I first heard the phrase “you can’t manage what you don’t measure” in the early ‘90s, when I joined the data networking industry. The phrase was used to help justify the need for our recently launched ethernet network management solution. The solution was a central repository for SNMP (simple network management protocol) messages from various networking hubs, bridges and routers, which kept network administrators informed about their network’s health and helped expedite troubleshooting when things went wrong. Boy, how things have evolved from those early days, especially the role of data in all aspects of today’s enterprise.
Today, machine data, or IoT data, have become arguably an organization’s most valuable asset – if, of course, the company is prepared to analyze that data. Derived from servers, sensors, security systems, networks and other devices, this data can help detect trends, troubleshoot problems, investigate security incidents and equip companies to run with more intelligence and efficiency. IDC also projects that machine data will comprise 42 percent of all data by 2020, meaning the potential for using such information to improve a company’s bottom line is growing as fast as the data itself.
However, to manage and transform machine data into business value, organizations need a few key assets on their side:
- A machine data analytics solution, such as Elastic Stack or Splunk;
- High-performance storage to support that solution and the frequently searched data it makes accessible;
- An economical storage solution to manage enormous amounts of cold and archival data; and
- Scalable compute and networking resources to rapidly collect, index and search machine data.
While there’s no one-size-fits-all approach to storage, a global storage network is an ideal architecture for reaping the full rewards made possible by machine data and IoT analytics platforms. These types of rapid, iterative analytics thrive when paired with highly consistent ingest rates, the infinite scaling of the cloud and integrated backup and disaster recovery (DR). On the other hand, machine data and IoT analytics solutions can suffer when they’re supported by multiple, disparate traditional storage technologies. This disconnected approach can also prove extremely costly for end users.
In the case of Splunk, using a dedicated caching network can augment hot, warm and cold tiers of data, giving users the chance to check the below storage goals off of their lists:
- Access all data with high performance and low latency to ensure optimal search performance;
- Eliminate the need for data migration;
- Simplify and enhance data protection and security;
- Make continuous ingest a reality; and
- Read data from the cloud using a dedicated, high-speed private network connection.
Machine data analytics deliver the critical intelligence companies need, and support from a global storage network helps avoid compromising performance or economics. Companies simply plug into the network to access high performance, unlimited scale and the wealth of knowledge contained in machine data.
Learn how to architect high-performance storage, mapped for Splunk success.