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IoT applications in Big Data

IoT applications in Big Data

$296.00
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Course access is prepared after purchase and delivered via email
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Self-paced • Lifetime updates
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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 IoT applications in Big Data course cover?

IoT applications in Big Data is covered here in 9 modules: Architecting Scalable IoT Data Ingestion Pipelines, Edge Computing and Distributed Data Processing, Time-Series Data Modeling and Storage and 6 more. The outline lists 72 specific topics, opening with selecting between MQTT, CoAP, and HTTP/2 for device-to-gateway communication based on power constraints and network reliability and closing with conducting capacity planning exercises.

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

The work is sequenced in 9 stages. It starts with Architecting Scalable IoT Data Ingestion Pipelines, moves through Edge Computing and Distributed Data Processing and Time-Series Data Modeling and Storage, and ends at Monitoring, Observability, and Lifecycle Management. Each stage carries its own topic list, so the sequence is followed rather than summarised.

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

Module 1 is Architecting Scalable IoT Data Ingestion Pipelines. It works through selecting between MQTT, CoAP, and HTTP/2 for device-to-gateway communication based on power constraints and network reliability, designing partitioning strategies in Apache Kafka to balance throughput and fault tolerance across thousands of device streams, implementing backpressure mechanisms to prevent ingestion pipeline overload during device burst events and 5 more.

How is the IoT applications in Big Data course delivered?

The IoT applications 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 IoT applications in Big Data course cost?

The IoT applications in Big Data course is $302 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: IoT Analytics in Big Data, IoT efficiency in Big Data, IoT technologies in Big Data, IoT Platform in Big Data Dataset.

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

This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and maintaining enterprise-scale IoT data systems, comparable to the depth required in ongoing internal capability initiatives for industrial IoT and smart infrastructure.

Module 1: Architecting Scalable IoT Data Ingestion Pipelines

  • Selecting between MQTT, CoAP, and HTTP/2 for device-to-gateway communication based on power constraints and network reliability
  • Designing partitioning strategies in Apache Kafka to balance throughput and fault tolerance across thousands of device streams
  • Implementing backpressure mechanisms to prevent ingestion pipeline overload during device burst events
  • Configuring edge buffering on constrained devices for handling intermittent connectivity to cloud endpoints
  • Choosing between batch and streaming ingestion based on SLA requirements for downstream analytics
  • Integrating device authentication at the ingestion layer using X.509 certificates or OAuth2 device flows
  • Deploying regional ingestion endpoints to comply with data sovereignty regulations
  • Monitoring ingestion latency and drop rates across heterogeneous device fleets using distributed tracing

Module 2: Edge Computing and Distributed Data Processing

  • Deciding which data preprocessing tasks (filtering, aggregation, anomaly detection) to execute on edge vs. cloud
  • Allocating compute resources on edge gateways for concurrent AI inference and data buffering under thermal limits
  • Deploying containerized analytics workloads (e.g., Docker, K3s) on resource-constrained edge devices
  • Implementing over-the-air (OTA) update mechanisms for edge application rollbacks and version control
  • Designing local failover logic for edge nodes when upstream connectivity is lost
  • Enforcing security policies on edge devices using hardware-based trusted execution environments (TEEs)
  • Measuring energy consumption trade-offs between local processing and data transmission
  • Calibrating edge model inference frequency to balance accuracy and battery life in mobile sensors

Module 3: Time-Series Data Modeling and Storage

  • Selecting time-series databases (e.g., InfluxDB, TimescaleDB, Amazon Timestream) based on query patterns and retention policies
  • Designing schema for high-cardinality device metadata without degrading query performance
  • Implementing data tiering strategies to move cold time-series data to lower-cost object storage
  • Configuring downsampling policies for long-term aggregation without losing diagnostic resolution
  • Indexing strategies for efficient retrieval of device data across geographic and organizational hierarchies
  • Handling out-of-order data arrival in time-series pipelines using event-time processing and watermarks
  • Defining retention policies aligned with regulatory requirements and business analytics needs
  • Validating data integrity across distributed time-series shards during cluster rebalancing

Module 4: Real-Time Stream Processing with AI Integration

  • Choosing between Apache Flink, Spark Streaming, and ksqlDB based on processing guarantees and latency SLAs
  • Embedding lightweight ML models (e.g., TensorFlow Lite) into stream processors for real-time anomaly scoring
  • Managing stateful operations (session windows, session joins) in fault-tolerant stream topologies
  • Implementing dynamic thresholding in stream processors using rolling statistical baselines
  • Handling schema evolution in streaming data when device firmware updates change payload structure
  • Scaling stream processing clusters elastically in response to seasonal device activity spikes
  • Instrumenting stream pipelines with metrics for detecting processing lag and backpressure
  • Securing inter-component communication in stream topologies using mutual TLS and service mesh

Module 5: Data Governance and Metadata Management

  • Establishing device data ownership and access controls across multi-tenant IoT platforms
  • Implementing data lineage tracking from sensor to dashboard using metadata registries
  • Classifying data sensitivity levels for IoT streams to enforce encryption and retention policies
  • Creating semantic models for device data to enable cross-domain analytics and discovery
  • Automating metadata extraction from device firmware and configuration management systems
  • Enforcing schema validation at ingestion to prevent downstream pipeline corruption
  • Integrating data catalog tools (e.g., Apache Atlas, DataHub) with IoT device registries
  • Managing consent workflows for personal data collected via wearable or consumer IoT devices

Module 6: Machine Learning for IoT Anomaly Detection and Predictive Maintenance

  • Selecting between supervised, unsupervised, and semi-supervised models based on labeled failure data availability
  • Engineering features from multivariate time-series signals (e.g., FFT, rolling entropy, cross-correlation)
  • Addressing concept drift in deployed models due to environmental or device aging effects
  • Designing feedback loops to incorporate operator validation of predicted anomalies into retraining
  • Deploying ensemble models to reduce false positives in high-consequence industrial settings
  • Implementing model versioning and A/B testing for iterative improvement of detection accuracy
  • Quantifying uncertainty in model predictions to support human-in-the-loop decision making
  • Optimizing model inference latency to meet real-time response requirements in control systems

Module 7: Security, Privacy, and Compliance in IoT Data Flows

  • Implementing end-to-end encryption for data in transit and at rest across edge-to-cloud pipelines
  • Designing role-based access control (RBAC) for device data across operational and IT teams
  • Conducting threat modeling for IoT architectures to identify attack surfaces in data pathways
  • Applying data minimization techniques to reduce storage of personally identifiable information (PII)
  • Generating audit logs for data access and modification in regulated environments (e.g., HIPAA, GDPR)
  • Hardening device firmware against tampering and unauthorized data exfiltration
  • Integrating SIEM systems with IoT platform logs for centralized security monitoring
  • Validating third-party device compliance with security baselines before onboarding

Module 8: System Integration and Interoperability

  • Mapping heterogeneous device data models to a unified enterprise data fabric using semantic ontologies
  • Integrating IoT data with ERP, MES, and CMMS systems using event-driven APIs
  • Resolving timestamp discrepancies across devices with unsynchronized clocks using NTP and PTP
  • Implementing data quality checks at integration points to prevent error propagation
  • Designing idempotent ingestion workflows to handle duplicate messages from unreliable transports
  • Orchestrating data synchronization between on-premise SCADA systems and cloud analytics platforms
  • Using API gateways to manage rate limiting, authentication, and versioning for IoT data services
  • Establishing SLAs for data availability and freshness in cross-system workflows

Module 9: Monitoring, Observability, and Lifecycle Management

  • Defining SLOs for data pipeline uptime, latency, and completeness across device cohorts
  • Instrumenting device firmware with telemetry for connectivity, power, and transmission success
  • Correlating infrastructure metrics (CPU, memory) with data throughput in edge processing nodes
  • Creating alerting rules that minimize false positives in high-volume sensor environments
  • Tracking device firmware versions and patch compliance across distributed fleets
  • Automating root cause analysis for data gaps using dependency graphs of pipeline components
  • Managing device decommissioning workflows to archive data and revoke credentials securely
  • Conducting capacity planning exercises based on historical growth of device data volume
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