Splunk: Tips and tricks for effective data management

by Riversafe

effective data management

Effective data management is crucial for any organisation that wants to extract insights and make smart decisions. This is especially true for those using Splunk, a powerful platform that enables businesses to collect, analyse, index, query, and visualise raw data from various sources. 

As a Big Data platform, Splunk is designed to take vast amounts of data and turn it into something useful. Integratable with NoSQL and relational databases, Splunk connects with a huge number of workflows, tools, and data sources. As Splunk ingests more data, it gets better at analysing information, spotting patterns, and unearthing constructive insights from even the most unstructured of data. 

But the more data you collect, the more challenging (and potentially costly) it becomes to manage it.  

As you scale up your operational intelligence strategy, the volume of data you’re feeding into Splunk and the speed at which it’s being generated will increase. That’s why it’s important to nail down a proper data management plan as early as possible; one that can grow with your Splunk instance, and make sure your data is being handled in the most effective way.  

Here are a few tips to help you better manage your Splunk data.  

Define a data retention policy 

Outline some rules around how long you need to keep certain types of data; and be ruthless. If it’s not being actively queried, if no one’s accessed it for some time, and if it’s not needed for compliance purposes, do you really need to keep it? 

Defining how long you want to retain your data in Splunk will help you optimise your storage usage and avoid unnecessary costs. Once you’ve worked out an appropriate retention period based on your data and how you’re using it, you can configure data retention policies in Splunk to automatically delete data that is no longer needed for analytical or compliance purposes. 

Use data lifecycle management (DLM) 

There are several factors to take into account when putting together your data management strategy. Data lifecycle management is a big one. Finding the right type of storage, for the right type of data, at the right time in its lifespan can help you manage data more frugally, but you have to make sure you’re not burying data that you might need to access. Balancing cost-effectiveness with performance, searchability, and accessibility can be tricky.  

Data lifecycle management can help you automate the process of moving data from one storage tier to another, based on specific criteria. This can help you optimise storage costs and ensure that regularly queried data is stored on a high-performance searchable storage option, while less frequently accessed data is moved to slower and cheaper storage. 

Splunk stores data in tiers—which buckets of data are stored on which tier will be determined by your data lifecycle policy, and how data in each bucket is being used.  

If you’re actively writing new data to a bucket, the bucket will be in a ‘hot’ state. Once a hot bucket is full, or you close it down, it becomes ‘warm’. Both hot and warm buckets are stored on Splunk’s warm storage tier—this is the fastest, most accessible storage level, and therefore the most costly to use.  

As your allotted warm storage space fills up, older buckets will be moved to the ‘cold’ tier. Buckets in cold storage are still searchable, but are not as demanding in terms of performance, meaning you can use lower-quality hardware to access them.  

Similarly, once this cold storage space becomes limited, or a bucket approaches the end of its life as per your data retention policy, it’ll be moved to the ‘frozen’ tier and be deleted (unless you configure Splunk to do otherwise). The frozen storage tier is useful for housing data needed for compliance purposes, that doesn’t need to be searched or accessed regularly. (It’s worth noting that any data you place in frozen storage will have its associated metadata deleted, but this data can be restored when moved to a warmer tier.) 

Use index time and search time field extraction 

Another way to minimise the amount of data that needs to be indexed while keeping certain aspects of it searchable is index and search time field extractions.  

As the name suggests, field extractions can help you extract specific fields from your data and make them available for search and analysis. Index time field extractions can help you reduce the amount of data that needs to be indexed, while search time field extractions can help you extract fields on demand. 

You can extract fields either before or after events have been indexed. To help you take advantage of this functionality, Splunk provides a field extractor tool to help you dynamically create these custom fields. 

Monitor your data usage 

It sounds obvious but you’d be surprised how quickly data usage can rack up if you don’t keep an eye on it. Make sure you’re not exceeding your storage limits or incurring unnecessary costs by using the Monitoring Console in Splunk to track your data usage, and set up alerts to let you know when certain thresholds are exceeded. 

Consider using a data-archiving solution 

If you have large volumes of data that you need to retain for compliance or regulatory reasons, you might want to consider using a data archiving solution that integrates with Splunk. This can help you reduce your storage costs and improve search performance by moving older data to a separate archive. 

Splunk’s Dynamic Data Self Storage service helps you move data out of Splunk and into alternative private storage buckets hosted on AWS or Google Cloud Platform. This data can be restored to Splunk at a later date if needed. 

Try SmartStore 

Another alternative indexing method to consider is SmartStore, a utility that enables Splunk customers to use remote S3 and block storage services, such as Amazon S3, Google GCS, or Azure Blob Storage to store data.  

SmartStore helps users reduce the amount of data held in Splunk indexers. Instead, data is placed in remote storage while the indexer maintains a local cache of the most recently used data, including hot buckets and copies of active warm buckets.  

Hosting the bulk of this previously indexed data elsewhere on more cost-effective storage solutions can significantly reduce Splunk storage costs while keeping the data you need searchable and close at hand.  

Is your Splunk instance up to scratch?

As a Splunk Accredited Professional Services Provider, and Premier Managed Service Provider, RiverSafe has the experience and know-how to help you do more with Splunk. 

Find out if your Splunk instance is delivering maximum protection and value with our free Splunk Health Check.  


By Riversafe

Experts in DevOps, Cyber Security and Data Operations