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Co-Authors: Shweta Patira , Ankan Saha , Yilin Li, and Manas Somaiya Earlier this year, we launched Collaborative Articles with the vision of making LinkedIn the one-stop destination for all work-related questions. Among our 1 billion members, there are seasoned experts who have encountered every conceivable workplace problem. If we could present their thoughts on LinkedIn, then mentors, experts, and coaches would be right at our members' fingertips.
👋 Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. In every issue, I cover topics related to Big Tech and startups through the lens of engineering managers and senior engineers. In this article, we cover thee out of nine topics from today’s subscriber-only issue: The Past and Future of Modern Backend Practices.
Summary The dbt project has become overwhelmingly popular across analytics and data engineering teams. While it is easy to adopt, there are many potential pitfalls. Dustin Dorsey and Cameron Cyr co-authored a practical guide to building your dbt project. In this episode they share their hard-won wisdom about how to build and scale your dbt projects.
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
We recently announced our AI-generated documentation feature, which uses large language models (LLMs) to automatically generate documentation for tables and columns in Unity.
Even though you may be thinking now about Data+AI Summit 2024, I still owe you my retrospective for the 2023 edition. Let's start with the first part covering stream processing talks!
This article aims to introduce and explore the practice of splitting debug symbols away from C/C++ build artifacts to save space and time when building large codebases. Note that we want to retain access to the debug symbols if and when they are needed at a later date, hence we don’t want to merely remove (aka strip ) the debug symbols. 1 This exploration is largely inspired and based on what I have learned in various places around the web, most notably: Improving C++ Builds with Split DWARF, by
Large Language Models (LLMs) have now become an integral part of various applications. This article provides an extensive list of resources for anyone interested to dive into the world of LLMs.
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.
AI transforms how we interact with technology, make decisions, and solve complex problems. It has been at the heart of many innovations over the past two years, powering everything from the chatbots that enhance our customer experiences to the predictive analytics engines that help us make financial decisions. What defines a successful AI initiative, and how can your organization ensure that your investments and hard work deliver maximum value for your organization?
The enterprise app market has been growing faster than ever before, due to the recent spike in demand for AI / ML workloads. These new types of apps operate over large sets of data, have increasingly higher compute demands, require strict data privacy protections, provide very sophisticated web experiences, and need to be secure at all stages of their life cycles.
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.
Improving Efficiency Of Goku Time Series Database at Pinterest (Part — 1) Monil Mukesh Sanghavi, Kapil Bajaj, Ming-May Hu, Xiao Li and Zhenxiao Luo Introduction At Pinterest, one of the pillars of the observability stack provides internal engineering teams (our users) the opportunity to monitor their services using metrics data and set up alerting on it.
Python plays a big part at Meta. It powers Instagram’s backend and plays an important role in our configuration systems, as well as much of our AI work. Meta even made contributions to Python 3.12 , the latest version of Python. On this episode of the Meta Tech Podcast , Meta engineer Pascal Hartig ( @passy ) is joined by Amethyst Reese, a production engineer at Meta, to discuss all things Python at Meta.
Generative AI is an exciting and fast-moving area of research and application. Check out these 5 courses to get up to speed and stay ahead of the curve.
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
If you want to try your hand at fine-tuning an LLM (Large Language Model): one of the first things you’re going to need to know is “will it fit on my GPU”. The pre-eminent guide to estimating (VRAM) memory requirements is Transformer Math 101. It bears mentioning, though, that its heuristics are written in the context of frameworks such as GPT-NeoX and Megatron-DeepSpeed.
Upskilling through role-based pathways to accelerate your data + AI career Databricks has spent years crafting and iterating technical trainings for learners across.
Product Owners and Scrum Masters are two integral roles in Scrum, an Agile project management methodology. While both roles are essential for successful Scrum teams, their responsibilities are distinct. When talking about Product Owner vs Scrum Master responsibilities, it is necessary to note that the Product Owner looks after the vision and content of the product.
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
The greater tech community was front row for a high-stakes corporate saga this past weekend, complete with more plot twists than the Succession series finale. The unexpected dismissal of OpenAI CEO Sam Altman, followed by a threatened employee mutiny, followed by Microsoft’s fastest hire ever (I’m not sure that I believe that Sam cleared all the HR requirements in that time), followed by the reinstatement of Sam Altman as the CEO of OpenAI, has reignited a crucial conversation in the tech commun
by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset. This not only reduces the cost of compute resources but also reduces the execution time in a significant manner.
The world is gradually having a paradigm shift from desktop computers to mobile devices- it doesn’t mean that the desktop experience is not good. According to a recent mobile survey , many mobile users have confirmed that it is more appealing, unique, user-friendly and functional. Here are some guides that will help you in learning how to optimise website for mobile users: Choose your site layout You need to ensure your website layout is well streamlined.
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!
What this article is about What kind of changes we had to make to the codebase How we did the actual upgrade What challenges we faced How we did the data transfer How the data was kept in sync What this article is not A step-by-step guide on how to upgrade Elasticsearch (read on to find out why). Who we are We are a team from the Search & Browse department, the department in Zalando that is responsible for all things search (read: relevance, personalisation, sorting, filters, full text searc
Given that the DevOps engineer's salary has become increasingly lucrative in recent years, it has gained much attention amongst aspiring tech professionals. DevOps engineers are essential to the administration, maintenance, and integration of applications. The key concepts of DevOps are process automation and process unification. For all of these tasks, it's essential to comprehend not only development life cycles but also DevOps culture, with all of its guiding concepts, practices, and tools.
Master algorithms, including deep learning like LSTMs, GRUs, RNNs, and Generative AI & LLMs such as ChatGPT, with Packt's 50 Algorithms Every Programmer Should Know.
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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