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).
Data science has exploded as a practice in the past decade and has become an undisputed driver of innovation. The forcing factors behind the rising interest in Machine Learning, a not so new concept, have consolidated and created an unparalleled capacity for Deep Learning, a subset of Artificial Neural Networks with many “hidden layers”, to thrive in the years to come.
