DEVWorld Conference’s Post

CDC Is a Feature Not a Product During and after my time as the lead of Debezium, a widely used open-source platform for Change Data Capture (CDC) for a variety of database, I got repeatedly asked whether I’d be interested in creating a company around CDC. VCs, including wellknown household names, did and do reach out to me, pitching this idea. #ChangeDataCapture #CDC #DatabaseEngineering #SoftwareArchitecture #OpenSource #Debezium #DataIntegration #DataStreaming #RealTimeData #EnterpriseSoftwareArchitecture https://lnkd.in/g9Kk8s5t

Join our next webinar, "State- and migration-based deployment models: Finding the winning combination" to learn the risks of choosing the wrong database deployment model & how to avoid them. We’ll explain the differences between state-based and migration-based approaches and include a live demo! 📅 July 25 ⏰ 11 AM CT Register & get the recording after: https://hubs.li/Q02Gn9Ff0 #DatabaseDevOps #Liquibase

Data Integration & Ingestion are crucial components of any data pipeline. I have recently published a blog on real-time data ingestion using Debezium & Kafka Connect as a data ingestion tool and Oracle as a data source. Also discussing a No-Code solution like Hevo Data for similar use cases. Feel free to drop any curiosity or feedback. Link to the blog: https://lnkd.in/ebCntSdS Follow Raju M. for more similar content. #datapipeline #debezium #nocode

Dive into the intricacies of table complexity in data migration with Sunil Kumar. Explore factors influencing complexity classifications, the approaches to determine complexity and the significance of this classification in understanding manual migration efforts. https://lnkd.in/g8ykCVSD Check out the live demo of the GenAI powered DBShift™ https://lnkd.in/gXidN8QS Revolutionize your data migration strategies with GenAI powered DBShift™ Reach out today https://lnkd.in/grKwhZWj #DataMigration #DataManagement #TechInnovation #ArtificialIntelligence #GenAI #DBShift #ITSolutions #DataComplexity #LiveDemo #TechTrends #DataStrategies #MachineLearning #BigData #TechDemo #InnovationInTech #DigitalTransformation

🌟 Unlocking the Power of Kafka's Exactly-Once Semantics 🌟 In data-driven world, ensuring the reliability and consistency of data streams is paramount. This is where Kafka's exactly-once semantics come into play, offering robust solutions to prevent data loss and duplication. What are Kafka's exactly-once semantics? Kafka's exactly-once semantics guarantee that records are: ✅ Never Lost: Ensuring data integrity even in the face of failures. ✅ Never Processed More Than Once: Preventing duplication and ensuring accurate data processing. How is this achieved? 🔄 Idempotent Producers: These producers assign unique IDs to each message, allowing Kafka to detect and discard duplicate records. 📝 Transactional Writes: This feature groups a series of writes into a single transaction. Either all the writes succeed, or none do, ensuring data consistency. 🗂️ Atomic Writes to Multiple Topics: Kafka ensures that writes to multiple topics are all-or-nothing, preventing partial updates and maintaining data integrity. With Kafka's exactly-once semantics, you can trust that your data pipeline remains robust, reliable, and ready to meet the demands of modern data processing. #Kafka #DataStreaming #DataConsistency #TechInnovation #DataIntegrity

At RavenDB we empower developers to focus on building amazing products while reducing errors and saving time. In this video, Dejan Milicic Milicic, Head of DevRel at RavenDB shares his exciting journey from traditional relational databases to discovering the power of RavenDB—a NoSQL database that has transformed the way he develops software. Watch the video below to learn more about what he has to say⬇️ #NoSQL #RavenDB #SoftwareDevelopment #DatabaseDesign

🎯 Open Source Data Platforms: From Complexity to Clarity An excellent presentation by Priyanka Naik on building robust data platforms using open source tools. Key insights shared: 📊 Transitioning from batch to real-time streaming 🔄 Modular architecture for better maintainability 🎯 Data catalogs for non-technical user engagement ⚡ Apache Camel connectors for optimized Kafka workflows With 17 years of experience, Priyanka offered valuable perspectives on data democratization and quality management in modern architectures. Watch here: https://lnkd.in/gPcm8sZs #DataEngineering #OpenSource #Kafka #DataPlatform #Engineering

Actian Zeenea offer a unified data intelligence and governance solution: Understanding the quality of data throughout its lifecycle is critical for maintaining its usefulness, reliability, and value. High-quality data supports effective decision-making, ensures operational efficiency, enables regulatory compliance, and fosters trust within the organization. By actively managing data quality at each stage from creation to archival organizations can avoid costly mistakes, gain more value from their data, and drive better outcomes across all areas of the business. We can help you grow and innovate. reach out to learn more

Lamatic provides Seamless integration to Postgres 🐘 Here’s how it can simplify your workflow: #1. ☑ Direct Sync → Easily pull data from your Postgres tables and views into Lamatic without the hassle. No more manual transfers or outdated information! #2. ☑ Automated Workflows → Once your data hits Lamatic, workflows can trigger automatically. Imagine the time saved by eliminating manual processes! #3. ☑ Empower Complex GenAI → Whether you’re developing AI-driven applications or automating decision-making, the Postgres integration provides the solid data foundation you need to scale. #4. ☑ Enhanced Efficiency → Less time on data wrangling means more time for innovation and focusing on what really matters to your business. 💡 This update simplifies the complexity, enabling any business to leverage GenAI with real-time data from Postgres. How do you currently manage your data workflows?We’d love to hear your thoughts!

We've been building DataLine in the open for over a year now, and intuition about what to build has taken us pretty far. Now however, we're at a point where decisions need to be made about what to build next as we can go in many directions! Doing product development for an open source project has proven challenging. We don't know who our users are, and people who use our product host it themselves or run it locally. So for the past month, we've been running lots of user interviews and implementing optional anonymous and safe tracking features that'll allow us to understand how DataLine features are being used and measure improvements or regressions in quality over time. That brings us to the latest release! We've fixed some bugs and implemented these optional analytics, and we hope people trust us enough to enable them. The code is open source after all! Aside from that, you can now "refresh" connections if the underlying data structure changes (ex. database migrations). More info on the roadmap soon 😏 Upgrade to the latest release if you haven't already! Instructions below 👇