DataForge

?? 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

?? Advancing Data Engineering with DataForge ?? We’re excited to share a new video from Matt Kosovec, CEO and Co-founder of DataForge, where he outlines the key innovations of the DataForge Framework. In this video, Matt discusses: ?? The inefficiencies of traditional data frameworks ?? Moving from table-level to column-level transformations for scalability ?? The introduction of column-pure and row-pure transformations to simplify development and automate workflows This is essential viewing for data engineers and architects aiming to optimize and future-proof their data platforms. Watch the full video here: https://lnkd.in/g5Yrh9w3 Check out the full blog here: https://lnkd.in/eac2gmK3 For further details, contact us at [email protected]. #DataEngineering #DataAutomation #DataPipelines #CloudData #DataForge

?? Introducing Our Latest Feature: Stream Processing with Lambda Architecture We’re excited to announce a deep dive into the power of Stream Processing in our latest blog post! As real-time data demands grow, stream processing enables scalable, low-latency pipelines that can transform how you manage and analyze data. In this post, we also explore how Lambda Architecture combines batch and stream processing, ensuring both real-time insights and long-term data reliability. Discover how this hybrid approach can elevate your data infrastructure. ?? Learn more about leveraging stream processing and Lambda Architecture in your projects: https://lnkd.in/gNdZWuPJ #StreamProcessing #LambdaArchitecture #DataEngineering #RealTimeData #BigData #DataPipelines #FeatureUpdate #DataForge

?Introducing Sub-Sources for DataForge!?? In DataForge Cloud 8.1, we've taken complex data type processing to a whole new level with Sub-Sources, allowing you to work with nested complex arrays (NCAs) using standard SQL without tedious normalization. If you’re struggling with denormalized data structures like ARRAY<STRUCT<…>>, Sub-Sources empower you to handle them as if they were flat tables—keeping your data intact and eliminating manual normalization processes. Say goodbye to extra storage layers and complex pipelines! ?? Key Benefits: - Native SQL-based transformations - Work directly with NCAs without altering underlying structures - Maintain parent-child data relationships - Optimize nested data processing at scale Want to learn more? Read the full blog and see how Sub-Sources can accelerate your data workflows. https://lnkd.in/gjGTgJVW Start your 30-day free trial of DataForge Cloud today! #DataEngineering #DataTransformation #SQL #DataForge #CloudInnovation #ComplexData