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Data Architecture in Big Data

Data Architecture in Big Data

$298.00
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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 Data Architecture in Big Data course cover?

Data Architecture in Big Data is covered here in 9 modules: Defining Scalable Data Ingestion Strategies, Distributed Storage Architecture and Optimization, Data Cataloging and Metadata Management and 6 more. The outline lists 72 specific topics, opening with select between batch and streaming ingestion based on SLA requirements, data source volatility, and downstream processing latency tolerance.

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

The work is sequenced in 9 stages. It starts with Defining Scalable Data Ingestion Strategies, moves through Distributed Storage Architecture and Optimization and Data Cataloging and Metadata Management, and ends at Multi-Cloud and Hybrid Data Architecture. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Defining Scalable Data Ingestion Strategies. It works through select between batch and streaming ingestion based on SLA requirements, data source volatility, and downstream processing latency tolerance., design schema evolution handling in ingestion pipelines to support backward and forward compatibility across Avro or Protobuf formats., implement idempotent data ingestion to prevent duplication in the event of retries or consumer rebalancing.

How is the Data Architecture in Big Data course delivered?

The Data Architecture 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 Data Architecture in Big Data course cost?

The Data Architecture 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 Architecture Toolkit, Big Data Architecture and Data Architecture Kit, Enterprise Architecture Data Governance in Big Data, Big Data Processing and Data Architecture Kit.

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

This curriculum spans the design and operational rigor of a multi-workshop technical advisory engagement, addressing data architecture decisions across ingestion, storage, governance, and cross-cloud systems with the depth required to guide enterprise-scale big data platform development.

Module 1: Defining Scalable Data Ingestion Strategies

  • Select between batch and streaming ingestion based on SLA requirements, data source volatility, and downstream processing latency tolerance.
  • Design schema evolution handling in ingestion pipelines to support backward and forward compatibility across Avro or Protobuf formats.
  • Implement idempotent data ingestion to prevent duplication in the event of retries or consumer rebalancing in Kafka consumers.
  • Configure backpressure mechanisms in streaming pipelines to prevent system overload during traffic spikes.
  • Integrate authentication and authorization for data sources using OAuth, Kerberos, or mutual TLS in ingestion agents.
  • Choose between push and pull ingestion models based on source system capabilities and network topology constraints.
  • Instrument ingestion pipelines with distributed tracing to isolate latency bottlenecks across microservices.
  • Manage ingestion pipeline versioning and deployment using CI/CD workflows with rollback capabilities.

Module 2: Distributed Storage Architecture and Optimization

  • Select file formats (Parquet, ORC, Delta Lake) based on query patterns, update requirements, and compute engine compatibility.
  • Implement partitioning and bucketing strategies to minimize data scan volume for common analytical queries.
  • Configure storage tiering policies between hot (SSD), warm (HDD), and cold (object storage) tiers based on access frequency.
  • Design data lifecycle management workflows to automate archival and deletion according to compliance policies.
  • Optimize data layout for locality in distributed file systems to reduce cross-node data transfers.
  • Enforce encryption at rest with customer-managed keys and integrate with centralized key management systems (KMS).
  • Balance replication factor settings against durability requirements and storage cost in HDFS or object storage.
  • Monitor and remediate small file problems in distributed storage to prevent NameNode or metadata service overload.

Module 3: Data Cataloging and Metadata Management

  • Integrate automated metadata extraction from ingestion and ETL pipelines into a centralized catalog (e.g., Apache Atlas, DataHub).
  • Define ownership and stewardship fields for datasets and enforce metadata completeness as part of pipeline deployment gates.
  • Implement lineage tracking across batch and streaming jobs to support impact analysis and regulatory audits.
  • Standardize business glossary terms and map them to technical schema attributes for cross-functional alignment.
  • Configure access-controlled metadata views to restrict visibility of sensitive datasets in the catalog UI.
  • Design metadata retention policies aligned with data retention schedules to avoid orphaned entries.
  • Enable full-text and semantic search over metadata using indexing engines like Elasticsearch.
  • Automate classification of sensitive data fields using pattern matching and NLP models within the catalog pipeline.

Module 4: Governance, Compliance, and Data Security

  • Implement row- and column-level security in query engines (e.g., Apache Ranger, Unity Catalog) based on user roles and attributes.
  • Design audit logging for data access and modification at storage and compute layers to meet SOX or GDPR requirements.
  • Integrate data masking and tokenization in query results for PII fields based on user clearance levels.
  • Map data processing activities to regulatory frameworks (e.g., CCPA, HIPAA) and document data flow diagrams.
  • Enforce data retention and right-to-be-forgotten workflows across distributed systems with cross-system coordination.
  • Conduct data protection impact assessments (DPIAs) before launching new data products involving personal data.
  • Validate consent management signals from source systems and propagate them through the data pipeline.
  • Implement secure cross-account or cross-tenant data sharing using signed URLs or secure views.

Module 5: Building Reliable Data Processing Pipelines

  • Select orchestration frameworks (Airflow, Prefect, Dagster) based on scheduling complexity, monitoring needs, and team expertise.
  • Implement pipeline idempotency and retry logic with state tracking to ensure consistency after partial failures.
  • Define SLA monitoring and alerting for pipeline execution duration and data freshness using Prometheus and Grafana.
  • Structure pipeline DAGs to minimize cascading failures through conditional branching and circuit breakers.
  • Manage configuration drift across environments using version-controlled pipeline definitions and templating.
  • Isolate pipeline execution contexts using containers or serverless functions to prevent resource contention.
  • Validate data quality at pipeline checkpoints using constraints (e.g., Great Expectations) and fail fast on violations.
  • Implement pipeline version rollback using artifact repositories and deployment manifests.

Module 6: Real-Time Stream Processing Architecture

  • Choose between Kafka Streams, Flink, and Spark Structured Streaming based on state management and exactly-once semantics needs.
  • Design event time processing with watermarking to handle late-arriving data in time-windowed aggregations.
  • Implement state backend configuration (RocksDB, Redis) for fault-tolerant stream processing with low recovery time.
  • Scale stream processors dynamically based on lag metrics and CPU utilization in Kubernetes deployments.
  • Ensure end-to-end encryption and authentication in message brokers for regulated data streams.
  • Manage schema registry enforcement to prevent incompatible consumer-producer interactions in Avro topics.
  • Optimize serialization overhead by selecting binary formats and tuning buffer sizes in producer/consumer clients.
  • Monitor and control consumer group rebalancing frequency to minimize processing interruptions.

Module 7: Data Quality and Observability Engineering

  • Embed data quality checks (completeness, uniqueness, consistency) into ingestion and transformation stages.
  • Deploy statistical profiling workflows to detect anomalies in data distributions over time.
  • Establish data freshness SLAs and trigger alerts when upstream data delays exceed thresholds.
  • Instrument pipelines with structured logging and correlation IDs to trace data lineage across systems.
  • Build dashboards to visualize data quality KPIs across domains for operational oversight.
  • Implement automated quarantine of bad data records and notify stewards via incident management systems.
  • Correlate data quality events with infrastructure metrics (e.g., disk I/O, network latency) to identify root causes.
  • Define data quality scoring models to prioritize remediation efforts across datasets.

Module 8: Cost Management and Performance Tuning

  • Right-size compute clusters based on workload patterns using autoscaling policies and historical utilization data.
  • Implement query cost estimation and blocking for ad hoc queries exceeding resource thresholds.
  • Optimize shuffle operations in Spark by tuning partition counts and enabling adaptive query execution.
  • Negotiate reserved capacity or spot instance usage for non-critical workloads to reduce cloud spend.
  • Monitor storage-to-compute data transfer costs and co-locate workloads with data where feasible.
  • Use materialized views or pre-aggregated tables to accelerate frequent analytical queries.
  • Conduct regular cost attribution reporting by team, project, or dataset using tagging and cloud billing APIs.
  • Implement data compaction routines to reduce file count and improve query performance on object storage.

Module 9: Multi-Cloud and Hybrid Data Architecture

  • Design data replication strategies across cloud providers using change data capture (CDC) tools like Debezium.
  • Implement federated query engines to access data across on-prem and cloud storage without full migration.
  • Standardize data interchange formats and APIs to ensure interoperability between heterogeneous environments.
  • Address data residency requirements by routing workloads to region-specific clusters with local data copies.
  • Establish secure data transfer mechanisms (e.g., private interconnects, VPC peering) across environments.
  • Manage identity federation across cloud providers using SAML or OIDC integration with enterprise directories.
  • Evaluate data egress costs and throttling policies when designing cross-cloud data workflows.
  • Test disaster recovery failover procedures for data and metadata services across geographic regions.
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