1. Learning the Basics of
Apache NiFi for IoT
Timothy Spann
Principal DataFlow Field Engineer
Cloudera
#ossummit @PaasDev

2. #ossummit #lfelc
Speaker - Timothy Spann
Principal DataFlow Field Engineer
@PaasDev
DZone Zone Leader and Big Data MVB
Princeton NJ Future of Data Meetup
https://github.com/tspannhw
https://www.datainmotion.dev/

3. #ossummit #lfelc
Future of Data - Princeton (Global via YouTube)
@Pa
asDe
https://www.meetup.com/futureofdata-princeton/
From Big Data to AI to Streaming to Containers to
Cloud to Analytics to Cloud Storage to Fast Data to
Machine Learning to Microservices to ...

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BASICS of APACHE NIFI
•A general overview of capabilitiesWhat Is NiFi
•Navigating the Apache NiFi canvasGrand Tour
•Examples of processing IoT data from edge to consumptionExample IoT Flows

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Learning the Basics of Apache NiFi

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STORAGE LAYER
sensors
IoT REFERENCE ARCHITECTURE
Apache NiFi
Apache Kafka
DATA SYNDICATION
SERVICE BY KAFKA
Kafka Topic
iot
DATA FLOW APPS
POWERED BY NIFI
Apache Impala
Deep Learning & Machine
Learning
MODEL EXECUTION
REST

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End to End Logs Pipeline
Routers
Databases
Firewalls
Logs
Logs
Errors
Aggregates
Alerts
Other data
ETL
Analytics
Enterprise Analysis Real Time Analytics
Complexity Reduction
Events

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Apache Hue
VISUALIZATION
SQL and Query Editor & Performance Diagnostics
Tool for the Cloudera Data Platform

9. What is Apache NiFi?

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Apache NiFi
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Apache NiFi High Level Capabilities
• Scale horizontal and vertically
• Scale your data flow to millions event/s
• Ingest TB to PB of data per day
• Adapt to your flow requirements
• Back pressure & Dynamic prioritization
• Loss tolerant vs guaranteed delivery
• Low latency vs high throughput
• Secure
• SSL, HTTPS, SFTP, etc.
• Governance and data provenance
• Extensible
• Build your own processors and Controller services (providers)
• Integrate with external systems (Security, Monitoring, Governance, etc)

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FLOW FILES ARE LIKE HTTP DATA
HTTP Data FlowFile
HTTP/1.1 200 OK
Date: Sun, 10 Oct 2010 23:26:07 GMT
Server: Apache/2.2.8 (CentOS) OpenSSL/0.9.8g
Last-Modified: Sun, 26 Sep 2010 22:04:35 GMT
ETag: "45b6-834-49130cc1182c0"
Accept-Ranges: bytes
Content-Length: 13
Connection: close
Content-Type: text/html
Hello world!
Standard FlowFile Attributes
Key: 'entryDate’ Value: 'Fri Jun 17 17:15:04 EDT
2016'
Key: 'lineageStartDate’ Value: 'Fri Jun 17 17:15:04 EDT
2016'
Key: 'fileSize’ Value: '23609'
FlowFile Attribute Map Content
Key: 'filename’ Value: '15650246997242'
Key: 'path’Value: './’
Binary Content *
Header
Content

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Apache NiFi
Enable easy ingestion, routing, management and delivery of any data anywhere (Edge, cloud,
data center) to any downstream system with built in end-to-end security and provenance
• Over 300 Prebuilt Processors
• Easy to build your own
• Parse, Enrich & Apply Schema
• Filter, Split, Merger & Route
• Throttle & Backpressure
• Guaranteed Delivery
• Full data provenance
• Eco-system integration
Advanced tooling to industrialize flow development
(Flow Development Life Cycle)
FTP
SFTP
HL7
UDP
XML
HTTP
EMAIL
HTML
IMAGE
SYSLOG
FTP
SFTP
HL7
UDP
XML
HTTP
EMAIL
HTML
IMAGE
SYSLOG
HASH
MERGE
EXTRACT
DUPLICATE
SPLIT
ROUTE TEXT
ROUTE CONTENT
ROUTE CONTEXT
CONTROL RATE
DISTRIBUTE LOAD
GEOENRICH
SCAN
REPLACE
TRANSLATE
CONVERT
ENCRYPT
TALL
EVALUATE
EXECUTE

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Provenance/Lineage

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Prioritization
• Configure a prioritizer per
connection
• Determine what is important for
your data – time based, arrival
order, importance of a data set
• Funnel many connections down
to a single connection to
prioritize across data sets
• Develop your own prioritizer if
needed

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SQL BASED ROUTING WITH NiFi’s QueryRecord Processor
• QueryRecord Processor- Executes a SQL
statement against records and writes the results
to the flow file content.
• CSVReader: Looking up schema from SR, it will
converts CSV Records into ProcessRecords
• SQL execution via Apache Calcite: execute
configured SQL against the ProcessRecords for
routing
• CSVRecordSetWriter: Converts the result of
the query from Process records into CSV for the
for the flow file content
Do routing(routing geo and speed streams) using standard SQL as opposed to complex regular expressions.

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STATELESS ENGINE
• Granular containers
per flow
• Flows From NiFi
Registry
https://www.datainmotion.dev/2019/11/exploring-apache-nifi-110-parameters.html
bin/nifi.sh stateless RunFromRegistry Continuous --file kafka.json
https://github.com/apache/nifi/blob/ea1becac4fc519c54b8b4d21773e68f8da364755/nifi-nar-bundles/nifi-framework-bundle/nifi-
framework/nifi-stateless/README.md

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STATELESS ENGINE
• See also Parameters
• Docker
• YARN
• Kubernetes (K8)
• Stateful NiFi clusters
• Apache OpenWhisk
(FaaS)
https://www.datainmotion.dev/2019/11/exploring-apache-nifi-110-parameters.html
{"registryUrl": "http://tspann-mbp15-hw14277:18080",
"bucketId": "140b30f0-5a47-4747-9021-19d4fde7f993",
"flowId": "0540e1fd-c7ca-46fb-9296-e37632021945",
"ssl": {
"keystoreFile": "","keystorePass": "","keyPass": "","keystoreType": "",
"truststoreFile":
"/Library/Java/JavaVirtualMachines/amazon-corretto-11.jdk/Contents/Home/lib/sec
urity/cacerts",
"truststorePass": "changeit", "truststoreType": "JKS"
},
"parameters": {
"broker" : "4.317.852.100:9092",
"topic" : "iot",
"group_id" : "nifi-stateless-kafka-consumer",
"DestinationDirectory" : "/tmp/nifistateless/output2/",
"output_dir": "/Users/tspann/Documents/nifi-1.10.0-SNAPSHOT/logs/output"
}
}
https://github.com/tspannhw/stateless-examples

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PARAMETER CONTEXT
• Parameters
• Parameter
Context
https://www.datainmotion.dev/2019/11/exploring-apache-nifi-110-parameters.html

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PARAMETERS
• Parameters
• Parameter
Context
https://www.datainmotion.dev/2019/11/exploring-apache-nifi-110-parameters.html

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RETRYFLOWFILE
• Configurable Retries
• Maximum #
• Penalties
• When to Fail
• Reuse Mode
https://medium.com/@abdelkrim.hadjidj/apache-nifi-1-10-series-simplifying-error-handling-7de86f130acd

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BACKPRESSURE PREDICTION
OrdinaryLeastSquares
SimpleRegression
Enable analytics feature
http://lonnifi.blogspot.com/2019/11/back-pressure-prediction-deep-dive.html?es_id=5233333939
https://youtu.be/Tt8TSlHu7PE

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PARQUET READER AND WRITER
• Native Record Processors for Apache
Parquet Files!
• CSV <-> Parquet
• XML <-> Parquet
• AVRO <-> Parquet
• JSON <-> Parquet
• More...
https://www.datainmotion.dev/2019/11/exploring-apache-nifi-110-parameters.html
https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apac
he_7.html

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MANY OTHER FEATURES
• Prometheus Reporting Task
• Experimental Encrypted content
repository
• PublishKafka Partition Support
• Toolkit module to generate and build
Swagger
• GeoEnrichIPRecord Processor
• Command Line Diagnostics
• RocksDB FlowFile Repository
• PutBigQueryStreaming Processor
• Enhanced DevOps and CD/CI
ELT/ETL Lookup Services
• DatabaseRecordLookupService
• KuduLookupService
• HBase_2_ListLookupService

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Scalable and distributed architecture

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NiFi Flow Registry

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Example of NiFi Transformations
Data enrichment
Enrich events by adding the classification
based on the host
Use reference lookup table from a CSV file
[ {
"time" : ”7845800765",
"host" : ”web-...",
"sourcetype" : ”cpu_resource_usage",
"source" : "...",
"index" : "_metrics",
"meta" : "...",
"event" : "..."}}",
"classification" : internal
},
...
[ {
"time" : ”7845800765",
"host" : ”web-...",
"sourcetype" : ”cpu_resource_usage",
"source" : "...",
"index" : "_metrics",
"meta" : "...",
"event" : "..."}}",
"classification" : null
},
...

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INGEST RDBMS TABLES
https://community.cloudera.com/t5/Community-Articles/Incrementally-Streaming-RDBMS-Data-to-Your-Hadoop-DataLake/ta-p/247927
https://community.cloudera.com/t5/Community-Articles/Ingesting-Golden-Gate-Rec
ords-From-Apache-Kafka-and/ta-p/247557
https://community.cloudera.com/t5/Community-Articles/Incremental-Fetch-in-NiFi-
with-QueryDatabaseTable/ta-p/247073

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EXAMPLE IoT Flows

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IoT Reference Architecture
STORAGE LAYER
sensors
Apache NiFi
Apache Kafka
DATA SYNDICATION
SERVICE BY KAFKA
Kafka Topic
iot
DATA FLOW APPS
POWERED BY NIFI
Apache Impala
Deep Learning & Machine
Learning
MODEL EXECUTION
REST

31. #ossummit #lfelc
Best Practices
https://www.datainmotion.dev/2020/06/no-more-spaghetti-flows.html
● Reduce, Reuse, Recycle. Use Parameters to reuse
common modules.
● Put flows, reusable chunks into separate Process
Groups.
● Write custom processors if you need new or
specialized features
● Use Cloudera supported NiFi Processors
● Use Record Processors everywhere

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Cloudera Communities
Got questions? Leverage community.cloudera.com
Join our meetup:
www.meetup/pro/futureofdata