Building SSL for Hosting Mobile Sites on NiFi

Building SSL For Hosting Mobile Web Sites on Apache NiFi

 openssl req -x509 -newkey rsa:2048 -keyout admin-private-key.pem -out admin-cert.pem -days 365 -subj "/CN=Admin Q. User/C=US/L=Seattle" -nodes

openssl pkcs12 -inkey admin-private-key.pem -in admin-cert.pem -export -out admin-q-user.pfx -passout pass:"SuperSecret"


keytool -genkeypair -alias nifiserver -keyalg RSA -keypass SuperSecret -storepass SuperSecret -keystore server_keystore.jks -dname "CN=Test NiFi Server" -noprompt

keytool -genkeypair -alias nifiserver -keyalg RSA -keypass SuperSecret -storepass SuperSecret -keystore server_keystore.jks -dname "CN=Test NiFi Server" -noprompt

keytool -importcert -v -trustcacerts -alias admin -file admin-cert.pem -keystore server_truststore.jks  -storepass SuperSecret -noprompt

# then import into browser / ssl / key certs

[FLaNK] Smart Weather Websocket Application - Kafka Consumer

 [FLaNK] Smart Weather Websocket Application - Kafka Consumer

Part 2 of 2

This is based on Koji Kawamura's excellent GIST:

As part of my Smart Weather Application, I wanted to display weather information as it arrives in a webpage using web sockets.   Koji has an excellent NiFi flow that does it.   I tweaked it and add some things since I am not using Zeppelin.   I am hosting my webpage with NiFi as well.

We simply supply a webpage that makes a websocket connection to NiFi and NiFi keeps a cache in HBase to know what the client is doing.  This cache is updated by consuming from Kafka.   We can then feed events as they happen to the page.

Here is the JavaScript for the web page interface to websockets:

function sendMessage(type, payload) {
websocket.send(makeMessage(type, payload));

function makeMessage(type, payload) {
return JSON.stringify({
'type': type,
'payload': payload

var wsUri = "ws://edge2ai-1.dim.local:9091/test";

websocket = new WebSocket(wsUri);
websocket.onopen = function(evt) {

sendMessage('publish', {
"message": document.getElementById("kafkamessage")

websocket.onerror = function(evt) {console.log('ERR', evt)};
websocket.onmessage = function(evt) {
var dataPoints = JSON.parse(;

var output = document.getElementById("results");
var dataBuffer = "<p>";
for(var i=0;i<dataPoints.length;i++)
dataBuffer += " <img src=\"" + dataPoints[i].icon_url_base + dataPoints[i].icon_url_name + "\"> &nbsp;" + dataPoints[i].location +
dataPoints[i].station_id + "@" + dataPoints[i].latitude + ":" +
dataPoints[i].longitude + "@" + dataPoints[i].observation_time +
dataPoints[i].temperature_string + "," + dataPoints[i].relative_humidity + "," +
dataPoints[i].wind_string +"<br>";

output.innerHTML = output.innerHTML + dataBuffer + "</p><br>";


Video Walkthrough:

Source Code:

Kafka Topic

weathernj Schema

The schema registry has a live Swagger interface to it's REST API

NiFi Flow Overview

Ingest Via REST All US Weather Data from Zipped XML

As Data Streamings In, We Can Govern It

Ingested Data is Validated Against It's Schema Then Pushed to Kafka as Avro

We consume that Kafka data in store it in Kudu for analytics

We host a web page for our Websockets Application in NiFi with 4 simple processors.

Listen and Put Web Socket Messages Between NiFi Server and Web Application

Kafka Data is Cached for Websocket Applications

Set the Port for WebSockets via Jetty Web Server

Use HBase As Our Cache

We can monitor our Flink SQL application from the Global Flink Dashboard

We can query our Weather data store in Apache Kudu via Apache Impala through Apache Hue 

Kudu Visualizations of Our Weather Data in Cloudera Visual Applications

[FLaNK] Smart Weather Applications with Flink SQL

 [FLaNK] Smart Weather Applications with Flink SQL 

Sometimes you want to acquire, route, transform, live query and analyze all the weather data in the United States while those reports happen.   With FLaNK, it's a trivial process to do.

From Kafka to Kudu for Any Schema of Any Type of Data, No Code, Two Steps

The Schema Registry has full Swagger-ized Runnable REST API Documentation.   Integrate, DevOps and Migration in a simple script

Here's your schemas, upload, edit and compare.

Validating Data Against a Schema With Your Approved Level of Tolerance.   You want extra fields allowed, you got it.


Feed that data to beautiful visual applications running in Cloudera Machine Learning.

You like drill down maps, you got them.

Query your data fast with Apache Hue against Apache Kudu tables through Apache Impala.

Let's ingest all the US weather stations even though they are a zipped directory of a ton of XML files.

Weather Ingest is Easy Automagically

View All Your Topic Data Enabled by Schema Registry Even in Avro Format






INSERT INTO weathernj
SELECT `location`, station_id,latitude,longitude,observation_time,weather,
temperature_string, temp_f,temp_c,relative_humidity,wind_string,wind_dir,wind_degrees,wind_mph,
wind_kt, pressure_in,dewpoint_string,dewpoint_f,dewpoint_c
FROM weather
`location` is not null and `location` <> 'null' and trim(`location`) <> '' and `location` like '%NJ';

Kafka Insert


Example Slack Output

========================================================= Cincinnati/Northern Kentucky International Airport, KY Station KCVG
Temperature: 49.0 F (9.4 C)
Humdity: 83
Wind East at 3.5 MPH (3 KT)
Dewpoint 44.1 F (6.7 C)Observed at Tue, 27 Oct 2020 11:52:00 -0400---- tracking info ----          UUID: 2cb6bd67-148c-497d-badf-dfffb4906b89
  Kafka offset: 0
Kafka Timestamp: 1603818351260