ELK Engineer Interview Questions with Answers [2025]

Core Concepts

1. What is the primary role of the ELK Stack in log management?

The ELK Stack, comprising Elasticsearch, Logstash, and Kibana, serves as a powerful open-source solution for log management and analysis. Elasticsearch stores and searches logs, Logstash processes and transforms data, and Kibana visualizes insights. It enables real-time monitoring, troubleshooting, and analytics in enterprise environments, supporting scalability for large data volumes and integration with DevOps tools for efficient infrastructure oversight.

Explore ELK basics in real-time Linux questions.

2. Why do companies prefer the ELK Stack over other logging solutions?

• Cost-Effective: Open-source with no licensing costs.
• Scalable: Handles massive log volumes in MNCs.
• Flexible: Custom pipelines for data processing.
• Visual: Kibana dashboards for insights.
• Integrations: Works with Beats and cloud services.

ELK’s ecosystem reduces vendor dependency, ensuring reliable log analysis in dynamic environments.

3. When is the ELK Stack most effective in a DevOps environment?

The ELK Stack is most effective during high-volume log generation, such as in microservices or cloud deployments, for real-time troubleshooting. Use it post-incident for root cause analysis or during scaling to monitor performance.

It excels in CI/CD pipelines for log aggregation, preventing outages. Less suited for simple setups, ELK shines in distributed systems needing advanced analytics.

4. Where is the main configuration file for Logstash located?

The main configuration file for Logstash is typically at /etc/logstash/conf.d/ in Linux installations. It defines input, filter, and output pipelines. Proper organization ensures efficient data flow, with validation via logstash --config.test_and_exit confirming error-free setups in enterprise clusters.

5. Who is responsible for deploying the ELK Stack in an organization?

• DevOps Engineers: Lead pipeline configurations.
• Sysadmins: Handle Elasticsearch clustering.
• Data Analysts: Set up Kibana visualizations.
• Security Teams: Ensure log security.

Collaboration aligns ELK with business needs, with junior engineers managing basic deployments.

6. Which component of the ELK Stack is responsible for data storage?

Elasticsearch is responsible for data storage in the ELK Stack, using inverted indexes for fast search and analysis. It supports distributed clustering for scalability, handling petabytes of logs. Kibana visualizes this data, while Logstash ingests it, making Elasticsearch core for enterprise log retention and querying.

7. How does Logstash process incoming logs?

Logstash processes incoming logs through input plugins like beats or file inputs, applies filters for parsing and transformation, and outputs to Elasticsearch. It supports grok patterns for structured data, enabling real-time enrichment in pipelines.

• Input: Receives logs from sources.
• Filter: Parses and enriches data.
• Output: Sends to Elasticsearch.

8. What are the core components of the ELK Stack?

• Elasticsearch: Stores and searches data.
• Logstash: Processes log ingestion.
• Kibana: Visualizes dashboards.
• Beats: Lightweight shippers.
• X-Pack: Adds security features.

This structure enables MNCs to manage logs efficiently.

9. Why is Elasticsearch indexing critical to the ELK Stack?

Elasticsearch indexing creates inverted indexes for fast full-text search, enabling real-time log analysis. It supports sharding for scalability, ensuring quick queries on large datasets. Without proper indexing, performance suffers, making it essential for enterprise troubleshooting.

• Speed: Enables sub-second searches.
• Scalability: Distributes data across nodes.
• Relevance: Scores results for accuracy.

Learn about search optimization in policy as code.

10. When should you use Filebeat in the ELK Stack?

Use Filebeat for lightweight log shipping from servers to Logstash or Elasticsearch, ideal for high-volume environments like containers. It’s efficient for real-time forwarding without heavy processing, reducing resource use in distributed setups.

11. Where do you define Logstash pipelines?

• Location: /etc/logstash/conf.d/ directory.
• Structure: Input, filter, output sections.
• Validation: Use logstash --config.test_and_exit.
• Modularity: Separate files for pipelines.

Pipelines sync via version control for consistency.

12. Who manages Elasticsearch clusters in an enterprise?

DevOps engineers and database admins manage Elasticsearch clusters, focusing on indexing and scaling. Monitoring teams optimize performance, while security teams handle access. Collaboration ensures high availability in MNC environments.

13. Which features make Kibana powerful for visualization?

• Dashboards: Customizable panels.
• Discover: Log exploration tool.
• Visualize: Charts and maps.
• Timelion: Time series analysis.

Kibana enhances enterprise data insights.

14. How do you configure Elasticsearch for high availability?

Configure Elasticsearch for high availability by setting up a cluster with multiple nodes, defining discovery settings, and enabling replication. Use dedicated master nodes for stability, ensuring data redundancy in enterprise deployments.

cluster.name: my-cluster node.master: true discovery.zen.ping.unicast.hosts: ["node1", "node2"]

15. What steps are required to install the ELK Stack on Ubuntu?

Update system with apt update, install Java, then add Elasticsearch, Logstash, and Kibana repositories. Install packages via apt install, configure elasticsearch.yml for clustering, and start services. Set up firewall rules for ports 9200, 5601. This establishes a basic ELK setup for enterprise logging.

• Dependencies: Install OpenJDK.
• Repositories: Add official ELK repos.
• Services: Enable and start systemd units.

16. Why use Grok filters in Logstash?

Grok filters parse unstructured logs into structured fields using patterns, enabling easier querying in Elasticsearch. They support custom regex for proprietary formats, improving data usability in enterprise log analysis.

17. When do you use the date filter in Logstash?

• Timestamp Parsing: Extracts dates from logs.
• Indexing: Sets @timestamp field.
• Timezone Handling: Adjusts for locales.
• Validation: Use logstash --config.test_and_exit.

Date filters ensure accurate time-based queries.

Understand date handling in configuration drift detection.

Configuration Management

18. Where are Kibana index patterns defined?

Kibana index patterns are defined in the Management section, specifying indices like logstash-* for log matching. They map fields for visualization, ensuring data accessibility in enterprise dashboards.

19. Who handles ELK security in an organization?

Security engineers handle ELK security, configuring X-Pack roles and SSL. DevOps teams integrate authentication, while auditors ensure compliance. Collaboration protects sensitive logs in MNCs.

Automated tools streamline security across teams.

20. Which settings control Elasticsearch shard allocation?

• cluster.routing.allocation: Enables/disables shards.
• index.number_of_shards: Sets shard count.
• discovery.zen.minimum_master_nodes: For stability.
• Validation: Check with curl API.

These optimize enterprise cluster performance.

21. How do you validate Logstash pipelines?

Validate Logstash pipelines with logstash --config.test_and_exit /etc/logstash/conf.d/pipeline.conf to check syntax and logic. Test with sample data, monitor logs for errors, and integrate into CI/CD for enterprise reliability.

22. What is the purpose of the mutate filter in Logstash?

The mutate filter modifies fields, like renaming or adding tags, for data normalization. It supports gsub for string replacement, essential for cleaning logs before indexing in enterprise pipelines.

23. Why centralize Logstash configurations?

Centralizing Logstash configurations ensures consistency across instances, reducing errors. Use Git for version control, Ansible for deployment in MNCs, streamlining audits and compliance for large-scale log processing.

• Consistency: Uniform pipelines across sites.
• Automation: Ansible for updates.
• Audits: Simplifies compliance checks.

24. How do you organize ELK configurations for multiple environments?

Organize by environment-specific directories in /etc/elasticsearch/, using ansible roles for deployment. Use index templates for data separation, syncing via Git for scalability in enterprise setups.

25. What tools complement ELK configuration?

• Ansible: Automates ELK deployments.
• Git: Tracks configuration changes.
• Kibana Dev Tools: Tests queries.
• Elastic HQ: Manages clusters.

These enhance efficiency in enterprise environments.

Discover complementary tools in policy as code tools.

26. Why use index templates in Elasticsearch?

Index templates apply settings like shard count and mappings to new indices automatically. They ensure consistency, optimize performance, and reduce manual configuration in dynamic enterprise logging.

27. When to use the aggregate filter in Logstash?

Use the aggregate filter to group events, like correlating multi-line logs or calculating metrics. It’s ideal for session aggregation, supporting timeouts for efficient processing in enterprise pipelines.

28. Where do you store Elasticsearch mappings?

• Location: Defined in index templates.
• Dynamic: Auto-generated or explicit.
• Validation: Use _mapping API.
• Modularity: Separate for indices.

Mappings enhance data structure in enterprises.

29. What are the essential plugins for Logstash?

Essential plugins include input-beats for shipping, filter-grok for parsing, and output-elasticsearch for indexing. Codec-multiline handles multi-line logs, sourced from Elastic for enterprise use.

• Input Plugins: Beats, file.
• Filter Plugins: Grok, date.
• Output Plugins: Elasticsearch, kafka.

30. Why create custom Logstash filters?

Custom filters parse proprietary log formats, using Ruby code for complex transformations. They enable tailored enrichment, improving query accuracy in unique enterprise data streams.

31. When should you use Beats in the ELK Stack?

• Lightweight Shipping: Filebeat for logs.
• Metric Collection: Metricbeat for metrics.
• High Volume: Reduces Logstash load.
• Security: Configure modules for compliance.

Beats ensure efficient data ingestion in enterprises.

32. Where can you source additional Logstash plugins?

Source plugins from RubyGems or Elastic's repository. Install with bin/logstash-plugin install, test in pipelines, and define in configurations for production enterprise use.

33. Who develops and maintains ELK plugins?

Elastic and community developers maintain plugins on GitHub. MNC teams create custom plugins for proprietary needs, ensuring compatibility with enterprise versions.

Contributions via pull requests keep plugins updated.

Learn about development in trunk-based development.

Plugins and Extensions

34. Which plugin is best for parsing JSON logs in Logstash?

• filter-json: Parses JSON fields.
• filter-mutate: Renames keys.
• Configuration: Use json codec.
• Use Case: Structured log handling.

These ensure clean data in enterprises.

35. How do you write a custom Logstash filter?

Write custom filters in Ruby, extending LogStash::Filters::Base. Define register and filter methods for logic. Install as plugin, test in pipelines for enterprise log processing.

class LogStash::Filters::Custom < LogStash::Filters::Base config_name "custom" def filter(event) event.set("parsed", event.get("raw").gsub(/pattern/, "replacement")) end end

36. What is the expected output format for Logstash events?

Logstash events output as JSON with @timestamp, @metadata, and fields. Filters add structured data, ensuring compatibility with Elasticsearch for consistent enterprise indexing.

37. What are Elasticsearch queries in the ELK Stack?

• Match Query: Full-text search.
• Term Query: Exact matches.
• Bool Query: Combines conditions.
• Aggregations: Group data.

Queries enable efficient enterprise log searching.

38. Why use Kibana scripted fields?

Scripted fields compute values on-the-fly using Painless scripts, like calculating durations. They enhance visualizations without altering data, useful for dynamic enterprise analytics.

• Computation: Runtime field calculations.
• Flexibility: Custom logic.
• Performance: Avoids reindexing.

39. When do Kibana visualizations get updated?

Kibana visualizations update in real-time with auto-refresh intervals or on dashboard load. Use saved searches for efficiency, reducing load in enterprise monitoring.

40. Where do you configure Elasticsearch replicas?

• Index Settings: index.number_of_replicas.
• Cluster Level: dynamic cluster settings.
• API: PUT /index/_settings.
• Validation: Check with GET /_cluster/health.

Replicas ensure data redundancy in enterprises.

41. Who uses Kibana for reporting in an enterprise?

Data analysts use Kibana for reporting, creating dashboards and PDFs. DevOps teams monitor logs, while executives view summaries. LDAP integration assigns access in MNCs.

Role-based access ensures secure reporting.

Explore reporting tools in SBOM compliance.

Notifications and Alerts

42. Which features enable alerting in Kibana?

Kibana alerting uses Watcher for rules on indices, triggering actions like emails. Define thresholds for metrics, integrating with Slack for notifications in enterprise setups.

Alerts ensure proactive monitoring across time zones.

43. How do you set up alerting in the ELK Stack?

Set up alerting with X-Pack Watcher, defining watch rules on queries. Configure actions like email or webhook, test with simulate API for reliable enterprise notifications.

PUT _xpack/watcher/watch/log_alert { "trigger": { "schedule": { "interval": "5m" } }, "input": { "search": { "request": { "indices": ["logs-*"] } } }, "condition": { "compare": { "ctx.payload.hits.total": { "gt": 100 } } }, "actions": { "email": { "email": { "to": "[email protected]" } } } }

44. What is Watcher in the ELK Stack?

• Rule Engine: Defines alert conditions.
• Triggers: Schedule or event-based.
• Actions: Email, Slack, webhook.
• Integration: With X-Pack security.

Watcher enables proactive enterprise alerting.

45. Why use threshold alerts in Kibana?

Threshold alerts trigger on metric crossings, like CPU >80%, notifying teams. They support complex conditions, reducing manual monitoring in enterprise environments.

46. What is X-Pack in the ELK Stack?

X-Pack adds security, alerting, and monitoring to ELK, with features like role-based access and machine learning. It’s essential for enterprise deployments requiring compliance and advanced analytics.

47. When to use machine learning in ELK?

• Anomaly Detection: Identifies unusual patterns.
• Forecasting: Predicts trends.
• Jobs: Run on time series data.
• Integration: With Kibana visualizations.

Machine learning enhances enterprise predictive monitoring.

48. Where does Elasticsearch store indices?

Elasticsearch stores indices in /var/lib/elasticsearch/ by default, with data paths configurable in elasticsearch.yml. Shards distribute data across nodes for enterprise scalability.

49. Who configures X-Pack security?

Security admins configure X-Pack, setting roles and SSL. DevOps teams integrate with LDAP, ensuring secure access in MNCs for compliant log management.

Configuration aligns with enterprise security standards.

Learn about security in container scanning tools.

50. Which features enhance ELK scalability?

• Sharding: Distributes data.
• Replication: Ensures redundancy.
• Hot-Warm Architecture: Tiered storage.
• Cloud Integration: AWS Elasticsearch.

These support high-volume enterprise logging.

51. How do you scale Elasticsearch clusters?

Scale Elasticsearch by adding nodes, adjusting shard counts, and using dedicated masters. Monitor with _cat/health API, ensuring balanced allocation for enterprise performance.

PUT _cluster/settings { "persistent": { "cluster.routing.allocation.enable": "all" } }

52. What role does Beats play in ELK alerting?

Beats ship logs to Logstash for alerting, with modules for predefined configurations. They enable lightweight data collection, supporting Watcher triggers in enterprise setups.

53. Why use SSL in ELK communications?

• Encryption: Protects data in transit.
• Authentication: Verifies nodes.
• Compliance: Meets standards like GDPR.
• Configuration: In elasticsearch.yml.

SSL secures enterprise log flows.

54. How does ELK handle real-time alerting?

ELK handles real-time alerting with Watcher executing on schedules or events, querying indices for conditions. Actions trigger immediately, ensuring proactive enterprise monitoring.

Advanced Features and Integration

55. What common errors occur in ELK configurations?

• Syntax Errors: Invalid pipeline configs.
• Connection Issues: Elasticsearch unreachable.
• Memory Errors: Heap size too low.
• Validation: Use test commands.

Logs aid troubleshooting in enterprises.

56. When to restart Logstash after changes?

Restart Logstash after pipeline changes using systemctl restart logstash. For minor tweaks, reload configs without downtime, scheduling during low traffic for enterprise stability.

57. Where to find ELK logs for debugging?

Logs are in /var/log/logstash/ for Logstash, /var/log/elasticsearch/ for Elasticsearch. Rotate with logrotate, use grep for errors in enterprise debugging.

Explore logging in change failure rate.

58. Who troubleshoots ELK in a team environment?

Senior DevOps troubleshoot using _cat APIs or logs. Teams collaborate on issues, with MNCs monitoring ELK itself for proactive maintenance.

Documentation ensures consistency.

59. Which commands verify ELK cluster status?

• curl localhost:9200/_cluster/health: Checks status.
• curl localhost:9200/_cat/indices: Lists indices.
• logstash --version: Verifies Logstash.
• kibana --version: Checks Kibana.

These ensure enterprise ELK health.

60. How do you debug a failing Logstash pipeline?

Debug with logstash -f pipeline.conf --log.level debug, check worker threads, and review event flows. Use stdin input for testing in enterprise setups.

61. What best practices for ELK performance tuning?

• Heap Size: Set to 50% RAM.
• Shard Sizing: 20-50GB per shard.
• Pipeline Workers: Match CPU cores.
• Monitoring: Use X-Pack.

Tune for enterprise high-load scenarios.

62. Why backup Elasticsearch indices regularly?

Backups use snapshot API to repositories like S3, preventing data loss. In MNCs, automate via cron, versioning for quick recovery in enterprise logging.

63. How to handle high cardinality in Elasticsearch?

Handle high cardinality by using keyword fields for aggregations, sampling data, or dynamic mappings. Monitor with _field_caps API, optimizing for enterprise query performance.

Troubleshooting and Best Practices

64. What is the role of ELK in cloud monitoring?

ELK integrates with clouds like AWS via CloudWatch plugins, monitoring logs and metrics. It supports hybrid setups, ensuring consistent oversight with AI for anomaly detection in enterprises.

• Plugins: CloudWatch input.
• Hybrid: Unifies logs.
• Scalability: Auto-scales.

65. When to migrate from self-hosted to Elastic Cloud?

Migrate to Elastic Cloud for managed scaling, updates, and security during growth or if maintenance burdens increase. It offers pay-as-you-go for enterprise flexibility.

Understand migration in multi-cloud deployments.

66. Where does ELK fit in DevOps pipelines?

• CI/CD: Logs pipeline execution.
• Integration: With Jenkins plugins.
• Health: Monitors build servers.
• Automation: Triggers alerts.

ELK enhances DevOps visibility.

67. Who benefits from ELK certifications?

DevOps professionals benefit, validating skills for roles. Certified staff manage MNC setups, covering deployment and optimization, boosting careers in enterprise logging.

68. Which integrations are trending for ELK?

Trending integrations include Kubernetes with Fluentd, Prometheus for metrics, and AWS Lambda for serverless. They support microservices, aligning with enterprise IT trends.

These keep ELK relevant.

69. How does ELK support container logging?

ELK supports container logging with Filebeat in Docker, parsing JSON logs. Define pipelines for Kubernetes, scaling with orchestration for enterprise container oversight.

filebeat.inputs: - type: container paths: - '/var/lib/docker/containers/*/*.log'

70. What challenges in scaling ELK for MNCs?

• Data Volume: High ingestion rates.
• Storage: Index management.
• Query Performance: Slow searches.
• Solution: Hot-warm clusters.

Plan for petabyte-scale in enterprises.

71. Why adopt X-Pack for ELK?

X-Pack adds security, alerting, and ML to ELK, essential for enterprise compliance. It simplifies management, while open-source alternatives suit cost-sensitive setups.

72. How to customize Kibana for enterprise use?

Customize Kibana with saved objects, role-based spaces, and plugins. Use advanced settings for branding, tailoring for enterprise roles like analysts or admins.

Enterprise and Future Trends

73. What is Elastic Agent in the ELK Stack?

• Purpose: Unified shipper.
• Features: Fleet management.
• Use Case: Multi-data source.
• Integration: With Beats.

It simplifies enterprise data collection.

Explore agents in self-service platforms.

74. When use ELK for security analytics?

Use ELK for security analytics with SIEM app, correlating logs for threats. It supports anomaly detection, providing proactive defense in enterprise security.

75. Where to find community resources for ELK?

Resources are on discuss.elastic.co, GitHub, and Stack Overflow, offering tips, plugins, and troubleshooting for ELK users in global enterprise communities.

76. Who contributes to ELK development?

Elastic and community contributors update via GitHub. MNC teams add custom integrations, ensuring relevance for cloud and containers in enterprise logging.

Contributions drive innovation.

77. Which security features protect ELK?

• X-Pack Security: Roles, SSL.
• Encryption: Data at rest.
• Audit Logging: Tracks access.
• IP Filtering: Network security.

These secure enterprise deployments.

78. How to optimize ELK for IoT logging?

Optimize with lightweight Beats for IoT, using efficient pipelines. Configure for low-bandwidth, ensuring scalability for edge logging in enterprise IoT setups.

filebeat.inputs: - type: log enabled: true paths: - /iot/logs/*.log

79. What trends in ELK for 2025?

Trends include AI for anomaly detection, serverless ELK, and multi-cloud support. Enhanced security and real-time analytics ensure ELK's future in enterprise monitoring.

80. Why use ELK in hybrid environments?

• Unified Logging: Across on-prem, cloud.
• Consistency: Standard pipelines.
• Plugins: Cloud integrations.
• Scalability: Handles hybrid complexity.

ELK bridges hybrid gaps.

81. How to measure ELK effectiveness?

Measure via query latency, ingestion rate, and alert accuracy using X-Pack monitoring. Analyze retention costs and search relevance, guiding improvements in enterprise logging.

Learn about metrics in DORA metrics.

82. What is Elastic Security in ELK?

Elastic Security provides SIEM capabilities, correlating logs for threats. It includes detection rules and response workflows, aiding enterprise security operations.

83. When to use ELK for microservices logging?

Use ELK for microservices with centralized logging via Fluentd, correlating traces. It supports distributed tracing, ensuring visibility in enterprise microservices.

84. Where to store ELK backups?

• S3 Repositories: Cloud storage.
• Shared Filesystems: NFS.
• Automation: Snapshot lifecycle.
• Retention: Policy-based.

Backups ensure enterprise resilience.

85. Who is accountable for ELK performance?

DevOps and data engineers optimize ELK, tuning shards and pipelines. SREs monitor health, ensuring uptime in MNC environments.

Accountability supports operational goals.

86. Which metrics are critical for ELK monitoring?

• Ingestion Rate: Logs per second.
• Query Latency: Search time.
• Cluster Health: Node status.
• Storage Usage: Index size.

These ensure enterprise efficiency.

87. How to monitor Elasticsearch cluster health?

Monitor with _cluster/health API, checking status, active shards, and unassigned. Use Kibana Monitoring for visuals, alerting on yellow/red states in enterprise clusters.

GET _cluster/health

88. What is the role of ILM in Elasticsearch?

Index Lifecycle Management (ILM) automates index phases like hot, warm, delete. It optimizes storage, ensuring compliance in enterprise log retention.

89. Why use transforms in Elasticsearch?

• Aggregation: Pivots data.
• Performance: Reduces index size.
• Analytics: Continuous transforms.
• Integration: With Kibana.

Transforms enhance enterprise analytics.

Explore data processing in Kubernetes provisioning.

90. When to use continuous transforms in ELK?

Use continuous transforms for real-time aggregations, like rolling up logs hourly. They reduce storage, ideal for long-term enterprise analytics.

91. Where to configure Kibana spaces?

• Management: Spaces section.
• Roles: Assign access.
• Objects: Migrate between spaces.
• Security: X-Pack enabled.

Spaces organize enterprise dashboards.

92. Who maintains ELK documentation?

Elastic maintains documentation on elastic.co, with community contributions on GitHub. MNC teams create internal guides for enterprise-specific use cases.

Updates cover new features.

93. Which plugins support ELK integrations?

• Kafka Input: Streaming data.
• JDBC Output: Database sync.
• HTTP Output: API integrations.
• Configuration: Plugin installs.

These enable enterprise connectivity.

94. How to integrate ELK with Kubernetes?

Integrate with EFK stack, using Fluentd daemonset for logging. Configure Elasticsearch operator for scaling, ensuring visibility in enterprise Kubernetes clusters.

apiVersion: v1 kind: ConfigMap metadata: name: fluentd-config

95. What is the role of rollover in Elasticsearch?

Rollover creates new indices when conditions like size or age are met, managing growth. It supports aliasing for seamless querying in enterprise setups.

96. Why use snapshot lifecycle management in ELK?

• Automation: Schedules snapshots.
• Retention: Deletes old ones.
• Storage: Optimizes repositories.
• Integration: With ILM.

SLM ensures enterprise data protection.

97. When to use search templates in Elasticsearch?

Use search templates for reusable queries with parameters, reducing duplication. They support Mustache for variables, ideal for dynamic enterprise searches.

Learn about templates in git hooks.

98. Where to find ELK performance metrics?

Performance metrics are in X-Pack Monitoring indices, visualized in Kibana. Use _nodes/stats API for node-level data in enterprise clusters.

Metrics guide optimization.

99. Who is responsible for ELK testing?

DevOps and QA teams test ELK pipelines with sample data. Analysts validate queries, ensuring reliability in production enterprise environments.

100. Which tools integrate with ELK for alerting?

• PagerDuty: Incident management.
• Slack: Team notifications.
• Email: Basic alerts.
• Webhook: Custom actions.

These enhance enterprise alerting.

101. How to monitor Logstash performance?

Monitor Logstash with --log.level debug, tracking pipeline workers and event rates. Use API /_node/stats for metrics, ensuring efficiency in enterprise pipelines.

GET _nodes/stats/pipeline

102. What is the role of Kibana Canvas?

Kibana Canvas creates custom reports with elements like text and charts. It supports storytelling, aiding enterprise data presentation.

103. Why automate ELK deployments?

• Efficiency: Reduces manual effort.
• Consistency: Uniform setups.
• Scalability: Supports growth.
• Tools: Ansible, Terraform.

Automation aligns with enterprise DevOps.