Revenue monitoring not only involves monitoring huge amounts of data in real-time but also finding correlations between thousands, if not millions, of customer experience and other metrics. Are traditional monitoring methods capable of detecting a correlation between a drop in user log-ins and a drop in revenue as it’s happening? For many reasons, the answer is no.
I wanted to better understand how COVID-19 has been developing in South America. As I’ve recently started playing with InfluxDB, the open source time series database, I created a dashboard of cases and deaths using InfluxData’s platform. I usually use InfluxDB, Chronograf, Grafana, Zabbix and other similar solutions to monitor services and systems. However, until this point, I hadn’t used them to process and visualize other kinds of data.
Is it the middle of Spring already? (And Fall, for our Southern Hemisphere readers!) A lot has happened at Splunk since our last developer updates at .conf19. In case you missed any of the great developer sessions there, grab some time to watch what you missed!
The large volumes of logs, metrics, and traces generated by scaling cloud environments can be overwhelming, but they must be collected to identify and respond to production issues or other signals showing business or application issues. To collect, monitor, and analyze this data, many teams choose between open source or proprietary observability solutions.
As an open-source, distributed, general-purpose cluster-computing framework, Apache Spark is popular for machine learning, data processing, ETL, and data streaming. While Spark manages the scheduling and processing needed for big data workloads and applications, it requires resources like vCPUs and memory to run on.
From dealing with security concerns to production monitoring, businesses need to analyze the log data of their systems to ensure everything is functioning normally. In a computing context, a log refers to automatically produced and time-stamped documentation of events related to a particular system. Analysis of log data helps businesses comply with regulations, security policies and audits, understand online consumer behavior, and comprehend system troubleshoots.
Giraffe is the open source React-based visualization library that’s used to implement InfluxDB’s v2 UI. It employs clever algorithms to handle the challenge of visualizing the incredibly high volume of data that InfluxDB can ingest and query. We’ve just published documentation describing how developers can take advantage of this library and I’ve tried to create a companion tutorial to further illustrate the power of this library.
At Splunk, we're listening to our customers and offering more predictable, flexible, and familiar pricing options as part of our Data-to-Everything Pricing model. In particular, Splunk’s new infrastructure pricing metric changes the paradigm of how much data you can analyze with Splunk, allowing users to move toward a value-driven pricing model that better aligns what you pay with real value you can extract from using Splunk products.
When you perform a search in Elasticsearch, results are ordered so that documents which are relevant to your query are ranked highly. However, results that may be considered relevant for one application may be considered less relevant for another application. Because Elasticsearch is super flexible, it can be fine-tuned to provide the most relevant search results for your specific use case(s).
