Why And When Should We Consider Stream Processing In Our Solutions Teqnation 2023
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Session Recording on Youtube https://www.youtube.com/watch?v=uWPZQ_HMy10 - Session Description Do you find yourself bombarded with buzzwords and overwhelmed by the rapid emergence of new technologies? "Stream Processing" is a tech buzzword that has been around for some time but is still unfamiliar to many. Join this session to discover its potential in software systems. I will share insights from Apache Flink, Apache Beam, Google Dataflow, and my experiences at Bol.com (the biggest e-commerce platform in the Netherlands) as we cover: - Stream Processing overview: main concepts and features - Apache Beam vs. Spring Boot comparison - Key Considerations for Using Stream Processing - Learning strategies to navigate this evolving landscape.
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Why And When Should We Consider Stream Processing In Our Solutions Teqnation 2023
- 1. Company/speaker Presentation title #teqnation2021 CO-SPONSORS MAIN SPONSOR Why and when should we consider Stream Processing in our solutions Soroosh Khodami May 17 2023 @ Teqnation
- 2. Agenda What is Stream Processing? Frameworks & Platforms Basic Concepts & Patterns Demo Time Benefits & Drawbacks + Considerations Use Cases For Different Industries How to start ?
- 3. This Talk is For Software Developers Tech Leads / Software Architects Data Engineers / Data Scientist / AI Engineers Product Owners / Product Managers / Business Analysts
- 4. $ whoami I’m Soroosh Khodami Full-Stack Developer at Bol.com & Code Nomads Working with Stream Processing at Scale in Bol.com Software Architecture Enthusiastic @SorooshKh linkedin.com/in/sorooshkhodami/ Slides & Code Repository Link Will Be Shared At The End
- 6. RIGHT TOOL FOR THE JOB
- 7. What is Stream Processing? Event Processing? Event Driven?
- 9. Stream (Data) Processing Stream processing is a big data technique that focuses on continuously reading data, processing the data individually or joining it with related data sets in real-time or near real- time, and then sending the output to other applications, data-stores, or systems.
- 10. Event Processing Trigger Actions Decision Making Event Payment Received
- 12. Stream Processing Frameworks & Platforms
- 14. Stream Processing Universe 2023 Code will be executed on a Runner Standalone / Alongside other frameworks
- 15. Stream Processing Universe 2023 Cloud Platforms
- 16. Hardened at Scale Powered By Flink https://flink.apache.org/powered-by/
- 17. + Examples Stream Processing Basic Concepts & Patterns
- 18. Bounded Stream / Unbounded Stream Time Now Past Future Unbounded Stream Bounded Stream #1 Start End Time Now Past Future Bounded Stream #2 Start End
- 19. Event Time & Processing Time Processing Time Event Time 1 Login 1 2 3 4 5 6 7 2 Search 3 View 4 View 5 View 6 Play 1 Login 2 Search 3 View 4 View 5 View 6 Play 1 2 3 4 5 6 7
- 20. Delivery Guarantees Learn More (Important) Streaming Concepts - Exactly Once Fault Tolerance Guarantees youtube.com/watch?v=9pRsewtSPkQ Rundown of Flink's Checkpoints - youtube.com/watch?v=hoLeQjoGBkQ Understanding exactly-once processing and windowing in streaming pipelines - youtube.com/watch?v=DraQGkARegE At Most Once At Least Once Exactly Once Messages can be lost, but never duplicated (Fire & Forget) Messages can be duplicated Messages are delivered & processed exactly once
- 21. IoT Farm Context +1000 Sensors Multiple Sensors per location Not reliable internet connection Large amount of continious sensors data Requirements Aggregated Sensors Data Per Location Correct Order Of Data No Duplicates
- 22. Read Source Operators & Transform Transforms Sink Operator(s) Operator(s) Operator(s) Basic Building Blocks
- 23. Read Soil Moisture Sensors Operators & Transform Sink IOT Farm Example Operator(s) Operator(s) Read Optical Sensors Read Temperture Sensors Filter Selected Locations Join & Aggregate Operator(s) Operator(s)
- 24. Operators & Transform Images From: http://ibmstreams.github.io/streamsx.documentation/docs/spl/quick-start/qs-2/ Analyzing tweets using Cloud Dataflow pipeline templates https://cloud.google.com/blog/products/gcp/analyzing-tweets-using-cloud-dataflow-pipeline-templates/
- 25. Time 5 4 4 1 7 2 2 6 4 1 Windowing Sum: 19 Count: 5 2 3 6 4 4 7 2 2 6 4 1 2 • Divides an unbounded, continuous data stream into smaller, finite segments • Allows to perform operations and calculations on manageable chunks of data. • It’s not feasible to load/keep entire stream into memory • Useful for analyzing data over specific time periods or fixed numbers of events. Window of Data Learn More Basics of Windowing - https://www.youtube.com/watch?v=oJ-LueBvOcM&t=1s Advanced Windowing Concepts - https://www.youtube.com/watch?v=MuFA6CSti6M
- 26. Time 5 4 4 1 7 2 2 6 4 1 5 seconds Time Based Windows No Overlaps between windows elements Tumbling/Fixed Window 5 1 4 7 2 4 5 seconds 5 seconds 4 2 1 Sum:11 Count: 4 Sum: 19 Count: 5 Sum: 5 Count: 2 Time 5 2 3 4 4 1 7 2 2 6 4 1 Size Based Windows 5 2 3 1 4 7 2 4 4 2 6 1 Sum: 11 Count: 4 Sum: 17 Count: 4 Sum: 13 Count: 4 2 3 2 3 Time 5 2 3 4 4 1 7 2 2 6 4 1 Time & Size Based Windows 5 2 3 1 4 7 2 4 4 2 6 1 Sum: 11 Count: 4 Sum: 17 Count: 4 Sum: 7 Count: 3 5 seconds 5 seconds 5 seconds
- 27. Sliding Window Time Success Success Success Success Success Error WARN WARN Error WARN Window #1 Window #2 Window #3 Window #N Window #N+1 Time Based Windows Error Error Error Error Error Error Error Error Success : 4 Warn : 0 Error : 0 Success : 3 Warn : 0 Error : 1 Success : 1 Warn : 2 Error : 1 ……….. Success : 0 Warn : 0 Error : 4 Last 10 Second Every 5 Seconds + Overlaps Between Windows
- 28. Session Window Time User #1 Play Heartbeat Heart Beat Seek Seek Heartbeat Seek Heart Beat Heartbeat Heartbeat Seek Pause Window #1 Window #2 10 sec User #2 Play Heartbeat Heart Beat Seek Heartbeat Heartbeat Window #1 Window #2 20 sec Close the window based on GAP Duration = 10 sec
- 29. Watermarks 1 2 3 4 7 Window #1 Window #2 5 seconds 5 seconds 1 2 3 4 7 Window #1 Window #2 5 seconds 5 seconds 4 Learn More Basics of Windowing - https://www.youtube.com/watch?v=oJ-LueBvOcM&t=1s Advanced Windowing Concepts - https://www.youtube.com/watch?v=MuFA6CSti6M
- 30. Basic Concepts & Patterns Bounded Stream / Unbounded Stream Operators & Transforms Event Time & Processing Time Event Delivery Guarantee Windowing ( Fixed , Sliding, Session, Watermark ) States & Stateful Stream Processing Joining Streams & Enrichment Pattern
- 31. Learn More Stream Join in Flink: from Discrete to Continuous - Xingcan Cui https://www.youtube.com/watch?v=3YVRluJUKIw Webinar: 99 Ways to Enrich Streaming Data with Apache Flink - Konstantin Knauf - https://www.youtube.com/watch?v=cJS18iKLUIY 2 5 3 2 1 2 1 3 4 5 Temperature Sensor Stream Moisture Sensor Stream Window Window Inner Join 2 1 1 2 Window Cross Join (CoGroup) 3 2 1 5 2 1 Joining Streams & Enrichment Pattern Device-2 , Temp : 28 Device-2 , Moisture : 876 Device-2 Moisture : 876 Temp : 28 Inner Join
- 32. States & Stateful Stream Processing Learn More Introduction to Stateful Stream Processing with Apache Flink - Robert Metzger https://www.youtube.com/watch?v=DkNeyCW-eH0 Webinar: Deep Dive on Apache Flink State - Seth Wiesman - https://www.youtube.com/watch?v=9GF8Hwqzwnk State Stateful Operator Streams Stateless Operator Stateless Operator Stateless Operator Stateless Operator Stateless Operator Stateless Operator Stateful Operator Stateless Operator Stateless Operator Stateless Operator State
- 33. States & Stateful Stream Processing Login Attempts State: Last Threshold Breach : Nullable Read Windowing Last 15 Minutes Count Enrich With Previous Breache and Update Last Breach Group By IP Brute Force Login Monitoring Sink Security Alerts Learn More Introduction to Stateful Stream Processing with Apache Flink - Robert Metzger https://www.youtube.com/watch?v=DkNeyCW-eH0 Webinar: Deep Dive on Apache Flink State - Seth Wiesman - https://www.youtube.com/watch?v=9GF8Hwqzwnk Login Attempts Login Attempts Filter Above Threshold
- 34. Group By Key / KeyBy [4Geeks] Play Heartbeat Heart Beat Seek Seek Heartbeat Seek Heart Beat Heartbeat Heartbeat Seek Group By Action Play Play Play Group By Customer Seek Heartbeat Heartbeat Heartbeat Seek Play Play Learn More Apache Flink Specifying Keys https://medium.com/big-data-processing/apache-flink-specifying-keys-81b3b651469 Branching & merging PCollections with Apache Beam - https://youtu.be/RYD40js20a4
- 35. DEMO TIME Apache Beam Code
- 37. IP Monitoring ( Apache Beam )
- 38. IP Monitoring ( Apache Beam )
- 39. What You Just Saw Hidden Code Behind The Functions
- 40. Order Enrichment With Customer Data [4Geeks] Apache Beam + Dataflow vs Spring Boot Customers Events (CDC) Orders Events Enriched Orders With Customer Data Enrich Order Data Code Repository & Slides @SorooshKh
- 41. Insights 1 Dataflow Worker with Default Spec 120k message processed in 3 minutes Apache Beam + Dataflow Order Enrichment Test Results Note: Please note that the insights provided above are not derived from a fully accurate benchmark. ~ 700 msg/second Higher Costs For Keeping Job Running Tested on Minimum Kubernetes Hardware on GCP 120k message processed in 5 minutes Spring Boot ~ 400 msg/second Lower Costs For Keeping Job Running
- 42. Order Enrichment With Customer Data [4Geeks] Customer CDC Read Enrich Order With Customer Data Sink EnrichedOrder Orders Read Store Customer in Redis Get Customer Information from Redis Spring Boot + Redis
- 43. Order Enrichment With Customer Data [4Geeks] Customer CDC State: Customer Read CoGroupByKey EnrichOrderWithCusto merData Sink EnrichedOrder Orders Read KeyBy CustomerID KeyBy CustomerID Update Customer in State Customer(123) (123, Customer(123)) (123, Customer(123)) Order(1005, CustomerId =123) (123, Order(1005, CustomerId=123)) (123, Order(1005, CustomerId=123)) OrderWithCustomerData - Order - Customer Learn More Stream Join in Flink: from Discrete to Continuous - Xingcan Cui https://www.youtube.com/watch?v=3YVRluJUKIw Webinar: 99 Ways to Enrich Streaming Data with Apache Flink - Konstantin Knauf - https://www.youtube.com/watch?v=cJS18iKLUIY Apache Beam + Dataflow
- 44. Why Should We Consider It Benefits, Drawbacks & Considerations
- 45. Benefits & Drawbacks Fast & High-Throughput Easy to Scale Exactly Once Processing / Fault Tolerant Customizable Advanced features in scale: Windowing, Watermarks, Stateful Functions and .. ✖ Complexity ✖ Implementation & Maintenance ✖ Testing & Debugging is challenging ✖ Changing the data pipelines are hard ✖ Error handling is not simple ✖ Data consistency is not easy Drawbacks Benefits Stream Processing Frameworks
- 46. Stream Data Integration vs Stream Analytics Learn More Stream Processing – Concepts and Frameworks (Guido Schmutz, Switzerland) https://www.youtube.com/watch?v=vFshGQ2ndeg | https://www.slideshare.net/gschmutz/introduction-to-stream-processing-132881199 (Stream ETL) Stream Data Integration Stream Analytics Reading Input Map Filter Simple Enrich Stateful Processing Pattern Matching Complex Calculations / Aggregations
- 47. Considerations Learn More ( Important ) Apache Flink Worst Practices - Konstantin Knauf - https://www.youtube.com/watch?v=F7HQd3KX2TQ Learning Curve Project Timeline Hard to Find Developer Limited Docs/Resources Community Support Costs Stream Data Integration 1 – 2 Weeks Stream Analytics 2 – 3 Months 3 – 4 Engineers 4 – 6 Months 0 -> Stability Cloud Providers Helps a Bit
- 48. Stream Processing When should we consider it in our solutions?
- 49. DECISION MAKING FACTORS Requirements (FRs + NFRs + Roadmap) Development Cost (Capex) Maintenance Cost (Opex) Complexity Limitations Industry Best Practices
- 50. When should we consider it in our solutions? Case: Stream Data Integration Context / Conditions
- 51. When should we consider it in our solutions? Case: Stream Data Integration Context / Conditions • Events / second < 1K • Experience of Stream processing : No • Business queries are changing frequently • Time to market : Very tight • 3 – 4 Mid-Senior Developers Learn More Apache Flink Worst Practices - Konstantin Knauf https://www.youtube.com/watch?v=F7HQd3KX2TQ Note: The cases incorporated within this presentation are designed to demonstrate the reasoning process.
- 52. When should we consider it in our solutions? Learn More Apache Flink Worst Practices - Konstantin Knauf https://www.youtube.com/watch?v=F7HQd3KX2TQ Context / Conditions Case: Stream Analytics • Events / second > 10K • Experience of Stream processing : No • Business queries are clear and not changing frequently • Real time/near real time insights are crucial ? Yes • 3 – 4 Mid-Senior Developers Note: The cases incorporated within this presentation are designed to demonstrate the reasoning process.
- 53. Quick Look On Stream Processing Use Cases
- 54. Usecases Video Streaming Playback Analytics IOT GPS Tracking Telecom Billing / Charging System Finance Fraud Detection E-Commerce User Analytics Gaming Industry Anti-Cheat
- 55. Video Platforms Use cases Playback Analytics Content Provider Shares Pay Per Minute Fraud Detection Personalized Recommendation Learn More Massive Scale Data Processing at Netflix using Flink - Snehal Nagmote & Pallavi Phadnis youtube.com/watch?v=lC0d3gAPXaI Custom, Complex Windows at Scale using Apache Flink - Matt Zimmer (Netflix) youtube.com/watch?v=XUvqnsWm8yo SF 2017: Monal Daxini - Stream Processing with Flink at Netflix youtube.com/watch?v=sPB8w-YXX1s Real-time Processing with Flink for Machine Learning at Netflix - Elliot Chow youtube.com/watch?v=o4C7TDneH00
- 56. Gaming Industry Use cases Learn More Kafka and Big Data Streaming Use Cases in the Gaming Industry https://www.confluent.io/online-talks/kafka-and-big-data-streaming-use-cases-in-the-gaming- industry/ Let's Play Flink – Fun with Streaming in a Gaming Company https://www.youtube.com/watch?v=8BNKEmt47UM Game Telemetry Analytics Rewards (In-Game) Live In-Game Changes (NPC, Quests, .. ) IoT Integration Loyalty Service Anti-Cheat Chat Service Monitoring Match Making Payment Fraud Detection In-Game Recommendation Advertiseme AI Training Payment
- 57. Application Analytics Use cases Learn More Implementing Google Analytics: A Case Study - Making Sense of Stream Processing by Martin Kleppmann https://www.oreilly.com/library/view/making-sense-of/9781492042563/ch01.html Martin Kleppmann — Event Sourcing and Stream Processing at Scale https://www.youtube.com/watch?v=avi-TZI9t2I Singles Day 2018: Data in a Flink of an eye https://www.ververica.com/blog/singles-day-2018-data-in-a-flink-of-an-eye
- 58. Learn More 7 Reasons to use Apache Flink for your IoT Project https://www.youtube.com/watch?v=Q0LBTmT4W9o Fleet management / GPS Tracking Anomaly detection Smart home automation Energy management Environmental monitoring Predictive maintenance Self-Driving Cars Internet Of Things Use cases
- 59. Billing Network Optimization Security Fraud Detection Learn More Maciej Próchniak - Stream processing in telco - case study based on Apache Flink & TouK Nussknacker @ Devoxx Poland https://www.youtube.com/watch?v=WLfEB__fM-4 Telecommunication Use cases
- 60. Fraud detection Algorithmic trading Risk management Real-time portfolio analysis Customer analytics Regulatory compliance Profit & Lost Insights Learn More Real Time Fraud Detection with Stateful Functions https://www.youtube.com/watch?v=RxDlksbsdQ0 Fast Data at ING - Martijn Visser & Bas Geerdink (ING) https://www.youtube.com/watch?v=e-_6gijUGAw Stream ING Models – Real time model deployment of ML Capabilities https://www.youtube.com/watch?v=Do7C4UJyWCM Financial Systems Use cases
- 61. Stream Processing How to start learning ?
- 62. How to start learning? [1] https://youtu.be/65lmwL7rSy4 [2] https://youtube.com/playlist?list=PL8bzd7vku-WhVHzJgmXoCxx3aB4PxTQLP [3] https://beamsummit.org/ [3] https://www.flink-forward.org/ [4] https://beam.apache.org/documentation/ [4] https://nightlies.apache.org/flink/flink-docs-stable/ 1 2 3 4 IMPORTANT NOTE Creating a Stream Processing service isn't as straightforward as crafting CRUD APIs. Relying solely on Google, development tools, Stackoverflow, and copy-pasting won't get you far. It's crucial to dedicate ample time to thoroughly learn and understand the underlying concepts. Google Cloud Apache Beam Debi Cabrera Apache Beam Step By Step Atul Raina BEAM SUMMIT & FLINK FORWARD Official Documentation
- 63. Slides & Code Repository Any Question ? Send me a message on twitter or Linkedin Thanks for your Attention ! @SorooshKh linkedin.com/in/sorooshkhodami/ Please Rate This Session And Share Your Feedback
Notas del editor
- What is Stream Processing ? Why We Should Learn It ?
- Developer By Day, Furniture Assembelr By Night I learned that using Right tool is the most important part of assembling
- Question 1: Who has heard these technologies a lot ? Question 2: Who has used this technologies in production ? Everyday that we wake up, we hear some new Apache technologies ..
- Okay, Not for me I'm not fan of complex definitions. let's get to a simple definition
- reading data multiple source processing Data itself. payload itself individually or joined with other data sending out to another system
- Event processing is a technique that focuses on listening for specific events or patterns of events within a system, enabling decision-making and triggering actions based on the information contained in the events.
- Services communicates with Events
- We need to chunk the data to make it feasible to process
- Bounded Stream Example : Processing list of last month records for Train Check in – Checkout for Analysis purpose
- 1 Minute : You are watching netflix on Airplane / Subway . Your actions will be synced afterward
- We have three type of guarantees, no gurantee , at least one delivery, exactly once delivery Flink -> Checkpointing Don’t forget to check learn more
- Ok, wait. Hold your horse , So you said a lot of definitions, what is the usecase ..
- Transforms are Filter , Map , Aggregate , Join, Custom Functions
- 30s – 1 Minute
- 1 minute
- 1 minute We cannot carry two watermelon with one hand We need to chunk the data to make it feasible to process Ok, right. We should devide . but how we are going to divide the data ?
- It’s very similar to a shuttle, isn’t it ?
- Let’s imagine that we are receiving request logs
- Watching Video on in the Subway or during the flight Phone Call How Stream Processing can do this ? Session Window is based on Group By Key
- 1 Minute : Thing that we need to learn, they are too much. So we make it easier by Examples ! How can we do it in our current applications, without Stream processing frame works ?
- Some times we need to store some data, and later looking back to stored data similar to what we used to do with Redis / Database.
- Key By is most commong Transformation partition the data stream similar to group by in SQL Some times we need to group some of the data together Some times it may cause a network shuffle that will partition the stream on different nodes
- 5 minute
- val failedLogins = p.apply("Read PubSub Messages", readFromPubSubSubscription()) val ipCounts = failedLogins .apply("Window", failedLoginWindowingStrategy()) .apply("Map to KV <IP,MSG>", mapToKVIPAddr()) .apply("Group by Key IP-Addr", GroupByKey.create()) .apply("Count per IP", countNumberOfAttempts()) val alerts = ipCounts .apply("Filter by Threshold", isCountOfAttempAboveThresholdFilter()) .apply("Enrich with Old Breaches Last Month", enrichWithOldBreachesLastMonth()) alerts.apply("Write Alerts to PubSub", publishToPubSubTopic())
- val failedLogins = p.apply("Read PubSub Messages", readFromPubSubSubscription()) val ipCounts = failedLogins .apply("Window", failedLoginWindowingStrategy()) .apply("Map to KV <IP,MSG>", mapToKVIPAddr()) .apply("Group by Key IP-Addr", GroupByKey.create()) .apply("Count per IP", countNumberOfAttempts()) val alerts = ipCounts .apply("Filter by Threshold", isCountOfAttempAboveThresholdFilter()) .apply("Enrich with Old Breaches Last Month", enrichWithOldBreachesLastMonth()) alerts.apply("Write Alerts to PubSub", publishToPubSubTopic())
- Stream Processing Applications and especially when you start to have Stateful functions are not really easy.
- Complexity Handling out-of-order events, windowing, and state management Increased complexity compared to batch processing Implementation and Maintenance Expertise required in distributed systems, fault tolerance, and specific stream processing frameworks Maintenance effort for business logic and data flow changes Testing and Debugging Complex testing scenarios and simulation of various events and failures Difficulties in debugging due to real-time and distributed nature of processing Error Handling Managing errors and edge cases can be challenging Recovery mechanisms and failure scenarios require careful consideration Data Consistency Ensuring exactly-once processing and data consistency can be challenging Requires robust handling of distributed systems and failures Learning Curve and Project Timeline 2-3 months for a medior developer to become proficient 4-6 months for a project to reach stability from start Resource Intensiveness Real-time processing may consume more resources than batch processing Cloud services can help mitigate infrastructure costs
- In Short Stream Data Integration is Map Transform Filter Enrich Stream Data Integration is also using States , Windowing , State Management, Event Pattern
- Learning Curve Stream Data Integration : 1 – 2 weeks Stream Analytics: 2 – 3 months For not very basic project, expect 2-4 months from project initiation to reach stability It’s not easy to find developers with extensive stream processing experience. For most of Stream processing frameworks, there are not many step by step documentation & stack overflow questions with working answers. You need to connect the dots yourself. Decent community support available, but not as extensive as Spring or other popular frameworks Stream processing can be resource-intensive, ( Cloud services helps us here )
- Case Stream Data Integration: (Map, Filter, Basic Enrichment) You are not getting much out of using Stream processing frameworks. You can achieve almost same results with other tools with possibility to scale up. Case Stream Analytics : You should start investing on your stream processing solution and building a team by help of professional consultants to lead/faciliate/boost the process. In the mean time, you can use other available tools to support part of your business requirements. ( Like BigQuery, Monitoring tools)
- Case Stream Data Integration: (Map, Filter, Basic Enrichment) You are not getting much out of using Stream processing frameworks. You can achieve almost same results with other tools with possibility to scale up. Case Stream Analytics : You should start investing on your stream processing solution and building a team by help of professional consultants to lead/faciliate/boost the process. In the mean time, you can use other available tools to support part of your business requirements. ( Like BigQuery, Monitoring tools)
- Case Stream Data Integration: (Real time ETL) You are not getting much out of using Stream processing frameworks. You can achieve almost same results with other tools with possibility to scale up. Case Stream Analytics : You should start investing on your stream processing solution and building a team by help of professional consultants to lead/faciliate/boost the process. In the mean time, you can use other available tools to support part of your business requirements. ( Like BigQuery, Monitoring tools)
- Anomaly detection: Stream processing can help identify unusual patterns or behaviors in IoT device data, enabling early detection of potential issues or failures. For example, it can be used to monitor sensor data from industrial equipment or vehicles to detect anomalies that may indicate a malfunction or maintenance need. Smart home automation: In a smart home environment, stream processing can be used to analyze data from various sensors and devices to trigger automated actions, such as adjusting lighting or temperature based on occupancy, time of day, or user preferences. Fleet management: Stream processing can analyze data from GPS trackers, vehicle sensors, and other devices in real-time to optimize fleet operations. This may include route planning, vehicle maintenance scheduling, fuel efficiency analysis, or driver behavior monitoring. Environmental monitoring: IoT devices can be deployed to monitor various environmental parameters, such as air quality, water levels, or temperature. Stream processing can be used to analyze this data in real-time, enabling rapid response to environmental changes or potential hazards. Energy management: Stream processing can be used to analyze energy consumption data from smart meters, IoT devices, and sensors in real-time, helping to optimize energy usage and reduce costs. This can be applied to smart grids, microgrids, or individual buildings. Predictive maintenance: By analyzing IoT sensor data in real-time, stream processing can help predict when a machine or equipment may require maintenance or is likely to fail. This allows for proactive maintenance scheduling, reducing downtime and increasing operational efficiency.