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Cloud Computing in Big Data

Cloud Computing in Big Data

$298.00
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.
When you get access:
Course access is prepared after purchase and delivered via email
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Self-paced • Lifetime updates
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What does the Cloud Computing in Big Data course cover?

Cloud Computing in Big Data is covered here in 9 modules: Architecting Scalable Data Ingestion Pipelines, Cloud Storage Optimization for Large-Scale Datasets, Distributed Processing Frameworks in the Cloud and 6 more. The outline lists 72 specific topics, opening with selecting between batch and streaming ingestion based on SLA requirements and source system capabilities and closing with enforcing encryption and access controls on.

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

The work is sequenced in 9 stages. It starts with Architecting Scalable Data Ingestion Pipelines, moves through Cloud Storage Optimization for Large-Scale Datasets and Distributed Processing Frameworks in the Cloud, and ends at Multi-Cloud and Hybrid Data Strategies. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Architecting Scalable Data Ingestion Pipelines. It works through selecting between batch and streaming ingestion based on SLA requirements and source system capabilities, designing idempotent ingestion workflows to handle duplicate messages in distributed systems, implementing schema validation at ingestion to enforce data quality before storage and 5 more. It sets the vocabulary the remaining 8 modules build on.

How is the Cloud Computing in Big Data course delivered?

The Cloud Computing 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 Cloud Computing in Big Data course cost?

The Cloud Computing in Big Data course is $296 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: Cognitive Computing in Big Data, Cloud Computing Toolkit, Cloud Computing in Cloud Migration, Cloud Computing and High Performance Computing 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 cloud data platform rollout, reflecting the technical depth and cross-functional coordination required in large-scale data engineering programs across distributed teams.

Module 1: Architecting Scalable Data Ingestion Pipelines

  • Selecting between batch and streaming ingestion based on SLA requirements and source system capabilities
  • Designing idempotent ingestion workflows to handle duplicate messages in distributed systems
  • Implementing schema validation at ingestion to enforce data quality before storage
  • Choosing appropriate serialization formats (Avro, Parquet, JSON) based on query patterns and storage cost
  • Configuring retry mechanisms and dead-letter queues for failed ingestion attempts
  • Integrating change data capture (CDC) tools with transactional databases for real-time replication
  • Scaling ingestion workers dynamically in response to throughput spikes using auto-healing groups
  • Encrypting data in transit and at rest during ingestion using cloud-native KMS integrations

Module 2: Cloud Storage Optimization for Large-Scale Datasets

  • Partitioning data by time or key dimensions to reduce query scan costs in object storage
  • Implementing lifecycle policies to transition cold data from hot to archive tiers automatically
  • Choosing between object storage and managed file systems based on access patterns and concurrency needs
  • Enforcing immutability and versioning for compliance-sensitive datasets
  • Designing cross-region replication strategies for disaster recovery and data locality
  • Applying server-side encryption with customer-managed keys for regulatory compliance
  • Monitoring storage growth trends and setting alerts for anomalous usage spikes
  • Optimizing file sizes to balance metadata overhead and query performance

Module 3: Distributed Processing Frameworks in the Cloud

  • Right-sizing cluster node types based on memory, CPU, and network I/O requirements for workloads
  • Configuring autoscaling policies for Spark clusters using metrics like queue depth and backlog
  • Managing dependency conflicts in shared execution environments across multiple jobs
  • Optimizing shuffle partitions to avoid excessive small files or memory pressure
  • Implementing checkpointing for long-running streaming jobs to ensure fault tolerance
  • Isolating production and development workloads using resource queues or separate clusters
  • Integrating custom metrics into monitoring systems for job-specific performance tracking
  • Choosing between serverless and managed cluster options based on cost and control trade-offs

Module 4: Data Orchestration and Workflow Management

  • Defining dependency graphs with conditional branching for complex ETL workflows
  • Implementing idempotency in tasks to allow safe reruns without side effects
  • Securing access to orchestration platforms using role-based access control and service accounts
  • Parameterizing workflows to support multi-environment deployments (dev, staging, prod)
  • Setting up alerting for task failures, timeouts, and SLA breaches
  • Version-controlling DAGs and workflow definitions using Git integration
  • Managing secrets for database credentials and API keys using secure vault integrations
  • Reconciling clock skew and timezone issues across distributed workflow triggers

Module 5: Real-Time Stream Processing at Scale

  • Selecting windowing strategies (tumbling, sliding, session) based on business logic requirements
  • Handling out-of-order events using watermarking and allowed lateness in stream jobs
  • Scaling consumer groups to match partition counts in message queues
  • Designing stateful processing with fault-tolerant backends like RocksDB or managed storage
  • Monitoring end-to-end latency and backpressure in streaming pipelines
  • Integrating stream processing with external systems using exactly-once semantics where possible
  • Implementing circuit breakers to prevent cascading failures during downstream outages
  • Choosing between Kafka Streams, Flink, and managed services based on operational overhead

Module 6: Cloud-Native Data Warehousing and Analytics

  • Designing star or snowflake schemas optimized for columnar storage and query patterns
  • Managing materialized views and aggregates to accelerate common reporting queries
  • Implementing row-level security policies for multi-tenant or departmental access
  • Controlling query concurrency and resource allocation to prevent resource starvation
  • Estimating and monitoring compute costs per query to enforce budget constraints
  • Integrating external data sources via federated queries while managing performance impact
  • Scheduling and prioritizing maintenance operations like vacuuming and compaction
  • Validating data freshness and pipeline completeness before enabling dashboards

Module 7: Security, Compliance, and Data Governance

  • Implementing attribute-based access control for fine-grained data permissions
  • Conducting data classification to identify PII, PHI, and other regulated data elements
  • Generating audit logs for data access and modification across cloud services
  • Enforcing data retention and deletion policies in alignment with legal requirements
  • Configuring data loss prevention (DLP) tools to detect and redact sensitive content
  • Mapping data lineage across transformations to support impact analysis and compliance reporting
  • Integrating with enterprise identity providers using SAML or OIDC for centralized authentication
  • Conducting regular access reviews and certification for privileged roles

Module 8: Monitoring, Observability, and Cost Management

  • Instrumenting pipelines with custom metrics for business-level KPIs and data quality
  • Correlating logs, metrics, and traces across distributed components for root cause analysis
  • Setting up anomaly detection on data volume, latency, and error rates
  • Allocating cloud costs by team, project, or workload using tagging and labeling strategies
  • Identifying and eliminating orphaned resources and idle clusters to reduce waste
  • Creating operational runbooks for common failure scenarios and escalation paths
  • Validating backup integrity and recovery procedures through periodic drills
  • Establishing SLOs and error budgets for critical data services

Module 9: Multi-Cloud and Hybrid Data Strategies

  • Designing data egress strategies to minimize bandwidth costs between cloud providers
  • Implementing consistent identity and policy management across cloud environments
  • Choosing between data replication and data federation for cross-cloud analytics
  • Managing metadata consistency in hybrid environments with on-prem and cloud sources
  • Addressing latency and reliability challenges in long-haul data transfers
  • Standardizing data formats and APIs to reduce vendor lock-in risks
  • Evaluating managed services across providers for feature parity and operational fit
  • Enforcing encryption and access controls on data moving through public networks
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