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Database Migration in Big Data

Database Migration in Big Data

$299.00
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.
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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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