Big Data in Application Development

Description

What does the Big Data in Application Development course cover?

Big Data in Application Development is covered here in 9 modules: Strategic Alignment of Big Data with Application Lifecycle, Data Architecture for Scalable Application Systems, Real-Time Data Ingestion and Stream Processing and 6 more. The outline lists 72 specific topics, opening with define data-driven KPIs that align with application performance and business outcomes during product roadmap planning.

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

The work is sequenced in 9 stages. It starts with Strategic Alignment of Big Data with Application Lifecycle, moves through Data Architecture for Scalable Application Systems and Real-Time Data Ingestion and Stream Processing, and ends at Cross-System Data Integration and Interoperability. 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 Application Development course?

Module 1 is Strategic Alignment of Big Data with Application Lifecycle. It works through define data-driven KPIs that align with application performance and business outcomes during product roadmap planning., select application domains where big data integration delivers measurable ROI versus traditional data approaches., integrate data strategy into agile sprint planning by prioritizing data-intensive user stories with high business impact. and 5 more.

How is the Big Data in Application Development course delivered?

The Big Data in Application Development 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 Application Development course cost?

The Big Data in Application Development 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.

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

This curriculum spans the technical and operational breadth of a multi-workshop program focused on embedding big data practices into enterprise application development, comparable to an internal capability build-out for data-intensive systems across product, platform, and compliance functions.

Module 1: Strategic Alignment of Big Data with Application Lifecycle

Define data-driven KPIs that align with application performance and business outcomes during product roadmap planning.
Select application domains where big data integration delivers measurable ROI versus traditional data approaches.
Integrate data strategy into agile sprint planning by prioritizing data-intensive user stories with high business impact.
Establish cross-functional data squads comprising developers, data engineers, and product owners to co-design data-enabled features.
Conduct feasibility assessments for real-time data ingestion versus batch processing based on SLA requirements.
Balance technical debt accumulation from rapid data feature deployment against long-term data architecture sustainability.
Negotiate data access and latency requirements with stakeholders during application requirement gathering.
Map data lineage from source systems to application outputs to support auditability and compliance.

Module 2: Data Architecture for Scalable Application Systems

Choose between Lambda and Kappa architectures based on application consistency requirements and operational complexity tolerance.
Design schema evolution strategies for Avro or Protobuf to support backward and forward compatibility in microservices.
Implement polyglot persistence by selecting appropriate data stores (e.g., Cassandra for time-series, Elasticsearch for search) per use case.
Partition large datasets by business key (e.g., tenant, region) to enable efficient data isolation and query performance.
Define data sharding strategies in distributed databases to prevent hotspots under high write loads.
Optimize data serialization formats across service boundaries to reduce network overhead and deserialization latency.
Enforce data contract validation at API gateways to prevent malformed data from entering the pipeline.
Implement caching layers with TTL and cache invalidation logic to reduce load on backend data sources.

Module 3: Real-Time Data Ingestion and Stream Processing

Configure Kafka topics with appropriate replication factor, partition count, and retention policies based on throughput and durability needs.
Handle backpressure in Flink or Spark Streaming applications by tuning micro-batch intervals and buffer sizes.
Implement exactly-once processing semantics using transactional sinks and idempotent writers in stateful stream jobs.
Deploy change data capture (CDC) tools like Debezium to stream database changes into real-time application workflows.
Monitor end-to-end event latency from source to sink to detect processing bottlenecks in streaming pipelines.
Design fault-tolerant stream processing topologies with checkpointing and state backend configuration.
Filter and transform high-volume streams at ingestion points to reduce downstream processing load.
Secure Kafka clusters using SSL/TLS encryption and SASL authentication for inter-service communication.

Module 4: Data Quality and Governance in Production Applications

Embed data validation rules (e.g., range checks, referential integrity) within application logic at ingestion points.
Implement automated data profiling jobs to detect schema drift and anomaly patterns in incoming datasets.
Assign data ownership roles within development teams to enforce accountability for data accuracy and timeliness.
Log data quality metrics (completeness, uniqueness, accuracy) alongside application telemetry for root cause analysis.
Apply data masking or tokenization in non-production environments to comply with privacy regulations.
Version critical datasets and track changes using metadata repositories for reproducibility.
Integrate data quality gates into CI/CD pipelines to prevent deployment of data-breaking changes.
Respond to data incident alerts by triggering rollback procedures or circuit breakers in dependent services.

Module 5: Machine Learning Integration in Application Workflows

Design feature stores with consistent training and serving views to eliminate training-serving skew.
Version machine learning models and associate them with specific application releases for traceability.
Implement A/B testing frameworks to compare model performance across user cohorts in production.
Monitor model drift using statistical tests on prediction distributions and trigger retraining workflows.
Cache model predictions with expiration policies to reduce inference latency for frequently accessed inputs.
Isolate ML inference workloads using container orchestration to manage resource contention.
Expose model endpoints via REST/gRPC APIs with rate limiting and authentication for secure access.
Log prediction inputs and outputs for audit trails and regulatory compliance in high-stakes applications.

Module 6: Performance Optimization and Cost Management

Right-size cluster resources for Spark jobs by analyzing executor memory, core utilization, and shuffle spill metrics.
Implement data compaction and file format optimization (e.g., Parquet with Z-Ordering) to reduce query costs.
Apply query pushdown and predicate filtering in data sources to minimize data movement across network.
Negotiate reserved instance pricing for long-running data processing clusters to reduce cloud expenditure.
Use autoscaling policies with cooldown periods to handle variable data loads without overprovisioning.
Monitor I/O patterns and cache frequently accessed data in memory or SSD-backed storage tiers.
Optimize join strategies (broadcast vs. shuffle) based on dataset size and skew distribution.
Implement data lifecycle policies to archive cold data to low-cost storage and delete obsolete records.

Module 7: Security, Privacy, and Regulatory Compliance

Enforce role-based access control (RBAC) on data platforms to restrict access by application component and user role.
Encrypt data at rest using customer-managed keys and in transit with TLS 1.3 or higher.
Conduct data protection impact assessments (DPIA) before launching applications handling PII.
Implement audit logging for all data access and modification events with immutable storage.
Apply differential privacy techniques in analytics features to prevent re-identification attacks.
Design data residency strategies to comply with jurisdiction-specific regulations (e.g., GDPR, CCPA).
Integrate data subject request workflows (e.g., right to erasure) into application CRUD operations.
Validate third-party data processors for SOC 2 or ISO 27001 compliance before integration.

Module 8: Monitoring, Observability, and Incident Response

Instrument data pipelines with distributed tracing to diagnose latency across microservices and data stores.
Define SLOs for data freshness, pipeline uptime, and query latency with corresponding error budgets.
Correlate application errors with data pipeline failures using shared context identifiers (e.g., trace IDs).
Set up anomaly detection on data volume, schema, and null rate metrics using statistical baselines.
Configure alerting thresholds to minimize false positives while ensuring critical data incidents are escalated.
Conduct blameless postmortems for data outages to identify systemic weaknesses in application design.
Simulate data pipeline failures in staging environments to validate application resilience.
Maintain runbooks for common data incidents with step-by-step recovery procedures.

Module 9: Cross-System Data Integration and Interoperability

Design idempotent data synchronization jobs to reconcile discrepancies between transactional and analytical systems.
Implement event-driven integration patterns using message queues to decouple application components.
Map heterogeneous data models across systems using canonical data formats and transformation layers.
Negotiate API contracts with external partners for reliable and versioned data exchange.
Handle rate limiting and retry logic when consuming third-party data feeds with variable availability.
Validate data consistency across distributed systems using reconciliation jobs and checksums.
Use service mesh patterns to manage observability, retries, and timeouts in data-dependent services.
Document data exchange protocols and metadata schemas for onboarding new integration partners.