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Sizing your Splunk architecture


There are three main considerations for proper sizing of architecture: data volume, hardware, and storage. Efficient data management, capacity planning, search optimization, and leveraging storage optimization techniques can help with data volume. More powerful hardware might improve performance, but it might not always be the most cost-effective solution. And storage solutions must be cost-efficient yet performant. This section details how to choose the right solutions for each of these considerations.

Data volume

Data volume plays a significant role in determining the total cost of ownership (TCO) for Splunk Enterprise. As the amount of data ingested and stored increases, various factors come into play that can impact the overall costs associated with managing and analyzing that data. Here are some key considerations:

  • Storage Costs: The primary cost driver with increasing data volume is storage. Splunk Enterprise requires storage capacity to retain data for indexing, searching, and analysis. As data volumes grow, organizations need to allocate sufficient storage resources, which can result in increased costs for storage infrastructure, such as disk space or remote/cloud storage.
  • Infrastructure Scaling: As data volumes expand, the infrastructure supporting Splunk Enterprise might need to be scaled to handle the increased workload. This could involve adding more servers, upgrading hardware specifications, or deploying distributed architectures. Scaling infrastructure incurs additional costs for hardware acquisition, maintenance, power consumption, and cooling.
  • Licensing Costs: The licensing model for Splunk Enterprise is based on the volume of data ingested or indexed. As data volume increases, organizations might need to purchase additional licenses or upgrade existing licenses to accommodate the higher data volumes. This can result in increased licensing costs, impacting the TCO.
  • Operational Expenses: Managing large data volumes requires additional operational efforts. This includes tasks like data onboarding, indexing, retention management, backup and restore procedures, overall system administration and Splunk administration. Organizations might need to allocate more resources, such as dedicated personnel or Splunk expertise, to efficiently handle the data management process. These operational expenses (OpEx) contribute to the TCO.
  • Data Retention and Archiving: Depending on regulatory requirements or business needs, organizations might need to retain data for extended periods. Longer data retention increases storage costs, as well as the associated costs for managing and archiving the data over time. Organizations must evaluate the trade-off between retention periods, and storage costs and archiving options, to meet OLA/SLAs and optimize TCO.
  • Performance Optimization: Large data volumes can impact search and indexing performance. To maintain acceptable performance levels, organizations might need to invest in additional hardware, optimization efforts, or specialized techniques such as data summarization or data tiering. These performance optimization measures can incur additional costs.

To effectively manage the TCO in the face of increasing data volumes, organizations can employ several strategies:

  • Data Lifecycle Management: Implement data lifecycle management practices to identify and categorize data based on its value, relevance, and retention requirements. This helps optimize storage costs by moving less frequently accessed or older data to cost-effective storage tiers or archiving solutions.
  • Data Pruning and Filtering: Apply data pruning and filtering techniques to reduce the amount of unnecessary or irrelevant data ingested into Splunk Enterprise. This minimizes storage requirements and associated costs.
  • Capacity Planning: Perform regular capacity planning exercises to anticipate future data growth and infrastructure needs. By accurately forecasting data volume, organizations can budget for the required storage, hardware, and licensing resources in advance, minimizing unexpected costs.
  • Search Optimization: Optimize search queries and indexing processes to reduce the computational resources required for analyzing large data volumes. Efficient search practices, such as targeted filtering, summarization, or data model acceleration, can improve performance and reduce hardware requirements.
  • Compression: Leverage compression and deduplication techniques to reduce storage needs without compromising data integrity. Splunk Enterprise provides built-in features to compress indexed data, which optimizes storage utilization.

Data volume has a significant impact on the TCO of Splunk Enterprise. By implementing strategies such as efficient data management, capacity planning, search optimization, and leveraging storage optimization techniques, organizations can effectively manage costs associated with data volume while ensuring optimal performance and maximizing the value derived from your Splunk investment.

Hardware requirements

Understanding your business requirements is the first step to getting the right hardware. Evaluate the volume and velocity of data you need to ingest, the desired search and indexing performance, and the expected growth rate of your data.

These factors influence the hardware specifications required to handle your workload effectively. The system requirements guide provides the minimum hardware specifications for running Splunk Enterprise. It outlines the recommended CPU, memory, and storage configurations based on the expected workload. If your business requirements align with the minimum specifications, this can be a starting point for hardware selection to meet basic functionality.

However, to ensure optimal performance and scalability, you should also refer to the reference hardware guide. This guide provides more detailed information on hardware configurations to handle various data volumes and user loads. It includes guidelines for small, medium, and large-scale deployments, helping you choose hardware that aligns with your business objectives and anticipated growth.

Consider factors such as the number of concurrent users, expected search and indexing performance, and the desired level of data retention. By analyzing these variables against the reference hardware recommendations, you can make informed decisions about the hardware that best supports your business objectives.

It's important to note that while investing in more powerful hardware might improve performance, it might not always be the most cost-effective solution. Assessing the balance between performance requirements and cost savings is crucial. This is where the guidance of skilled Splunk administrators and architects becomes invaluable. They can analyze your specific needs, fine-tune configurations, and provide optimization strategies to help achieve optimal performance while optimizing hardware costs.

Storage considerations

By selecting cost-efficient yet performant storage options and adopting a tiered storage approach, organizations can effectively manage data volumes and associated costs. Additionally, leveraging SmartStore can offer further advantages in terms of scalability and cost optimization.

  1. Cost-Efficient and Performant Storage Options: When considering storage for Splunk Enterprise, it is important to balance performance requirements with cost considerations. By carefully evaluating storage technologies and solutions, organizations can choose options that provide a suitable balance between performance and cost.
  2. Tiered Storage Approach: Adopting a tiered storage approach involves using different storage tiers based on data access patterns and retention requirements. This approach optimizes costs by allocating faster and more expensive storage for "hot" data that is frequently accessed or requires real-time analysis. As data ages or becomes less frequently accessed, it can be transitioned to slower and more cost-effective storage tiers, reducing overall storage costs while still maintaining accessibility.
  3. SmartStore: SmartStore is a feature that allows organizations to further optimize storage costs and scalability. It separates the storage of hot and warm data, leveraging object storage systems such as Amazon S3 or Google Cloud Storage. With SmartStore, hot data is retained in high-performance, local storage, while warm data is offloaded to cost-efficient object storage.

Should I consider migrating to SmartStore?

Splunk SmartStore is a powerful feature that offers organizations the opportunity to optimize storage costs, enhance scalability, and streamline data management within their Splunk Enterprise deployments. By migrating to SmartStore, organizations can unlock a range of benefits that contribute to improved operational efficiency and cost savings.

  1. Scalability and Elasticity: SmartStore enables organizations to seamlessly scale their Splunk environments to handle ever-growing data volumes. By leveraging cloud-based object storage systems, such as Amazon S3 or Google Cloud Storage, SmartStore eliminates the need for continuous hardware investments to accommodate expanding data storage requirements. This scalability allows businesses to stay agile and responsive, ensuring their Splunk infrastructure can adapt to evolving demands without disruptions.
  2. Cost Optimization: One of the primary drivers for migrating to SmartStore is the significant cost optimization it offers. By leveraging cost-efficient object storage for storing warm data, organizations can reduce their storage costs. Object storage providers typically offer lower per-terabyte storage costs compared to high-performance local storage options. SmartStore allows businesses to store less frequently accessed or older data in cost-effective storage tiers while maintaining accessibility. This approach allows for substantial cost savings, especially for organizations with large data volumes or long-term data retention requirements.
  3. Simplified Data Management: SmartStore simplifies data management by separating hot and warm data storage; cold storage does not apply to SmartStore. Hot data, which requires real-time analysis and frequent access, remains in high-performance local storage. Warm data, on the other hand, is automatically offloaded to object storage. This separation enables organizations to focus their local storage resources on critical, frequently accessed data, while utilizing more cost-effective object storage for infrequently accessed or historical data. This simplification of data management processes contributes to improved efficiency and reduced administrative overhead.
  4. Hybrid and Multi-Cloud Support: For organizations adopting hybrid cloud or multi-cloud strategies, SmartStore offers seamless integration with cloud object storage providers. This allows businesses to extend their Splunk deployments into cloud environments while maintaining consistent data access and management. SmartStore's compatibility with various cloud platforms provides flexibility in choosing the right cloud storage provider based on cost, performance, and specific business requirements. The ability to leverage cloud resources for Splunk data storage adds versatility and scalability to the overall IT infrastructure.
  5. Enhanced Performance: SmartStore, with its efficient caching mechanisms and metadata management, ensures that warm data stored in object storage remains readily accessible for analysis and searches. By intelligently managing the data retrieval process, SmartStore minimizes latency and delivers reliable performance even when accessing data from remote object storage. This allows organizations to achieve optimal performance while still benefiting from the cost savings provided by object storage.

Migrating to Splunk SmartStore offers organizations a powerful solution to optimize storage costs, improve scalability, and streamline data management within their Splunk Enterprise deployments. By embracing SmartStore, businesses can achieve significant cost savings, enhance performance, and seamlessly scale their Splunk infrastructure to handle growing data volumes. With simplified data management and support for hybrid and multi-cloud architectures, SmartStore empowers organizations to unlock the full potential of their data while driving operational efficiency and cost optimization. Refer to the Choosing SmartStore guide when considering migrating to SmartStore, and consider features not supported by SmartStore.

Next steps

This article is part of the Splunk Outcome Path, Reducing your infrastructure footprint. Click into that path to find more ways you can maximize your investment in Splunk software and achieve cost savings. 

In addition, these resources might help you implement the guidance provided in this article: