In this special guest feature, Ellen Rubin, CEO and co-founder of ClearSky Data, discusses three ways to extend the use of the cloud as effectively as possible when working to manage the increasing volume of machine generated data. Ellen is an experienced entrepreneur with a record in leading strategy, market positioning and go-to- market efforts for fast-growing companies. ClearSky Data’s global storage network simplifies the entire data lifecycle and delivers enterprise storage as a fully managed service. Most recently, Ellen was co-founder of CloudSwitch, a cloud-enablement software company that was acquired by Verizon in 2011.
Machine data growth is like a tidal wave; it’s gaining speed and it’ll soon be out of control. According to IDC, 42 percent of all data will be machine generated by 2020, which includes data from sensors, security systems, networks, servers, storage and applications. Due to the scale of the data coming from these sources, many organizations are struggling to stay afloat.
The storage systems of the past weren’t built to manage data of this scale. Logically, the public cloud should solve the machine data problem. Moving machine data off of enterprise infrastructure and into cloud services gives enterprises a chance to stop over-provisioning their physical storage capacity, and shrink their data center footprints – a goal that many CIOs and industry leaders are working toward. However, machine data is frequently generated by on-premises applications and processes. Hosting it in the cloud can create latency issues, rack up access fees and negate the cloud’s intended value.
Don’t cling to an on-premises life raft and risk drowning in machine data. Take the opportunity to use the cloud and ride the wave. Below are three ways to extend the use of the cloud as effectively as possible:
1 — Know your apps – and their needs
Maybe your organization invested in a machine data analytics solution, only to find that your storage is holding it up. By identifying the specific roadblocks in your way, you’ll be able to outline a path toward a solution.
For example, many enterprises use Splunk to monitor IT operations and analyze security data. Splunk analyzes terabytes of rapidly growing machine data each day, and for every terabyte of machine data it analyzes, Splunk can require up to 23 times that amount in tiered storage. To keep Splunk running effectively and fuel its indexing processes, IT teams need to shift data between hot, warm and cold tiers – and quickly move data between the cloud and on-premises infrastructure as needed.
Open-source alternatives for log analytics and machine data, such as Elasticsearch and the ELK Stack (which includes Elasticsearch, Logstash and Kibana), can also benefit from the scalability of the cloud while keeping data close to its source. With high-performance storage and low-latency access to source systems, organizations can reap more rewards from their Splunk, ELK and other machine data applications.
2 — Combine cloud services with an edge computing strategy
In a recent report, Gartner suggested using an edge computing model to solve latency and performance issues in the data center. Edge computing can accelerate cloud and hybrid initiatives, as it involves using distributed architectures to bring data center resources to the edge of networks. As a result, data can be analyzed and interacted with in real time, as if it were local.
For companies that already store massive amounts of data within on-premises infrastructure, require high performance, seek to avoid data access fees and are pursuing a hybrid cloud strategy, edge computing can help bypass issues that previously stalled all progress toward these goals. While machine data analytics applications benefit greatly from edge computing, the approach’s ability to deliver high-performance storage for growing data sets can also increase the value of operational analytics, multimedia content, financial trading and capital market applications.
3 — Don’t go it alone
When the perfect wave is headed your way, you pursue it with intent – you don’t leave it up to chance. The way you approach your company’s infrastructure and data is no different. Make sure you meet the needs of your IT team by assessing your organization’s applications, planning your strategy and pursuing it carefully. You’ll be all the more prepared to balance on the edge and ride the incoming wave of machine data to a successful end.
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