Name: The State of Stream Processing
Start: 2018-03-28T09:00:00Z
End: 2018-03-28T09:01:00.000Z
Location: BrightTALK
Rating: 5

Confluent is building the foundational platform for data in motion. Our cloud-native offering is designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organisation. With Confluent, organisations can create a central nervous system to innovate and win in a digital-first world.

What is Apache Kafka® and how does it work?

Apache Kafka® was built with the vision to become the central nervous system that makes real-time data available to all the applications that need to use it, with numerous use cases like stock trading and fraud detection, to transportation, data integration, and real-time analytics.

This 2 part series you will get an overview of what Kafka is, what it's used for, and the core concepts that enable it to power a highly scalable, available and resilient real-time event streaming platform. The series begins with an introduction to the shift toward real-time data streaming, and continues all the way through to best practices for developing applications with Apache Kafka® and how to integrate Kafka into your environment.

Whether you’re just getting started or have already built stream processing applications, you will find actionable insights in this series that will enable you to further derive business value from your data systems.

This training is comprised of the following topics::

1.Benefits of Stream Processing and Apache Kafka® Use Cases
2.Apache Kafka® Architecture & Fundamentals Explained
3.How Apache Kafka® Works
4. Integrating Apache Kafka® Into Your Environment
5. Confluent Cloud

Register now to learn Apache Kafka® from Confluent, the company founded by Kafka’s original developers.

Part 2: Fundamentals for Apache Kafka

Are you struggling to scale your data streaming use cases and break down organisational silos? Join Lyndon Hedderly and Burhan Nazir of Confluent as they share their expertise on deploying enterprise-wide data streaming and accelerating the speed to realising measurable business value derived from a data-streaming investment.

With years of experience enabling companies to deploy data streaming, Lyndon and Burhan have identified the missing factors in data streaming deployments, including a lack of general business visibility, an absence of allocated budget for Kafka or data streaming, and no senior-level or C-suite sponsorship.

During this webinar, you will learn five top tips on successfully implementing an enterprise-wide streaming platform. You will also discover how organisations are leveraging data streaming to scale their operations and break down silos.

Take advantage of this opportunity to learn from the experts in data streaming. Register now to join Lyndon and Burhan and take the first step towards realising the full potential of your data.

Five Tips for Enabling Enterprise-Wide Data Streaming

GenAI has the potential to revolutionize industries by improving efficiency, reducing costs, and providing better outcomes for individuals and businesses. This potential has come into being as a result of the availability of large amounts of data and the development of powerful computing systems that can process that data quickly. However, the current hype around AI is distracting businesses from its real value - the capacity to solve real-world problems and address practical challenges. As the AI hype dies down and productive tools begin to emerge, the integration between streaming data and AI will grow stronger.



Join Italo Nesi, Advisory Solutions Engineer at Confluent, for a webinar where he will discuss:



• Leading use cases for data streaming and GenAI
• How to tap into continuously enriched, trustworthy data streams to quickly scale and build real-time AI applications
• How to build governed, secured, and trusted AI
• How Confluent's depth of integration with Apache Flink makes it easier to build end-to-end AI pipelines that can handle data ingestion, processing, and analysis.

• The relationship between Machine Learning and Generative AI data products

Streaming & The Future: Artificial Intelligence as a Data Product

2024 allows retailers to leverage new technologies to provide customers with a more personalised, efficient, and seamless shopping experience and optimise business operations to drive profit. Making the right technology investments and data strategy choices will enable success in a challenging financial environment. Deploying a data streaming platform throughout your business allows a wide range of use cases that address some of the most challenging business objectives of 2024. 


Join the Confluent team for a webinar that will delve into the most impactful data streaming use cases, leaving you clear on the value these can deliver to your business and how to get started with deploying a data streaming platform. 


By attending, you will learn how a data streaming platform can enable the following:



•Last Mile Delivery: Discover how real-time data can enable retailers to optimise their last-mile delivery operations, reducing delivery times, improving efficiency, and providing a more personalised and convenient delivery experience.


•Customer Personalisation: Real-time data can enable retailers to provide a more personalised and relevant shopping experience for customers, improving customer satisfaction and loyalty and ultimately driving sales.


•Supply Chain and Logistics: A data streaming platform can enable retailers to optimise their supply chain and logistics operations, reducing costs, improving efficiency, and ensuring that products are delivered to customers on time.


•Dynamic Pricing: Use real-time data to enable retailers to implement dynamic pricing strategies responsive to changes in customer demand, competition, inventory levels, and customer behaviour and preferences.


•AI and Analytics: Discover how to train ML models with real-time data that is constantly streaming so your applications can act on every event with context and intelligence.

The Top Data Streaming Use Cases for Retail

Data mesh is a new approach to designing data architectures by embracing organisational and data-centric constructs, such as data management and governance. The idea is that data should be easily accessible and interconnected across the entire business.

In this talk, will cover:
 
- The basics of building a streaming data mesh with Kafka & how Confluent enables this. 
- The four principles of the data mesh: domain-driven decentralisation, data as a product, self-service data platform, and federated governance.
- The differences between working with event streams versus centralised approaches.
- How to onboard data from existing systems into a mesh, modelling the communication within the mesh
- How to deal with changes to your domain’s “public” data, give examples of global standards for governance
- The importance of taking a product-centric view of data sources and the data sets they share.

Apache Kafka®, Confluent, and the Data Mesh

This webinar explores the state of data streaming for the Financial Services Industry in 2023. Join this webinar to learn more about:

- What disruptive trends in financial services are driving the pace of innovation
- How data streaming provides the ability to integrate and correlate data in real-time at any scale.
- Which enterprise architectures are trending in the FinServ industry globally

The State of Data Streaming for Financial Services in 2023

Data Streaming with Apache Kafka and Apache Flink is one of the world's most relevant and talked about paradigms in technology. With the buzz around this technology growing, join Kai Waehner, Global Field CTO at Confluent, to hear his predictions for the 'Top Five Trends for Data Streaming in 2024'. In this webinar, Kai will discuss:

• Data Sharing: Decentralized data mesh architecture with independent data products building or buying cloud-native software components. Collaborating within and outside the organization with data sharing using streaming protocols like Kafka, APIs like REST/HTTP, and open standards like AsyncAPI

• Data Contracts: Policy enforcement for structure, integrity constraints, metadata, rules, and other requirements to ensure data governance and compliance across the end-to-end data streaming pipelines

• Serverless Stream Processing: Focusing on business value leveraging a fully managed, integrated, and secure infrastructure for data correlation to implement streaming ETL and application development.

• Multi-Cloud: Data bridge, migrations, or disaster recovery across cloud providers with reliable uni- or bidirectional data synchronization

• AI: Data fabric for machine learning infrastructures to train models, real-time model scoring, and integrating 3rd party services like GenAI LLMs or SaaS offerings

Top Five Data Streaming Trends for 2024

Stream processing is now at the forefront of many company strategies. Over the last couple of years we have seen streaming use cases explode and now proliferate the landscape of any modern business. 

Use cases including digital transformation, IoT, real-time risk, payments microservices and machine learning are all built on the fundamental that they need fast data and they need it at scale. 

Apache Kafka® has long been the streaming platform of choice, its origins of being dumb pipes for big data have long since been left behind and now it is the goto-streaming platform of choice. 

Stream processing beckons as being the vehicle for driving those streams, and along with it brings a world of real-time semantics surrounding windowing, joining, correctness, elasticity, and accessibility. The ‘current state of stream processing’ walks through the origins of stream processing, applicable use cases and then dives into the challenges currently facing the world of stream processing as it drives the next data revolution.

Neil is a Technologist in the Office of the CTO at Confluent, the company founded by the creators of Apache Kafka. He has over 20 years of expertise of working on distributed computing, messaging and stream processing. He has built or redesigned commercial messaging platforms, distributed caching products as well as developed large scale bespoke systems for tier-1 banks. After a period at ThoughtWorks, he went on to build some of the first distributed risk engines in financial services. In 2008 he launched a startup that specialised in distributed data analytics and visualization. Prior to joining Confluent he was the CTO at a fintech consultancy.

The State of Stream Processing

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Data streaming

Stream Processing

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Data Processing

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The State of Stream Processing

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Confluent