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Structured Data in Big Data

Structured Data in Big Data

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What does the Structured Data in Big Data course cover?

Structured Data in Big Data is covered here in 9 modules: Defining Structured Data in Modern Data Ecosystems, Data Modeling for Scalable Structured Systems, Ingestion and Pipeline Architecture and 6 more. The outline lists 72 specific topics, opening with selecting appropriate schema definitions for transactional systems versus analytical workloads in hybrid architectures and closing with conducting quarterly cost attribution reviews to allocate.

How do you approach Structured Data in Big Data step by step?

The work is sequenced in 9 stages. It starts with Defining Structured Data in Modern Data Ecosystems, moves through Data Modeling for Scalable Structured Systems and Ingestion and Pipeline Architecture, and ends at Monitoring, Cost, and Lifecycle Management. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Structured Data in Big Data course?

Module 1 is Defining Structured Data in Modern Data Ecosystems. It works through selecting appropriate schema definitions for transactional systems versus analytical workloads in hybrid architectures, evaluating when to enforce rigid schema-on-write versus flexible schema-on-read in data lakehouse implementations, integrating legacy relational data models with cloud-native structured data formats like Parquet and ORC and 5 more.

How is the Structured Data in Big Data course delivered?

The Structured Data 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 Structured Data in Big Data course cost?

The Structured Data in Big Data course is $302 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: Structured Insights in Big Data, Semi Structured Data in Big Data, Big Data in Big Data, Big Data Ethics in Big Data.

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

This curriculum spans the technical and operational complexity of a multi-workshop program focused on enterprise data platform engineering, covering the design, ingestion, governance, and optimization of structured data systems across hybrid and cloud environments.

Module 1: Defining Structured Data in Modern Data Ecosystems

  • Selecting appropriate schema definitions for transactional systems versus analytical workloads in hybrid architectures
  • Evaluating when to enforce rigid schema-on-write versus flexible schema-on-read in data lakehouse implementations
  • Integrating legacy relational data models with cloud-native structured data formats like Parquet and ORC
  • Mapping business entity definitions across departments to ensure consistent column semantics in shared datasets
  • Deciding between row-based and columnar storage based on query patterns in OLTP and OLAP systems
  • Implementing data type standardization across systems to prevent implicit casting errors in ETL pipelines
  • Assessing the impact of null handling policies on downstream reporting accuracy
  • Documenting metadata lineage for structured fields to support audit and compliance requirements

Module 2: Data Modeling for Scalable Structured Systems

  • Choosing between normalized, star, and wide-table models based on query performance and update frequency
  • Designing slowly changing dimensions for historical tracking in enterprise data warehouses
  • Partitioning large fact tables by time or region to optimize query performance and data lifecycle management
  • Implementing surrogate key strategies to decouple source system identifiers from analytical models
  • Resolving conformed dimension conflicts when integrating data from multiple operational systems
  • Denormalizing tables for analytical use cases while maintaining referential integrity constraints
  • Modeling time-series data with appropriate primary and clustering keys in distributed databases
  • Validating model assumptions against actual data distribution and cardinality post-ingestion

Module 3: Ingestion and Pipeline Architecture

  • Configuring batch frequency versus latency trade-offs for daily, hourly, or near-real-time structured data loads
  • Implementing change data capture (CDC) using log parsing versus polling mechanisms in RDBMS sources
  • Handling schema drift during ingestion by defining validation rules and error quarantine processes
  • Designing idempotent ingestion pipelines to prevent data duplication during retries
  • Selecting between micro-batch and streaming ingestion for structured event data from message queues
  • Compressing and encrypting structured data payloads in transit between on-prem and cloud environments
  • Monitoring ingestion pipeline backpressure and tuning parallelism in Spark or Flink workloads
  • Implementing data freshness checks and alerting for SLA-bound structured datasets

Module 4: Storage Formats and Optimization

  • Choosing Parquet, Avro, or ORC based on compression ratio, query speed, and schema evolution needs
  • Tuning Parquet row group size and page size for optimal I/O performance on cloud object storage
  • Implementing Z-Order or bin-packing clustering to co-locate related records in cloud data lakes
  • Managing file size fragmentation in append-heavy workloads using compaction strategies
  • Enabling predicate pushdown and column pruning in query engines by aligning schema design with access patterns
  • Encrypting structured data at rest using customer-managed or provider-managed keys in cloud storage
  • Versioning structured data files to support reproducibility and rollback in analytical environments
  • Applying data masking rules at ingestion time for PII fields stored in structured formats

Module 5: Query Engines and Performance Tuning

  • Selecting between Presto, Trino, Spark SQL, and cloud-native engines based on concurrency and cost
  • Configuring executor memory and shuffle partitions to avoid out-of-memory errors on large joins
  • Creating and maintaining statistics for cost-based optimizers in distributed SQL engines
  • Indexing strategies in columnar databases, including min/max, bloom filters, and secondary indexes
  • Optimizing join order and broadcast hints for skewed datasets in analytical queries
  • Partition pruning implementation to reduce scan volume in time-series queries
  • Monitoring and diagnosing slow queries using execution plan analysis and runtime profiling
  • Implementing materialized views or pre-aggregated tables for high-frequency reporting queries

Module 6: Data Quality and Validation

  • Defining and enforcing field-level constraints such as uniqueness, referential integrity, and allowed value sets
  • Implementing automated anomaly detection for numeric field distributions using statistical thresholds
  • Validating foreign key relationships across datasets in distributed environments where constraints aren't enforced
  • Configuring data quality scorecards with weighted rules for executive reporting
  • Handling missing or invalid records by routing to quarantine tables with remediation workflows
  • Tracking data completeness across expected ingestion windows using watermark validation
  • Integrating data profiling results into CI/CD pipelines for data model changes
  • Establishing ownership and escalation paths for recurring data quality failures

Module 7: Governance and Compliance

  • Classifying structured data fields as PII, PHI, or financial using pattern-based and dictionary-driven scanners
  • Implementing dynamic data masking policies in query engines based on user roles and sensitivity labels
  • Auditing access to sensitive structured tables using query logging and monitoring tools
  • Enforcing data retention policies with automated purging or archival based on regulatory requirements
  • Mapping data lineage from source systems to reports to support GDPR and CCPA data subject requests
  • Managing consent flags in structured customer records and propagating them through downstream systems
  • Documenting data ownership and stewardship responsibilities in a centralized catalog
  • Conducting impact analysis for schema changes using lineage to identify dependent reports and models

Module 8: Integration and Interoperability

  • Exposing structured data via REST or GraphQL APIs with pagination, filtering, and rate limiting
  • Synchronizing data between cloud data warehouses and on-prem BI tools using secure connectors
  • Transforming structured data for machine learning pipelines, including feature encoding and normalization
  • Implementing data sharing agreements using secure views or data exchange platforms
  • Handling time zone and calendar discrepancies when integrating structured data across global regions
  • Standardizing currency and unit conversions in financial structured datasets
  • Resolving identifier mismatches when merging customer data from disparate CRM and ERP systems
  • Generating OpenAPI specifications for structured data endpoints consumed by downstream applications

Module 9: Monitoring, Cost, and Lifecycle Management

  • Tracking storage growth of structured datasets to forecast cloud cost and capacity needs
  • Implementing auto-tiering policies to move cold structured data to lower-cost storage classes
  • Setting up alerts for abnormal query costs driven by full table scans on large structured tables
  • Right-sizing compute clusters based on historical utilization patterns for structured workloads
  • Archiving and decommissioning obsolete structured datasets with stakeholder approval workflows
  • Measuring and reporting on data pipeline success rates and error trends over time
  • Optimizing query caching strategies for frequently accessed structured reports
  • Conducting quarterly cost attribution reviews to allocate structured data expenses by business unit
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