Data Warehouse vs. Live Datamart - Comparison and Differences

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Data Warehouses have existed for many years in almost every company. While they are still as good and relevant for the same use cases as they were 20 years ago, they cannot solve new, existing challenges and those sure to come in a ever-changing digital world. The upcoming sections will clarify when to still use a Data Warehouse and when to use a modern Live Datamart instead.

Technologie

Difference between a Data Warehouse
and a Live Datamart
Kai Wähner
kwaehner@tibco.com
@KaiWaehner
www.kai-waehner.de
LinkedIn / Xing à Please connect!

Key Messages
–  A Data Warehouse is used for analysis and reporting of historical data!
–  A Live Datamart is used to manage operations in real time!
–  A Live Datamart combines automated streaming analytics and proactive human interaction

© Copyright 2000-2015 TIBCO Software Inc.
Agenda
What is a Data Warehouse?
What is a Live Datamart?
Spoilt for Choice

Data Warehouse
http://www.exforsys.com/tutorials/msas/data-warehouse-database-and-oltp-database.html
… used for storing
historical data.

Business Intelligence
= Reporting
+ Statistics
+ Data Discovery

Live Demo
Reporting and Analytics with TIBCO Spotfire

Live Datamart
Live Datamart
Continuous Query Processor Alerts

BusinessEvents

FTL

EMS

Ac4veSpaces

BusinessWorks

Social
Media
Data

Market
Data

Sensor
Data

Historical

Data

Ac4veSpaces
Datagrid
Enterprise

data

Market Data
IoT
Mobile
Social
Live UI
Command & Control
ACTION

Con4nuous
Query

… used for operations
in real time.

Live Demo
Operations in Real Time with TIBCO Live Datamart

© Copyright 2000-2015 TIBCO Software Inc.
Spoilt for Choice
Data Warehouse
Live Datamart
Historical Data
1) Reporting
2) Data Discovery to find Patterns
3) Optional: Analyze using Statistics
Real Time Data
(and historical data integrated)
4) Operations
5) Optional: Apply Statistics

Please contact me!
Kai Wähner
kwaehner@tibco.com
@KaiWaehner
www.kai-waehner.de
LinkedIn / Xing à Please connect!
Questions?

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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/

When NOT to use Apache Kafka?

Kai Wähner

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?

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Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable. Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden. This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents. We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse. Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.

Serverless Kafka and Spark in a Multi-Cloud Lakehouse Architecture

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

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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

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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

Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration. This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.

Kafka for Real-Time Replication between Edge and Hybrid Cloud

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

Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers. This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture. The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more. Video recording of this presentation: https://youtu.be/j7D29eyysDw Further reading: https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/ https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/ https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/

Kappa vs Lambda Architectures and Technology Comparison

Kai Wähner

I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe: Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter. Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka. Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments. Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka. Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.

The Top 5 Apache Kafka Use Cases and Architectures in 2022

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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

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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

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1. Difference between a Data Warehouse and a Live Datamart Kai Wähner kwaehner@tibco.com @KaiWaehner www.kai-waehner.de LinkedIn / Xing à Please connect!

2. Key Messages –  A Data Warehouse is used for analysis and reporting of historical data! –  A Live Datamart is used to manage operations in real time! –  A Live Datamart combines automated streaming analytics and proactive human interaction

5. Data Warehouse http://www.exforsys.com/tutorials/msas/data-warehouse-database-and-oltp-database.html … used for storing historical data.

6. Business Intelligence = Reporting + Statistics + Data Discovery

7. Live Demo Reporting and Analytics with TIBCO Spotfire

9. Live Datamart Live Datamart Continuous Query Processor Alerts BusinessEvents FTL EMS Ac4veSpaces BusinessWorks Social Media Data Market Data Sensor Data Historical Data Ac4veSpaces Datagrid Enterprise data Market Data IoT Mobile Social Live UI Command & Control ACTION Con4nuous Query … used for operations in real time.

10. Live Demo Operations in Real Time with TIBCO Live Datamart

12. © Copyright 2000-2015 TIBCO Software Inc. Spoilt for Choice Data Warehouse Live Datamart Historical Data 1) Reporting 2) Data Discovery to find Patterns 3) Optional: Analyze using Statistics Real Time Data (and historical data integrated) 4) Operations 5) Optional: Apply Statistics

13. Key Messages •  A Data Warehouse is used for analysis and reporting of historical data! •  A Live Datamart is used to manage operations in real time! •  A Live Datamart combines automated streaming analytics and proactive human interaction

14. Please contact me! Kai Wähner kwaehner@tibco.com @KaiWaehner www.kai-waehner.de LinkedIn / Xing à Please connect! Questions?

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