Meet the Experts: Visualize Your Time-Stamped Data Using the React-Based Giraffe Library
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This document discusses Giraffe, a React-based library for visualizing time-series data from InfluxData. It provides examples of using Giraffe to visualize data exported from Flux queries in InfluxData by converting the data to layers in Giraffe configurations. The document also contains code examples for connecting to InfluxData and executing Flux queries to export data to visualize in Giraffe.
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Their entire system architecture — including InfluxDB Cloud, Grafana, Kafka, and DigitalOcean
The importance of using time-stamped data to extend the life of their machines
Power Your Predictive Analytics with InfluxDB
If you're using InfluxDB to store and manage your time series data, you're already off to a great start. But why stop there? In our upcoming webinar, we'll show you how to take your data analysis to the next level by building predictive analytics using a variety of tools and techniques.
We will demonstrate how to use Quix to create custom dashboards and visualizations that allow you to monitor your data in real-time. We'll also introduce you to Hugging Face, a powerful tool for building models that can predict future trends and identify anomalies. With these tools at your disposal, you'll be able to extract valuable insights from your data and make more informed decisions about the future. Don't miss out on this opportunity to improve your data analysis skills and take your business to the next level!
What you will learn:
Use InfluxDB to store and manage time series data
Utilize Quix and Hugging Face to build models, visualize trends, and identify anomalies
Extract valuable insights from your data
Improve your data analysis skills to make informed decision
How Teréga Replaces Legacy Data Historians with InfluxDB, AWS and IO-Base
Are you considering replacing your legacy data historian and moving your OT data to the cloud? Join this technical webinar to learn how to adopt InfluxDB and IO Base - a digital platform used to improve operational efficiencies!
Teréga Solutions are the creators of digital solutions used to improve energy efficiencies and to address decarbonization challenges. Their network includes 5,000+ km of gas pipelines within France; they aim to help France attain carbon neutrality by 2050. With these impressive goals in mind, Teréga has created IO-Base — the digital platform to improve industrial performance, and increase profitability. Creating digital twins for their clients allows them to collect data from all production sites and view it in real time, from anywhere and at any time.
Discover how Teréga uses InfluxDB, Docker, and AWS to monitor its gas and hydrogen pipeline infrastructure. They chose to replace their legacy data historian with InfluxDB — the purpose built time series database. They are collecting more than 100K different metrics at various frequencies — some are collected every 5 seconds to only every 1-2 minutes. THey have reduced overall IT spend by 50% and collect 2x the amount of data at 20x frequency! By using various industrial protocols (Modbus, OPC-UA, etc.), Teréga improved output, reduced the TCO, and is now able to create added-value services: forecast, monitoring, predictive maintenance.
Join this webinar as Thomas Delquié dives into:
Teréga's approach to modernizing fossil fuel pipelines IT systems while improving yields and safety
Their centralized methodology to collecting sensor, hardware, and network metrics
The importance of time series data and why they chose InfluxDB
Build an Edge-to-Cloud Solution with the MING Stack
FlowForge enables organizations to reliably deliver Node-RED applications in a continuous, collaborative, and secure manner. Node-RED is the popular, low-code programming solution that makes it easy to connect different services using a visual programming environment. InfluxData is the creator of InfluxDB, the purpose-built time series database run by developers at scale and in any environment in the cloud, on-premises, or at the edge.
Jump-start monitoring your industrial IoT devices and discover how to build an edge-to-cloud solution with the MING stack. The MING stack includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana. This solution can be used to improve fleet management, enable predictive maintenance of industrial machines and power generation equipment (i.e. turbines and generators) and increase safety practices (i.e. buildings, construction sites). Join this webinar to learn best practices from industrial IoT SME's.
In this webinar, Robert Marcer and Jay Clifford dive into:
Best practices for monitoring sensor data collected by everyone — from the edge to the factory
Tips and tricks for using Node-RED and InfluxDB together
Demo — see Node-RED and InfluxDB live
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The document is an agenda for a discussion between the CTO and founder of Ockam, Mrinal Wadhwa, and the CTO and founder of InfluxData, Paul Dix, about rewriting products using the Rust programming language. It includes an introduction of the founders, an overview of the discussion topics like why they decided to rewrite in Rust and the challenges they faced, how they got their engineers comfortable with Rust, tips they learned in the process, benefits gained from moving to Rust, and how their communities responded to the switch.
Introducing InfluxDB Cloud Dedicated
InfluxData is excited to announce the general availability of InfluxDB Cloud Dedicated! It is a fully managed time series database service running on cloud infrastructure resources that are dedicated to a single tenant. With this new offering, we’re excited to provide our customers with additional security options, and more custom configuration options to best suit customers’ workload requirements. Join this webinar to learn more about InfluxDB Cloud, and the new dedicated database service offering!
In this webinar, Balaji Palani and Gary Fowler will dive into:
Key features of the new InfluxDB Cloud Dedicated solution
Use cases for using the newest version of the purpose-built time series database
Live demo
During this 1-hour technical webinar, you’ll also get a chance to ask your questions live.
Gain Better Observability with OpenTelemetry and InfluxDB
Many developers and DevOps engineers have become aware of using their observability data to gain greater insights into their infrastructure systems. InfluxDB is the purpose-built time series database used to collect metrics and gain observability into apps, servers, containers, and networks. Developers use InfluxDB to improve the quality and efficiency of their CI/CD pipelines. Start using InfluxDB to aggregate infrastructure and application performance monitoring metrics to enable better anomaly detection, root-cause analysis, and alerting.
This session will demonstrate how to record metrics, logs, and traces with one library — OpenTelemetry — and store them in one open source time series database — InfluxDB. Zoe will demonstrate how easy it is to set up the OpenTelemetry Operator for Kubernetes and to store and analyze your data in InfluxDB.
How a Heat Treating Plant Ensures Tight Process Control and Exceptional Quali...
American Metal Processing Company ("AMP") is the US' largest commercial rotary heat treat facility with customers in the automotive, construction, military, and agriculture industries. They use their atmosphere-protected rotary retort furnaces to provide their clients with three primary hardening services: neutral hardening (quench and temper), carburizing, and carbonitriding.
This furnace style ensures consistent, uniform heat treatment process vs. traditional batch-or-belt-style furnaces; excels at processing high volumes of smaller parts with tight tolerances; and improves the strength and toughness of plain carbon steels. Discover why AMP’s use of Telegraf, InfluxDB, Node-RED, and Grafana allows them to gain 24/7 insights into their plant operations and metallurgical results. Learn how they use time-stamped data to gain accurate metrics about their consumables usage, furnace profiles, and machine status.
Join this webinar as Grant Pinkos dives into:
American Metal Processing's approach to heat treating in a digitized environment through connected systems
Their approach to collecting and measuring sensor data to enable predictive maintenance and improve product quality
Why they need a time series database for managing and analyzing vast amounts of time-stamped data
How Delft University's Engineering Students Make Their EV Formula-Style Race ...
Delft University is the oldest and largest technical university in the Netherlands with 25,000+ students. Since 1999, they have had a team of students (undergraduate and graduate) designing, building, and racing cars, as part of the Formula Student worldwide competition. The competition has grown to include teams from 1K+ universities in 20+ countries. Students are responsible for all aspects of car manufacturing (research, construction, testing, developing, marketing, management, and fundraising). Delft University's team includes 90 students across disciplines.
Discover how Delft University's team uses Marple and InfluxDB to collect telemetry and sensor metrics while they develop, test, and race their electrics cars. They collect sensor data about their EV's control systems using a time series platform. During races, they are collecting IoT data about their batteries, accelerometer, gyroscope, tires, etc. The engineers are able to share important car stats during races which help the drivers tweak their driving decisions — all with the goal of winning. After races, the entire team are able to analyze data in Marple to understand what to do better next time. By using Marple + InfluxDB, their team are able to collect, share and analyze high frequency car data used to make their car faster at competitions.
Join this webinar as Robbin Baauw and Nero Vanbiervliet dive into:
Marple's approach to empowering engineers to organize, analyze, and visualize their data
Delft University's collaborative methodology to building and racing their Formula-style race car
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Introducing InfluxDB’s New Time Series Database Storage Engine
InfluxData is excited to announce the general availability of InfluxDB Cloud's new storage engine! It is a cloud-native, real-time, columnar database optimized for time series data. InfluxDB's rebuilt core was coded in Rust and sits on top of Apache Arrow and DataFusion. InfluxData's team picked Apache Parquet as the persistent format. In this webinar, Paul Dix and Balaji Palani will demonstrate key product features including the removal of cardinality limits!
They will dive into:
The next phase of the InfluxDB platform
How using Apache Arrow's ecosystem has improved InfluxDB's performance and scalability
Key features of InfluxDB Cloud's new core — including SQL native support
Start Automating InfluxDB Deployments at the Edge with balena
balena.io helps companies develop, deploy, update, and manage IoT devices. By using Linux containers and other cloud technologies, balena enables teams to quickly and easily build fleets of connected devices. Developers are able to use containers with the language of choice and pull IoT sensor data from 70+ different single board computers into balenaCloud. Discover how to use balena.io to automate your InfluxDB deployments at the edge!
During this one-hour session, experts from balena and InfluxData will demonstrate how to build and deploy your own air quality IoT solution. You will learn:
The fundamentals of IoT sensor deployment and management using balena.
How to use a time series platform to collect and visualize metrics from edge devices.
Tips and tricks to using balenaCloud to automate InfluxDB deployments and Telegraf configurations.
How to use InfluxDB's Edge Data Replication feature to collect sensor data and push it to InfluxDB Cloud for analysis.
No coding experience required, just a curiosity to start your own IoT adventure.
Understanding InfluxDB’s New Storage Engine
Learn more about InfluxDB’s new storage engine! The team developed a cloud-native, real-time, columnar database optimized for time series data. We built it all in Rust and it sits on top of Apache Arrow and DataFusion. We chose Apache Parquet as the persistent format, which is an open source columnar data file format. This new storage engine provides InfluxDB Cloud users with new functionality, including the removal of cardinality limits, so developers can bring in massive amounts of time series data at scale.
In this webinar, Anais Dotis-Georgiou will dive into:
Requirements for rebuilding InfluxDB’s core
Key product features and timeline
How Apache Arrow’s ecosystem is used to meet those requirements
Stick around for a demo and live Q&A
Streamline and Scale Out Data Pipelines with Kubernetes, Telegraf, and InfluxDB
RudderStack — the creators of the leading open source Customer Data Platform (CDP) — needed a scalable way to collect and store metrics related to customer events and processing times (down to the nanosecond). They provide their clients with data pipelines that simplify data collection from applications, websites, and SaaS platforms. RudderStack's solution enables clients to stream customer data in real time — they quickly deploy flexible data pipelines that send the data to the customer's entire stack without engineering headaches. Customers are able to stream data from any tool using their 16+ SDK's, and they are able to transform the data in-transit using JavaScript or Python. How does RudderStack use a time series platform to provide their customers with real-time analytics?
Join this webinar as Ryan McCrary dives into:
RudderStack's approach to streamlining data pipelines with their 180+ out-of-the-box integrations
Their data architecture including Kapacitor for alerting and Grafana for customized dashboards
Why using InfluxDB was crucial for them for fast data collection and providing single-sources of truths for their customers
Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...
Customers using ThingWorx and the Manufacturing Solutions often need to store property data longer than the Solutions default to. These customers are recommended to use InfluxDB, and this presentation will cover the key considerations for moving to InfluxDB vs the standard ThingWorx value streams. Join this session as Ward highlights ThingWorx’s solution and its easy implementation process.
Scott Anderson [InfluxData] | New & Upcoming Flux Features | InfluxDays 2022
Two new features are coming to Flux that add flexibility
and functionality to your data workflow—polymorphic
labels and dynamic types. This session walks through
these new features and shows how they work.
Steinkamp, Clifford [InfluxData] | Closing Thoughts | InfluxDays 2022
This document outlines the schedule for Day 2 of InfluxDays 2022, an event hosted by InfluxData. The schedule includes sessions on building developer experience, how developers like to work, an overview of the InfluxDB developer console and API, demos of client libraries and the InfluxDB v2 API, tips for getting involved in the InfluxDB community and university, use cases for networking monitoring, crypto/fintech, monitoring/observability, and IIoT, and closing thoughts. Recordings of all sessions will be made available to registered attendees by November 7th. Upcoming events include advanced Flux training in London and resources through the community forums, Slack channel, and online university.
Steinkamp, Clifford [InfluxData] | Welcome to InfluxDays 2022 - Day 2 | Influ...
This document contains the agenda for Day 2 of InfluxDays 2022, which includes:
- Welcome and introductory remarks from Zoe Steinkamp and Jay Clifford of InfluxData.
- Fireside chats and presentations on building great developer experiences, how developers like to work, and use cases for InfluxDB from companies like Tesla, InfluxData, and others.
- Sessions on the InfluxDB developer console, APIs, client libraries, getting involved in the community, accelerating time to awesome with InfluxDB University, and tips for analyzing IoT data with InfluxDB.
- Closing thoughts from Zoe Steinkamp and Jay Clifford, as well as
Steinkamp, Clifford [InfluxData] | Closing Thoughts Day 1 | InfluxDays 2022
The document summarizes the agenda and sessions for Day 1 of InfluxDays 2022. It includes sessions on InfluxDB data collection, scripting languages like Flux, the InfluxDB time series engine, tasks, storage, and a closing discussion. The agenda involves talks from InfluxData employees on building applications with real-time data, navigating the developer experience, solving problems, the InfluxDB platform, community, education, use cases in crypto/fintech and IIoT, and tips/tricks for analysis.
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How Bevi Uses InfluxDB and Grafana to Improve Predictive Maintenance and Redu...
How Bevi Uses InfluxDB and Grafana to Improve Predictive Maintenance and Redu...
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Introducing InfluxDB’s New Time Series Database Storage Engine
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Start Automating InfluxDB Deployments at the Edge with balena
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Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...
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Scott Anderson [InfluxData] | New & Upcoming Flux Features | InfluxDays 2022
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Steinkamp, Clifford [InfluxData] | Closing Thoughts | InfluxDays 2022
Steinkamp, Clifford [InfluxData] | Closing Thoughts | InfluxDays 2022
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Steinkamp, Clifford [InfluxData] | Welcome to InfluxDays 2022 - Day 2 | Influ...
Steinkamp, Clifford [InfluxData] | Closing Thoughts Day 1 | InfluxDays 2022
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- 1. A React-Based Library Kristina Robinson Software Engineering Manager, InfluxData January 2021 Visualize Your Time-Stamped Data Using Giraffe
- 2. © 2021 InfluxData. All rights reserved. 2 • •
- 3. © 2021 InfluxData. All rights reserved. 3 • • • • • • • ▪ ▪ ▪
- 4. © 2021 InfluxData. All rights reserved. 4 • const fluxResultCSV = `#datatype,string,long,dateTime:RFC3339,dateTime:RFC3339,dateTi me:RFC3339,double,string,string,string,string #group,false,false,true,true,false,false,true,true,true,true #default,_result,,,,,,,,, ,result,table,_start,_stop,_time,_value,_field,_measurement,exa mple,location ,,0,2020-03-25T20:58:15.731129Z,2020-04-24T20:58:15.731129Z,202 0-04-03T18:31:33.95Z,29.9,value,temperature,index.html,browser ,,0,2020-03-25T20:58:15.731129Z,2020-04-24T20:58:15.731129Z,202 0-04-03T18:55:23.863Z,28.7,value,temperature,index.html,browser ,,0,2020-03-25T20:58:15.731129Z,2020-04-24T20:58:15.731129Z,202 0-04-03T19:50:52.357Z,15,value,temperature,index.html,browser
- 5. © 2021 InfluxData. All rights reserved. 5
- 6. © 2021 InfluxData. All rights reserved. 6
- 7. © 2021 InfluxData. All rights reserved. 7
- 8. © 2021 InfluxData. All rights reserved. 8
- 9. © 2021 InfluxData. All rights reserved. 9
- 10. © 2021 InfluxData. All rights reserved. 10
- 11. © 2021 InfluxData. All rights reserved. 11
- 12. © 2021 InfluxData. All rights reserved. 12
- 13. © 2021 InfluxData. All rights reserved. 13
- 14. © 2021 InfluxData. All rights reserved. 14 • • • • • • • •
- 15. © 2021 InfluxData. All rights reserved. 15 const body = { query: query, dialect: { header: true, delimiter: ',', quoteChar: '"', commentPrefix: "#", annotations: ["datatype","group","default"]}, type: 'flux'} const headers = new Headers() headers.append('Authorization', `Token ${Constants.Influx.TOKEN}`) headers.append('Content-Type', 'application/json') var request = new Request(`${Constants.Influx.URL}/api/v2/query?org=${Constants.Influx.ORG}`, method: 'POST', headers: headers, body: JSON.stringify(body)})
- 16. © 2021 InfluxData. All rights reserved. 16 const { InfluxDB, flux } = require('@influxdata/influxdb-client') // vars to connect to bucket in influxdb const baseURL = process.env.INFLUX_URL // url of your cloud instance const influxToken = process.env.INFLUX_TOKEN; // all access token from the UI const orgID = process.env.ORG_ID // export your org id; const bucket = process.env.BUCKET_NAME //export the name of your bucket // connect to influxdb const influxDB = new InfluxDB({ url: baseURL, token: influxToken }) const queryApi = influxDB.getQueryApi(orgID) let clientQuery = flux``+query queryApi.queryLines(clientQuery, { next(line) { csv = `${csv}${line}n`; }, complete() { res.end(JSON.stringify({ csv: csv })) }, })
- 17. © 2021 InfluxData. All rights reserved. 17 • • // fluxResultsCSV is annotated CSV const dataFromFlux = fromFlux(fluxResultCSV) const config = { table: dataFromFlux.table, layers: [lineLayer], } OR const config = { fluxResponse, layers: [lineLayer], } • const lineLayer = { type: "line", x: "_time", y: "_value", }
- 18. © 2021 InfluxData. All rights reserved. 18 • • •
- 19. © 2021 InfluxData. All rights reserved. 19 • Settings Templates • Step 2 • Lookup • • • Data Dashboards Speedtest Dashboard •
- 20. © 2021 InfluxData. All rights reserved. 20
- 21. Q & A
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- 25. Thank You