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Why are so many companies migrating from one cloud data platform to another?  🔃 Consolidation, scalability, and optimization are big drivers, with the rise of the lakehouse architecture leading the charge. Whether it’s technical limitations or external pressures, migrations are here to stay. At our previous #dbt Meetup, Laszlo Pataki shared his experiences on switching the query engine under dbt projects, and we’ve got some key takeaways! It’s not just a "lift and shift" – migrations bring new governance, security, and integration challenges. With tools like SQLGlot + ChatGPT, Laszlo showed how AI can automate and simplify the process. 🧠  But it doesn’t stop at automation: he highlighted the importance of early testing and validation, leveraging tools like dbt’s audit_helper to ensure smooth cross-platform transitions. If you’re navigating a migration, don’t miss his insights 👉  check out the slideshow!

#Shiftleft, #realtimestreaming, and looking ahead at Confluent 's strategy are just some of the things Andrew Foo discussed recently with TechDay Asia. Read the full article here:

“From data chaos to clarity” – At Confluent, that’s exactly how we see data streaming untangling data complexities. At #KafkaSummit Bangalore, Jay Kreps, Co-Founder and Chief Executive Officer, and Shaun Clowes, Chief Product Officer, spoke to Frontier Enterprise's Rahul Joshi about the not-so-secret but powerful weapon that helps businesses turn brittle data infrastructures to streamlined, efficient systems. Spoiler alert: It’s a data streaming platform! 😉 When you’ve got a platform that works as an intelligent connective tissue - enabling #realtime data and simplifying data integration across the organization - data sets truly become more than the sum of their parts. 🧩 Read more ⬇

Hear why Indian firms are transforming their data strategy with Confluent in this article by Frontier Enterprise:

Hhhhm, don't miss the significance of this news: https://lnkd.in/gnP-DhiK Snowflake picked up Datavolo (IMO, built on THE best orchestration tech on the planet), and now they're seeing if Redpanda is ready to step up to the show in a big way. Whoever is in charge of MNA is absolutely on the money with their vision. At very least we share the same vision and if they do pick up RedPanda then they could be setting themselves up to be the modernization data platform for the next decade. If you have thoughts please drop a comment below.

During the presentation, it was mentioned that the performance of Confluent Cloud is 16x that of Apache Kafka. Where does that massive performance differential come from? When you talk about performance, the first element at play is the networking that connects the client producing data to the service receiving it. If you’re running it on-premises, you’re somewhat limited by the available networking and the hardware in that rack. In the cloud, there have been significant advancements in optimisations. Once you collect data at the network layer, the next step is processing it to ensure it’s stored exactly once, with all the transactional pieces in place. When you run this on-premises, you’re limited to the capabilities of that machine. The third part is writing data to the physical disks in that machine through a RAID (redundant array of independent disks) array or something similar. In the cloud, we can break these steps apart. We actually don’t run the same code as we do on-premises. In the cloud, for example, you don’t have a single hard drive or one machine. We use a process called disaggregation, where we split that single system into multiple layers and optimise those layers independently. A lot of our performance gains come from this approach. These interactions are handled very differently in the cloud. That’s why we have an engine called Kora. We write things differently because we’re in the cloud, allowing us to take advantage of many optimisations.

MinIO DataPod is a first-of-its-kind reference architecture for building data infrastructure to support exascale #AI and large-scale #datalake workloads. Object storage has become foundational for AI infrastructure, and with DataPod, we are building the blueprint for storing massive volumes of structured and unstructured data while providing performance at scale.

Enterprise clients needs strategic partners who can bring the right balance of IP and #services to build a solid #data foundation in the #cloud with proper #governance and monitoring ready to be leveraged for #GenAI use cases.

🌟 The Journey of Kafka and Confluent: Solving Real-World Problems, Shaping the Future of Data 🌟 In 2010, LinkedIn faced a daunting challenge: managing real-time data at scale. With millions of users generating continuous streams of data—from activity logs to notifications—traditional systems couldn't keep up. Out of this necessity, Apache Kafka was born, revolutionizing how we think about data pipelines and event streaming. But this was just the beginning. In 2014, Kafka's creators—Jay Kreps, Neha Narkhede, and Jun Rao—took it a step further by founding Confluent, a company with a vision to make event-driven architectures accessible to every organization, regardless of scale. 💡 The Future of Confluent Confluent has grown into a market leader in the data streaming space, empowering businesses across industries like finance, e-commerce, healthcare, and media. Their roadmap is filled with ambitious goals: Fully Managed Platforms: Expanding Confluent Cloud to simplify event-streaming operations, allowing organizations to focus on innovation rather than infrastructure. Infinite Storage: Enhancing Kafka’s tiered storage capabilities to handle limitless data while keeping costs low. Global Event Streaming: Introducing tools to seamlessly integrate real-time data across multi-cloud and on-premise systems. 🚀 Innovations Underway Confluent is working on cutting-edge technologies to: Support AI & ML Pipelines: Enabling real-time data feeds for machine learning models to improve predictions and automate insights. Strengthen Data Security: Enhancing features like role-based access control and data masking for sensitive streams. Serverless Architecture: Pushing toward a serverless Kafka experience to eliminate operational complexity. 🔍 Emerging Challenges and Opportunities Despite its success, Confluent and Kafka face a dynamic landscape: Data Regulation: With growing concerns about data privacy and compliance (GDPR, CCPA), Kafka must evolve to handle regional data residency laws. Edge Computing: As IoT and edge devices proliferate, real-time event streaming at the edge becomes a critical challenge. Competition: New players and alternatives like Pulsar and Flink are entering the market, pushing for continued innovation. At its core, Kafka is more than a tool—it's a philosophy that data should be fast, reliable, and actionable in real-time. And Confluent is leading the charge to ensure organizations can adapt to a world that never stops streaming. #ApacheKafka #Confluent #DataStreaming #RealTimeData #Innovation #FutureTech #EventDrivenArchitecture

Hello All, I wondered how the companies using the Microservice Architecture monitor their services, and how they ensure the services have less latency. And I found they use something called "Observability" for this one. Many tools in the market provide the Observability, one such platform is Grafana. They provide something called LGTM. I love the LGTM because of the simplicity and Customization. Even they provide good docs and tutorials for getting started with Grafana. Wants to know more check out here: https://lnkd.in/gjvfSuKE #grafana #loki #lgtm #opensource #observability #kubernetes