Nark - Steroids for graphite
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The document describes Nark, a tool for visualizing and monitoring time series metrics data stored in Graphite. It provides dashboards with interactive graphs, alerts, and aims to address limitations in Graphite like lack of dashboards and alerting. Nark is being developed by Lucid Software using Play, Scala, and other technologies. It is open source and the developers welcome contributions.
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The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks.
To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library.
Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph.
A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML.
This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.
DataLyzer Spectrum Brochure
DataLyzer Spectrum is stand-alone statistical process control (SPC) software that automates data collection, charting, and analysis. It allows data entry from various sources and displays control charts in real-time. The software produces sophisticated analysis with minimal user interaction. It has been developed since the late 1970s and was one of the first commercial SPC and measurement system analysis software packages.
Spark + AI Summit 2020 イベント概要
Spark + AI Summit 2020 had over 35,000 attendees from 125 countries. The majority of participants were data engineers and data scientists. Apache Spark is now widely used with Python and SQL. Spark 3.0 includes improvements like adaptive query execution that accelerate queries by 2-18x. Delta Engine is a new high performance query engine for data lakes built on Spark 3.0.
Spark streaming , Spark SQL
Spark Streaming allows processing of live data streams in Spark. It integrates streaming data and batch processing within the same Spark application. Spark SQL provides a programming abstraction called DataFrames and can be used to query structured data in Spark. Structured Streaming in Spark 2.0 provides a high-level API for building streaming applications on top of Spark SQL's engine. It allows running the same queries on streaming data as on batch data and unifies streaming, interactive, and batch processing.
Data Science Challenge presentation given to the CinBITools Meetup Group
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires social network analysis to recommend users to follow on a social media platform based on click data. The document discusses the approaches, tools, and algorithms used to solve each problem at scale using Apache Spark and Hadoop technologies.
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The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires analyzing a social network graph to recommend users to follow. The document discusses the approaches, tools, and results for each problem.
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Nark - Steroids for graphite
- 1. NARK STEROIDS FOR GRAPHITE Alyssa Stringham, Software Developer Matthew Barlocker,Chief Architect Lucid Software Inc
- 2. ABOUTALYSSA Software Developer at Lucid Software Inc BYU graduate with Bachelors in ComputerScience I love playing the carillon and piano fast-paced board games hats traveling playing foosball
- 3. ABOUT "THE BARLOCKER" Chief Architect at Lucid Software Inc Bachelors Degree from BYU in ComputerScience I love to play board games go four-wheeling wrestle my sons fly airplanes Follow me on nineofclouds.blogspot.com
- 4. GRAPHITE • Graphite is a realtime, scalable analytics & graphing tool • Works well with StatsD • Stores time series data • Data organized by path name stats.environment.servers.servername.path.to.metric stats.environment.servers.servername.path.to.metric2 stats.environment.servers.servername2.path.to.metric
- 5. GRAPHITE Simple to store data Send variable name, value, and timestamp stats.environment.servername.metricname 10 1399523324879 Simple to retrieve data API to retrieve graphs & data http://graphite.readthedocs.org/en/latest/render_api.html 98 complex functions Aggregate Functions (sumSeries, divideSeries, stacked, etc.) Filtering Functions (maxSeries, exclude, highestAverage, removeBelowValue, etc.) Transform Functions (abs, log, integral, derivative, etc.) Sorting Functions (sortByName, sortByMinima, etc.) See all at http ://graphite.readthedocs.org/en/latest/functions.html Demo
- 8. GRAPHITE -WHAT IT'S MISSING Dashboards Interactive graphs (static images aren't good enough) Variables in targets Sharing of dashboards Adaptability for different screen sizes Alerting Need reliable, highly-available alerting Tattle andCyren aren't good enough
- 9. WE NEEDTO KNOWWHEN DISASTERS HIT
- 10. WE NEEDTO KNOWWHEN DISASTERS HIT
- 11. NARK Definitions: 1: "An annoying person or thing"
- 12. NARK Definitions: 1: "An annoying person or thing" 2: "Someone that will report any misbehaving to a higher authority like police, parents and teachers [and the ops team]"
- 13. NARK - DASHBOARDS Graphs Normal vs Stacked Axes Full vs Power of 2 vs Power of 10 Targets UseGraphite functions Variable Replacement Variables Defaults Options Interactivity Tagging Demo
- 21. NARK - ALERTING Normal and DynamicAlerts Thresholds Alert History Subscribing Single alert All alerts with tag Manage notification settings Demo
- 23. FUTUREOF NARK MoreGranular Subscriptions AlertAcknowledgement ViewCurrentAlerts Favorite Dashboards ImprovingVariables User Roles (admins)
- 24. NARK -TECHNOLOGY Built on Play & Scala MySQL Bootstrap Dygraph.js OpenID HighAvailability OnGithub https://github.com/lucidchart/nark
- 26. JOINTHETEAM • Building the next generation of collaborative web applications • VC funded • Profitable • Graduates from MIT, Harvard, Stanford • Former Google, Amazon, Microsoft employees https://www.golucid.co/jobs