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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.

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| © Copyright 2023, InfluxData
Start Automating
InfluxDB Deployments at
the Edge with balena
January 24, 2023
| © Copyright 2023, InfluxData
Past life: Sales Engineer for IIoT Solutions.
Passion: Autonomous and Vision based projects.
Driven: To make IoT accessible to all.
Belief: Industrial IoT’s success belongs to the
domain experts.
Jay Clifford
Developer Advocate, InfluxData
| © Copyright 2023, InfluxData
Past life: IoT and Smart Cities during the last 15 years.
Passion: Educative IoT devices.
Driven: Make the planet a better place with IoT.
Belief: IoT success will come when everyone will
collaborate.
Marc Pous
IoT Giant
Developer Advocate, balena.io
| © Copyright 2023, InfluxData
Part 3
Edge Data Replication
and analysing our data
in the cloud.
Part 2
Intro to balenAir
Part 4
Demo and next steps!
Part 1
What is balena?
4
| © Copyright 2023, InfluxData
What is balena?
| © Copyright 2023, InfluxData
balena - Anyone?
6
| © Copyright 2023, InfluxData
7
balena - Anyone?
| © Copyright 2023, InfluxData
8
balena - At a Glance
“Platform built to develop, deploy, and manage
fleets of connected devices at scale.”
| © Copyright 2023, InfluxData
9
balena - Fleet Management
| © Copyright 2023, InfluxData
10
balena - Fleet Management
| © Copyright 2023, InfluxData
11
balena - Fleet Management
| © Copyright 2023, InfluxData
balena - At a Glance
12
| © Copyright 2023, InfluxData
balena - At a Glance
13
| © Copyright 2023, InfluxData
balena - At a Glance
14
| © Copyright 2023, InfluxData
balena - At a Glance
15
. . .
| © Copyright 2023, InfluxData
balena - At a Glance
. . .
16
https://hub.balena.io/organizations/marc6/apps/MING
| © Copyright 2023, InfluxData
balena - At a Glance
17
. . .
| © Copyright 2023, InfluxData
balena - At a Glance
. . .
18
| © Copyright 2023, InfluxData
balena - What is a Block
19
https://blog.balena.io/introducing-balenablocks-jumpstart-your-iot-app-development/
| © Copyright 2023, InfluxData
balena - Connector Block
20
https://hub.balena.io/organizations/balenalabs/blocks/connector
The connector block wraps Telegraf in code which discovers and intelligently connects data
sources and sinks.
For example, the connector block will find an MQTT broker running on the device and
subscribe to a sensor topic. An application developer can simply send JSON-formatted data to
this topic and it will be ingested by connector. Similarly, the block will find an instance of
InfluxDB running on the device, and send any ingested data to it.
The use of environment variables allows a developer or fleet owner to enable lots of other data
sources and sinks, both on the device and externally hosted.
https://github.com/balena-labs-projects/connector
| © Copyright 2023, InfluxData
balenAir
| © Copyright 2023, InfluxData
What is balenAir ?
A “build it yourself” Indoor Air Quality
Device - IAQ.
Motivation: Dedicated to making
environmental information visibly more
accessible.
| open source | highly customizable |
| easy to use | privacy |
IAQ = (C02, VOC and particulate sensors)
0 (best air quality) - 99 (hazardous)
22
| © Copyright 2023, InfluxData
Sensors and Hardware
PMSA003I SCD-40
Raspberry PI 3 / 4b
SparkFun Qwiic LED Stick
23
| © Copyright 2023, InfluxData
Demo Architecture
IAQ MQTT
Broker
Connector
InfluxDB
Grafana
Edge
downsampled
co2
24
| © Copyright 2023, InfluxData
Edge Data Replication
| © Copyright 2023, InfluxData
Cloud
Edge
generator
northbound
Write
Endpoint
replication queue
26
Telegraf
| © Copyright 2023, InfluxData
Analyzing our data in the cloud
| © Copyright 2023, InfluxData
A functional language designed for
querying, analyzing, and acting on data.
Flux
28
| © Copyright 2023, InfluxData
Anomaly Detection Forecasting
29
| © Copyright 2023, InfluxData
Let’s start!
| © Copyright 2023, InfluxData
Deployment with balena
31
| © Copyright 2023, InfluxData
Tips and Tricks
| © Copyright 2023, InfluxData
33
Connector block tips & tricks
https://github.com/balena-labs-projects/connector
| © Copyright 2023, InfluxData
34
Unsupervised and rule-based
time series anomaly detection
The ADTK package allows
you to easily build an
effective detection model
from a variety or rule-based
anomaly detection methods.
“Prophet is a procedure for
forecasting time series data
based on an additive model
where non-linear trends are
fit with yearly, weekly, and
daily seasonality, plus holiday
effects.”
A Neural Network based
Time-Series model, inspired
by Facebook Prophet and
AR-Net (Autoregressive
neural network), built on
PyTorch.
“A new library to analyze time
series data. Kats is a
lightweight, easy-to-use, and
generalizable framework for
generic time series analysis”
Anomaly Detection Toolkit
(ADTK)
FB Prophet Neural Prophet Kats
Favourite Python Libraries
| © Copyright 2023, InfluxData
What next?
| © Copyright 2023, InfluxData
Balena Hub
36
https://hub.balena.io/organizations/g_jay_clifford/apps/balenAir_InfluxDB_V2 https://forums.balena.io
| © Copyright 2023, InfluxData
https://influxdbu.com/ https://influxcommunity.slack.com/
Learning and Community
37
| © Copyright 2023, InfluxData
Any Questions?
| © Copyright 2023, InfluxData
www.influxdata.com
| © Copyright 2023, InfluxData
Step-by-step tutorial
| © Copyright 2023, InfluxData
Deployment with balena
41
| © Copyright 2023, InfluxData
Deployment with balena
https://github.com/InfluxCommunity/balenair/
42
| © Copyright 2023, InfluxData
Deployment with balena
https://github.com/InfluxCommunity/balenair/blob/master/docker-compose.yml
43
| © Copyright 2023, InfluxData
Deployment with balena → flux
https://github.com/InfluxCommunity/balenair/blob/master/docker-compose.yml
44
| © Copyright 2023, InfluxData
Deployment with balena
45
| © Copyright 2023, InfluxData
Deployment with balena
46
| © Copyright 2023, InfluxData
Deployment with balena
47
| © Copyright 2023, InfluxData
Deployment with balena
48
| © Copyright 2023, InfluxData
Deployment with balena
49
| © Copyright 2023, InfluxData
Deployment with balena
50
| © Copyright 2023, InfluxData
Deployment with balena
51
| © Copyright 2023, InfluxData
Deployment with balena
52
| © Copyright 2023, InfluxData
InfluxDB Cloud
53
| © Copyright 2023, InfluxData
InfluxDB Cloud
54
from(bucket: "C02-Demo")
|> range(start: v.timeRangeStart,
stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"]
== "mqtt_consumer")
|> filter(fn: (r) => r["_field"] ==
"co2")
|> filter(fn: (r) => r["location"] ==
"ES")
|> aggregateWindow(every: 30s, fn:
mean, createEmpty: false )
|> yield(name: "actual")
|> holtWinters(n: 8, interval: 30s,
withFit: true)
|> yield(name: "forcast")
import
"contrib/anaisdg/anomalydetection"
from(bucket: "C02-Demo")
|> range(start: v.timeRangeStart,
stop: v.timeRangeStop)
|> filter(fn: (r) => r["_measurement"]
== "mqtt_consumer")
|> filter(fn: (r) => r["_field"] ==
"co2")
|> anomalydetection.mad(threshold:
3.0)
|> filter(fn: (r) => r["level"] ==
"anomaly")
// Notice how you can filter for the
actual MAD value to better discover
anomalous series.
|> filter(fn: (r) => r["MAD"] > 3.0)
|> group(columns: ["location"],
mode:"by")
|> aggregateWindow(every: 1m, fn:
count, createEmpty: false )
|> yield()

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Start Automating InfluxDB Deployments at the Edge with balena

  • 1. | © Copyright 2023, InfluxData Start Automating InfluxDB Deployments at the Edge with balena January 24, 2023
  • 2. | © Copyright 2023, InfluxData Past life: Sales Engineer for IIoT Solutions. Passion: Autonomous and Vision based projects. Driven: To make IoT accessible to all. Belief: Industrial IoT’s success belongs to the domain experts. Jay Clifford Developer Advocate, InfluxData
  • 3. | © Copyright 2023, InfluxData Past life: IoT and Smart Cities during the last 15 years. Passion: Educative IoT devices. Driven: Make the planet a better place with IoT. Belief: IoT success will come when everyone will collaborate. Marc Pous IoT Giant Developer Advocate, balena.io
  • 4. | © Copyright 2023, InfluxData Part 3 Edge Data Replication and analysing our data in the cloud. Part 2 Intro to balenAir Part 4 Demo and next steps! Part 1 What is balena? 4
  • 5. | © Copyright 2023, InfluxData What is balena?
  • 6. | © Copyright 2023, InfluxData balena - Anyone? 6
  • 7. | © Copyright 2023, InfluxData 7 balena - Anyone?
  • 8. | © Copyright 2023, InfluxData 8 balena - At a Glance “Platform built to develop, deploy, and manage fleets of connected devices at scale.”
  • 9. | © Copyright 2023, InfluxData 9 balena - Fleet Management
  • 10. | © Copyright 2023, InfluxData 10 balena - Fleet Management
  • 11. | © Copyright 2023, InfluxData 11 balena - Fleet Management
  • 12. | © Copyright 2023, InfluxData balena - At a Glance 12
  • 13. | © Copyright 2023, InfluxData balena - At a Glance 13
  • 14. | © Copyright 2023, InfluxData balena - At a Glance 14
  • 15. | © Copyright 2023, InfluxData balena - At a Glance 15 . . .
  • 16. | © Copyright 2023, InfluxData balena - At a Glance . . . 16 https://hub.balena.io/organizations/marc6/apps/MING
  • 17. | © Copyright 2023, InfluxData balena - At a Glance 17 . . .
  • 18. | © Copyright 2023, InfluxData balena - At a Glance . . . 18
  • 19. | © Copyright 2023, InfluxData balena - What is a Block 19 https://blog.balena.io/introducing-balenablocks-jumpstart-your-iot-app-development/
  • 20. | © Copyright 2023, InfluxData balena - Connector Block 20 https://hub.balena.io/organizations/balenalabs/blocks/connector The connector block wraps Telegraf in code which discovers and intelligently connects data sources and sinks. For example, the connector block will find an MQTT broker running on the device and subscribe to a sensor topic. An application developer can simply send JSON-formatted data to this topic and it will be ingested by connector. Similarly, the block will find an instance of InfluxDB running on the device, and send any ingested data to it. The use of environment variables allows a developer or fleet owner to enable lots of other data sources and sinks, both on the device and externally hosted. https://github.com/balena-labs-projects/connector
  • 21. | © Copyright 2023, InfluxData balenAir
  • 22. | © Copyright 2023, InfluxData What is balenAir ? A “build it yourself” Indoor Air Quality Device - IAQ. Motivation: Dedicated to making environmental information visibly more accessible. | open source | highly customizable | | easy to use | privacy | IAQ = (C02, VOC and particulate sensors) 0 (best air quality) - 99 (hazardous) 22
  • 23. | © Copyright 2023, InfluxData Sensors and Hardware PMSA003I SCD-40 Raspberry PI 3 / 4b SparkFun Qwiic LED Stick 23
  • 24. | © Copyright 2023, InfluxData Demo Architecture IAQ MQTT Broker Connector InfluxDB Grafana Edge downsampled co2 24
  • 25. | © Copyright 2023, InfluxData Edge Data Replication
  • 26. | © Copyright 2023, InfluxData Cloud Edge generator northbound Write Endpoint replication queue 26 Telegraf
  • 27. | © Copyright 2023, InfluxData Analyzing our data in the cloud
  • 28. | © Copyright 2023, InfluxData A functional language designed for querying, analyzing, and acting on data. Flux 28
  • 29. | © Copyright 2023, InfluxData Anomaly Detection Forecasting 29
  • 30. | © Copyright 2023, InfluxData Let’s start!
  • 31. | © Copyright 2023, InfluxData Deployment with balena 31
  • 32. | © Copyright 2023, InfluxData Tips and Tricks
  • 33. | © Copyright 2023, InfluxData 33 Connector block tips & tricks https://github.com/balena-labs-projects/connector
  • 34. | © Copyright 2023, InfluxData 34 Unsupervised and rule-based time series anomaly detection The ADTK package allows you to easily build an effective detection model from a variety or rule-based anomaly detection methods. “Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.” A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net (Autoregressive neural network), built on PyTorch. “A new library to analyze time series data. Kats is a lightweight, easy-to-use, and generalizable framework for generic time series analysis” Anomaly Detection Toolkit (ADTK) FB Prophet Neural Prophet Kats Favourite Python Libraries
  • 35. | © Copyright 2023, InfluxData What next?
  • 36. | © Copyright 2023, InfluxData Balena Hub 36 https://hub.balena.io/organizations/g_jay_clifford/apps/balenAir_InfluxDB_V2 https://forums.balena.io
  • 37. | © Copyright 2023, InfluxData https://influxdbu.com/ https://influxcommunity.slack.com/ Learning and Community 37
  • 38. | © Copyright 2023, InfluxData Any Questions?
  • 39. | © Copyright 2023, InfluxData www.influxdata.com
  • 40. | © Copyright 2023, InfluxData Step-by-step tutorial
  • 41. | © Copyright 2023, InfluxData Deployment with balena 41
  • 42. | © Copyright 2023, InfluxData Deployment with balena https://github.com/InfluxCommunity/balenair/ 42
  • 43. | © Copyright 2023, InfluxData Deployment with balena https://github.com/InfluxCommunity/balenair/blob/master/docker-compose.yml 43
  • 44. | © Copyright 2023, InfluxData Deployment with balena → flux https://github.com/InfluxCommunity/balenair/blob/master/docker-compose.yml 44
  • 45. | © Copyright 2023, InfluxData Deployment with balena 45
  • 46. | © Copyright 2023, InfluxData Deployment with balena 46
  • 47. | © Copyright 2023, InfluxData Deployment with balena 47
  • 48. | © Copyright 2023, InfluxData Deployment with balena 48
  • 49. | © Copyright 2023, InfluxData Deployment with balena 49
  • 50. | © Copyright 2023, InfluxData Deployment with balena 50
  • 51. | © Copyright 2023, InfluxData Deployment with balena 51
  • 52. | © Copyright 2023, InfluxData Deployment with balena 52
  • 53. | © Copyright 2023, InfluxData InfluxDB Cloud 53
  • 54. | © Copyright 2023, InfluxData InfluxDB Cloud 54 from(bucket: "C02-Demo") |> range(start: v.timeRangeStart, stop: v.timeRangeStop) |> filter(fn: (r) => r["_measurement"] == "mqtt_consumer") |> filter(fn: (r) => r["_field"] == "co2") |> filter(fn: (r) => r["location"] == "ES") |> aggregateWindow(every: 30s, fn: mean, createEmpty: false ) |> yield(name: "actual") |> holtWinters(n: 8, interval: 30s, withFit: true) |> yield(name: "forcast") import "contrib/anaisdg/anomalydetection" from(bucket: "C02-Demo") |> range(start: v.timeRangeStart, stop: v.timeRangeStop) |> filter(fn: (r) => r["_measurement"] == "mqtt_consumer") |> filter(fn: (r) => r["_field"] == "co2") |> anomalydetection.mad(threshold: 3.0) |> filter(fn: (r) => r["level"] == "anomaly") // Notice how you can filter for the actual MAD value to better discover anomalous series. |> filter(fn: (r) => r["MAD"] > 3.0) |> group(columns: ["location"], mode:"by") |> aggregateWindow(every: 1m, fn: count, createEmpty: false ) |> yield()