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A universal router and receiver to send your data to and from the most cost-effective destinations, cutting costs on licensing and infrastructure.
Logging systems and other observability tools are at capacity. As data continues to grow 25%-30% a year, organizations face ballooning infrastructure costs and push up against their daily licensing limits. To mitigate these challenges, they engage in data gymnastics to make it all fit – day after day. Adding to this, long-term retention of data in analytics tools can be expensive and can slow the performance of queries and correlations.
It’s easy to get data into most log analytics systems (compared to traditional databases and data warehouses), and it’s typically easy to query it once it gets there – but unfortunately, that’s where the easy part ends. If you’re working with a tool that is optimized for fast search, the cost of retaining the data in that system grows linearly with retention time. For a deeper dive into the astounding cost ramp-up of indexing and storing data in your logging tool, review Why Log Systems Require So Much Infrastructure.
Your log system of choice is probably pulling triple-duty for your enterprise, driving unneeded expenses. You may have started out on a quest to index unstructured data for faster searches, but that same platform is likely now housing all your company’s logs, leaving you with higher license and storage costs – and limited benefits from your data.
Instead, use Cribl Stream to customize the processing and routing of each data source type, optimizing, enriching, and routing the data based on its content, value, and purpose. Some data should be stored as metrics, other data in indexed log tools, or sent to low-cost storage, while some data should be dropped altogether.
Instead of cramming every data source into a single, expensive log analytics system, Cribl Stream offers you cost effective log data management that allows you to optimize the treatment of each of your data sources–saving you time and money.
See how this industry titan broke free from a complex legacy data pipeline infrastructure with Stream – reducing duplicate data by 15%!