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Data engineering refers to the design of systems that are capable of collecting, analyzing, and storing data at a large scale. In manufacturing, data engineering aids in optimizing operations and enhancing productivity while ensuring curated data that is both compliant and high in integrity. The increased efficiency in data “wrangling” means that more accurate modeling and planning may be done, enabling manufacturers to make stronger data-driven decisions.
I get asked every now and then if I offer 1:1 mentoring for either software engineers or engineering managers or leaders. While I used to do this in the past, I don't offer this any more. I collected much of the advice I have to offer for software engineers in The Software Engineer's Guidebook. I also write The Pragmatic Engineer Newsletter where I do cover topics like what it means to be a senior engineer at various companies , how to deal with a low-quality engineering culture , and
Introduction Docker containers have emerged as indispensable tools in the fast-evolving landscape of software development and deployment, providing a lightweight and efficient way to package, distribute, and run applications. This article delves into the top 20 Docker containers across various categories, showcasing their features, use cases, and contributions to streamlining development workflows.
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
2023 was the second full year of The Pragmatic Engineer Newsletter , and this newsletter is now almost two and a half years old; the first issue came out on 26 August 2021. Thank you for being a reader, I greatly value your support. This year, 102 newsletter issues were published, and this is number 103. You received a deepdive issue on Tuesdays, and every Thursday it was “The Pulse” – formerly The Scoop.
On July 5, 2023, Meta launched Threads, the newest product in our family of apps, to an unprecedented success that saw it garner over 100 million sign ups in its first five days. A small, nimble team of engineers built Threads over the course of only five months of technical work. While the app’s production launch had been under consideration for some time, the business finally made the decision and informed the infrastructure teams to prepare for its launch with only two days’ advance notice.
On July 5, 2023, Meta launched Threads, the newest product in our family of apps, to an unprecedented success that saw it garner over 100 million sign ups in its first five days. A small, nimble team of engineers built Threads over the course of only five months of technical work. While the app’s production launch had been under consideration for some time, the business finally made the decision and informed the infrastructure teams to prepare for its launch with only two days’ advance notice.
The rise of generative AI (gen AI) is inspiring organizations to envision a future in which AI is integrated into all aspects of their operations for a more human, personalized and efficient customer experience. However, getting the required compute infrastructure into place, particularly GPUs for large language models (LLMs), is a real challenge. Accessing the necessary resources from cloud providers demands careful planning and up to month-long wait times due to the high demand for GPUs.
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.
Here are our top posts of 2023, including: 5 Free Books to Master Data Science • 5 Free Courses to Master Machine Learning • 3 Ways to Access GPT-4 for Free • and much more!
Over the past six months, we've been working with NVIDIA to get the most out of their new TensorRT-LLM library. TensorRT-LLM provides an easy-to-use Python interface to integrate with a web server for fast, efficient inference performance with LLMs. In this post, we're highlighting some key areas where our collaboration with NVIDIA has been particularly important.
When businesses share sensitive first-party data with outside partners or customers, they must do so in a way that meets strict governance requirements around security and privacy. Data clean rooms have emerged as the technology to meet this need, enabling interoperability where multiple parties can collaborate on and analyze sensitive data in a governed way without exposing direct access to the underlying data and business logic.
Even though nowadays data processing frameworks and data stores have smart query planners, they don't take our responsibility to correctly design the job logic.
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.
Summary The "modern data stack" promised a scalable, composable data platform that gave everyone the flexibility to use the best tools for every job. The reality was that it left data teams in the position of spending all of their engineering effort on integrating systems that weren't designed with compatible user experiences. The team at 5X understand the pain involved and the barriers to productivity and set out to solve it by pre-integrating the best tools from each layer of the s
Co-authors: Max Kanat-Alexander and Grant Jenks Today we are open-sourcing the LinkedIn Developer Productivity & Happiness Framework (DPH Framework) - a collection of documents that describe the systems, processes, metrics, and feedback systems we use to understand our developers and their needs internally at LinkedIn. Now more than ever, developers are navigating so much change and new opportunity in this new era of Generative AI, so ensuring teams have the systems, processes, metrics and f
HawkEye is the powerful toolkit used internally at Meta for monitoring, observability, and debuggability of the end-to-end machine learning (ML) workflow that powers ML-based products. HawkEye supports recommendation and ranking models across several products at Meta. Over the past two years, it has facilitated order of magnitude improvements in the time spent debugging production issues.
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
Request a meeting with Databricks executives/thought leaders at NRF! Each January, thousands of leaders from retailers around the globe gather at Javits Center.
Sherwood Media, LLC has added U.K.-based Chartr Limited, a data-driven media company and newsletter publisher, to its portfolio through an acquisition by Robinhood Markets, Inc. Chartr’s visual storytelling turns complex data into easy-to-understand narratives, and will now give the tens of millions of readers of Sherwood Media the ability to better understand the finer details of important trends and the news of the day.
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
We will learn a simple way to install and use Llama 2 without setting up Python or any program. Just download the files and run a command in PowerShell.
Sometimes I just need something new and interesting to work on, to keep me engaged. A few days ago I was lying by the river next to a fire, with the cold air blowing on my face and the eagles soaring above. Thinking about and contemplating life and data engineering … something flitted across my […] The post Datafusion SQL CLI – Look Ma, I made a new ETL tool. appeared first on Confessions of a Data Guy.
2023 has been a year of breakthrough innovation for many, and a deer-in-headlights moment for others. I keep flashing back to the 90s when the Internet created new businesses and destroyed others—LLMs are doing the same, only with more velocity. From CDAOs to VCs alike, the rate of creative destruction is faster, but there is also an intense focus on value.
Historically, only a few AI experts within an organization could develop insights using machine learning (ML) and predictive analytics. Yet in this new wave of AI, democratizing ML to more data teams is crucial—and for Snowflake SQL users, it’s now a reality. With the general availability of ML-based forecasting and anomaly detection functions in Snowflake Cortex, data analysts and other SQL users can now build more accurate forecasts and identify outliers for their time-series data in Snowflake
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!
We've partnered with Springboard, the leading data science bootcamp offering personalized 1:1 mentorship, dedicated career support, proven outcomes, and an unbeatable money-back job guarantee, to present a handpicked collection of resources to supercharge your data science journey in the coming year.
Technology is evolving at breakneck speed, and the information we consume every day continues to grow exponentially with every passing day. Analyzing this complex mountain of data to make the right decisions informed by this data has become ever more challenging. Traditional models of project management , like the waterfall method and hierarchical team structures are too rigid to respond to the fast-paced change organizations are facing today.
Introduction Anomaly detection is widely applied across various industries, playing a significant role in the enterprise sector. This blog focuses on its application.
Welcome to Snowflake’s Startup Spotlight, where we feature awesome companies building businesses on Snowflake. In this edition, Patch.tech Co-Founder and CPO Whelan Boyd talks about how frustration with clogged data pipelines sparked the idea for Patch’s code packages, which allow engineers to distribute data sets with all the built-in elements that analysts and developers need to create apps.
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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