DataForge

In this article, you will learn about the benefits and best practices for implementing Medallion architecture in data pipelines, a strategy for organizing and managing data in a lake house with three tiers of data structure referred to as bronze, silver, and gold. It also provides code examples to help readers with practical implementation. #MedallionArchitecture #DataPipeline #DataTransformation

Struggling with a legacy data platform with thousands of lines of code? Feel stuck fixing issues all day rather than building the next great data product? Check out the latest video series about a customer case study where DataForge transformed their legacy system of ~5,000 SQL scripts and 500,000 lines of code into 56 files and <2,000 lines of code in less than 5 weeks. https://lnkd.in/gyt-tFwt #dataengineering #agile #datapipeline

Interested how DataForge works? Check out Part 2 of DataForge CEO Matt Kosovec's introduction series covering the DataForge Object Model and Framework. In this blog and video, Matt walks through the key concepts and structures of DataForge that re-define data engineering code management using modern computer science concepts. Blog and Video: https://lnkd.in/gRqn_bQD GitHub: https://lnkd.in/eHPc9uG8

🚀 New Video & Blog! Data refresh patterns are critical for efficient pipeline management. Join our CTO Vadim Orlov in his latest content where he dives into the six key refresh patterns available in DataForge Cloud, including Full, None, Timestamp, Sequence, and Custom Refresh Types. Learn how to: - Streamline data pipeline development. - Optimize performance using incremental refresh. - Leverage advanced features like watermarks and custom merge patterns. Whether you're working with small datasets or tackling complex, real-world challenges, these patterns will help you build your pipelines declaratively and manage your platform at scale. 📺 Watch the video: https://lnkd.in/gj2Upc7z 📝 Read the blog: https://lnkd.in/gQb9s9AV 📂 Check us out on GitHub: https://lnkd.in/eHPc9uG8 We’d love your feedback—share your thoughts or questions in the comments! #DataEngineering #DataPipeline #Automation #DataForge

💡 Building robust and scalable BI data models is all about making the right engineering choices at every stage. In our latest blog and video, we explore key decisions in traditional ETL pipeline design—like specifying data types early, managing granularity, and balancing modularity with performance. These choices impact data stability, processing efficiency, and model integrity. Discover how thoughtful stage design can lead to more reliable data pipelines and support evolving analytics demands. 🔗 Watch and read more: https://lnkd.in/gWSVsduV ⭐ Don’t forget to star and follow our open-source library for DataForge Core on GitHub: https://lnkd.in/eHPc9uG8 #DataEngineering #ETL #DataTransformation #BI #DataModeling #DataForge

In this article, you will learn about the key concepts and techniques of data transformation, including data discovery, processing, enrichment, governance, and automation, as well as the benefits of different approaches (ETL, ELT, reverse ETL) and the value of the layered medallion architecture. #datatransformation #datatools #dataengineering

🎬 New Video: Building Reusable Data Architecture with DataForge Cloud We’re pleased to share a new video featuring Vadim Orlov, CTO and Co-founder of DataForge, where he covers how DataForge Cloud can streamline data engineering tasks and reduce repetitive coding. In this session, Vadim demonstrates how Templates and Cloning in DataForge Cloud support a DRY (Don’t Repeat Yourself) approach, making data transformation more efficient and easier to manage. He shows how to set up Rule, Relation, and Connection Templates, consolidate data across similar systems, and scale data operations across subsidiaries, providing a practical guide to simplifying complex data pipelines. 📹 Watch the video to learn more about managing data architecture with reusable code! 🔗 https://lnkd.in/g96FdDaE

🌟 Curious about how DataForge measures up? 🌟 We're excited to share our new Product Comparison Guide, crafted to provide an in-depth look at how DataForge stands alongside leading code frameworks like dbt and SQLMesh, as well as ELT tools like Coalesce and Matillion. This guide offers a clear, side-by-side evaluation, helping you quickly identify the unique strengths of each tool and understand how they fit into today’s evolving data landscape. Explore the guide on our website to see how DataForge can help reimagine your data transformation workflows. And we’d love to hear from you—which tools or platforms should we add next? We worked hard to make this as factual as possible, but we may have missed something—let us know! https://lnkd.in/g4Nq75rz #DataForge #DataTransformation #ProductGuide #DataEngineering #ELT #ModernDataStack

📄 Mastering Schema Evolution & Type Safety with DataForge 📄 We’ve published a new blog exploring two key challenges in data pipeline development: schema evolution and type safety. Based on recent discussions in the data engineering community, schema changes in source data remain one of the primary causes of pipeline failures. In this post, we cover: 🔹 How schema evolution impacts pipelines, especially when adding, removing, or modifying attributes. 🔹 Why SQL, despite being a strongly typed language, often struggles with enforcing type safety. 🔹 How DataForge addresses these issues with compile-time type safety and automated schema evolution strategies. By leveraging these features, data engineers can: ✅ Improve pipeline reliability and reduce debugging time. ✅ Automate handling schema changes across datasets. ✅ Ensure consistent downstream logic across data lakehouse architectures. Read the full blog and see how DataForge can streamline schema management in your pipelines. 👉 https://lnkd.in/gZ4SYSB7 #DataEngineering #SchemaEvolution #TypeSafety #DataPipelines #DataForge

In this article, you will learn about the different types of data transformations, their purposes, and implementation best practices, which include hands-on examples. #datatransformation #datatools #dataengineering