FLiP-Py-Pi-GasThermal: Building an IoT Edge Application with Apache Pulsar and Python for TVOC and CO2 Ingest

 FLiP-Py-Pi-GasThermal

Source:  https://github.com/tspannhw/FLiP-Py-Pi-GasThermal

tags:  Apache Pulsar, Python, Raspberry Pi, Gas Sensor + Thermal Camera Sensors, Apache Flink, Trino/Presto SQL

Sensors

• Pimoroni BreakoutGarden: SGP30
  • Sensiron SGP30 TVOC and eCO2 sensor (datasheet)
  • TVOC sensing from 0-60,000 ppb (parts per billion)
  • CO2 sensing from 400 to 60,000 ppm (parts per million)
• Pimoroni BreakoutGarden: MLX90640 Thermal Camera

Architecture

  

Build

bin/pulsar-admin topics create persistent://public/default/garden3

bin/pulsar-client consume "persistent://public/default/garden3" -s "garden3reader" -n 0

class Garden(Record):
    cpu = Float()
    diskusage = String()
    endtime = String()
    equivalentco2ppm = String()
    host = String()
    hostname = String()
    ipaddress = String()
    macaddress = String()
    memory = Float()
    rowid = String()
    runtime = Integer()
    starttime = String()
    systemtime = String()
    totalvocppb = String()
    ts = Integer()
    uuid = String()

----- got message -----
key:[garden3_uuid_yvs_20220306191528], properties:[], content:{
 "cpu": 0.0,
 "diskusage": "103496.6 MB",
 "endtime": "1646594128.2460103",
 "equivalentco2ppm": "  413",
 "host": "garden3",
 "hostname": "garden3",
 "ipaddress": "192.168.1.198",
 "macaddress": "dc:a6:32:32:98:20",
 "memory": 9.2,
 "rowid": "20220306191528_707b34d4-7299-4233-a495-d2d97393e834",
 "runtime": 0,
 "starttime": "03/06/2022 14:15:28",
 "systemtime": "03/06/2022 14:15:29",
 "totalvocppb": "    5",
 "ts": 1646594129,
 "uuid": "garden3_uuid_yvs_20220306191528"
}

presto> select * from pulsar."public/default"."garden3";
 cpu |  diskusage  |      endtime       | equivalentco2ppm |  host   | hostname |   ipaddress   |    macaddress     | memory |                        rowid                        | runtime |      
-----+-------------+--------------------+------------------+---------+----------+---------------+-------------------+--------+-----------------------------------------------------+---------+------
 6.5 | 103496.5 MB | 1646594650.7116666 |   418            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192410_197b6b0c-b86c-4191-9e9c-11777767825e |       0 | 03/06
 6.7 | 103496.5 MB | 1646594651.7441382 |   418            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192411_4dab3212-423b-46a9-ae39-b10eb363336d |       0 | 03/06
 1.3 | 103496.5 MB | 1646594652.7764313 |   421            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192412_d24a819b-4ca1-489a-9683-da48bc37185c |       0 | 03/06
 0.2 | 103496.5 MB | 1646594653.810233  |   421            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192413_f4e43177-2486-4a36-b27a-903028d6aacf |       0 | 03/06
 0.0 | 103496.5 MB | 1646594654.8467774 |   416            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192414_558d3db8-725a-46ec-ad1f-89f8067142f8 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594655.880628  |   420            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192415_1eeea533-7f7e-4945-a089-d4b2f8681e14 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594656.9145741 |   418            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192416_d7d8c6ea-2adf-4704-8f49-3108a7328f26 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594657.9489982 |   425            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192417_c71cbb6e-c59f-4855-84ae-b796fd3d7a76 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594658.9828157 |   420            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192418_c14204dd-4c1c-4e76-a272-8ddecd11a97c |       0 | 03/06
 0.0 | 103496.5 MB | 1646594660.0187812 |   420            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192419_c7583063-06e0-4601-806a-96619b6bb136 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594661.0531507 |   428            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192421_57e4a8ed-af6f-4dd0-b143-f29f1a211fdb |       0 | 03/06
 0.0 | 103496.5 MB | 1646594662.087301  |   421            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192422_cf2ce36e-c996-41ac-baec-db292cffdd37 |       0 | 03/06
 6.2 | 103496.5 MB | 1646594663.1214898 |   415            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192423_30b13428-ac2e-4929-b9f5-c4c3fbc3312a |       0 | 03/06
 6.5 | 103496.5 MB | 1646594664.1541135 |   424            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192424_d1517315-85fa-4923-b634-9b673c5b20ba |       0 | 03/06
 3.6 | 103496.5 MB | 1646594665.1890867 |   417            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192425_3984b0bc-a92e-4458-834c-e65259bb7a4d |       0 | 03/06
 0.0 | 103496.5 MB | 1646594666.221572  |   422            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192426_e0debd83-47e8-43f7-8ec5-cf0dbc27b87e |       0 | 03/06
 0.0 | 103496.5 MB | 1646594667.2555919 |   424            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192427_6ec920e7-56d2-4730-bddc-e18592cf1210 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594668.2893167 |   428            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192428_434b4458-6f89-4161-b967-f29796d8bf5d |       0 | 03/06
 0.0 | 103496.5 MB | 1646594669.3234618 |   426            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192429_5bb29c2e-d078-405d-b8f6-967ea4753b3f |       0 | 03/06
 0.0 | 103496.5 MB | 1646594670.359024  |   421            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192430_05b86f6b-4d28-497c-acd6-d14f7ce27157 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594671.392967  |   432            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192431_b7a2955a-6f39-4439-8eb9-27d7a2633836 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594672.4271743 |   426            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192432_29861384-fc30-4deb-bc1d-b7f2d27ef89d |       0 | 03/06
 0.0 | 103496.5 MB | 1646594673.4611707 |   424            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192433_25435335-39fe-4294-b913-95c63537a743 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594674.4951062 |   424            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192434_a52a90e8-b11c-467b-b87f-936432bc8988 |       0 | 03/06
 3.3 | 103496.5 MB | 1646594675.531778  |   436            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192435_48bab6a9-9f2e-443b-9fed-f096f6c24ebd |       0 | 03/06
 6.5 | 103496.5 MB | 1646594676.5642908 |   433            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192436_761f79dc-06f6-4ed3-8062-5227b6842b77 |       0 | 03/06
 6.2 | 103496.5 MB | 1646594677.5965276 |   421            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192437_6d8d49c9-0519-4825-b6b7-ed435e9fe747 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594678.6290672 |   423            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192438_a920872d-f152-4b18-94a7-b4dfe5641482 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594679.6629703 |   418            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192439_032ce574-10f6-4b50-b5e8-102b466af65a |       0 | 03/06
 0.0 | 103496.5 MB | 1646594680.699419  |   420            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192440_cde6f78a-d028-4f87-a95b-156bac0ee0c2 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594681.7338123 |   424            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192441_7400689a-6974-4ff2-a688-96c317d45d03 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594682.767776  |   417            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192442_46696fa7-295c-4fe6-bf49-8d8405e21cf5 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594683.8017883 |   420            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192443_4de9f79c-0169-49d6-b337-fb57dcb5cf7e |       0 | 03/06
 0.0 | 103496.5 MB | 1646594684.8360054 |   422            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192444_63081ac4-2c87-462d-bc16-ab506e9c6db2 |       0 | 03/06
 0.0 | 103496.5 MB | 1646594685.8720167 |   420            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192445_fee13e36-26c6-4f06-894c-8b96cb0f3bae |       0 | 03/06
 0.0 | 103496.5 MB | 1646594686.90594   |   431            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192446_8aaa423d-bfe3-43e8-8653-d554c2afb8d6 |       0 | 03/06
 0.8 | 103496.4 MB | 1646594687.939747  |   416            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192447_bfe339cd-3fb4-4d2a-b612-3a2363e1b83b |       0 | 03/06
 6.5 | 103496.4 MB | 1646594688.9728699 |   424            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192448_8809fc3e-75f8-4aca-a141-6e5d4872b0de |       0 | 03/06
 6.5 | 103496.4 MB | 1646594690.0051649 |   412            | garden3 | garden3  | 192.168.1.198 | dc:a6:32:32:98:20 |    9.2 | 20220306192449_0d0b4b62-0321-4bcd-952b-14607333d9f5 |       0 | 03/06

presto> desc pulsar."public/default"."garden3";
      Column       |   Type    | Extra |                                   Comment                                   
-------------------+-----------+-------+-----------------------------------------------------------------------------
 cpu               | real      |       | ["null","float"]                                                            
 diskusage         | varchar   |       | ["null","string"]                                                           
 endtime           | varchar   |       | ["null","string"]                                                           
 equivalentco2ppm  | varchar   |       | ["null","string"]                                                           
 host              | varchar   |       | ["null","string"]                                                           
 hostname          | varchar   |       | ["null","string"]                                                           
 ipaddress         | varchar   |       | ["null","string"]                                                           
 macaddress        | varchar   |       | ["null","string"]                                                           
 memory            | real      |       | ["null","float"]                                                            
 rowid             | varchar   |       | ["null","string"]                                                           
 runtime           | integer   |       | ["null","int"]                                                              
 starttime         | varchar   |       | ["null","string"]                                                           
 systemtime        | varchar   |       | ["null","string"]                                                           
 totalvocppb       | varchar   |       | ["null","string"]                                                           
 ts                | integer   |       | ["null","int"]                                                              
 uuid              | varchar   |       | ["null","string"]                                                           
 __partition__     | integer   |       | The partition number which the message belongs to                           
 __event_time__    | timestamp |       | Application defined timestamp in milliseconds of when the event occurred    
 __publish_time__  | timestamp |       | The timestamp in milliseconds of when event as published                    
 __message_id__    | varchar   |       | The message ID of the message used to generate this row                     
 __sequence_id__   | bigint    |       | The sequence ID of the message used to generate this row                    
 __producer_name__ | varchar   |       | The name of the producer that publish the message used to generate this row 
 __key__           | varchar   |       | The partition key for the topic                                             
 __properties__    | varchar   |       | User defined properties    
 

Presto/Trino gives us access to all that tasty meta data for each message/event/row/record/thing/data stuff. All with special names to help prevent collisions.

Meta Data

• partition - if we have a partitioned topic, which partition does this message belong to.
• event_time - you know what time it is! Timestamp in ms for event action.
• publish_time - when was this message published to the topic?
• message_id - unique id for this message
• sequence_id - ordering information for this message
• producer_name - who sent me this?
• key - did you assign a key like I asked you to?
• properties - all those extra fields you added around the payload

Spark Structured Streaming

• https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-sinks

val dfPulsar = spark.readStream.format("pulsar").option("service.url", "pulsar://localhost:6650").option("admin.url", "http://localhost:8080").option("topic", "persistent://public/default/garden3").load()
dfPulsar.printSchema()
val pQuery = dfPulsar.selectExpr("*").writeStream.format("parquet").option("truncate", false) .option("checkpointLocation", "/tmp/checkpoint").option("path", "/opt/demo/gasthermal").start()
    
pQuery.explain()
pQuery.awaitTermination()
pQuery.stop()

// can be "orc", "json", "csv", etc.

Show Me The Data

We can visualize data from Apache Pulsar by consuming it through the web sockets interface in a simple JQuery Single Page Web Application like below.

Example Parquet Files

pip3 install parquet-tools -U

parquet-tools inspect part-00000-b7e1f8dc-956d-4130-bc59-7b1435e41391-c000.snappy.parquet

############ file meta data ############
created_by: parquet-mr version 1.12.1 (build 2a5c06c58fa987f85aa22170be14d927d5ff6e7d)
num_columns: 23
num_rows: 1
num_row_groups: 1
format_version: 1.0
serialized_size: 5071


############ Columns ############
cpu
diskusage
endtime
equivalentco2ppm
host
hostname
ipaddress
macaddress
memory
rowid
runtime
starttime
systemtime
totalvocppb
ts
uuid
__key
__topic
__messageId
__publishTime
__eventTime
key
value

############ Column(cpu) ############
name: cpu
path: cpu
max_definition_level: 1
max_repetition_level: 0
physical_type: FLOAT
logical_type: None
converted_type (legacy): NONE

############ Column(diskusage) ############
name: diskusage
path: diskusage
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(endtime) ############
name: endtime
path: endtime
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(equivalentco2ppm) ############
name: equivalentco2ppm
path: equivalentco2ppm
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(host) ############
name: host
path: host
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(hostname) ############
name: hostname
path: hostname
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(ipaddress) ############
name: ipaddress
path: ipaddress
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(macaddress) ############
name: macaddress
path: macaddress
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(memory) ############
name: memory
path: memory
max_definition_level: 1
max_repetition_level: 0
physical_type: FLOAT
logical_type: None
converted_type (legacy): NONE

############ Column(rowid) ############
name: rowid
path: rowid
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(runtime) ############
name: runtime
path: runtime
max_definition_level: 1
max_repetition_level: 0
physical_type: INT32
logical_type: None
converted_type (legacy): NONE

############ Column(starttime) ############
name: starttime
path: starttime
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(systemtime) ############
name: systemtime
path: systemtime
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(totalvocppb) ############
name: totalvocppb
path: totalvocppb
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(ts) ############
name: ts
path: ts
max_definition_level: 1
max_repetition_level: 0
physical_type: INT32
logical_type: None
converted_type (legacy): NONE

############ Column(uuid) ############
name: uuid
path: uuid
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(__key) ############
name: __key
path: __key
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: None
converted_type (legacy): NONE

############ Column(__topic) ############
name: __topic
path: __topic
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(__messageId) ############
name: __messageId
path: __messageId
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: None
converted_type (legacy): NONE

############ Column(__publishTime) ############
name: __publishTime
path: __publishTime
max_definition_level: 1
max_repetition_level: 0
physical_type: INT96
logical_type: None
converted_type (legacy): NONE

############ Column(__eventTime) ############
name: __eventTime
path: __eventTime
max_definition_level: 1
max_repetition_level: 0
physical_type: INT96
logical_type: None
converted_type (legacy): NONE

############ Column(key) ############
name: key
path: __messageProperties.key_value.key
max_definition_level: 2
max_repetition_level: 1
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(value) ############
name: value
path: __messageProperties.key_value.value
max_definition_level: 3
max_repetition_level: 1
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

Flink

CREATE CATALOG pulsar WITH (
   'type' = 'pulsar',
   'service-url' = 'pulsar://pulsar1:6650',
   'admin-url' = 'http://pulsar1:8080',
   'format' = 'json'
);

USE CATALOG pulsar;

SHOW TABLES;

Flink SQL> describe garden3;
+------------------+--------+------+-----+--------+-----------+
|             name |   type | null | key | extras | watermark |
+------------------+--------+------+-----+--------+-----------+
|              cpu |  FLOAT | true |     |        |           |
|        diskusage | STRING | true |     |        |           |
|          endtime | STRING | true |     |        |           |
| equivalentco2ppm | STRING | true |     |        |           |
|             host | STRING | true |     |        |           |
|         hostname | STRING | true |     |        |           |
|        ipaddress | STRING | true |     |        |           |
|       macaddress | STRING | true |     |        |           |
|           memory |  FLOAT | true |     |        |           |
|            rowid | STRING | true |     |        |           |
|          runtime |    INT | true |     |        |           |
|        starttime | STRING | true |     |        |           |
|       systemtime | STRING | true |     |        |           |
|      totalvocppb | STRING | true |     |        |           |
|               ts |    INT | true |     |        |           |
|             uuid | STRING | true |     |        |           |
+------------------+--------+------+-----+--------+-----------+
16 rows in set

select equivalentco2ppm, totalvocppb, cpu, starttime, systemtime, ts, cpu, diskusage, endtime, memory, uuid from garden3;

select max(equivalentco2ppm) as MaxCO2, max(totalvocppb) as MaxVocPPB from garden3;


// TODO

Add your own table

  publishTime TIMESTAMP(3) METADATA,
  WATERMARK FOR publishTime AS publishTime - INTERVAL '5' SECOND
  

  

Apache NiFi (FLiPN)

• Choose a processor from the palette

• Consume messages from Apache Pulsar

• Full Apache NiFi Data Flow with Apache Pulsar and MongoDB

MongoDB

mongo -u username1 -p password1 --authenticationDatabase admin pulsar1:27017/inventory

show databases

db.createCollection("garden3")

show collections

db.garden3.find().pretty()

{
        "_id" : ObjectId("622f7315f99b9a338d60592f"),
        "cpu" : 0,
        "diskusage" : "101615.9 MB",
        "endtime" : "1647276083.2033532",
        "equivalentco2ppm" : "  407",
        "host" : "garden3",
        "hostname" : "garden3",
        "ipaddress" : "192.168.1.199",
        "macaddress" : "dc:a6:32:32:98:20",
        "memory" : 8.8,
        "rowid" : "20220314164123_0e19c5e6-45f5-405e-bd93-9aed05b37630",
        "runtime" : 0,
        "starttime" : "03/14/2022 12:41:23",
        "systemtime" : "03/14/2022 12:41:24",
        "totalvocppb" : "  5",
        "ts" : 1647276084,
        "uuid" : "garden3_uuid_xrl_20220314164123"
}

 

References

• https://pypi.org/project/parquet-tools/
• https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/dev/table/sqlclient/
• https://github.com/tspannhw/StreamingAnalyticsUsingFlinkSQL/
• https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/connectors/datastream/pulsar/
• https://shop.pimoroni.com/products/sgp30-air-quality-sensor-breakout?variant=30924091719763
• https://shop.pimoroni.com/products/mlx90640-thermal-camera-breakout?variant=12536948654163
• https://github.com/tspannhw/minifi-gasthermal
• https://dev.to/tspannhw/cloudera-edge2ai-minifi-java-agent-with-raspberry-pi-and-thermal-camera-and-air-quality-sensor-part-1-3oo9
• https://www.datainmotion.dev/2020/01/cloudera-edge2ai-minifi-java-agent-with.html
• https://github.com/tspannhw/FLiP-Pi-Thermal
• https://github.com/pimoroni/mlx90640-library

Additional Heat Images

   

ODPI's OpenDS4All - Open Source Data Science Content To Teach the World

OpenDS4All

Start learning now:

https://github.com/odpi/OpenDS4All/tree/master/opends4all-resources/opends4all-data-wrangling-and-integration

ODPI has officially announced this recently and it looks great.

https://www.odpi.org/news/2020/02/28/odpi-announces-the-opends4all-project

There is a ton of amazing materials including slides, notes, documentation, homework, exercises and Jupyter notebooks covering Data Wrangling, Data Science, the Basics and Apache Spark.   

This“starter set” of training materials can help you build a Data Science program for yourself, your company, your university or your non-profit.    I am going to bring some of these to my meetups and hopefully can help give back with new materials, updates and suggestions.

These are college level materials developed by the University of Pennsylvania and open source via the ODPI with IBM leading.   The code and slides look great.   I can see these helping to enable the world adding another million desperately needed Data Scientists, Data Engineers and Data Science Enabled professionals.

I have been running some of this via Cloudera Machine Learning in my CDP cluster in AWS and it works great.   This is really well made.   I am hoping to create a module on Streaming Data Science to contribute.

https://www.meetup.com/futureofdata-princeton/