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Summary Modern businesses aspire to be data driven, and technologists enjoy working through the challenge of building data systems to support that goal. Data governance is the binding force between these two parts of the organization. Nicola Askham found her way into data governance by accident, and stayed because of the benefit that she was able to provide by serving as a bridge between the technology and business.
Open source file and table formats have garnered much interest in the data industry because of their potential for interoperability — unlocking the ability for many technologies to safely operate over a single copy of data. Greater interoperability not only reduces the complexity and costs associated with using many tools and processing engines in parallel, but it would also reduce potential risks associated with vendor lock-in.
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.
This acquisition will bring Bitstamp’s globally-scaled crypto exchange to Robinhood, with retail and institutional customers across the EU, UK, US and Asia. This strategic combination better positions Robinhood to expand outside of the US and will bring a trusted and reputable institutional business to Robinhood. Expected to close in the first half of 2025, subject to customary closing conditions, including regulatory approvals.
Nothing will raise the hackles on the backs of hairy and pale programmers who’ve been stuck in their mom’s basement for a decade like bringing up OOP (Object Oriented Programming), especially in the context of Python. It’s like two fattened calves prepared for slaughter, sharpen your knives, and take your place, it’s time to feast […] The post Is Python OOP the Devil?
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.
Python’s popularity has grown significantly, quickly becoming the preferred language for development across machine learning, application development, pipelines and more. At Snowflake we are deeply committed to delivering a best-in-class platform for Python developers. In line with this commitment, we’re thrilled to announce the public preview support of Snowpark pandas API, enabling seamless execution of distributed pandas at scale in Snowflake.
We are excited to announce a new data type called variant for semi-structured data. Variant provides an order of magnitude performance improvements compared.
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
Each year, we are humbled and honored to look back on the contributions from the Snowflake Partner Network (SPN) and recognize their hard work with the Snowflake Partner Awards. Our partners help drive customer success and build an ever-expanding open ecosystem of solutions built on the AI Data Cloud. In the midst of this year’s AI Data Cloud Summit , we announced the 2024 Snowflake Partner Awards, recognizing 36 partners that are winning together with Snowflake and honoring them for their conti
Is AI taking over the world? Umm, not yet, at least. However, according to a recently published report , almost 35% of global companies report using AI to optimize their business. In this article, we will take a closer look at two of the most talked about and widely used AI technologies of 2024 – generative AI and predictive AI. Table of Contents Generative AI vs Predictive AI – Comparison Table Generative AI 101: A Revolutionary Cocktail of Technology and Art How Does Generative AI
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
In today’s world, innovation doesn’t happen in a vacuum; collaboration can help technological breakthroughs happen faster. The rise of AI, for example, will depend on the collaboration between data and development. We’re increasingly seeing software engineering workloads that are deeply intertwined with a strong data foundation. Whether you’re part of a global data team or a solo developer, Snowflake’s AI Data Cloud is a single platform that helps you run development tasks (building apps, pipeli
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!
Generative AI presents enterprises with the opportunity to extract insights at scale from unstructured data sources, like documents, customer reviews and images. It also presents an opportunity to reimagine every customer and employee interaction with data to be done via conversational applications. These opportunities also come with challenges for data and AI teams, who must prioritize data security and privacy while rapidly deploying new use cases across the organization.
A constant flow of breaking news from the data lakehouse space is making notable tech headlines this week. On Tuesday, Databricks announced that it will acquire Tabular, a data management company founded by the creators of Apache Iceberg, Ryan Blue, Daniel Weeks, and Jason Reidfor. The deal was for an unconfirmed sum, but some reports suggest that amount to be between $1B and $2B (and allegedly outbidding Snowflake).
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
With the rapid adoption of generative AI (gen AI) and large language models (LLMs), organizations are racing to unlock as much business value as possible from their content across data, apps and models. This requires supercharging more employees with the ability to immediately find and collaborate on relevant content to derive insights faster. At the same time, organizations must ensure the right people have access to the right content, while also protecting sensitive and/or Personally Identifia
Dive into how we tackled flakiness among thousands of tests in our CI pipelines with a modularized and configurable approach across our diverse codebase, strengthening reliability of testing infrastructure, improving efficiency in identifying and resolving issues.
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.
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