Data In Motion

Patient Monitoring Example Architecture

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Apache Kafka
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Apache NiFi
Cloudera Schema Registry
Apache Hive
Apache Druid
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Apache Kafka Streams (KStreams)
Apache Ranger
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Apache NiFi - MiNiFi
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For Upcoming Projects

Cloudera Edge Manager

Using Sensors At Scale on the Edge Part 1 : Apache NiFi : MiNiFi : CEM

In the next release I will be adding CEM.

This is an update to an existing flow that adds new features based on Apache NiFi 1.9.1. We will also be migrating this to MiNiFi 0.6.0 C++ with Cloudera CEM.

*Will be updated with CEM and MFC++ Agent 0.6.0

Links:

Source:

Advanced XML Processing with Apache NiFi 1.9.1

With the latest version of Apache NiFi, you can now directly convert XML to JSON or Apache AVRO, CSV or any other format supported by RecordWriters. This is a great advancement. To make it even easier, you don't even need to know the schema before hand. There is a built-in option to Infer Schema.

The results of an RSS (XML) feed converted to JSON and displayed in a slack channel.

Besides just RSS feeds, we can grab regular XML data including XML data that is wrapped in a Zip file (or even in a Zipfile in an email, SFTP server or Google Docs).

Get the Hourly Weather Observation for the United States

Decompress That Zip

Unpack That Zip into Files

One ZIP becomes many XML files of data.

An example XML record from a NOAA weather station.

Converted to JSON Automagically

Let's Read Those Records With A Query and Convert the results to JSON Records

Read RSS Feed of Travel Warnings

In this one simple example, we are ingesting all of the observed weather from all observation stations in the United States via one downloaded ZIP file containing all of the hourly XMLs. Apache NiFi can easily acquire this file, decompress it and unpack the files from the zip. We can then convert all of them as records by inferring their schema and building new records in the output format of our choice. We can then pull apart values we need or push this new cleaned format to one or more storage options including HBase, HDFS, Hive, SQL Database, MongoDB or elsewhere. We can also send this data on via Apache Kafka to a streaming engine such as Kafka Streams or Spark Structured Streaming for more processing or joining with other datasets.

QueryRecord is allowing us to write a SQL query such as SELECT * FROM FLOWFILE reading XML records and producing JSON records as a result. We can change fields or add things like SUMs and AVGs.

References:

Hourly Update

https://w1.weather.gov/xml/current_obs/all_xml.zip