Building a data management strategy

Dynamic Data Active Searchable (DDAS)

DDAS represents the most performant tier of data storage within the Splunk platform, designed for short-term and fast access. It is ideal for scenarios that require immediate data retrieval and high-speed search capabilities. Commonly used for real-time monitoring, anomaly detection, and security investigations, DDAS ensures that critical data is accessible without delay.

Dynamic Data Active Archive (DDAA)

DDAA offers a cost-effective solution for archiving data that is no longer needed for immediate access but still holds value for historical analysis or compliance purposes. This Splunk-managed capability allows users to restore sets of archived data to facilitate investigations and audits. DDAA is particularly useful for medium-term data storage where rapid restoration is required but immediate access is not critical.

Dynamic Data Self Storage (DDSS)

DDSS provides a customer-managed workflow for archiving data in external object storage solutions like Amazon S3. This capability is suitable for long-term data retention where the likelihood of access is either minimal or predictable, making it the least expensive dynamic data storage tier. DDSS requires self-managed Splunk infrastructure for data restoration, offering flexibility and control over storage costs while still supporting high-performance search.

Federated search

Federated search allows users to search data across multiple Splunk instances or supported storage locations without the need for duplication or reindexing. This capability extends to supported object storage like Amazon S3, enabling in-place searches that democratize data access while optimizing storage costs. Federated search is ideal for scenarios where data is stored externally but needs to be accessible for periodic analysis or reporting.

Ingest actions

Ingest actions enables users to define transformation rules at ingest time using a user-friendly interface. These rules can mask, filter, or route data to multiple destinations, including Splunk indexes and external storage. Ingest actions is an essential capability for ensuring data hygiene and compliance, particularly when sensitive information must be redacted before indexing. Ingest actions is built into all Splunk indexers, heavy forwarders, and Splunk Cloud Platform, making implementation easy and intuitive.

Splunk Edge Processor

Splunk Edge Processor operates close to the data source, offering the ability to filter, mask, transform, and route data on the journey to its final destination. Hosted on customer-managed infrastructure, this capability provides advanced processing using SPL2 pipelines, allowing for complex data manipulation and routing decisions at the edge.

Splunk Ingest Processor

Similar to Edge Processor, Splunk Ingest Processor focuses on data transformation and routing within the Splunk SaaS environment. It supports all the features of Edge Processor as well as additional advanced features like logs to metrics conversion for routing to Splunk Observability Cloud. Because it's a service hosted in Splunk Cloud Platform, there's no need for customer-managed infrastructure and no configuration changes are needed to upstream agents or clients. This capability is ideal for organizations prioritizing cloud-native data management solutions.

OpenTelemetry (OTEL)

OTEL is an open-source framework for collecting and exporting telemetry data, including logs, metrics, and traces. It is designed to facilitate observability in cloud-native environments, supporting integration with Splunk Observability Cloud as well as Splunk Enterprise and Splunk Cloud Platform. This capability is particularly useful for standardizing data collection across diverse infrastructure and application landscapes.

Business objectives and use cases

Aligning data management strategies with specific business objectives and use cases is paramount. A clear understanding of how data will be used to drive business outcomes will dictate the appropriate capabilities to adopt. Including the relevant stakeholders in these decisions will minimize iteration and disruption.

Example: A financial services company can prioritize the rapid detection of fraudulent activities by using DDAS for real-time processing of transactional data, quickly identifying suspicious patterns and ensuring customer trust and regulatory compliance.

Data value and accessibility

The relative value of data tends to diminish over time, and the frequency with which it needs to be accessed impacts storage and retrieval strategies. Determining the optimal balance between cost-effective storage and timely accessibility is crucial.

Example: A healthcare provider can store recent patient records in DDAS for instant access during consultations, while archiving older records in DDAA for retrospective studies.

Regulatory compliance

Data management strategies must adhere to relevant regulatory requirements, such as GDPR, HIPAA, or industry-specific mandates. Implementing appropriate data governance policies and security measures is essential for maintaining compliance and avoiding penalties.

Example: An e-commerce platform operating globally can use ingest actions to redact customer PII before indexing, ensuring compliance with GDPR across different jurisdictions.

Cost management

Data storage and processing costs can quickly escalate. Evaluating different storage tiers and data retention policies on an event-level basis can help minimize expenses without compromising data value.

Example: A retail chain facing budget constraints can use Ingest Processor to send point-of-sale audit data to S3, while routing only security-critical data to the Splunk platform for near-term analysis. Federated search for S3 can later be used to search archived data as needed for audit and compliance.

Performance and scalability

Data management capabilities must handle current data volumes and processing demands while scaling to accommodate future growth. Choosing scalable capabilities and optimizing performance are essential for maintaining responsiveness and avoiding bottlenecks.

Example: A tech startup experiencing rapid user data growth can use Ingest Processor to scale processing without incurring additional infrastructure costs, converting logs to metrics to maintain high performance in user analytics.

Security and data protection

Protecting sensitive data from unauthorized access, breaches, or loss is a top priority. Implementing robust security measures such as classification, redaction, and access control is crucial for safeguarding data assets.

Example: A government agency managing sensitive communications can use Edge Processor to filter and mask data at the source before transmission, safeguarding against potential interception or leaks during transport to centralized systems.

Integration and interoperability

Data management capabilities must seamlessly integrate with existing IT systems and data sources. Ensuring interoperability and compatibility is essential for facilitating data sharing, analysis, and reporting across the organization.

Example: A multinational corporation using diverse IT systems can use OpenTelemetry to standardize telemetry data collection across its global operations, ensuring consistent monitoring, analysis, and deployment while enabling IT to maintain comprehensive oversight and optimize system performance.

Future-proofing and innovation with AI and ML

Data management capabilities should be adaptable to emerging trends and evolving business needs. Embracing innovation and future-proofing your capability choices can help organizations stay ahead of the curve and maximize the value of their data assets.

Example: An automotive company investing in AI-driven predictive maintenance can use ML models for analyzing vehicle sensor data or leverage the Splunk platform to gain further insights into the state of its LLM and vector infrastructures, enhancing operational efficiency and positioning the company at the forefront of industry innovation.

DDAS for real-time monitoring

Scenario: A cybersecurity firm uses DDAS to manage Windows logs for real-time threat detection, routing logs from various endpoints directly into DDAS storage and configuring Splunk indexes for high-speed retrieval.

1. Set up Splunk universal forwarders on Windows servers to collect event logs.
2. Configure routing to DDAS storage, ensuring optimal index settings for rapid search performance.
3. Implement monitoring dashboards to visualize log data and alert on anomalies in real-time.

DDAA for historical data access

Scenario: A healthcare provider archives patient records in DDAA to balance cost and accessibility, transitioning data from DDAS as it ages while maintaining accessibility for audits or historical research.

1. Determine which data fits into the 0-90 day window of search and which data can be migrated to DDAA in 0-30 days.
2. Establish data lifecycle policies in Splunk indexes to automatically transition records from DDAS to DDAA based on age.
3. Configure Edge Processor or Ingest Processor to set the destination index of classified data to the appropriate index for the appropriate retention settings.
4. Use Splunk self-service restoration features to retrieve archived data when needed, supporting compliance audits or research projects.

Federated search for S3 for long-term compliance

Scenario: A retail chain archives point-of-sale audit data using Ingest Processor and federated search for Amazon S3 to comply with security regulations. Some data is stored in DDAS for near-term security detections, while all data is stored in Amazon S3 to reduce costs while fulfilling retention requirements.

1. Identify criteria for audit data critical to detections.
2. Set up Amazon S3 for archived data, ensuring proper access controls and security measures are in place.
3. Configure Edge Processor or Ingest Processor to route all point-of-sale audit source types directly to Amazon S3, while routing a subset of critical events to DDAS for near-term search.
4. Run detections on DDAS data, allowing data to expire after a short time window to save on storage and indexer workload.

Federated search for cross-platform analysis

Scenario: A multinational corporation uses federated search to analyze security logs stored across different AWS regions or business units, querying data in external S3 buckets without duplication in Splunk indexes.

1. Create pipelines in Edge Processor or Ingest Processor to intercept specific security data best suited for federation and route that data directly to regional S3 buckets.
2. Configure federated search indexes in the Splunk platform to link with external object storage locations.
3. Develop queries that combine results from distinct federated indexes to enable comprehensive analysis of security data.

Ingest actions for data hygiene and compliance

Scenario: An e-commerce platform uses ingest actions to redact PII from customer logs before indexing, ensuring compliance with GDPR while maintaining data integrity for business analysis.

1. Define transformation rules in ingest actions to automatically mask sensitive fields within incoming logs for specific source types.
2. Use the preview UI to validate transformation rules and ensure data is correctly redacted before storage.
3. Continuously monitor and update ingest actions settings to comply with evolving regulatory requirements.

Edge Processor for advanced data processing

Scenario: A government agency deploys Edge Processor to filter and mask sensitive communications at the source prior to transmission, limiting the footprint of sensitive data in transit to centralized systems without compromising data integrity.

1. Install Edge Processor instances in the same VPC as the origin servers producing sensitive data.
2. Configure pipelines using SPL2 to identify and mask sensitive or otherwise restricted data.
3. Use the pipeline preview UI to validate masking of restricted data prior to routing.
4. Route the data to Splunk indexes.
5. Establish data governance and audit searches to quickly identify and remediate new sensitive data.

Ingest Processor for cloud-native solutions

Scenario: A tech startup uses Ingest Processor to convert application logs to metrics for enhanced observability, reducing infrastructure overhead while integrating logs with other observability data.

1. Identify logs that contain business-critical metrics.
2. Configure Ingest Processor pipelines using SPL2 to transform logs into metrics, including dimensional data important for search-time correlation.
3. Route metric data to Splunk Observability Cloud.

Dynamic Data tiering (DDAS, DDAA, DDSS)

• Align the lifecycle requirements of individual use cases and data sources with suitable storage tiers and archival policies.
• Use complementary capabilities like stream processing and search federation to optimize these tiers against cost and search access goals.
• Leverage built-in data lifecycle management features to automate the movement of data between tiers.
• Monitor storage utilization and search performance to optimize tier assignments over time.

Ingest actions, Edge Processor, and Ingest Processor

• Develop clear data governance policies to guide the use of these capabilities.
• Thoroughly test all transformation rules and routing configurations in a non-production environment before deploying to production.
• Monitor the performance of these capabilities to ensure they are not introducing latency or data loss.

Federated search

• Carefully plan the architecture of your federated search environment, considering network bandwidth, security, and data access controls.
• Optimize search queries to minimize the impact on external storage systems.
• Monitor the performance of federated searches and adjust configuration as needed.

OpenTelemetry (OTEL)

• Adopt a consistent approach to telemetry data collection across all applications and infrastructure.
• Use OTEL to enrich telemetry data with contextual information.
• Integrate OTEL with Splunk Observability Cloud for comprehensive monitoring and analysis.

Enabling strategic business objectives

• Improved decision-making: Ensuring data is readily accessible, properly transformed, and stored in the appropriate tier supports strategic planning, risk management, and operational optimization.
• Enhanced agility and innovation: A flexible and scalable set of data management capabilities enables organizations to adapt quickly to changing business needs, experiment with new capabilities, and drive innovation.
• Strengthened security posture: Proactive security monitoring and threat detection—enabled by efficient data routing and storage capabilities—protect against cyber threats and minimize the impact of security incidents.
• Proactive compliance: Implementing data governance policies and automating compliance processes using the right capabilities helps reduce the risk of regulatory penalties and maintain a strong reputation for data privacy and security.
• Increased operational efficiency: Automating data routing, transformation, and archiving using these capabilities minimizes manual intervention, freeing up valuable IT resources for more strategic initiatives.

Concrete cost benefits

• Filtering noisy data: Filtering irrelevant or low-value events—such as debug logs and verbose messages—using ingest actions, Edge Processor, or Ingest Processor directly reduces the volume of data ingested and stored.
• Data structure and format conversion: Converting verbose formats such as JSON or XML into more concise structures such as CSV or metrics using Edge Processor or Ingest Processor reduces data size and optimizes storage efficiency.
• Removal of empty or unused data: Identifying and removing empty fields or unused data from logs using these capabilities further reduces data volume and storage overhead.
• Optimizing index retention: Setting appropriate retention policies based on data value and access frequency reduces the amount of data stored in DDAS and DDAA, lowering both storage costs and SVC consumption.
• Event categorization: Properly categorizing events and targeting index retention policies reduces the amount of data that needs to be searched, measurably reducing SVC consumption.
• Fewer search-time knowledge objects: Transforming and enriching data at ingest time using Edge Processor or Ingest Processor reduces the need for complex search-time knowledge objects such as lookups and calculated fields, improving search performance and reducing SVC consumption.
• Logs to metrics conversion: Converting verbose logs into metrics using Edge Processor or Ingest Processor reduces the volume of data indexed and searched, delivering SVC savings particularly for monitoring use cases.
• Better distribution of events: Routing events to the appropriate indexes based on their characteristics and search frequency keeps searches targeted and efficient, reducing SVC consumption.
• Reduced infrastructure: Optimized data ingestion and processing using these capabilities reduces the number of indexers required, lowering infrastructure costs in customer-managed environments and reducing SVC costs in Splunk Cloud Platform.
• Avoidance of regulatory non-compliance costs: Data redaction and masking via ingest actions or Edge Processor ensures compliance with regulations such as GDPR and HIPAA, reducing the risk of penalties ranging from 4% of annual global turnover under GDPR to $50,000 per HIPAA violation.

By quantifying these cost benefits alongside the strategic value described above, organizations can build a compelling case for investment in Splunk data management capabilities. A well-defined data management strategy is not just a technical implementation; it is a strategic enabler that drives business success and delivers tangible financial returns.

Start small, think big

Overambitious initial projects can overwhelm teams and lead to failure. Instead:

• Identify a specific, well-defined use case with measurable outcomes
• Focus on a single data source or application initially

These practices allow for rapid iteration and demonstrable success, while reducing risk, building confidence, and providing a template for scaling to more complex scenarios.

Example: Rather than implementing a comprehensive data tiering strategy across the entire organization, begin by tiering Windows event logs for a specific business unit, focusing on security use cases.

Understand your data and stakeholders

Implementing data management capabilities without understanding the data's context and the needs of its users leads to ineffective solutions. Instead:

• Conduct thorough data discovery
• Identify data owners, consumers, and their specific requirements
• Understand data sensitivity, retention, regulatory requirements, and access patterns
• Engage stakeholders early and often to gather feedback and ensure alignment

These practices ensure your choice of capabilities and strategy meets the needs of the business and avoids disruption.

Example: Before implementing ingest actions to mask PII, consult with legal and compliance teams to understand the specific regulatory requirements, and engage with security analysts to understand how masking might impact their investigations.

Prioritize value, outcomes, and ability to implement

Focusing on technically interesting but low-impact projects can waste resources and fail to deliver business value. Instead:

• Prioritize use cases based on their potential impact on key business objectives such as cost reduction, improved security, and enhanced compliance, as well as your ability to implement them.
• Consider the ease of implementation and the availability of required data and expertise.

These practices help you achieve quick wins, build momentum, and demonstrate the ROI of your data management capabilities.

Example: Prioritize implementing federated search for S3 for archived data required for compliance audits, as this directly addresses a critical business need and provides immediate cost savings by avoiding data re-ingestion.

Plan, test, and monitor rigorously

Implementing new capabilities without proper planning and testing can lead to unexpected disruptions and data loss. Instead:

• Develop detailed implementation plans including rollback procedures.
• Test all changes in a non-production environment.
• Establish comprehensive monitoring dashboards and alerts to track data flow, storage utilization, and search performance.

These practices help to minimize risk, ensure data integrity, and provide early warning of potential issues.

Example: Before enabling Edge Processor pipelines to transform and route data, thoroughly test the pipelines in a non-production environment using sample data, then monitor performance after deployment to ensure no latency or data loss is introduced.

Implement iteratively, evaluate continuously

A "set it and forget it" approach to data management capabilities leads to stagnation and missed optimization opportunities. Instead:

• Implement changes in small, manageable increments.
• Continuously monitor performance to identify areas for improvement.
• Iterate on the strategy based on feedback, changing business needs, and evolving technologies.

These practices ensure your data management strategy and chosen capabilities remain effective, efficient, and aligned with business objectives over time.

Example: After implementing Dynamic Data tiering, regularly review storage utilization and search performance to identify opportunities to optimize data retention policies and storage tier assignments.

Capture and communicate

Lack of documentation hinders knowledge sharing, troubleshooting, and future enhancements. Instead:

• Document all aspects of your data management strategy including data sources, data flows, transformation rules, retention policies, security measures, and monitoring procedures.
• Create clear and concise documentation that is accessible to all stakeholders.

These practices facilitate collaboration, reduce reliance on individual expertise, and ensure the long-term sustainability of your data management strategy.