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By Braeden Quirante
Raw data from IoT devices, like GPS trackers or electronic logging devices (ELDs), often lacks meaning on its own. However, if combined with information from other business systems, such as inventory management or customer relationship management (CRM), this data can now provide a richer, more complete picture for more effective decision-making. For example, combining GPS data with inventory levels can optimize logistics and delivery routes.
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By Confluent
IT teams urged to resolve 'data delays' as UK executives struggle to access and use relevant business data.
In today's data-driven world, staying ahead means acting on the most up-to-date information. That's where change data capture (CDC) comes in. CDC is a design pattern that tracks your database tables, capturing every row-level insert, update, and delete as it happens. This real-time monitoring allows downstream systems to react to changes instantly, without batch-based updates and resource-intensive full scans.
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By Colin McCabe
We are proud to announce the release of Apache Kafka 3.9.0. This is a major release, the final one in the 3.x line. This will also be the final major release to feature the deprecated Apache ZooKeeper mode. Starting in 4.0 and later, Kafka will always run without ZooKeeper.
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By Zion Samuel
A year in at Confluent, Product Manager Surabhi Singh has learned a lot about data streaming—and even more about herself. In this fast-paced environment, Surabhi is highly motivated and committed to her work strategically planning, coordinating, and delivering product improvements for customers whose business operations depend on Confluent Platform.
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By Confluent
2025 will see UK businesses undertake a major shake up of their IT and data practices, new research shows.
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By Adam Bellemare
The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both operational and analytical use cases. Streams provide low-latency capabilities to enable timely reactions to events, while tables provide higher-latency but extremely batch-efficient querying capabilities. You simply choose the most relevant processing head for your requirements and plug it into the data.
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By Adam Bellemare
The headless data architecture is an organic emergence of the separation of data storage, management, optimization, and access from the services that write, process, and query it. With this architecture, you can manage your data from a single logical location, including permissions, schema evolution, and table optimizations. And, to top it off, it makes regulatory compliance a lot simpler, because your data resides in one place, instead of being copied around to every processing engine that needs it.
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By Danica Fine
Welcome back to the third installment of our blog series where we’re diving into the beautiful black box that is Apache Kafka to better understand how we interact with the cluster through producer and consumer clients. Earlier in the series, we took a look at the Kafka producer to see how the client works before following a produce request as it’s processed by the cluster.
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By Confluent
As the speed of decisions increases, new Confluent research shows half of C-level executives are relying on 'gut feel' due to a lack of real-time data.
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By Confluent
In this short video, a Confluent Solutions Engineering will run through the high-level steps on how to get started with your migration. And even better, once you’re done watching, you can download our comprehensive migration kit for a step by step guide of everything I’ve talked about and more.
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By Confluent
Kafka Connectors provide a way to get data flowing between your sources and sinks, and Confluent Cloud. This video reviews your options and shows you how to get started quickly.
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By Confluent
This video provides a tour of the main Confluent Cloud interface, and shows you how to set up your first cluster.
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By Confluent
Data visualization is cool, but have you tried setting up a chart of real-time data? In this video, Lucia Cerchie shows you how to create a live visualization of market data. She starts by producing data to a topic in Confluent Cloud from an Alpaca API websocket, then processes that data with Flink SQL, and finally uses a Streamlit component for a real-time visualization.
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By Confluent
In this recap video from Current 2024, attendees share their favorite moments from the event. From insightful talks on data streaming innovation to hands-on workshops and networking opportunities, hear what participants found most valuable.
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By Confluent
Apache Flink SQL makes it easy to implement analytics that summarize important attributes of real-time data streams. There are four different types of time-based windows in Flink SQL: tumbling, hopping, cumulating, and session. Learn how these various window types behave, and how to work with the table-valued functions that are at the heart of Flink SQL’s support for windowing.
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By Confluent
In today’s fast-paced financial services landscape, customers have a shorter attention span than ever. To meet clients’ growing demands for real-time access to information and keep innovating in areas like fraud detection and personalized financial advice, Thrivent needed to overhaul its data infrastructure. With data scattered across siloed legacy systems, diverse tech stacks, and multiple cloud environments, the challenge was a bit daunting. But by adopting Confluent Cloud, Thrivent was able to unify its disparate data systems into a single source of truth.
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By Confluent
Learn how GEP, an AI-powered supply chain and procurement company, harnesses real-time data streaming through Confluent Cloud to fuel its generative AI solutions. With seamless integration into Azure OpenAI services and GPT models, GEP’s generative AI chatbot delivers document summaries and risk management insights to its customers.
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By Confluent
Discover how GEP, an AI-powered procurement company, utilized Confluent's data streaming platform to transform its generative AI capabilities. Integrating real-time data into their AI models enabled GEP to provide a contextual chat-based service. This chatbot allowed GEP customers to build their own tools simply by communicating in English with a chatbot.
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By Confluent
Learn how replication works in Apache Kafka. Deep dive into its critical aspects, including: Whether you're a systems architect, developer, or just curious about Kafka, this video provides valuable insights and hands-on examples. Don't forget to check out our GitHub repo to get all of the code used in the demo, and to contribute your own enhancements.
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By Confluent
Traditional messaging middleware like Message Queues (MQs), Enterprise Service Buses (ESBs), and Extract, Transform and Load (ETL) tools have been widely used for decades to handle message distribution and inter-service communication across distributed applications. However, they can no longer keep up with the needs of modern applications across hybrid and multi cloud environments for asynchronicity, heterogeneous datasets and high volume throughput.
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By Confluent
Why a data mesh? Predicated on delivering data as a first-class product, data mesh focuses on making it easy to publish and access important data across your organization. An event-driven data mesh combines the scale and performance of data in motion with product-focused rigor and self-service capabilities, putting data at the front and center of both operational and analytical use-cases.
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By Confluent
When it comes to fraud detection in financial services, streaming data with Confluent enables you to build the right intelligence-as early as possible-for precise and predictive responses. Learn how Confluent's event-driven architecture and streaming pipelines deliver a continuous flow of data, aggregated from wherever it resides in your enterprise, to whichever application or team needs to see it. Enrich each interaction, each transaction, and each anomaly with real-time context so your fraud detection systems have the intelligence to get ahead.
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By Confluent
Many forces affect software today: larger datasets, geographical disparities, complex company structures, and the growing need to be fast and nimble in the face of change. Proven approaches such as service-oriented (SOA) and event-driven architectures (EDA) are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but as this practical ebook demonstrates, they provide a more holistic and compelling approach when applied together.
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By Confluent
Data pipelines do much of the heavy lifting in organizations for integrating, transforming, and preparing data for subsequent use in data warehouses for analytical use cases. Despite being critical to the data value stream, data pipelines fundamentally haven't evolved in the last few decades. These legacy pipelines are holding organizations back from really getting value out of their data as real-time streaming becomes essential.
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By Confluent
In today's fast-paced business world, relying on outdated data can prove to be an expensive mistake. To maintain a competitive edge, it's crucial to have accurate real-time data that reflects the status quo of your business processes. With real-time data streaming, you can make informed decisions and drive value at a moment's notice. So, why would you settle for being simply data-driven when you can take your business to the next level with real-time data insights??
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By Confluent
Data pipelines do much of the heavy lifting in organizations for integrating and transforming and preparing the data for subsequent use in downstream systems for operational use cases. Despite being critical to the data value stream, data pipelines fundamentally haven't evolved in the last few decades. These legacy pipelines are holding organizations back from really getting value out of their data as real-time streaming becomes essential.
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By Confluent
Shoe retail titan NewLimits relies on a jumble of homegrown ETL pipelines and batch-based data systems. As a result, sluggish and inefficient data transfers are frustrating internal teams and holding back the company's development velocity and data quality.
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Connect and process all of your data in real time with a cloud-native and complete data streaming platform available everywhere you need it.
Data streaming enables businesses to continuously process their data in real time for improved workflows, more automation, and superior, digital customer experiences. Confluent helps you operationalize and scale all your data streaming projects so you never lose focus on your core business.
Confluent Is So Much More Than Kafka:
- Cloud Native: 10x Apache Kafka® service powered by the Kora Engine.
- Complete: A complete, enterprise-grade data streaming platform.
- Everywhere: Availability everywhere your data and applications reside.
Apache Kafka® Reinvented for the Data Streaming Era