Kevin Kardian’s Post

🔄 The Data Economics Revolution: ETL as the New Digital Marketplace 📊 Something remarkable is happening in tech: ETL pipelines are transforming from simple data movers into vibrant marketplaces. The "datanomics" revolution is here. Here's what's driving this shift: 💎 Data-as-a-Product: Every transformation step creates new value, turning raw data into premium business intelligence products 🏪 Marketplace Evolution: ETL platforms now function as trading floors where organizations buy, sell, and exchange data transformations 🌐 Network Effects: As more players join these data pipelines, the ecosystem becomes exponentially more valuable The future belongs to developers who think like data economists. The question isn't just "How do we move data?" but "How do we maximize its market value?" What's your take on this transformation? Are you already participating in the data marketplace? #DataEngineering #ETL #FutureOfTech #DataEconomics 🚀

#LogicalDataManagement is truly transformative, enabling organizations to achieve trusted, #AI-ready data, seamless #SelfService access, and unparalleled customer personalization. Ravi Shankar’s blog offers a fantastic deep dive into these benefits, along with an exclusive preview of O'Reilly’s forthcoming book on Logical Data Management. Don’t miss the opportunity to explore how this innovative approach can empower your #DataStrategy!

🌟 Insightful Read! Ivo Velitchkov's article, "Apps Break Data," delves into the challenges of application-centric systems fragmenting data, echoing Dave McComb's Data-Centric Revolution. Key Takeaways: Application-Centric Pitfalls: Traditional IT investments often prioritize applications over data, leading to fragmented information silos. Historical Accidents: Application boundaries are often determined by past experiences and chance, not by strategic data considerations. Functional Bias: There's a prevalent bias towards functional requirements, often at the expense of data quality and interoperability. Market Forces: Software engineering practices and market dynamics perpetuate the fragmentation of data. This article serves as a compelling reminder of the need to shift towards a data-centric approach, where data is treated as a first-class citizen, independent of applications. Recommended Actions: Evaluate IT Investments: Assess whether your IT investments prioritize data integrity and interoperability over mere application functionality. Challenge the Status Quo: Question existing biases towards functional requirements and consider the long-term implications on data quality. Embrace Data-Centric Architectures: Adopt architectures that treat data as the core asset, ensuring it remains coherent and accessible across applications. For a deeper understanding, read the full article here: https://lnkd.in/eV4EJc9a Let's continue the conversation: How is your organization addressing the challenges of data fragmentation? 🚀 #DataCentric #DataInteroperability #KnowledgeGraphs #DataQuality #DigitalTransformation

The recent mindboggling AI-powered changes turned the collocation “data product” into a major buzzword, and the data industry – into one of the fastest-growing domains, getting a 62% market value boost in just 6 years. Users seek data-driven products, businesses invest heavily in building them.     But are there any clouds on the horizon for companies embarking on the process of developing data solutions?     📍Uncovering the major don’ts of the data product development journey here in this article https://lnkd.in/dnUq9hx3  #data #dataproduct #softwaredevelopment #technology

Some thoughts I put together on the different layers we see data quality emerging from (or being compromised by when not done right). The application layer as data creator is something that can get overlooked, and getting it right involves many skills and disciplines. https://lnkd.in/ei7AceZQ #softwareengineering #dataengineering #eqengineered

Struggling with #DataExtraction efficiently? You're not alone! Our blog reveals strategic solutions to help you overcome these challenges and optimize your data workflow. Read now! https://bit.ly/4cI2HiT #OracleDataExtractionTools #DataExtractionSoftware #DataExtractionChallenges #RiteSoftware

Legacy data tools: making promises, then ghosting your ROI. 👻 If your data products feel stuck in the 2010s, it’s time for a modern approach. See how Metaphor makes data product dreams a reality, minus the outdated baggage: https://hubs.li/Q02T7lkG0 #DataProducts #Innovation #DataTransformation

🌟 Understanding API Pagination: A Key Strategy for Managing Data Efficiently 🌟 When dealing with APIs, pagination is a fundamental concept to structure large datasets for better performance and user experience. It breaks data into manageable chunks (pages), ensuring scalability and efficient data transfer. Why API Pagination Matters 🚀 1️⃣ Improved Performance: Fetch smaller data sets to optimize resource use. 2️⃣ Reduced Resource Usage: Limit memory and bandwidth consumption. 3️⃣ Enhanced User Experience: Enable faster, responsive applications. 4️⃣ Efficient Data Transfer: Streamline handling of large datasets. 5️⃣ Scalability: Adapt as the dataset or user base grows. Common Pagination Techniques 💡 1️⃣ Offset and Limit Pagination: Retrieve data using offset (start point) and limit (page size). Example: /api/posts?offset=0&limit=10 2️⃣ Cursor-Based Pagination: Use unique identifiers like primary keys or timestamps for stable navigation, even with changing datasets. Example: /api/posts?cursor=eyJpZCI6MX0 3️⃣ Page-Based Pagination: Use page and limit parameters for simpler navigation, often including metadata like total pages or records. Example: /api/posts?page=2&limit=20 4️⃣ Time-Based Pagination: Fetch data by time ranges (startTime & endTime) to retrieve historical or recent data. Example: /api/events?start_time=2023-01-01&end_time=2023-01-31 5️⃣ Keyset Pagination: Rely on sorting and unique attributes (e.g., last seen key) for efficient and stable data retrieval. Example: /api/products?last_key=XYZ123 Best Practices for API Pagination ✅ 📌 Use consistent naming conventions for pagination parameters. 📌 Include pagination metadata in API responses (e.g., total pages). 📌 Choose optimal page sizes to balance performance and usability. 📌 Offer sorting and filtering options to refine results. 📌 Ensure pagination stability for a seamless user experience. 📌 Handle edge cases (e.g., empty pages or invalid parameters). 📌 Leverage caching to reduce repetitive calls.

it is released Welcome to a new era of data management with #DenodoPlatform9! We’re thrilled to introduce the latest version of our platform, designed to revolutionize how your team manages and innovates with data through intelligent #datadelivery. Read this new #DataManagement blog by @Kevin Bohan, Director of Product Marketing at @Denodo, to discover the new features that make this release a game-changer for data enthusiasts and professionals alike, and how #DenodoPlatform 9 can transform your #data operations! https://buff.ly/45McjGS