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Music city data Hail Hydrate! from stream to lake

Music city data Hail Hydrate! from stream to lake with apache pulsar, apache flink, apache nifi, datalake, cloud, streaming data, IoT.

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https://github.com/tspannhw https://www.datainmotion.dev/
https://github.com/tspannhw https://www.datainmotion.dev/
Tim Spann, Developer Advocate
DZone Zone Leader and Big Data MVB
Data DJay
https://github.com/tspannhw
https://www.datainmotion.dev/
https://github.com/tspannhw/SpeakerProfile
https://dev.to/tspannhw
https://sessionize.com/tspann/
https://www.slideshare.net/bunkertor
@PaasDev
AGENDA
Use Case - Populate the Data Lake
Key Challenges
▪ Their Impact
▪ A Solution
▪ Outcome
Why Apache NiFi and Apache Pulsar?
Successful Architecture
Demo
5
USE CASE
IoT Ingestion: High-volume streaming sources, multiple message formats, diverse
protocols and multi-vendor devices creates data ingestion challenges.
6
KEY CHALLENGES
Visibility: Lack visibility of end-to-end streaming data flows,
inability to troubleshoot bottlenecks, consumption patterns etc.
Data Ingestion: High-volume streaming sources, multiple message
formats, diverse protocols and multi-vendor devices creates data
ingestion challenges.
Real-time Insights: Analyzing continuous and rapid inflow
(velocity) of streaming data at high volumes creates major
challenges for gaining real-time insights.
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Music city data Hail Hydrate! from stream to lake

  • 3. Tim Spann, Developer Advocate DZone Zone Leader and Big Data MVB Data DJay https://github.com/tspannhw https://www.datainmotion.dev/ https://github.com/tspannhw/SpeakerProfile https://dev.to/tspannhw https://sessionize.com/tspann/ https://www.slideshare.net/bunkertor @PaasDev
  • 4. AGENDA Use Case - Populate the Data Lake Key Challenges ▪ Their Impact ▪ A Solution ▪ Outcome Why Apache NiFi and Apache Pulsar? Successful Architecture Demo
  • 5. 5 USE CASE IoT Ingestion: High-volume streaming sources, multiple message formats, diverse protocols and multi-vendor devices creates data ingestion challenges.
  • 6. 6 KEY CHALLENGES Visibility: Lack visibility of end-to-end streaming data flows, inability to troubleshoot bottlenecks, consumption patterns etc. Data Ingestion: High-volume streaming sources, multiple message formats, diverse protocols and multi-vendor devices creates data ingestion challenges. Real-time Insights: Analyzing continuous and rapid inflow (velocity) of streaming data at high volumes creates major challenges for gaining real-time insights.
  • 7. 7 IMPACT Delays: Decreasing user satisfaction and delay in project delivery. Missed revenue and opportunities. Code Sprawl: Custom scripts over various qualities proliferate across environments to cope with the complexity. Costs: Increasing costs of development and maintenance. Too many tools, not enough experts, waiting for contractors or time delays as developers learn yet another tool, package or language. Maintaining multiple messaging clusters.
  • 8. 8 SOLUTION Visibility: Apache Pulsar and Apache NiFi provenance provides insights, metrics and control over the entire end-to-end stream across clouds. Data Ingestion: Apache Pulsar and Apache NiFi provide tools to handle high-volume streaming sources, multiple message formats, diverse protocols and multi-vendor devices. Variety of Data: Apache Pulsar and Apache NiFi offer many OOTB connectors for sinks and sources.  
  • 9. 9 OUTCOME Agility: Reduction of new data source onboarding time from weeks to days. More data in your data warehouse now. New Applications: Enablement of new innovative use cases in compressed timeframe. No more waiting for data to arrive, Data Analysts and Data Scientists focus on innovation. Savings: Cost reduction thanks to technologies offload, reduced consultant costs and simplification of ingest processes.
  • 10. FLiPN Stack for Cloud Data Engineers - Events Multiple users, protocols, frameworks, languages, clouds, data sources & clusters CLOUD DATA ENGINEER • Experience in ETL/ELT • Coding skills in Python or Java • Knowledge of database query languages such as SQL • Experience with Streaming • Knowledge of Cloud Tools • Expert in ETL (Eating, Ties and Laziness) • Edge Camera Interaction • Typical User • No Coding Skills • Can use NiFi • Questions your cloud spend CAT AI / Deep Learning / ML / DS • Can run in Apache NiFi • Can run in Apache Pulsar Functions • Can run in Apache Flink • Can run in Apache Flink SQL • Can run in Apache Pulsar Clients • Can run in Apache Pulsar Microservices • Can run in Function Mesh https://functionmesh.io/
  • 11. StreamNative Solution Application Messaging Data Pipelines Real-time Contextual Analytics Tiered Storage APP Layer Computing Layer Storage Layer StreamNative Platform IaaS Layer Micro Service Notification Dashboard Risk Control Auditing Payment ETL
  • 12. 12 What is Apache NiFi? Apache NiFi is a scalable, real-time streaming data platform that collects, curates, and analyzes data so customers gain key insights for immediate actionable intelligence.
  • 13. Why Apache NiFi? • Guaranteed delivery • Data buffering - Backpressure - Pressure release • Prioritized queuing • Flow specific QoS - Latency vs. throughput - Loss tolerance • Data provenance • Supports push and pull models • Hundreds of processors • Visual command and control • Over a sixty sources • Flow templates • Pluggable/multi-role security • Designed for extension • Clustering • Version Control
  • 14. 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)
  • 15. 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 ACQUIRE PROCESS DELIVER • Over 300 Prebuilt Processors • Easy to build your own • Parse, Enrich & Apply Schema • Filter, Split, Merger & Route • Throttle & Backpressure • Guaranteed Delivery • Full data provenance from acquisition to delivery • Diverse, Non-Traditional Sources • 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
  • 16. 16 What is Apache Pulsar? Apache Pulsar is an open source, cloud-native distributed messaging and streaming platform.
  • 17. Apache Pulsar Enable Geo-Replicated Messaging ● Pub-Sub ● Geo-Replication ● Pulsar Functions ● Horizontal Scalability ● Multi-tenancy ● Tiered Persistent Storage ● Pulsar Connectors ● REST API ● CLI ● Many clients available ● Four Different Subscription Types ● Multi-Protocol Support ○ MQTT ○ AMQP ○ JMS ○ Kafka ○ ...
  • 18. Apache Pulsar: Key Features (1) Multi-tenancy ✓ Data is stored in one system and shared by multiple organizations ✓ Apply access control policy to ensure data stay compliant Cloud-Native Architecture ✓ Separate computing layer from storage layer ✓ Instant elasticity and scalability ✓ Rebalance-free to save labor cost ✓ Streamlined operations Tiered storage ✓ Enable historical data to be offloaded to cloud-native storage ✓ Effectively store event streams for indefinite periods of time Geo-replication ✓ Pulsar supports multi-datacenter (n-mesh) replication with both asynchronous and synchronous replication for built-in disaster recovery
  • 19. Apache Pulsar: Key Features (2) Converged Messaging ✓ Support both application messaging and data pipelines ✓ Store one copy of data ✓ Consume with different subscriptions Unified Batch and Stream Storage ✓ Tiered storage enables Pulsar to store real-time data and historic data in one system ✓ Tightly integrated with Flink for unified batch and stream processing Serverless Streaming ✓ Pulsar Functions provides an easy-to-use stream processing framework to process streams in a serverless way Pluggable Protocols ✓ Support popular messaging protocols: Kafka, AMQP, MQTT ✓ Provide full protocol compatibility ✓ Zero migration cost
  • 20. ● “Bookies” ● Stores messages and cursors ● Messages are grouped in segments/ledgers ● A group of bookies form an “ensemble” to store a ledger ● “Brokers” ● Handles message routing and connections ● Stateless, but with caches ● Automatic load-balancing ● Topics are composed of multiple segments ● ● Stores metadata for both Pulsar and BookKeeper ● Service discovery Store Messages Metadata & Service Discovery Metadata & Service Discovery Pulsar Cluster
  • 21. Reader and Batch API Pulsar IO/Connectors Stream Processor Applications Prebuilt Connectors Custom Connectors Microservices or Event-Driven Architecture Pub/Sub API Publisher Subscriber Admin API Operators & Administrators Teams Tenant Pulsar API Design 21
  • 22. Subscription Modes Different subscription modes have different semantics: Exclusive/Failover - guaranteed order, single active consumer Shared - multiple active consumers, no order Key_Shared - multiple active consumers, order for given key Producer 1 Producer 2 Pulsar Topic Subscription D Consumer D-1 Consumer D-2 Key-Shared < K 1 ,V 1 0 > < K 1 ,V 1 1 > < K 1 ,V 1 2 > < K 2 ,V 2 0 > < K 2 ,V 2 1 > < K 2 ,V 2 2 > Subscription C Consumer C-1 Consumer C-2 Shared < K 1 ,V 1 0 > < K 2 ,V 2 1 > < K 1 ,V 1 2 > < K 2 ,V 2 0 > < K 1 ,V 1 1 > < K 2 ,V 2 2 > Subscription A Consumer A Exclusive Subscription B Consumer B-1 Consumer B-2 In case of failure in Consumer B-1 Failover
  • 23. Unified Messaging Model Streaming Messaging Producer 1 Producer 2 Pulsar Topic/Partition m0 m1 m2 m3 m4 Consumer D-1 Consumer D-2 Consumer D-3 Subscription D < k 2 , v 1 > < k 2 , v 3 > <k3,v2 > < k 1 , v 0 > < k 1 , v 4 > Key-Shared Consumer C-1 Consumer C-2 Consumer C-3 Subscription C m1 m2 m3 m4 m0 Shared Failover Consumer B-1 Consumer B-0 Subscription B m1 m2 m3 m4 m0 In case of failure in Consumer B-0 Consumer A-1 Consumer A-0 Subscription A m1 m2 m3 m4 m0 Exclusive X
  • 24. A Unified Messaging Platform Message Queuing Data Streaming
  • 25. FLiP Stack (FLink -integrate- Pulsar) https://hub.streamnative.io/data-processing/pulsar-flink/2.7.0/
  • 26. A cloud-native, real-time messaging and streaming platform to support multi-cloud and hybrid cloud strategies. Powered by Pulsar Built for Containers Flink SQL Cloud Native
  • 27. All Data - Anytime - Anywhere - Any Cloud Multi- inges t Multi- inges t Multi-i ngest Merge Priority
  • 29. Demo Walk Through A cloud data lake that is empty is not useful to anyone. How can you quickly, scalably and reliably fill your cloud data lake with diverse sources of data you already have and new ones you never imagined you needed. Utilizing open source tools from Apache, the FLaNK stack enables any data engineer, programmer or analyst to build reusable modules with low or no code. FLaNK utilizes Apache NiFi, Apache Kafka, Apache Flink and MiNiFi agents to load CDC, Logs, REST, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before. I will teach you how to fish in the deep end of the lake and return a data engineering hero. Let's hope everyone is ready to go from 0 to Petabyte hero. Create Apache Pulsar Tenants and Namespaces bin/pulsar-admin tenants create stocks bin/pulsar-admin namespaces create stocks/inbound bin/pulsar-admin topics create persistent://stocks/inbound/stocks bin/pulsar-admin topics create persistent://stocks/inbound/stocks2 bin/pulsar-admin topics list stocks/inbound/ bin/pulsar-client consume -n 0 -s "subs" -p Earliest persistent://stocks/inbound/stocks ● https://github.com/tspannhw/FLiP-IoT ● https://github.com/tspannhw/FLiP-SQL // Example Java Code ProducerBuilder<byte[]> producerBuilder = client.newProducer().topic(topic) .producerName("jetson"); Producer<byte[]> producer = producerBuilder.create(); String message = "" + jct.message; MessageId msgID = producer.newMessage().key(pulsarKey).value(message. getBytes()) .property("device",OS).send();
  • 30. 30 WAITING FOR DATA FROM YOUR PIPELINE Sometimes data from your pipeline never arrives Sometimes it’s late Trying to debug hybrid cloud data streams can be hairy Apache Pulsar and Apache NiFi make this process transparent Apache NiFi shows logs, errors and provenance via UI, REST and CLI NiFi and Pulsar have many metrics available via CLI and REST and streamed to Grafana, Prometheus, … Use StreamNative Cloud for Easy Visibility
  • 31. 31 {"uuid": "rpi4_uuid_jfx_20200826203733", "amplitude100": 1.2, "amplitude500": 0.6, "amplitude1000": 0.3, "lownoise": 0.6, "midnoise": 0.2, "highnoise": 0.2, "amps": 0.3, "ipaddress": "192.168.1.76", "host": "rp4", "host_name": "rp4", "macaddress": "6e:37:12:08:63:e1", "systemtime": "08/26/2020 16:37:34", "endtime": "1598474254.75", "runtime": "28179.03", "starttime": "08/26/2020 08:47:54", "cpu": 48.3, "cpu_temp": "72.0", "diskusage": "40219.3 MB", "memory": 24.3, "id": "20200826203733_28ce9520-6832-4f80-b17d-f36c21fd8fc9", "temperature": "47.2", "adjtemp": "35.8", "adjtempf": "76.4", "temperaturef": "97.0", "pressure": 1010.0, "humidity": 8.3, "lux": 67.4, "proximity": 0, "oxidising": 77.9, "reducing": 184.6, "nh3": 144.7, "gasKO": "Oxidising: 77913.04 OhmsnReducing: 184625.00 OhmsnNH3: 144651.47 Ohms"} SHOW ME SOME DATA
  • 33. DEEPER CONTENT ● https://www.datainmotion.dev/2020/10/running-flink-sql-against-kafka-using.html ● https://www.datainmotion.dev/2020/10/top-25-use-cases-of-cloudera-flow.html ● https://github.com/tspannhw/EverythingApacheNiFi ● https://github.com/tspannhw/CloudDemo2021 ● https://github.com/tspannhw/StreamingSQLExamples ● https://www.linkedin.com/pulse/2021-schedule-tim-spann/ ● https://github.com/tspannhw/StreamingSQLExamples/blob/8d02e62260e82b027b43abb911b5c366 a3081927/README.md
  • 34. Connect with the Community & Stay Up-To-Date ● Join the Pulsar Slack channel - Apache-Pulsar.slack.com ● Follow @streamnativeio and @apache_pulsar on Twitter ● Subscribe to Monthly Pulsar Newsletter for major news, events, project updates, and resources in the Pulsar community
  • 35. Interested In Learning More? Function Mesh - Simplify Complex Streaming Jobs in Cloud The GitHub Source Code for Demo Manning's Apache Pulsar in Action O’Reilly Book [10/6] Pulsar Summit Europe Resources Free eBooks Upcoming Events
  • 36. Source: Data streaming service StreamNative takes in $23.7M
  • 37. Q&A