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Giraffe Visualization Library and InfluxDB

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.

Embrace Growing and Untapped Data Sources Without Price as a Limitation

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.

Dynamic presentations with Canvas

Canvas is data visualization and presentation tool that sits within Kibana. It allows us to pull live data directly from Elasticsearch and combine it with colours, images and text in order to create dynamic and visually appealing presentations. This talk will cover the basics of building your first presentation based on the live data from Elasticsearch. If you enjoy immersing yourself in the creative process while applying your technical skills, you should join us for this talk.

Improving search relevance with boolean queries

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).

Kubernetes for Data Science: meet Kubeflow

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.