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Big Data in Big Data

Big Data in Big Data

$302.00
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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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What does the Big Data in Big Data course cover?

Big Data in Big Data is covered here in 9 modules: Strategic Data Infrastructure Planning, Scalable Data Ingestion Architecture, Data Modeling for Analytical Scale and 6 more. The outline lists 72 specific topics, opening with selecting between cloud-native data lakehouses and on-premises Hadoop ecosystems based on compliance, latency, and data gravity constraints.

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

The work is sequenced in 9 stages. It starts with Strategic Data Infrastructure Planning, moves through Scalable Data Ingestion Architecture and Data Modeling for Analytical Scale, and ends at Enterprise Data Governance Frameworks. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Strategic Data Infrastructure Planning. It works through selecting between cloud-native data lakehouses and on-premises Hadoop ecosystems based on compliance, latency, and data gravity constraints., defining data domain ownership models across business units to prevent duplication and ensure accountability., evaluating vendor lock-in risks when adopting managed services like AWS Glue, Azure Synapse, or Google BigQuery. and 5 more.

How is the Big Data in Big Data course delivered?

The Big 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 Big Data in Big Data course cost?

The Big Data in Big Data course is $300 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: Big Data Ethics in Big Data, Big data utilization in Big Data, Big Data Testing in Big Data, Big data processing in Big Data.

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

This curriculum spans the technical and organizational complexity of a multi-workshop program focused on enterprise data platform modernization, comparable to advisory engagements that address data governance, infrastructure migration, and scalable analytics delivery across distributed systems.

Module 1: Strategic Data Infrastructure Planning

  • Selecting between cloud-native data lakehouses and on-premises Hadoop ecosystems based on compliance, latency, and data gravity constraints.
  • Defining data domain ownership models across business units to prevent duplication and ensure accountability.
  • Evaluating vendor lock-in risks when adopting managed services like AWS Glue, Azure Synapse, or Google BigQuery.
  • Establishing data center interconnect bandwidth requirements for hybrid data pipelines with real-time synchronization.
  • Designing multi-region replication strategies for disaster recovery while minimizing cross-region egress costs.
  • Implementing data retention policies that align with legal hold requirements and storage cost optimization.
  • Negotiating SLAs with infrastructure providers for data durability, availability, and repair time objectives.
  • Planning for incremental data migration from legacy EDWs to modern data platforms with zero downtime.

Module 2: Scalable Data Ingestion Architecture

  • Choosing between batch, micro-batch, and streaming ingestion based on source system capabilities and downstream latency needs.
  • Configuring Kafka producers with appropriate serialization, partitioning, and ACK policies to balance throughput and reliability.
  • Implementing idempotent consumers to handle message replay scenarios in event-driven pipelines.
  • Managing schema evolution in Avro or Protobuf across producer-consumer boundaries using schema registry enforcement.
  • Deploying change data capture (CDC) tools like Debezium with transaction log polling frequency tuned to source DB load.
  • Securing data in transit using mutual TLS and encrypting payloads for sensitive PII ingestion.
  • Throttling ingestion rates from high-volume sources to prevent backpressure on downstream systems.
  • Validating data shape and completeness at ingestion points using schema-on-write enforcement.

Module 3: Data Modeling for Analytical Scale

  • Choosing between star schema, Data Vault 2.0, and anchor modeling based on auditability and agility requirements.
  • Partitioning large fact tables by time and bucketing by high-cardinality dimensions to optimize query performance.
  • Implementing slowly changing dimensions (SCD Type 2) with automated versioning and expiry logic.
  • Denormalizing dimension attributes into wide column formats for OLAP workloads with known query patterns.
  • Managing surrogate key generation across distributed data sources with collision-resistant algorithms.
  • Designing immutable fact tables with transaction time and system time for temporal analysis.
  • Indexing Parquet files using min/max statistics and Bloom filters to reduce I/O in analytical queries.
  • Versioning data models to support backward compatibility during schema migrations.

Module 4: Distributed Processing Frameworks

  • Tuning Spark executors for memory overhead, core allocation, and dynamic allocation in YARN or Kubernetes.
  • Optimizing shuffle partitions based on data volume and cluster node count to avoid skew and OOM errors.
  • Choosing between DataFrame, Dataset, and RDD APIs based on type safety and optimization needs.
  • Implementing broadcast joins for small lookup tables to reduce shuffle traffic.
  • Configuring checkpointing intervals for long-running streaming jobs to balance recovery time and storage cost.
  • Managing Python UDF serialization overhead in PySpark using vectorized Pandas functions.
  • Deploying Flink applications with savepoints for stateful processing and version upgrades.
  • Monitoring GC pressure and spill-to-disk events to diagnose performance bottlenecks in processing jobs.

Module 5: Data Quality and Observability

  • Defining data quality rules (completeness, consistency, accuracy) per domain with business stakeholder sign-off.
  • Integrating Great Expectations or Deequ into CI/CD pipelines for data test automation.
  • Setting up anomaly detection on data volume, freshness, and distribution drift using statistical baselines.
  • Instrumenting data pipelines with structured logging and distributed tracing for root cause analysis.
  • Creating data lineage graphs using metadata extraction from ETL jobs and query logs.
  • Alerting on SLA breaches for pipeline completion time using time-series monitoring tools.
  • Implementing data profiling jobs to detect unexpected null rates or value outliers in staging layers.
  • Establishing data incident response protocols with escalation paths and remediation runbooks.

Module 6: Security and Compliance Governance

  • Implementing column- and row-level security in Snowflake or Databricks using dynamic masking policies.
  • Enforcing attribute-based access control (ABAC) integrated with corporate identity providers.
  • Auditing data access patterns using query logs to detect unauthorized PII exposure.
  • Classifying data sensitivity levels using automated scanners and tagging frameworks.
  • Managing encryption keys for data-at-rest using customer-managed KMS with rotation policies.
  • Conducting data protection impact assessments (DPIAs) for new data collection initiatives.
  • Implementing data anonymization techniques (k-anonymity, differential privacy) for regulated analytics.
  • Documenting data processing agreements (DPAs) for third-party data sharing under GDPR or CCPA.

Module 7: Performance Optimization and Cost Control

  • Right-sizing cluster configurations based on historical utilization metrics and auto-scaling policies.
  • Implementing query result caching for frequently accessed reports with cache invalidation rules.
  • Converting cold data to cheaper storage tiers (S3 Glacier, Azure Archive) with retrieval time SLAs.
  • Optimizing file sizing and compaction strategies to reduce small file overhead in data lakes.
  • Using materialized views to pre-aggregate high-latency queries on large datasets.
  • Enforcing query timeouts and resource quotas to prevent runaway jobs in shared clusters.
  • Monitoring compute-to-data ratios to identify inefficient data locality and network transfer waste.
  • Conducting cost attribution by tagging workloads with project, team, and cost center metadata.

Module 8: Machine Learning Pipeline Integration

  • Versioning training datasets using DVC or MLflow to ensure reproducible model builds.
  • Serving feature vectors from a feature store with low-latency APIs for online inference.
  • Scheduling retraining pipelines based on data drift detection thresholds and model decay metrics.
  • Validating model inputs against schema and distribution expectations in production serving layers.
  • Logging prediction requests and outcomes for monitoring, bias detection, and audit trails.
  • Managing model registry lifecycle with staging transitions (dev → staging → prod) and rollback procedures.
  • Deploying models using serverless inference endpoints with auto-scaling and cold start mitigation.
  • Integrating A/B testing frameworks to compare model performance in production traffic splits.

Module 9: Enterprise Data Governance Frameworks

  • Establishing a centralized data catalog with automated metadata harvesting from sources and pipelines.
  • Implementing data stewardship roles with defined responsibilities for domain-specific data assets.
  • Enforcing metadata completeness requirements (owner, SLA, sensitivity) before production promotion.
  • Integrating data governance tools with DevOps pipelines for policy-as-code enforcement.
  • Conducting quarterly data inventory audits to identify shadow data systems and redundant datasets.
  • Defining data product contracts with API-level SLAs for downstream consumer reliability.
  • Mapping data flows across systems to comply with regulatory data mapping requirements.
  • Operating a data governance council with cross-functional representation to resolve policy conflicts.
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