When NOT to use Apache Kafka?

•

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Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka. No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck. A detailed article about this topic: https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/

Software

When NOT to use
Apache Kafka?
kai-waehner.de | @KaiWaehner | Field CTO @ Confluent

Data Streaming with Apache Kafka
DWH
APP
STREAM
PROCESSING
CONNECTORS
ksqlDB
KStreams
APP
Streaming ETL
Data Processing
Real-time Analytics
Stateless and Stateful
Business Applications
Fully-managed
Pipelines
Connectivity to
Data Infrastructure,
SaaS, AI/ML
Data Governance
Connectivity
Filtering and Routing
Change Data Capture
Built-in Scale and Fault Tolerance
Oracle
DB
ORACLE CDC
SOURCE
PREMIUM
CONNECTOR
Real-time Data Sharing
across Hybrid and Multi-Cloud
Storage
Backpressure Handling
Slow Consumers
Replayability
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?

Markets
DaaS
Digital
replatforming/
Legacy Modernization
Customer
360
Faster transactional
processing / analysis
incl. Machine Learning / AI
Microservices
Architecture
Online Fraud
Detection
Online Security
(syslog, log aggregation,
Splunk replacement)
Middleware
replacement
Website / Core
Operations /
Payments
(Central Nervous System)
Real-time
app updates
Customer
Experience
Core Business
Platform
Operational
Efficiency (Agility)
Migrate to
Cloud
Fraud
Detection
Regulatory
Increase
Revenue
(make money)
Decrease
Costs
(save money)
Mitigate
Risk
(protect money)
Business Value
10 business
use case
Strategic
Driver
20 business
use case
Data Eng. /
Infrastructure
use case
Use Cases for Data Streaming by Business Value
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?

When NOT to use
Apache Kafka?
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?

Kafka is a Database BUT NOT for Complex Analytics
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?
Durable
Fault-tolerant
Tiered Storage
Compacted Topics
Exactly-once Semantics
RocksDB on Client Side
ksqlDB
Interactive Queries
“You Name It”
Connect

Kafka is NOT a Proxy for Millions of Clients
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?
“Last Mile” Integration
is usually a Proxy
(like HTTP or MQTT)

Kafka is NOT an API Management Platform
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?
Orders Customers
Payments
Stock
API
(HTTP/REST)
Data Streaming
Data Integration
Real-Time Apps
API Gateway
API Lifecycle
Data Sharing
Monetization
REST
Proxy
Stream
Exchange

Kafka is NOT the right tool for processing large messages *
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?
Claim Check Enterprise
Integration Pattern:
* BUT works well for some use cases, e.g.:
- Splitting large legacy CSV files
- Externalizing large payloads on-the-fly
- Image processing at the edge
- Uploading large files into the DWH
Pre-Processing and Data Correlation
e.g. enrich with other metadata
(ksqlDB)
Store big files in data lake
(e.g. AWS S3)
Consume and correlate
metadata
(Kafka Streams)
Automated
Orchestration
(Kafka Clients)
Real time analytics and
other business applications
(Kafka Clients + other tools)
Send metadata
including link to video
in object store
(Kafka Producer)
Download big files
from data lake

Kafka is NOT an IoT Platform *
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?
Siemens S7
Kafka
Connect
Storage
Kafka Streams
/
ksqlDB
Stateless +
Stateful
REST Proxy
HTTP(S)
SCADA
DCS
ERP
MES
Cloud
Factory
* BUT Kafka is a fundamental
part of most IoT projects, e.g.:
- Scalable real-time data hub
for IoT data AND IT data
- Edge and hybrid cloud
- Direct integration with IoT
protocols
- Integration via 3rd party with
IoT protocols
Analytics
Database
Data Lake
CRM
Kafka Connect
Cluster Linking

Kafka is NOT for hard real-time requirements
kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?
OT - Connected Vehicle
(Car, Train, Drone)
OT - Manufacturing
(Field Bus, PLC, Machine, Robot)
IT – Enterprise Software
(Data Center, Cloud, Car IT)
Central Data Center / Public Cloud
Vehicle Data
Robot Data All Data
C
C++
Rust
C
C++
Rust
Java
Python
Go
[#] Hard Real Time
= Deterministic network
with zero spikes + zero latency
[#] Soft Real Time
+ Near Real Time
+ Batch
Cluster Linking Cluster Linking

TL;DR
== de facto standard for DATA STREAMING

Kai Waehner
Field CTO
kai.waehner@confluent.io
@KaiWaehner
confluent.io
kai-waehner.de
linkedin.com/in/kaiwaehner
Questions? Feedback?
Let’s connect!

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Live commerce combines instant purchasing of a featured product and audience participation. This talk explores the need for real-time data streaming with Apache Kafka between applications to enable live commerce across online stores and brick & mortar stores across regions, countries, and continents in any retail business. The discussion covers several building blocks of a live commerce enterprise architecture, including transactional data processing, omnichannel, natural language processing, augmented reality, edge computing, and more.

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

Kai Wähner

If there were a buzzword of the hour, it would certainly be "data mesh"! This new architectural paradigm unlocks analytic data at scale and enables rapid access to an ever-growing number of distributed domain datasets for various usage scenarios. As such, the data mesh addresses the most common weaknesses of the traditional centralized data lake or data platform architecture. And the heart of a data mesh infrastructure must be real-time, decoupled, reliable, and scalable. This presentation explores how Apache Kafka, as an open and scalable decentralized real-time platform, can be the basis of a data mesh infrastructure and - complemented by many other data platforms like a data warehouse, data lake, and lakehouse - solve real business problems. There is no silver bullet or single technology/product/cloud service for implementing a data mesh. The key outcome of a data mesh architecture is the ability to build data products; with the right tool for the job. A good data mesh combines data streaming technology like Apache Kafka or Confluent Cloud with cloud-native data warehouse and data lake architectures from Snowflake, Databricks, Google BigQuery, et al.

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

Kai Wähner

Enterprise integration is more challenging than ever before. The IT evolution requires the integration of more and more technologies. Applications are deployed across the edge, hybrid, and multi-cloud architectures. Traditional middleware such as MQ, ETL, ESB does not scale well enough or only processes data in batch instead of real-time. This presentation explores why Apache Kafka is the new black for integration projects, how Kafka fits into the discussion around cloud-native iPaaS (Integration Platform as a Service) solutions, and why event streaming is a new software category. A concrete real-world example shows the difference between event streaming and traditional integration platforms respectively cloud-native iPaaS. Video Recording of this presentation: https://www.youtube.com/watch?v=I8yZwKg_IJc&t=2842s Blog post about this topic: https://www.kai-waehner.de/blog/2021/11/03/apache-kafka-cloud-native-ipaas-versus-mq-etl-esb-middleware/

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

Kai Wähner

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation. The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Kai Wähner

Hybrid cloud architectures are the new black for most companies. A cloud-first strategy is evident for many new enterprise architectures, but some use cases require resiliency across edge sites and multiple cloud regions. Data streaming with the Apache Kafka ecosystem is a perfect technology for building resilient and hybrid real-time applications at any scale. This talk explores different architectures and their trade-offs for transactional and analytical workloads. Real-world examples include financial services, retail, and the automotive industry. Video recording: https://qconlondon.com/london2022/presentation/resilient-real-time-data-streaming-across-the-edge-and-hybrid-cloud

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Kai Wähner

Agenda: 1) Defence, Modern Warfare, and Cybersecurity in 202X 2) Data in Motion with Apache Kafka as Defence Backbone 3) Situational Awareness 4) Threat Intelligence 5) Forensics and AI / Machine Learning 6) Air-Gapped and Zero Trust Environments 7) SIEM / SOAR Modernization Technologies discussed in the presentation include Apache Kafka, Kafka Streams, kqlDB, Kafka Connect, Elasticsearch, Splunk, IBM QRadar, Zeek, Netflow, PCAP, TensorFlow, AWS, Azure, GCP, Sigma, Confluent Cloud,

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Kai Wähner

Real-World Deployments of Data Streaming with Apache Kafka across the Healthcare Value Chain using open source and cloud-native technologies and serverless SaaS: 1) Legacy Modernization and Hybrid Cloud: Optum (UnitedHealth Group, Centene, Bayer) 2) Streaming ETL (Bayer, Babylon Health) 3) Real-time Analytics (Cerner, Celmatix, CDC/Centers for Disease Control and Prevention) 4) Machine Learning and Data Science (Recursion, Humana) 5) Open API and Omnichannel (Care.com, Invitae)

Apache Kafka in the Healthcare Industry

Kai Wähner

The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka. Use Cases for Data in Motion in the Healthcare Industry: - Know Your Patient (= “Customer 360”) - Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.) - IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc) Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.

Apache Kafka in the Healthcare Industry

Kai Wähner

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Kai Wähner

The manufacturing industry is moving away from just selling machinery, devices, and other hardware. Software and services increase revenue and margins. Equipment-as-a-Service (EaaS) even outsources the maintenance to the vendor. This paradigm shift is only possible with reliable and scalable real-time data processing leveraging an event streaming platform such as Apache Kafka. This talk explores how Kafka-native Condition Monitoring and Predictive Maintenance help with this innovation. More details: https://www.kai-waehner.de/blog/2021/10/25/apache-kafka-condition-monitoring-predictive-maintenance-industrial-iot-digital-twin/ Video recording: https://youtu.be/tfOuN5KeI9w

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Kai Wähner

Today, in 2022, Apache Kafka is the central nervous system of many applications in various areas related to the automotive and manufacturing industry for processing analytical and transactional data in motion across edge, hybrid, and multi-cloud deployments. This presentation explores the automotive event streaming landscape, including connected vehicles, smart manufacturing, supply chain optimization, aftersales, mobility services, and innovative new business models. Afterwards, many real-world examples are shown from companies such as Audi, BMW, Porsche, Tesla, Uber, Grab, and FREENOW. More detail in the blog post: https://www.kai-waehner.de/blog/2022/01/12/apache-kafka-landscape-for-automotive-and-manufacturing/

Apache Kafka Landscape for Automotive and Manufacturing

Kai Wähner

Technical thought leadership presentation to discuss how leading organizations move to real-time architecture to support business growth and enhance customer experience. This is a forum to discuss use cases with your peers to understand how other digital-native companies are utilizing data in motion to drive competitive advantage. Agenda: - Data in Motion with Event Streaming and Apache Kafka - Streaming ETL Pipelines - IT Modernisation and Hybrid Multi-Cloud - Customer Experience and Customer 360 - IoT and Big Data Processing - Machine Learning and Analytics

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

Kai Wähner

The Rise of Data in Motion in the Public Sector powered by event streaming with Apache Kafka. Citizen Services: - Health services, e.g. hospital modernization, track & trace - Covid distance control - Public administration - reduce bureaucracy, data democratization across government departments - eGovernment - Efficient and digital citizen engagement, e.g. personal ID application process Smart City - Smart driving, parking, buildings, environment Waste management - Open exchange – e.g. mobility services (1st and 3rd party) Energy - Smart grid and utilities infrastructure (energy distribution, smart home, smart meters, smart water, etc.) - National Security Law enforcement, surveillance, police/interior security data exchange - Defense and military (border control, intelligent solider) Cybersecurity for situational awareness and threat intelligence

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Kai Wähner

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

Kai Wähner

Apache Kafka in the Transportation and Logistics

Kai Wähner

Data in Motion powered by the Apache Kafka ecosystem for Situational Awareness, Threat Detection, Forensics, Zero Trust Zones and Air-Gapped Environments. Agenda: 1) Cybersecurity in 202X 2) Data in Motion as Cybersecurity Backbone 3) Situational Awareness 4) Threat Intelligence 5) Forensics 6) Air-Gapped and Zero Trust Environments 7) SIEM / SOAR Modernization More details in the "Kafka for Cybersecurity" blog series: https://www.kai-waehner.de/blog/2021/07/02/kafka-cybersecurity-siem-soar-part-1-of-6-data-in-motion-as-backbone/

Apache Kafka for Cybersecurity and SIEM / SOAR Modernization

Kai Wähner

Connect all the things: An intro to event streaming for the automotive industry including connected cars, mobility services, and manufacturing / industrial IoT. Video recording of this talk: https://www.youtube.com/watch?v=rBfBFrcO-WU The Fourth Industrial Revolution (also known as Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using modern smart technology. Event Streaming with Apache Kafka plays a massive role in processing massive volumes of data in real-time in a reliable, scalable, and flexible way using integrating with various legacy and modern data sources and sinks. Other industries—retail, healthcare, government, financial services, energy, and more—also lean into Industry 4.0 technology to take advantage of IoT devices, sensors, smart machines, robotics, and connected data. The variety of these deployments goes from disconnected edge use cases across hybrid architectures to global multi-cloud deployments. In this presentation, I want to give you an overview of existing use cases for event streaming technology in a connected world across supply chains, industries and customer experiences that come along with these interdisciplinary data intersections: - The Automotive Industry (and it’s not only Connected Cars) - Mobility Services across verticals (transportation, logistics, travel industry, retailing, …) - Smart Cities (including citizen health services, communication infrastructure, …) Real-world examples include use cases from car makers such as Audi, BMW, Porsche, Tesla, plus many examples from mobility services such as Uber, Lyft, Here Technologies, and more.

Apache Kafka in the Automotive Industry (Connected Vehicles, Manufacturing 4....

Kai Wähner

The Rise of Data in Motion powered by Event Streaming - Use Cases and Architecture for IBM Cloud Pak with Confluent Platform. Including screenshots of the live demo (integration between IBM and Kafka via Confluent Platform and Kafka Connect connectors). Learn about the integration capabilities of IBM Cloud Pak for Integration, now with the industry’s leading event streaming platform from Confluent Platform powered by Apache Kafka.

IBM Cloud Pak for Integration with Confluent Platform powered by Apache Kafka

Kai Wähner

Microservices became the new black in enterprise architectures. APIs provide functions to other applications or end users. Even if your architecture uses another pattern than microservices, like SOA (Service-Oriented Architecture) or Client-Server communication, APIs are used between the different applications and end users. Apache Kafka plays a key role in modern microservice architectures to build open, scalable, flexible and decoupled real time applications. API Management complements Kafka by providing a way to implement and govern the full life cycle of the APIs. This session explores how event streaming with Apache Kafka and API Management (including API Gateway and Service Mesh technologies) complement and compete with each other depending on the use case and point of view of the project team. The session concludes exploring the vision of event streaming APIs instead of RPC calls. Understand how event streaming with Kafka and Confluent complements tools and frameworks such as Kong, Mulesoft, Apigee, Envoy, Istio, Linkerd, Software AG, TIBCO Mashery, IBM, Axway, etc. A Streaming API Data Exchangeprovides streaming replication between business units and companies. API Management with REST/HTTP is not appropriate for streaming data.

Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies?

Kai Wähner

The rise of data in motion in the insurance industry is visible across all lines of business including life, healthcare, travel, vehicle, and others. Apache Kafka changes how enterprises rethink data. This blog post explores use cases and architectures for event streaming. Real-world examples from Generali, Centene, Humana, and Telsa show innovative insurance-related data integration and stream processing in real-time.

Apache Kafka in the Insurance Industry

Kai Wähner

Mais de Kai Wähner (20)

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Apache Kafka in the Healthcare Industry

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Apache Kafka Landscape for Automotive and Manufacturing

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

Apache Kafka in the Transportation and Logistics

Apache Kafka for Cybersecurity and SIEM / SOAR Modernization

Apache Kafka in the Automotive Industry (Connected Vehicles, Manufacturing 4....

IBM Cloud Pak for Integration with Confluent Platform powered by Apache Kafka

Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies?

Apache Kafka in the Insurance Industry

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When NOT to use Apache Kafka?

1. When NOT to use Apache Kafka? kai-waehner.de | @KaiWaehner | Field CTO @ Confluent

2. Data Streaming with Apache Kafka DWH APP STREAM PROCESSING CONNECTORS ksqlDB KStreams APP Streaming ETL Data Processing Real-time Analytics Stateless and Stateful Business Applications Fully-managed Pipelines Connectivity to Data Infrastructure, SaaS, AI/ML Data Governance Connectivity Filtering and Routing Change Data Capture Built-in Scale and Fault Tolerance Oracle DB ORACLE CDC SOURCE PREMIUM CONNECTOR Real-time Data Sharing across Hybrid and Multi-Cloud Storage Backpressure Handling Slow Consumers Replayability kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?

3. Markets DaaS Digital replatforming/ Legacy Modernization Customer 360 Faster transactional processing / analysis incl. Machine Learning / AI Microservices Architecture Online Fraud Detection Online Security (syslog, log aggregation, Splunk replacement) Middleware replacement Website / Core Operations / Payments (Central Nervous System) Real-time app updates Customer Experience Core Business Platform Operational Efficiency (Agility) Migrate to Cloud Fraud Detection Regulatory Increase Revenue (make money) Decrease Costs (save money) Mitigate Risk (protect money) Business Value 10 business use case Strategic Driver 20 business use case Data Eng. / Infrastructure use case Use Cases for Data Streaming by Business Value kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?

4. When NOT to use Apache Kafka? kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka?

5. Kafka is a Database BUT NOT for Complex Analytics kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka? Durable Fault-tolerant Tiered Storage Compacted Topics Exactly-once Semantics RocksDB on Client Side ksqlDB Interactive Queries “You Name It” Connect

6. Kafka is NOT a Proxy for Millions of Clients kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka? “Last Mile” Integration is usually a Proxy (like HTTP or MQTT)

7. Kafka is NOT an API Management Platform kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka? Orders Customers Payments Stock API (HTTP/REST) Data Streaming Data Integration Real-Time Apps API Gateway API Lifecycle Data Sharing Monetization REST Proxy Stream Exchange

8. Kafka is NOT the right tool for processing large messages * kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka? Claim Check Enterprise Integration Pattern: * BUT works well for some use cases, e.g.: - Splitting large legacy CSV files - Externalizing large payloads on-the-fly - Image processing at the edge - Uploading large files into the DWH Pre-Processing and Data Correlation e.g. enrich with other metadata (ksqlDB) Store big files in data lake (e.g. AWS S3) Consume and correlate metadata (Kafka Streams) Automated Orchestration (Kafka Clients) Real time analytics and other business applications (Kafka Clients + other tools) Send metadata including link to video in object store (Kafka Producer) Download big files from data lake

9. Kafka is NOT an IoT Platform * kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka? Siemens S7 Kafka Connect Storage Kafka Streams / ksqlDB Stateless + Stateful REST Proxy HTTP(S) SCADA DCS ERP MES Cloud Factory * BUT Kafka is a fundamental part of most IoT projects, e.g.: - Scalable real-time data hub for IoT data AND IT data - Edge and hybrid cloud - Direct integration with IoT protocols - Integration via 3rd party with IoT protocols Analytics Database Data Lake CRM Kafka Connect Cluster Linking

10. Kafka is NOT for hard real-time requirements kai-waehner.de | @KaiWaehner | When NOT to use Apache Kafka? OT - Connected Vehicle (Car, Train, Drone) OT - Manufacturing (Field Bus, PLC, Machine, Robot) IT – Enterprise Software (Data Center, Cloud, Car IT) Central Data Center / Public Cloud Vehicle Data Robot Data All Data C C++ Rust C C++ Rust Java Python Go [#] Hard Real Time = Deterministic network with zero spikes + zero latency [#] Soft Real Time + Near Real Time + Batch Cluster Linking Cluster Linking

11. TL;DR == de facto standard for DATA STREAMING

12. Kai Waehner Field CTO kai.waehner@confluent.io @KaiWaehner confluent.io kai-waehner.de linkedin.com/in/kaiwaehner Questions? Feedback? Let’s connect!

When NOT to use Apache Kafka?

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