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In the world of data engineering, Maxime Beauchemin is someone who needs no introduction. One of the first data engineers at Facebook and Airbnb, he wrote and open sourced the wildly popular orchestrator, Apache Airflow , followed shortly thereafter by Apache Superset , a data exploration tool that’s taking the data viz landscape by storm. Currently, Maxime is CEO and co-founder of Preset , a fast-growing startup that’s paving the way forward for AI-enabled data visualization for modern companie
With an array of career options, all that matters is choosing the right career path. The right career path for one depends on their skill set, interest, job availability in that field, and, most importantly, your passion for the same. Speaking of job vacancies, the two careers have high demands till date and in upcoming years are Data Scientist and a Software Engineer.
Summary Monitoring and auditing IT systems for security events requires the ability to quickly analyze massive volumes of unstructured log data. The majority of products that are available either require too much effort to structure the logs, or aren't fast enough for interactive use cases. Cliff Crosland co-founded Scanner to provide fast querying of high scale log data for security auditing.
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
The new class of spot Bitcoin ETFs that were approved by the SEC yesterday are now available on Robinhood Earlier today, Robinhood started offering the new class of spot Bitcoin ETFs that were approved by the SEC on January 10. These 11 ETFs became tradable to all customers in the United States this morning in both retirement and brokerage accounts though Robinhood Financial.
The new class of spot Bitcoin ETFs that were approved by the SEC yesterday are now available on Robinhood Earlier today, Robinhood started offering the new class of spot Bitcoin ETFs that were approved by the SEC on January 10. These 11 ETFs became tradable to all customers in the United States this morning in both retirement and brokerage accounts though Robinhood Financial.
Many developers and enterprises looking to use machine learning (ML) to generate insights from data get bogged down by operational complexity. We have been making it easier and faster to build and manage ML models with Snowpark ML , the Python library and underlying infrastructure for end-to-end ML workflows in Snowflake. With Snowpark ML, data scientists and ML engineers can use familiar Python frameworks for preprocessing and feature engineering as well as training models that can be managed a
Summary Databases and analytics architectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. In this episode Tasso Argyros, CEO of ActionIQ, gives a summary of the major epochs in database technologies and how he is applying the capabilities of cloud data warehouses to the challenge of building more comprehensive experiences for end-users through a modern c
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.
I recently merged linear let- and where-bindings in GHC. Which means that we’ll have these in GHC 9.10, which is cause for celebration for me. Though they are much overdue, so maybe I should instead apologise to you. Anyway, I thought I’d take the opportunity to discuss some of GHC’s inner workings and how they explain some of the features of linear types in Haskell.
Previously you could read about transformation of a user job definition into an executable stream graph. Since this explanation was relatively high-level, I decided to deep dive into the final step executing the code.
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 Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up. As the sophistication increases, so does the complexity, leading to challenges for user experience.
Today, we are announcing the industry's first Generative AI Engineer learning pathway and certification to help ensure that data and AI practitioners have.
The premier of my latest talk covering The State of Data Engineering. I go through the history of the industry to see where we’re heading. This starts with data warehousing and goes into data science. I finish off by showing how data engineering can avoid the same fate as data warehousing and data science. Sorry, we didn’t have a microphone for the questions and I forgot to repeat some of the questions.
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
Want to make a successful career switch to data science? From learning data science concepts to cracking interviews, read this guide to move one step closer to your first data science job.
Hey ( credits ) Hey, new week new email. This is already end of January but I took time to travel and see people I did not see for a long time so I'm super happy how this new year is starting. Next week, I'll be wrapping up my DataOps lecture by incorporating how to deploy machine learning models. This is a fun part where students learn how to serve a simple classifier in production.
Data enrichment is one of common data engineering tasks. It's relatively easy to implement with static datasets because of the data availability. However, this apparently easy task can become a nightmare if used with inappropriate technologies.
Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
I once had an engineer tell me that they essentially didn’t want to consider cost as they were building a solution. I was baffled. Don’t get me wrong, yes, when you’re building, you iterate and aim to improve your solutions cost. But from my perspective, I don’t think completely ignoring costs from day one is… Read more The post Cutting Your Data Stack Costs: How To Approach It And Common Issues appeared first on Seattle Data Guy.
Learn data engineering, all the references ( credits ) This is a special edition of the Data News. But right now I'm in holidays finishing a hiking week in Corsica 🥾 So I wrote this special edition about: how to learn data engineering in 2024. The aim of this post is to create a repository of important links and concepts we should care about when we do data engineering.
Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
Even though I'm into streaming these days, I haven't really covered streaming in Delta Lake yet. I only slightly blogged about Change Data Feed but completely missed the fundamentals. Hopefully, this and next blog posts will change this!
Quantization is a technique for making machine learning models smaller and faster. We quantize Llama2-70B-Chat, producing an equivalent-quality model that generates 2.2x more.
Big data is big business these days. Organizations that hope to get ahead in crowded markets must utilize data from a variety of often highly disparate sources to understand how they’re performing and what customers are saying about them. However, data without the right analysis and reporting tools is just a waste of digital storage… Read more The post 7 Great Embedded Analytics Solutions – Which Embedded Analytics Solutions Should You Use?
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
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