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Introduction Companies can access a large pool of data in the modern business environment, and using this data in real-time may produce insightful results that can spur corporate success. Real-time dashboards such as GCP provide strong data visualization and actionable information for decision-makers. Nevertheless, setting up a streaming data pipeline to power such dashboards may […] The post Data Engineering for Streaming Data on GCP appeared first on Analytics Vidhya.
👋 Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. We cover one out of five topics in today’s subscriber-only The Scoop issue. If you’re not yet a full subscriber, you missed this week’s deep-dive into Figma’s engineering culture. To get full newsletters twice a week, subscribe here.
Buck2, our new open source, large-scale build system , is now available on GitHub. Buck2 is an extensible and performant build system written in Rust and designed to make your build experience faster and more efficient. In our internal tests at Meta, we observed that Buck2 completed builds 2x as fast as Buck1. Buck2, Meta’s open source large-scale build system, is now publicly available via the Buck2 website and the Buck2 GitHub repository.
In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate
LangChain is a Python library that helps you build GPT-powered applications in minutes. Get started with LangChain by building a simple question-answering app.
LangChain is a Python library that helps you build GPT-powered applications in minutes. Get started with LangChain by building a simple question-answering app.
Amidst the excitement and hype surrounding artificial intelligence, the significance of data engineering and its critical foundation—data modeling—can often be overlooked. This article is the first in a three-part series that will shine a spotlight on the fascinating world of data modeling, delving into its crucial importance within the broader context of data engineering.
Summary The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise.
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
Amidst the excitement and hype surrounding artificial intelligence, the significance of data engineering and its critical foundation—data modeling—can often be overlooked. This article is the first in a three-part series that will shine a spotlight on the fascinating world of data modeling, delving into its crucial importance within the broader context of data engineering.
Authors: Viktoras Truchanovicius and Selina Zhang At LinkedIn, developer productivity and happiness has always been a priority. It is critical for our engineering leaders to understand how efficiently and effectively their teams are operating to continuously deliver value-added features for our members and build an industry-leading engineering culture.
This article will explain how RAPIDS can help you speed up your next data science workflow. RAPIDS cuDF is a GPU DataFrame library that allows you to produce your end-to-end data science pipeline development all on GPU.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Spring has sprung—and with it comes a new crop of Snowflake Startup Challenge semi-finalists! The 2023 submission pool was the largest to date—twice as many submissions as last year—with entries that spanned not just the globe but the breadth of the Snowflake platform. Our judges put a lot of careful consideration into selecting the top 10, and we offer our sincere thanks to every company that sent in an entry this year—we know how much hard work goes into these submissions, and we appreciate it
Entity-centric modeling is a data modeling approach focusing on enriching tabular datasets with useful "features" to enable segmentation, cohort creation, and complex classification analyses easier.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
All the best data streaming resources, tips, and guides to help you learn introductory concepts, streaming architecture basics, common tools and technologies, and more.
Principles, practices, and examples for ensuring high quality data flows Source: DreamStudio (generated by author) Nearly 100% of companies today rely on data to power business opportunities and 76% use data as an integral part of forming a business strategy. In today’s age of digital business, an increasing number of decisions companies make when it comes to delivering customer experience, building trust, and shaping their business strategy begins with accurate data.
I will be sharing a couple of things I have learned in the past six months and tips that helped me stay dedicated and true to my journey in this article.
With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you
Machine learning and data streaming are a perfect match, but have diverging tech stacks. How can we overcome the pitfalls of SQL and the gulf between languages?
Building a Mental Model for Engineers and Anyone in Between Stream Processing can be handled gently and with care, or wildly, and almost out of control! You be the judge of what future you’d rather embrace. credit: @psalms original_photo Introduction In many cases, processing data in-stream, or as it becomes available, can help reduce an enormous data problem (due to the volume and scale of the flow of data) into a more manageable one.
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
For a software team to be successful, you need excellent communication. That is why we want to build systems that foster cross-team communication. Using a monorepo is an excellent way to do that. A monorepo provides: Visibility: by seeing the pull requests (PRs) of colleagues, you are easily informed of what other teams are doing. Uniformity: by working in one central repository, it is easier to share the configuration of linters, formatters, etc.
Learning a little about these tools and how to integrate them Photo by Nolan Krattinger on Unsplash Introduction A few weeks ago, while doing my mental stretch to think about new post ideas, I thought: Well, I need to learn (and talk) more about cloud and these things, I’ve practiced a lot on on-premise ambients, using open-source tools, and running away from proprietary solutions… But the world is cloud and I don’t think that this is gonna change any time soon… I then wrote a post about creati
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
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