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Database Migration in Big Data

Database Migration in Big Data

$298.00
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.
How you learn:
Self-paced • Lifetime updates
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Course access is prepared after purchase and delivered via email
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What does the Database Migration in Big Data course cover?

Database Migration in Big Data is covered here in 9 modules: Assessing Source System Architecture and Data Landscape, Designing Target Big Data Platform Architecture, Data Extraction and Change Data Capture (CDC) Strategy and 6 more. The outline lists 72 specific topics, opening with inventory and classify existing data sources by type (relational, NoSQL, flat files), volume, update frequency, and ownership domains.

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

The work is sequenced in 9 stages. It starts with Assessing Source System Architecture and Data Landscape, moves through Designing Target Big Data Platform Architecture and Data Extraction and Change Data Capture (CDC) Strategy, and ends at Post-Migration Optimization and Operations. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Assessing Source System Architecture and Data Landscape. It works through inventory and classify existing data sources by type (relational, NoSQL, flat files), volume, update frequency, and ownership domains., evaluate legacy schema designs for normalization anomalies, denormalized reporting tables, and embedded business logic in stored procedures., profile data quality across source systems to identify missing values, inconsistent formats, and referential.

How is the Database Migration in Big Data course delivered?

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

The Database Migration in Big Data 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.

Closely related courses: Database Migration in Cloud Migration, Big Data Migration in Cloud Migration, Database Migration Tools in Database Administration, Database Migration Planning in Database Administration.

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

This curriculum spans the technical and operational rigor of a multi-workshop migration engagement, addressing the full lifecycle from source assessment and platform design to cutover execution and ongoing operations, as typically encountered in large-scale data platform modernization programs.

Module 1: Assessing Source System Architecture and Data Landscape

  • Inventory and classify existing data sources by type (relational, NoSQL, flat files), volume, update frequency, and ownership domains.
  • Evaluate legacy schema designs for normalization anomalies, denormalized reporting tables, and embedded business logic in stored procedures.
  • Profile data quality across source systems to identify missing values, inconsistent formats, and referential integrity violations.
  • Determine data residency and compliance constraints that restrict movement of specific datasets across geographic regions.
  • Map source system dependencies, including ETL pipelines, reporting tools, and real-time consumers, to assess migration impact.
  • Document service-level agreements (SLAs) for source systems to establish baseline performance expectations for post-migration behavior.
  • Identify shadow IT data stores and undocumented integrations that may not appear in official architecture diagrams.
  • Conduct stakeholder interviews to uncover implicit data usage patterns not reflected in system logs.

Module 2: Designing Target Big Data Platform Architecture

  • Select appropriate storage layers (data lake, data warehouse, operational data store) based on query patterns, latency requirements, and governance needs.
  • Choose between batch, micro-batch, and streaming ingestion models based on source system capabilities and downstream use cases.
  • Define partitioning and bucketing strategies in distributed file systems (e.g., Parquet on S3 or HDFS) to optimize query performance and cost.
  • Implement schema evolution mechanisms (e.g., schema registry with Avro or Protobuf) to handle changing source data structures over time.
  • Design metadata management architecture using centralized catalog tools (e.g., AWS Glue Data Catalog, Apache Atlas) for discoverability and lineage.
  • Configure compute isolation and resource allocation (YARN queues, Kubernetes namespaces) to prevent workload interference.
  • Establish naming conventions and tagging standards for cloud resources to support cost allocation and access control.
  • Integrate monitoring agents and logging pipelines during platform provisioning to ensure observability from day one.

Module 3: Data Extraction and Change Data Capture (CDC) Strategy

  • Compare log-based CDC tools (Debezium, Oracle GoldenGate) against query-based extraction for transactional consistency and source system impact.
  • Configure database log retention policies to ensure CDC processes can recover from downtime without data loss.
  • Implement watermarking mechanisms to track progress of incremental extracts and support restartability.
  • Handle large object (LOB) columns by deciding between full extraction, sampling, or deferred processing based on usage patterns.
  • Encrypt sensitive data during extraction using client-side encryption before it leaves the source environment.
  • Throttle extraction processes to avoid performance degradation on production OLTP systems during peak hours.
  • Validate referential integrity across related tables during extraction when foreign keys are not enforced in source.
  • Design fallback mechanisms for extraction jobs that fail due to network instability or source schema changes.

Module 4: Schema Transformation and Data Modeling

  • Convert third-normal-form schemas to dimensional models (star/snowflake) based on analytical query patterns in the target environment.
  • Handle surrogate key generation in distributed environments using UUIDs, hash keys, or sequence emulators.
  • Implement slowly changing dimension (SCD) Type 2 logic using merge operations in Delta Lake or BigQuery MERGE statements.
  • Denormalize hierarchical data (e.g., JSON/XML) into relational structures while preserving path information for reconstruction.
  • Map data types across platforms (e.g., Oracle NUMBER to DECIMAL in Spark) to prevent precision loss during conversion.
  • Design conformed dimensions to enable cross-source reporting while resolving conflicting business definitions.
  • Preserve source system timestamps and apply timezone normalization based on business context, not technical default.
  • Implement data redaction or masking rules during transformation for compliance with privacy regulations.

Module 5: Migration Pipeline Orchestration and Automation

  • Select orchestration tools (Airflow, Prefect, Azure Data Factory) based on scheduling complexity, retry logic, and monitoring integration needs.
  • Design idempotent pipeline steps to allow safe reruns without duplicating or corrupting data.
  • Implement pipeline versioning using Git to track changes in transformation logic and support rollback capability.
  • Configure alerting thresholds for pipeline duration, row count variance, and failure rates to detect anomalies.
  • Parameterize pipeline configurations to support parallel execution across environments (dev, test, prod) with isolated resources.
  • Integrate pre- and post-execution data validation checks within the orchestration workflow to halt propagation of bad data.
  • Manage secret storage for database credentials using vault services (Hashicorp Vault, AWS Secrets Manager) instead of hardcoding.
  • Automate environment teardown and resource cleanup to control cloud spending during testing phases.

Module 6: Data Validation and Reconciliation

  • Develop row count and checksum comparisons at the table and partition level to verify completeness of data transfer.
  • Perform sample-based value validation by selecting random records and verifying field-level accuracy across source and target.
  • Run aggregate reconciliation queries (SUM, COUNT DISTINCT) to detect discrepancies in rolled-up metrics.
  • Compare data distributions (histograms, percentiles) for numeric fields to identify silent truncation or transformation errors.
  • Validate referential integrity in the target by checking for orphaned foreign keys after migration.
  • Use statistical sampling techniques when full reconciliation is infeasible due to data volume.
  • Document reconciliation exceptions and establish thresholds for acceptable variance based on business tolerance.
  • Automate reconciliation reports and integrate them into CI/CD pipelines for regression testing.

Module 7: Security, Access Control, and Compliance

  • Implement column- and row-level security policies in the target platform to enforce data access based on user roles.
  • Classify data elements by sensitivity level and apply encryption (at rest and in transit) accordingly.
  • Integrate with enterprise identity providers (LDAP, SAML, Okta) to maintain consistent user authentication.
  • Enable audit logging for data access and query execution to support compliance with SOX, HIPAA, or GDPR.
  • Conduct data lineage analysis to demonstrate compliance with data provenance requirements during audits.
  • Apply data retention and archival policies aligned with legal hold requirements and storage cost objectives.
  • Validate that PII masking functions operate correctly in both raw and aggregated query results.
  • Review cloud provider shared responsibility model to clarify security obligations for infrastructure and data layers.

Module 8: Cutover Planning and Production Deployment

  • Define cutover window based on business operations, considering peak usage times and downstream reporting cycles.
  • Execute parallel run period where both legacy and target systems process live data to validate accuracy under real load.
  • Implement dual-write mechanism during transition to maintain data consistency across systems.
  • Coordinate DNS or connection string updates with application teams to redirect queries to the new platform.
  • Prepare rollback plan with estimated recovery time objective (RTO) and data loss tolerance (RPO) for migration failure.
  • Freeze source system modifications during final sync phase to ensure data consistency at cutover.
  • Monitor application performance post-cutover for query latency, connection pooling, and result accuracy.
  • Decommission legacy systems only after confirming all dependent processes operate correctly on the new platform.

Module 9: Post-Migration Optimization and Operations

  • Review query execution plans and adjust indexing, clustering, or partitioning to improve performance.
  • Implement auto-scaling policies for compute resources based on historical workload patterns and forecasted demand.
  • Establish data quality monitoring to detect drift, null spikes, or out-of-range values in ongoing ingestion.
  • Optimize storage costs by moving cold data to lower-tier storage (e.g., S3 Glacier, Azure Archive).
  • Refactor inefficient queries identified through query log analysis and performance profiling.
  • Update documentation to reflect actual implementation details, including known limitations and workarounds.
  • Conduct root cause analysis for pipeline failures and implement preventive controls.
  • Rotate credentials and audit access permissions periodically to maintain security hygiene.
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