IDERA Software’s Post

Had the opportunity to explore Kedro a fast data pipeline management tool, I'll implement it in my latest project Kedro offers several benefits that make it a compelling choice over other data orchestration tools like Airflow, Metaflow, and Prefect: Modular and Reusable Code: Kedro's node-based architecture promotes modular and reusable code. Nodes encapsulate Python functions, allowing for easy testing, debugging, and code reuse across different pipelines. Data Catalog Integration: Kedro's Data Catalog provides a centralized registry for managing data sources and datasets. It supports various file types and file systems out-of-the-box, simplifying data access and management. Standardized Project Structure: Kedro enforces a standardized project structure with semantic naming conventions. This structure promotes collaboration, code organization, and better maintainability, especially for larger projects. Reproducibility: Kedro's configuration-driven approach and use of pipelines make it easier to reproduce and version-control data processing workflows, aiding in reproducible data science and machine learning experiments. Separation of Concerns: Kedro separates concerns by keeping data, configuration, notebooks, and source code in different directories, promoting better organization and easier collaboration. Extensibility: Kedro is designed to be extensible, allowing users to create custom plugins, datasets, and hooks to cater to specific project requirements. Active Community and Documentation: Kedro has an active community and comprehensive documentation, which can be helpful for learning, troubleshooting, and finding solutions to common problems. While tools like Airflow, Metaflow, and Prefect are also capable data orchestration tools, Kedro's focus on promoting modular and reusable code, its integrated Data Catalog, and its standardized project structure make it particularly well-suited for data science and machine learning projects, where reproducibility, collaboration, and code organization are crucial. Again tagging those who inspire me to be better and post regularly on LinkedIn Sunny Savita Boktiar Ahmed Bappy Krish Naik sudhanshu kumar

𝐃𝐕𝐂 𝐕𝐒 𝐂𝐨𝐝𝐞 𝐄𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧 𝐒𝐮𝐫𝐩𝐚𝐬𝐬𝐞𝐬 𝟒𝟎,𝟓𝟎𝟎 𝐈𝐧𝐬𝐭𝐚𝐥𝐥𝐬 𝐰𝐢𝐭𝐡 𝐚 𝐇𝐢𝐠𝐡 𝐔𝐬𝐞𝐫 𝐑𝐚𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐦𝐞𝐫𝐠𝐞 𝐚𝐬 𝐭𝐡𝐞 𝐭𝐨𝐩 𝟑 𝐕𝐒 𝐂𝐨𝐝𝐞 𝐞𝐱𝐭𝐞𝐧𝐬𝐢𝐨𝐧 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐝 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐬𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 𝐚𝐧𝐝 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬. We're excited to announce that the DVC extension for VS Code has achieved a significant milestone: 𝐨𝐯𝐞𝐫 𝟒𝟎,𝟓𝟎𝟎 𝐢𝐧𝐬𝐭𝐚𝐥𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐚𝐧 𝐢𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐯𝐞 𝐚𝐯𝐞𝐫𝐚𝐠𝐞 𝐫𝐚𝐭𝐢𝐧𝐠 𝐨𝐟 𝟒.𝟖 𝐨𝐮𝐭 𝐨𝐟 𝟓. This widespread adoption and positive user feedback demonstrate the extension's value in streamlining data science workflows. Since its launch, the DVC VS Code Extension has continuously evolved, adding valuable features like 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭, 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐦𝐨𝐝𝐞𝐥 𝐯𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠. These enhancements solidify the extension as a powerful tool for data scientists working within the VS Code environment. We extend our sincere gratitude to the amazing DVC community for embracing this extension and contributing to its success through feedback and contributions. We remain committed to providing the best possible experience for data science professionals and look forward to delivering even more advancements in the future. 𝐊𝐞𝐲 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 🔹𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭: Streamline data science workflows with seamless pipeline execution. 🔹𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Track and compare different experiment runs for informed decision-making. 🔹𝐌𝐨𝐝𝐞𝐥 𝐯𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠: Manage and deploy trained models with confidence through robust version control. 𝐆𝐞𝐭 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐃𝐕𝐂 𝐢𝐧 𝐕𝐒 𝐂𝐨𝐝𝐞: 🔹𝐈𝐧𝐬𝐭𝐚𝐥𝐥𝐚𝐭𝐢𝐨𝐧: Ensure DVC is installed and download the extension from the VS Code Marketplace by Microsoft 🔹𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐬𝐞𝐭𝐮𝐩: Initialize your project and verify extension settings. 🔹𝐁𝐚𝐬𝐢𝐜 𝐮𝐬𝐚𝐠𝐞: Explore features like dataset management, version control, and pipeline execution. 🔹𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐮𝐬𝐚𝐠𝐞: Leverage experiment tracking, model versioning, and custom commands. All links to the Additional resources will be in the comments. Follow Iterative as we are on a mission to solve the complexity of managing datasets, and ML model lifecycle management.

Check out this blog article penned by Max Kossatz, Financial Services Account Solution Architect @ Red Hat as he shares his perspective on the transition towards data science, and the lessons learned (or missed?) from the DevOps transition.

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐃𝐚𝐭𝐚 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 (𝐃𝐕𝐂)? Data Version Control (DVC) is a specialized tool designed for managing, versioning, and reproducing datasets, models, and other data files in the realms of data science and machine learning. It introduces Git-like version control capabilities to the world of data, enabling efficient tracking of changes, managing data pipelines, and facilitating collaborative projects. 𝐇𝐨𝐰 𝐢𝐬 𝐢𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐟𝐫𝐨𝐦 𝐆𝐢𝐭? DVC differs from Git primarily in its focus and 𝐡𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐨𝐟 𝐥𝐚𝐫𝐠𝐞 𝐟𝐢𝐥𝐞𝐬. Git is renowned for its prowess in versioning code but encounters challenges when dealing with large datasets and binary files. In contrast, DVC complements Git by specifically addressing the management of large data files and machine learning models. It achieves this by storing these files in 𝐫𝐞𝐦𝐨𝐭𝐞 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 rather than within the Git repository itself. This approach ensures that while Git handles metadata and smaller files effectively, DVC manages the actual data separately, thereby optimizing performance and scalability for data-intensive projects. By integrating seamlessly with Git for metadata management, DVC enhances reproducibility and collaboration in data science workflows without burdening the Git repository with large, binary data files. This distinction makes DVC an indispensable tool for data professionals seeking efficient version control and management solutions tailored to the demands of modern data science and machine learning initiatives. 𝘙𝘦𝘢𝘥 𝘵𝘩𝘦 𝘧𝘶𝘭𝘭 𝘢𝘳𝘵𝘪𝘤𝘭𝘦 𝘧𝘰𝘳 𝘣𝘦𝘵𝘵𝘦𝘳 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨, 𝘵𝘩𝘢𝘯𝘬𝘴 𝘵𝘰 Avi Chawla 𝘢𝘯𝘥 Daily Dose of Data Science 𝘧𝘰𝘳 𝘸𝘳𝘪𝘵𝘪𝘯𝘨 𝘵𝘩𝘪𝘴. https://lnkd.in/gXnjNngu

𝐃𝐞𝐦𝐲𝐬𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐀𝐩𝐚𝐜𝐡𝐞 𝐀𝐢𝐫𝐟𝐥𝐨𝐰: 𝐂𝐨𝐫𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐌𝐚𝐬𝐭𝐞𝐫𝐲 Are you tired of manually managing complex data pipelines? Enter Apache Airflow, a powerful open-source platform that streamlines data orchestration and workflow management. But for those new to Airflow, the core concepts can seem daunting. Fear not, data enthusiasts! This post is your guide to the fundamentals. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐀𝐢𝐫𝐟𝐥𝐨𝐰? Think of Airflow as the conductor of your data orchestra. It orchestrates and schedules the tasks that make up your data pipelines, ensuring everything runs smoothly and in the right order. 𝐊𝐞𝐲 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐨𝐟 𝐀𝐢𝐫𝐟𝐥𝐨𝐰: ➡ 𝐃𝐀𝐆𝐬 (𝐃𝐢𝐫𝐞𝐜𝐭𝐞𝐝 𝐀𝐜𝐲𝐜𝐥𝐢𝐜 𝐆𝐫𝐚𝐩𝐡𝐬): The blueprints for your workflows. DAGs define tasks and their dependencies, like a logical roadmap for your data processing. ➡ 𝐎𝐩𝐞𝐫𝐚𝐭𝐨𝐫𝐬: The workhorses of Airflow. These are Python functions that perform specific actions within a workflow, like data extraction, transformation, or loading. Common operators include BashOperator (for executing bash commands) and PythonOperator (for custom Python scripts). ➡ 𝐓𝐚𝐬𝐤𝐬: The individual units of work within a DAG. Tasks can be anything from fetching data from a database to training a machine learning model. ➡ 𝐄𝐱𝐞𝐜𝐮𝐭𝐨𝐫𝐬: The engines that carry out the tasks defined in your DAGs. Airflow supports various executors, including LocalExecutor for development and CeleryExecutor for distributed processing. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐔𝐬𝐢𝐧𝐠 𝐀𝐢𝐫𝐟𝐥𝐨𝐰: ➡ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: Airflow automates repetitive tasks, freeing you from manual data pipeline orchestration. Say goodbye to tedious scripting! ➡ 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Airflow can handle complex workflows with ease, scaling to accommodate growing data volumes as your needs evolve. ➡ 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Airflow ensures tasks run in the intended order and handles failures gracefully, minimizing disruptions in your data pipelines. ➡ 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: Airflow provides a user interface for monitoring your workflows and identifying issues proactively, allowing you to stay ahead of potential problems. Stay tuned, and let's unlock the power of Airflow together! #ApacheAirflow #DataEngineering #DataOps #MLOps #WorkflowManagement

Paul Nashawaty recently caught up with Mezmo at AppDev Field Day #ADFD1, and this DevOps Dialogue offers insight into their data management and observability platform, emphasizing its strength in extracting actionable value from telemetry data. The discussion explores how Mezmo stands out in the crowded market of observability tools by not just collecting data but also by making it comprehensible and useful for businesses. Check out the Mezmo presentation from AppDev Field Day for more detail!

#Docker Desktop with #Snowpark Container Services empower developers, data engineers, and data scientists with the tools to navigate the intricacies of incorporating data, including AI/ML, into their workflows. Accelerate deployment of data workloads with Docker and Snowpark in Snowflake https://lnkd.in/dbBPxyPG

Data Strangles Your Biz? Unleash the DataOps Kraken! Data stuck in slow, siloed pipelines? Say hello to DataOps, the DevOps for data that breaks down walls, automates workflows, and boosts collaboration. Why's everyone buzzing about it? Faster insights, happier stakeholders, and zero data delays. My latest blog dives deep: https://lnkd.in/gS6dwiJ9 Get ready to: Unleash data's power: Faster insights, smoother ops, and thrilled stakeholders. Ditch data delays: Streamlined pipelines for on-demand data. ⚡️ Break down the walls: Data teams & biz users, unite! So, ditch the data drudgery and embrace the DataOps revolution! Head over to my blog and let's get those pipelines pumping! #DataOps #BigData #Analytics #Innovation

🔮 Mage-AI | Workflow Orchestration 🌟 Check out my Mage project with DataTalksClub's Data Engineering Zoom Camp here https://lnkd.in/eamjDccs Mage-AI, symbolizes a groundbreaking fusion of mystical craftsmanship and cutting-edge technology. Serving as an advanced platform, it harnesses the power of AI algorithms in the realm of workflow orchestration. Here, workflow orchestration refers to the meticulous coordination and arrangement of magical procedures (pipelines) and ceremonies (triggers) to effortlessly achieve desired outcomes. Mage simplifies this intricate process by automating mundane tasks & overseeing resource allocation with precision. Its modern, user-friendly interface and adaptable nature ensure even novice practitioners can navigate complex projects with confidence and expertise. 🧙♂️ Mage Features: 🔄 Workflow Automation: It automates data processing tasks, reducing manual effort and streamlining workflows for enhanced efficiency and productivity. 💡 Resource Allocation: Mage optimises resource allocation within data pipelines, ensuring efficient utilisation of computing resources and minimising processing time. 🖥️ User-Friendly Interface: The platform provides an intuitive interface for data engineers to interact with, facilitating easy configuration, monitoring, and management of data workflows. 🛠️ Customisation: Mage allows for customisation of data pipelines to meet specific project requirements, enabling data engineers to tailor workflows to their unique needs. ⚖️ Scalability: Designed for seamless scalability, it handles large data volumes without compromising performance or reliability. 🚨 Monitoring and Alerting: Mage includes built-in monitoring and alerting functionalities to track the health and performance of data pipelines in real-time, ensuring timely intervention in case of issues or anomalies. 🔒 Data Governance: Supporting data governance practices, the platform offers features for data lineage tracking, metadata management, and access control, ensuring compliance with regulations and standards. 🔌 Built-in Connectors: Mage comes equipped with a wide range of connectors to various data sources and platforms, enabling seamless integration with popular databases, cloud services, and data warehouses. 🧩 Pre-built Code Blocks: It offers a library of pre-built code blocks for common data engineering tasks like data transformation, cleansing, and enrichment. These reusable code blocks accelerate development cycles and ensure consistency across data pipelines. 🔗 Integrated dbt (Data Build Tool): Mage includes built-in support for dbt, a popular data transformation tool. This integration enables data engineers to effortlessly build, test, and deploy data transformation workflows using dbt's familiar syntax and features, all within the Mage platform.