Lesson 1: Introduction to Real-Time Monitoring in IBM Blockchain
1.1. Understanding the critical need for real-time monitoring in blockchain networks.
1.2. Overview of key metrics and indicators for blockchain health.
1.3. Challenges of monitoring distributed ledger technology.
1.4. IBM Blockchain Platform’s built-in monitoring capabilities.
1.5. Distinction between passive and active monitoring.
1.6. The role of real-time data in proactive issue resolution.
1.7. Introduction to popular monitoring tools and their relevance to blockchain.
1.8. Setting the stage for advanced monitoring techniques.
1.9. Defining the scope of real-time monitoring in this course.
1.10. Expected outcomes for successful course completion.
Lesson 2: IBM Blockchain Platform Architecture for Monitoring
2.1. Deep dive into the components of an IBM Blockchain Platform network.
2.2. Identifying key monitoring points within the architecture (peers, orderers, CAs).
2.3. Understanding the data flow and potential bottlenecks.
2.4. The role of Kubernetes and OpenShift in the monitoring landscape.
2.5. Monitoring considerations for different deployment options (cloud, on-prem).
2.6. Interpreting logs and events from various components.
2.7. Analyzing resource utilization at the node level.
2.8. Understanding the impact of network topology on monitoring.
2.9. Identifying dependencies between blockchain components.
2.10. Mapping monitoring strategies to the architectural layers.
Lesson 3: Essential Metrics for IBM Blockchain Monitoring
3.1. Defining and tracking key performance indicators (KPIs) for blockchain.
3.2. Monitoring transaction throughput and latency.
3.3. Analyzing block propagation time and consistency.
3.4. Tracking resource utilization (CPU, memory, storage) of nodes.
3.5. Monitoring network connectivity and health.
3.6. Identifying and tracking error rates and exceptions.
3.7. Monitoring chaincode execution performance.
3.8. Tracking peer and orderer health status.
3.9. Monitoring certificate authority availability and performance.
3.10. Prioritizing metrics based on criticality.
Lesson 4: Leveraging IBM Cloud Monitoring with Sysdig
4.1. Introduction to IBM Cloud Monitoring with Sysdig.
4.2. Integrating Sysdig with IBM Blockchain Platform deployments.
4.3. Configuring Sysdig agents for data collection.
4.4. Creating custom dashboards for blockchain metrics in Sysdig.
4.5. Setting up alerts and notifications based on thresholds.
4.6. Analyzing container-level metrics for blockchain nodes.
4.7. Troubleshooting common monitoring issues with Sysdig.
4.8. Utilizing Sysdig for network and application performance monitoring.
4.9. Exploring advanced Sysdig features for anomaly detection.
4.10. Best practices for using Sysdig in a blockchain environment.
Lesson 5: Advanced Log Analysis for IBM Blockchain
5.1. Understanding the structure and format of IBM Blockchain logs.
5.2. Centralized log management strategies (e.g., ELK stack, LogDNA).
5.3. Configuring log forwarding from blockchain nodes.
5.4. Using log analysis tools for pattern recognition and anomaly detection.
5.5. Filtering and searching logs for specific events.
5.6. Correlating logs from different blockchain components.
5.7. Setting up alerts based on critical log events.
5.8. Troubleshooting common issues using log analysis.
5.9. Advanced techniques for log parsing and enrichment.
5.10. Implementing a robust log retention policy.
Lesson 6: Monitoring IBM Blockchain Platform on OpenShift
6.1. Understanding the monitoring capabilities of OpenShift for containerized applications.
6.2. Integrating IBM Blockchain Platform with OpenShift monitoring tools (e.g., Prometheus, Grafana).
6.3. Configuring Prometheus for scraping blockchain node metrics.
6.4. Creating custom Grafana dashboards for OpenShift-based blockchain.
6.5. Setting up alerts and notifications within the OpenShift monitoring framework.
6.6. Monitoring resource utilization and health of Kubernetes pods.
6.7. Troubleshooting monitoring issues in an OpenShift environment.
6.8. Leveraging OpenShift’s built-in logging capabilities.
6.9. Analyzing network traffic within the OpenShift cluster.
6.10. Best practices for monitoring IBM Blockchain on OpenShift.
Lesson 7: Monitoring IBM Blockchain Platform on Kubernetes
7.1. Understanding the monitoring capabilities of native Kubernetes.
7.2. Integrating IBM Blockchain Platform with Kubernetes monitoring tools (e.g., Prometheus, Grafana).
7.3. Configuring Prometheus for scraping blockchain node metrics in Kubernetes.
7.4. Creating custom Grafana dashboards for Kubernetes-based blockchain.
7.5. Setting up alerts and notifications within the Kubernetes monitoring framework.
7.6. Monitoring resource utilization and health of Kubernetes pods.
7.7. Troubleshooting monitoring issues in a Kubernetes environment.
7.8. Leveraging Kubernetes’ built-in logging capabilities.
7.9. Analyzing network traffic within the Kubernetes cluster.
7.10. Best practices for monitoring IBM Blockchain on Kubernetes.
Lesson 8: Advanced Prometheus and Grafana for Blockchain
8.1. Deep dive into advanced Prometheus configuration for blockchain metrics.
8.2. Writing custom Prometheus exporters for specific blockchain data.
8.3. Utilizing Prometheus Alertmanager for sophisticated alerting rules.
8.4. Designing advanced Grafana dashboards with complex queries and visualizations.
8.5. Leveraging Grafana variables and templates for dynamic dashboards.
8.6. Integrating Grafana with other data sources (e.g., databases) for enriched monitoring.
8.7. Troubleshooting performance issues in Prometheus and Grafana.
8.8. Implementing high availability for your monitoring stack.
8.9. Using Grafana annotations for event correlation.
8.10. Optimizing Prometheus and Grafana for large-scale blockchain networks.
Lesson 9: Monitoring Certificate Authorities (CAs)
9.1. Understanding the critical role of CAs in a blockchain network.
9.2. Key metrics for monitoring CA health and performance.
9.3. Monitoring CA availability and response time.
9.4. Tracking certificate issuance and revocation rates.
9.5. Analyzing CA log files for errors and security events.
9.6. Setting up alerts for CA-related issues.
9.7. Monitoring resource utilization of CA nodes.
9.8. Troubleshooting common CA monitoring challenges.
9.9. Implementing proactive measures for CA stability.
9.10. Best practices for securing and monitoring CAs.
Lesson 10: Monitoring Peers and Endorsement Policies
10.1. Understanding the role of peers in processing transactions.
10.2. Key metrics for monitoring peer health and performance.
10.3. Monitoring peer availability and connection status.
10.4. Tracking peer resource utilization (CPU, memory, storage).
10.5. Analyzing peer log files for errors and transaction processing issues.
10.6. Monitoring the status of installed and instantiated chaincode.
10.7. Tracking endorsement policy compliance and failures.
10.8. Setting up alerts for peer-related issues.
10.9. Troubleshooting common peer monitoring challenges.
10.10. Best practices for monitoring peer performance and endorsement.
Lesson 11: Monitoring Orderers and Consensus Mechanisms
11.1. Understanding the role of orderers in transaction ordering and block creation.
11.2. Key metrics for monitoring orderer health and performance.
11.3. Monitoring orderer availability and connection status.
11.4. Tracking orderer resource utilization (CPU, memory, storage).
11.5. Analyzing orderer log files for errors and consensus-related issues.
11.6. Monitoring the state of the consensus mechanism (e.g., Raft, BFT).
11.7. Tracking block creation rate and transaction inclusion.
11.8. Setting up alerts for orderer-related issues.
11.9. Troubleshooting common orderer monitoring challenges.
11.10. Best practices for monitoring orderer performance and consensus.
Lesson 12: Monitoring Network Connectivity and Latency
12.1. Understanding the importance of network health in a distributed ledger.
12.2. Key metrics for monitoring network connectivity between nodes.
12.3. Tracking network latency and jitter.
12.4. Utilizing network monitoring tools (e.g., ping, traceroute) within the monitoring framework.
12.5. Setting up alerts for network connectivity issues.
12.6. Analyzing network traffic patterns.
12.7. Identifying potential network bottlenecks.
12.8. Troubleshooting network-related monitoring challenges.
12.9. Implementing proactive measures for network stability.
12.10. Best practices for monitoring network performance in a blockchain environment.
Lesson 13: Monitoring Chaincode Execution and Performance
13.1. Understanding the lifecycle of chaincode execution.
13.2. Key metrics for monitoring chaincode performance.
13.3. Tracking chaincode invocation latency and throughput.
13.4. Analyzing chaincode resource utilization (CPU, memory).
13.5. Monitoring chaincode container health.
13.6. Identifying and troubleshooting chaincode errors.
13.7. Setting up alerts for chaincode-related issues.
13.8. Profiling chaincode execution for performance optimization.
13.9. Monitoring chaincode logging and output.
13.10. Best practices for monitoring and optimizing chaincode performance.
Lesson 14: Security Monitoring for IBM Blockchain
14.1. Understanding the security aspects of a blockchain network.
14.2. Key metrics for monitoring security-related events.
14.3. Tracking access attempts and authentication failures.
14.4. Monitoring for suspicious network activity.
14.5. Analyzing logs for security-related events and anomalies.
14.6. Setting up alerts for potential security breaches.
14.7. Monitoring certificate expiration and validity.
14.8. Tracking changes to critical configuration files.
14.9. Integrating security information and event management (SIEM) systems.
14.10. Best practices for implementing robust security monitoring.
Lesson 15: Monitoring Storage Utilization and Performance
15.1. Understanding the storage requirements of a blockchain network.
15.2. Key metrics for monitoring storage utilization of nodes.
15.3. Tracking storage growth rate and predicting future needs.
15.4. Monitoring disk I/O performance.
15.5. Analyzing storage-related errors in logs.
15.6. Setting up alerts for storage capacity issues.
15.7. Troubleshooting storage-related monitoring challenges.
15.8. Implementing proactive measures for storage management.
15.9. Best practices for optimizing storage performance.
15.10. Monitoring database performance for state data.
Lesson 16: Monitoring Resource Utilization and Capacity Planning
16.1. Understanding the resource demands of blockchain nodes.
16.2. Key metrics for monitoring CPU, memory, and network utilization.
16.3. Tracking resource usage trends over time.
16.4. Analyzing resource bottlenecks and their impact on performance.
16.5. Setting up alerts for high resource utilization.
16.6. Utilizing monitoring data for capacity planning.
16.7. Predicting future resource needs based on network growth.
16.8. Optimizing resource allocation for efficiency.
16.9. Troubleshooting resource-related monitoring challenges.
16.10. Best practices for resource monitoring and capacity planning.
Lesson 17: Monitoring Transaction Flow and Status
17.1. Understanding the lifecycle of a blockchain transaction.
17.2. Key metrics for monitoring transaction flow.
17.3. Tracking transaction submission rate and confirmation time.
17.4. Monitoring the status of individual transactions.
17.5. Analyzing transaction errors and failures.
17.6. Setting up alerts for transaction-related issues.
17.7. Tracing transactions through the network.
17.8. Troubleshooting transaction processing bottlenecks.
17.9. Implementing end-to-end transaction monitoring.
17.10. Best practices for monitoring transaction flow and status.
Lesson 18: Monitoring Channel Health and Activity
18.1. Understanding the role of channels in isolating transactions.
18.2. Key metrics for monitoring channel health.
18.3. Tracking channel activity and transaction volume.
18.4. Monitoring the state of the ledger on each channel.
18.5. Analyzing channel-related errors in logs.
18.6. Setting up alerts for channel-specific issues.
18.7. Monitoring the health of peers joined to a channel.
18.8. Troubleshooting channel-related monitoring challenges.
18.9. Implementing proactive measures for channel stability.
18.10. Best practices for monitoring channel health and activity.
Lesson 19: Monitoring Membership Services and User Access
19.1. Understanding the role of membership services in access control.
19.2. Key metrics for monitoring membership service health.
19.3. Tracking user registrations and enrollments.
19.4. Monitoring user access attempts and authentication status.
19.5. Analyzing membership service logs for security events.
19.6. Setting up alerts for membership service-related issues.
19.7. Monitoring the revocation list for compromised identities.
19.8. Troubleshooting membership service monitoring challenges.
19.9. Implementing proactive measures for membership service stability.
19.10. Best practices for monitoring membership services and user access.
Lesson 20: Monitoring Chaincode Instantiation and Upgrades
20.1. Understanding the process of chaincode instantiation and upgrades.
20.2. Key metrics for monitoring chaincode deployment status.
20.3. Tracking the success and failure of chaincode operations.
20.4. Analyzing logs for chaincode deployment errors.
20.5. Monitoring the availability of deployed chaincode.
20.6. Setting up alerts for chaincode deployment issues.
20.7. Troubleshooting chaincode deployment monitoring challenges.
20.8. Implementing proactive measures for successful deployments.
20.9. Best practices for monitoring chaincode lifecycle events.
20.10. Monitoring the impact of upgrades on network performance.
Lesson 21: Integrating IBM Blockchain Monitoring with IT Operations
21.1. Aligning blockchain monitoring with existing IT operations workflows.
21.2. Integrating monitoring data into centralized dashboards and reporting.
21.3. Utilizing service desk and ticketing systems for incident management.
21.4. Implementing automated remediation for common issues.
25.5. Defining escalation procedures for critical alerts.
21.6. Establishing communication channels for monitoring teams.
21.7. Integrating monitoring with change management processes.
21.8. Utilizing IT service management (ITSM) tools for monitoring.
21.9. Best practices for integrating blockchain monitoring into IT operations.
21.10. Measuring the effectiveness of integrated monitoring.
Lesson 22: Advanced Alerting Strategies and Incident Response
22.1. Designing sophisticated alerting rules based on multiple metrics.
22.2. Utilizing advanced alerting features (e.g., hysteresis, flapping).
22.3. Implementing tiered alerting based on severity.
22.4. Defining clear incident response procedures.
22.5. Utilizing runbooks for common incident scenarios.
22.6. Conducting post-incident reviews and analysis.
22.7. Automating incident response actions.
22.8. Integrating alerting with on-call rotation systems.
22.9. Best practices for effective alerting and incident response.
22.10. Measuring the efficiency of your incident response process.
Lesson 23: Performance Analysis and Optimization
23.1. Utilizing monitoring data for performance analysis.
23.2. Identifying performance bottlenecks in the blockchain network.
23.3. Analyzing the impact of configuration changes on performance.
23.4. Utilizing profiling tools for in-depth performance analysis.
23.5. Optimizing node configurations for improved performance.
23.6. Tuning chaincode for better execution speed.
23.7. Optimizing network configuration for reduced latency.
23.8. Utilizing historical monitoring data for performance trends.
23.9. Best practices for performance analysis and optimization.
23.10. Measuring the impact of optimization efforts.
Lesson 24: Capacity Planning and Scaling
24.1. Utilizing monitoring data for capacity planning.
24.2. Predicting future resource needs based on network growth.
24.3. Determining optimal scaling strategies based on load.
24.4. Monitoring the impact of scaling on network performance.
24.5. Utilizing monitoring to identify scaling bottlenecks.
24.6. Implementing automated scaling based on monitoring metrics.
24.7. Troubleshooting capacity-related monitoring challenges.
24.8. Best practices for capacity planning and scaling.
24.9. Measuring the effectiveness of your scaling strategy.
24.10. Utilizing simulation tools for capacity planning.
Lesson 25: Monitoring Multi-Cloud and Hybrid Deployments
25.1. Understanding the challenges of monitoring across different cloud providers.
25.2. Implementing a unified monitoring strategy for multi-cloud deployments.
25.3. Utilizing tools for monitoring across different cloud environments.
25.4. Monitoring network connectivity between cloud providers.
25.5. Analyzing log data from disparate sources.
25.6. Setting up alerts for cross-cloud issues.
25.7. Troubleshooting monitoring challenges in a multi-cloud environment.
25.8. Best practices for monitoring multi-cloud blockchain deployments.
25.9. Monitoring hybrid cloud deployments (on-prem and cloud).
25.10. Ensuring consistent monitoring standards across environments.
Lesson 26: Monitoring Security Certificates and Keys
26.1. Understanding the importance of security certificates and keys.
26.2. Key metrics for monitoring certificate expiration.
26.3. Tracking certificate revocation status.
26.4. Monitoring the integrity of private keys.
26.5. Analyzing logs for certificate-related errors.
26.6. Setting up alerts for certificate expiration or compromise.
26.7. Implementing automated certificate renewal.
26.8. Troubleshooting certificate-related monitoring challenges.
26.9. Best practices for monitoring security certificates and keys.
26.10. Integrating with certificate management systems.
Lesson 27: Monitoring Consensus State and Health
27.1. Deep dive into monitoring the consensus mechanism.
27.2. Key metrics for tracking consensus state (e.g., leader election, view changes).
27.3. Monitoring the health of consensus participants.
27.4. Analyzing logs for consensus-related errors.
27.5. Setting up alerts for consensus failures or anomalies.
27.6. Troubleshooting consensus monitoring challenges.
27.7. Understanding the impact of network partitions on consensus.
27.8. Implementing proactive measures for consensus stability.
27.9. Best practices for monitoring different consensus protocols.
27.10. Utilizing monitoring data for consensus performance analysis.
Lesson 28: Monitoring Event Hubs and Subscriptions
28.1. Understanding the role of event hubs in real-time updates.
28.2. Key metrics for monitoring event hub health.
28.3. Tracking event delivery rate and latency.
28.4. Monitoring the status of event subscriptions.
28.5. Analyzing logs for event hub errors.
28.6. Setting up alerts for event hub or subscription issues.
28.7. Troubleshooting event hub monitoring challenges.
28.8. Implementing proactive measures for event hub stability.
28.9. Best practices for monitoring event hubs and subscriptions.
28.10. Monitoring the impact of event volume on performance.
Lesson 29: Monitoring Chaincode Container Logs and Metrics
29.1. Understanding the containerized nature of chaincode.
29.2. Key metrics for monitoring chaincode containers.
29.3. Tracking container resource utilization (CPU, memory).
29.4. Analyzing chaincode container logs for errors and output.
29.5. Monitoring container health and restarts.
29.6. Setting up alerts for chaincode container issues.
29.7. Troubleshooting chaincode container monitoring challenges.
29.8. Utilizing container monitoring tools (e.g., Docker stats, cAdvisor).
29.9. Best practices for monitoring chaincode containers.
29.10. Integrating container monitoring with the overall blockchain monitoring framework.
Lesson 30: Monitoring External Integrations and Dependencies
30.1. Understanding the importance of monitoring external systems interacting with the blockchain.
30.2. Key metrics for monitoring external integrations (e.g., databases, APIs).
30.3. Tracking the health and availability of integrated systems.
30.4. Monitoring the performance of data exchange with external systems.
30.5. Analyzing logs for integration errors.
30.6. Setting up alerts for external integration issues.
30.7. Troubleshooting external integration monitoring challenges.
30.8. Implementing proactive measures for integration stability.
30.9. Best practices for monitoring external dependencies.
30.10. Utilizing monitoring data for optimizing integration performance.
Lesson 31: Utilizing AI and Machine Learning for Anomaly Detection
31.1. Introduction to AI and ML for monitoring anomaly detection.
31.2. Identifying use cases for AI/ML in blockchain monitoring.
31.3. Training ML models on historical monitoring data.
31.4. Implementing anomaly detection algorithms.
31.5. Setting up alerts for detected anomalies.
31.6. Analyzing the root cause of detected anomalies.
31.7. Troubleshooting AI/ML-based monitoring challenges.
31.8. Best practices for utilizing AI/ML in blockchain monitoring.
31.9. Evaluating the effectiveness of anomaly detection models.
31.10. Integrating AI/ML insights into your monitoring dashboards.
Lesson 32: Building Custom Monitoring Dashboards and Reports
32.1. Principles of effective dashboard design.
32.2. Choosing the right visualizations for blockchain metrics.
32.3. Creating custom dashboards in Grafana or other tools.
32.4. Utilizing dashboard features for drill-down analysis.
32.5. Designing reports for different stakeholders (technical, business).
32.6. Automating report generation and distribution.
32.7. Customizing dashboards for specific roles and responsibilities.
32.8. Best practices for building user-friendly dashboards and reports.
32.9. Utilizing dashboard templates for consistency.
32.10. Iterating on dashboard design based on user feedback.
Lesson 33: Implementing Proactive Monitoring Strategies
33.1. Shifting from reactive to proactive monitoring.
33.2. Identifying potential issues before they impact the network.
33.3. Utilizing predictive analytics for forecasting future problems.
33.4. Implementing synthetic transactions for testing network health.
33.5. Setting up health checks and readiness probes.
33.6. Utilizing monitoring data for preventative maintenance.
33.7. Troubleshooting proactive monitoring implementation challenges.
33.8. Best practices for implementing proactive monitoring.
33.9. Measuring the effectiveness of proactive monitoring.
33.10. Integrating proactive monitoring into your operational workflows.
Lesson 34: Monitoring for Regulatory Compliance
34.1. Understanding the regulatory requirements for blockchain networks.
34.2. Identifying key metrics for compliance monitoring.
34.3. Tracking data immutability and integrity.
34.4. Monitoring access controls and permissions.
34.5. Analyzing logs for compliance-related events.
34.6. Setting up alerts for compliance violations.
34.7. Generating reports for regulatory audits.
34.8. Troubleshooting compliance monitoring challenges.
34.9. Best practices for implementing compliance monitoring.
34.10. Integrating monitoring with compliance tools.
Lesson 35: Cost Monitoring and Optimization
35.1. Understanding the cost drivers of an IBM Blockchain Platform deployment.
35.2. Key metrics for monitoring resource costs.
35.3. Tracking cloud infrastructure costs related to blockchain nodes.
35.4. Analyzing cost trends over time.
35.5. Identifying areas for cost optimization.
35.6. Utilizing monitoring data for cost allocation.
35.7. Setting up alerts for cost overruns.
35.8. Best practices for monitoring and optimizing blockchain costs.
35.9. Integrating cost management tools with monitoring.
35.10. Measuring the impact of cost optimization efforts.
Lesson 36: Automating Monitoring Tasks and Remediation
36.1. Identifying repetitive monitoring tasks for automation.
36.2. Utilizing scripting and automation tools (e.g., Ansible, Terraform).
36.3. Implementing automated health checks and restarts.
36.4. Automating log analysis and reporting.
36.5. Automating incident response actions.
36.6. Utilizing infrastructure as code for monitoring setup.
36.7. Troubleshooting automation implementation challenges.
36.8. Best practices for automating monitoring and remediation.
36.9. Measuring the efficiency of automation.
36.10. Integrating automation with your monitoring framework.
Lesson 37: Disaster Recovery and Business Continuity Monitoring
37.1. Understanding the importance of DR/BC for blockchain networks.
37.2. Key metrics for monitoring DR/BC readiness.
37.3. Tracking replication status and data consistency.
37.4. Monitoring the health of DR sites.
37.5. Analyzing logs for DR/BC-related events.
37.6. Setting up alerts for DR/BC failures or issues.
37.7. Troubleshooting DR/BC monitoring challenges.
37.8. Implementing proactive measures for DR/BC readiness.
37.9. Best practices for monitoring DR/BC for blockchain.
37.10. Utilizing monitoring data during DR/BC exercises.
Lesson 38: Monitoring for Auditing and Forensics
38.1. Understanding the requirements for blockchain auditing.
38.2. Identifying key data points for forensic analysis.
38.3. Utilizing monitoring data for audit trails.
38.4. Analyzing logs for evidence of suspicious activity.
38.5. Implementing secure log storage and retention.
38.6. Setting up alerts for audit-related events.
38.7. Troubleshooting auditing and forensics monitoring challenges.
38.8. Best practices for monitoring for auditing and forensics.
38.9. Integrating monitoring with auditing tools.
38.10. Utilizing monitoring data for post-incident analysis.
Lesson 39: Advanced Troubleshooting with Monitoring Data
39.1. Utilizing monitoring data for complex troubleshooting scenarios.
39.2. Correlating metrics and logs from multiple sources.
39.3. Utilizing visualization tools for identifying patterns in data.
39.4. Leveraging historical data for root cause analysis.
39.5. Implementing structured troubleshooting methodologies.
39.6. Troubleshooting monitoring tool issues themselves.
39.7. Best practices for advanced troubleshooting with monitoring data.
39.8. Utilizing runbooks for troubleshooting common issues.
39.9. Documenting troubleshooting steps and resolutions.
39.10. Continuous improvement of your troubleshooting process.
Lesson 40: Future Trends in IBM Blockchain Monitoring
40.1. Emerging technologies for blockchain monitoring (e.g., AI Ops).
40.2. The role of decentralized monitoring solutions.
40.3. Monitoring blockchain interoperability and cross-chain transactions.
40.4. The impact of new consensus mechanisms on monitoring.
40.5. Monitoring for compliance in evolving regulatory landscapes.
40.6. The future of security monitoring in blockchain.
40.7. The role of cloud-native monitoring in blockchain.
40.8. Open source monitoring tools and their evolution.
40.9. Best practices for staying updated on monitoring trends.
40.10. The evolution of IBM Blockchain Platform monitoring capabilities.
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