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Big Data Analytics in Big Data

Big Data Analytics in Big Data

$300.00
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Self-paced • Lifetime updates
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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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What does the Big Data Analytics in Big Data course cover?

Big Data Analytics in Big Data is covered here in 9 modules: Defining Data Strategy and Business Alignment, Designing Scalable Data Ingestion Architectures, Building and Governing Data Storage Layers and 6 more. The outline lists 72 specific topics, opening with selecting use cases with measurable ROI, such as customer churn prediction or supply chain optimization, based on stakeholder pain points and data.

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

The work is sequenced in 9 stages. It starts with Defining Data Strategy and Business Alignment, moves through Designing Scalable Data Ingestion Architectures and Building and Governing Data Storage Layers, and ends at Establishing Data Governance and Operational Sustainability. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Defining Data Strategy and Business Alignment. It works through selecting use cases with measurable ROI, such as customer churn prediction or supply chain optimization, based on stakeholder pain points and data availability, mapping data initiatives to enterprise KPIs and securing executive sponsorship for cross-functional data projects, conducting feasibility assessments that evaluate data maturity, infrastructure readiness, and skill gaps before.

How is the Big Data Analytics in Big Data course delivered?

The Big Data Analytics 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 Analytics in Big Data course cost?

The Big Data Analytics 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: Big Data Analytics Toolkit, Big Data And Analytics Toolkit, Prescriptive Analytics in Big Data, Omnichannel Analytics in Big Data.

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

This curriculum spans the design and operationalization of enterprise-scale data platforms, comparable in scope to a multi-phase internal capability program that integrates data strategy, governance, and engineering practices across complex organizational units.

Module 1: Defining Data Strategy and Business Alignment

  • Selecting use cases with measurable ROI, such as customer churn prediction or supply chain optimization, based on stakeholder pain points and data availability
  • Mapping data initiatives to enterprise KPIs and securing executive sponsorship for cross-functional data projects
  • Conducting feasibility assessments that evaluate data maturity, infrastructure readiness, and skill gaps before project initiation
  • Establishing data ownership models across business units to prevent siloed analytics efforts
  • Aligning data architecture decisions with long-term business growth scenarios, including mergers and geographic expansion
  • Creating feedback loops between analytics teams and business units to refine use case scope and success metrics
  • Negotiating data access rights with legal and compliance teams for regulated domains such as healthcare or finance
  • Documenting data lineage from source systems to dashboards to support auditability and trust

Module 2: Designing Scalable Data Ingestion Architectures

  • Choosing between batch and streaming ingestion based on SLAs, data volume, and downstream processing requirements
  • Implementing change data capture (CDC) for real-time replication from OLTP databases without impacting transaction performance
  • Configuring message brokers like Apache Kafka for fault-tolerant, high-throughput data pipelines
  • Handling schema evolution in streaming data by implementing schema registry and backward compatibility policies
  • Designing retry and dead-letter queue mechanisms for failed ingestion jobs in distributed systems
  • Evaluating cloud-native ingestion services (e.g., AWS Kinesis, Azure Event Hubs) versus on-premise solutions for hybrid environments
  • Securing data in transit using TLS and managing credentials via secret management tools like HashiCorp Vault
  • Monitoring ingestion latency and throughput using observability tools like Prometheus and Grafana

Module 3: Building and Governing Data Storage Layers

  • Selecting file formats (Parquet, ORC, Avro) based on query patterns, compression needs, and schema evolution support
  • Partitioning and bucketing large datasets in data lakes to optimize query performance and reduce compute costs
  • Implementing data lifecycle policies to transition cold data from hot storage (SSD) to archival tiers (e.g., S3 Glacier)
  • Enforcing data access controls at the object and column level using Apache Ranger or AWS Lake Formation
  • Designing multi-zone, multi-region storage architectures for disaster recovery and data sovereignty compliance
  • Validating data integrity after ETL processes using checksums and row count reconciliation
  • Managing metadata consistency across data catalog tools like Apache Atlas or AWS Glue Data Catalog
  • Handling unstructured data (logs, images, text) by integrating object storage with content indexing systems

Module 4: Implementing Data Processing Frameworks

  • Choosing between Apache Spark, Flink, and cloud-managed services (e.g., Dataproc, EMR) based on latency and scalability needs
  • Optimizing Spark jobs by tuning executor memory, parallelism, and shuffle partitions for large datasets
  • Writing idempotent transformations to support replayability in case of pipeline failures
  • Integrating UDFs (user-defined functions) in Python or Scala while managing serialization and performance overhead
  • Validating data quality during processing using embedded rules for completeness, consistency, and validity
  • Implementing watermarking and windowing strategies in streaming jobs to handle late-arriving data
  • Managing dependencies and versioning for processing jobs using CI/CD pipelines and containerization
  • Monitoring resource utilization and job failures using cluster-level metrics and alerting systems

Module 5: Ensuring Data Quality and Observability

  • Defining data quality rules (e.g., null rate thresholds, value distributions) per dataset and integrating them into pipelines
  • Implementing automated data validation checks using tools like Great Expectations or Deequ
  • Setting up anomaly detection on data drift using statistical baselines and alerting on deviations
  • Creating data quality dashboards that show health scores across pipelines and datasets
  • Establishing ownership for data issue resolution and defining SLAs for incident response
  • Logging data lineage and transformation history to support root cause analysis during data incidents
  • Conducting root-cause analysis when data discrepancies impact business decisions or reporting
  • Integrating data observability into DevOps workflows to catch quality issues before production deployment

Module 6: Enabling Secure and Compliant Data Access

  • Implementing role-based access control (RBAC) and attribute-based access control (ABAC) in data platforms
  • Masking sensitive data (PII, PCI) dynamically at query time using row- and column-level security
  • Conducting data protection impact assessments (DPIAs) for new analytics projects under GDPR or CCPA
  • Managing data residency requirements by deploying region-specific data zones and access gateways
  • Encrypting data at rest using customer-managed keys and rotating keys on a defined schedule
  • Auditing data access patterns and generating compliance reports for internal and external auditors
  • Integrating with enterprise identity providers (e.g., Active Directory, Okta) for centralized authentication
  • Handling data subject access requests (DSARs) by building traceability from individual records to source systems

Module 7: Deploying Analytics and Machine Learning at Scale

  • Orchestrating complex workflows using Apache Airflow or cloud schedulers with dependency management and retry logic
  • Versioning datasets and models using tools like DVC or MLflow to ensure reproducibility
  • Deploying machine learning models as REST APIs with auto-scaling and load balancing in containerized environments
  • Monitoring model performance decay and triggering retraining based on data drift or accuracy thresholds
  • Integrating feature stores (e.g., Feast, Tecton) to ensure consistency between training and serving data
  • Validating model inputs for schema and distribution compliance before inference
  • Managing A/B testing frameworks to evaluate model impact on business metrics in production
  • Logging prediction requests and outcomes for audit, debugging, and regulatory compliance

Module 8: Optimizing Performance and Cost Efficiency

  • Right-sizing compute clusters based on workload patterns and leveraging auto-scaling policies
  • Implementing data compaction and indexing strategies to reduce query execution time and cost
  • Using spot instances or preemptible VMs for fault-tolerant batch workloads to reduce cloud spend
  • Setting budget alerts and cost allocation tags to track spending by team, project, or dataset
  • Optimizing query performance through predicate pushdown, column pruning, and caching
  • Consolidating underutilized clusters and retiring unused datasets to reduce operational overhead
  • Conducting regular cost reviews with finance and engineering to align spending with business value
  • Evaluating total cost of ownership (TCO) when choosing between open-source and managed cloud services

Module 9: Establishing Data Governance and Operational Sustainability

  • Forming a data governance council with representatives from legal, IT, and business units to set policies
  • Defining and enforcing data classification standards (public, internal, confidential, restricted)
  • Creating data dictionaries and business glossaries to standardize terminology across teams
  • Implementing data stewardship roles to manage quality, metadata, and compliance for critical datasets
  • Conducting regular data governance audits to assess policy adherence and identify gaps
  • Integrating data governance tools with CI/CD pipelines to enforce standards during deployment
  • Developing runbooks and escalation procedures for data incidents and outages
  • Planning for technical debt by scheduling refactoring of legacy pipelines and deprecated schemas
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