-premises systems during hybrid phase.
Document operational runbooks for common failure scenarios, including data rollback and recovery.

Module 9: Organizational Change and Capability Transfer

Redesign data team roles and responsibilities to reflect cloud-native operational models and tooling.
Conduct hands-on workshops to transition DBAs and analysts from on-premises to cloud query interfaces.
Standardize SQL dialects and naming conventions across teams to reduce cognitive load and errors.
Integrate data migration knowledge into internal wikis with versioned runbooks and decision logs.
Establish cross-functional data reliability engineering (DRE) practices for incident response.
Define ownership models for cloud data assets, including chargeback or showback accountability.
Implement feedback loops from business users to refine data models and reporting post-migration.