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After 10 years of Data Engineering work, I think it’s time to hang up the proverbial hat and ride off into the sunset, never to be seen again. I wish. Everything has changed in 10 years, yet nothing has changed in 10 years, how is that even possible? Sometimes I wonder if I’ve learned anything […] The post What I’ve Learned After A Decade Of Data Engineering appeared first on Confessions of a Data Guy.
Welcome to the snow world ( credits ) Every year, the competition between Snowflake and Databricks intensifies, using their annual conferences as a platform for demonstrating their power. This year, the Snowflake Summit was held in San Francisco from June 2 to 5, while the Databricks Data+AI Summit took place 5 days later, from June 10 to 13, also in San Francisco.
Summary Stripe is a company that relies on data to power their products and business. To support that functionality they have invested in Trino and Iceberg for their analytical workloads. In this episode Kevin Liu shares some of the interesting features that they have built by combining those technologies, as well as the challenges that they face in supporting the myriad workloads that are thrown at this layer of their data platform.
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
I’m excited to share that OpenAI has completed the acquisition of Rockset. We are thrilled to join the OpenAI team and bring our technology and expertise to building safe and beneficial AGI. From the start, our vision at Rockset was to fundamentally transform the way data-driven applications were built. We developed our search and analytics database, taking full advantage of the cloud, to eliminate the complexity inherent in the data infrastructure needed for these apps.
Image by author Model deployment is the process of trained models being integrated into practical applications. This includes defining the necessary environment, specifying how input data is introduced into the model and the output produced, and the capacity to analyze new data and provide relevant predictions or categorizations.
One of the big challenges in streaming Delta Lake is the inability to handle in-place changes, like updates, deletes, or merges. There is good news, though. With a little bit of effort on your data provider's side, you can process a Delta Lake table as you would process Apache Kafka topics, hence without in-place changes.
One of the big challenges in streaming Delta Lake is the inability to handle in-place changes, like updates, deletes, or merges. There is good news, though. With a little bit of effort on your data provider's side, you can process a Delta Lake table as you would process Apache Kafka topics, hence without in-place changes.
Thousands of customers have worked with Snowflake to cost-effectively build a secure data foundation as they look to solve a growing variety of business problems with more data. Increasingly customers are looking to expand that powerful foundation to a broader set of data across their enterprise. Snowflake is now making it even easier for customers to bring the platform’s usability, performance, governance and many workloads to more data with Iceberg tables (now generally available), unlocking f
Understanding LLMs is pivotal in unlocking the full potential of AI-driven solutions across various domains. As we navigate the process of building AI-driven solutions, it is essential to approach the development and deployment of LLMs with a focus on responsible AI practices.
Productivity Update! Learn how to override default parameter values for geoprocessing tools in ArcGIS Pro 3.3. Override Geoprocessing Tool Defaults in ArcGIS Pro 3.
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.
The telecom industry is undergoing an unprecedented transformation. Fueled by tech advancements such as 5G, cloud computing, Internet of Things (IoT) and machine learning (ML), telecoms have the opportunity to reshape and streamline operations and make significant improvements in service delivery, customer experience and network optimization. Key to these technologies is generative AI (gen AI), a dynamic form of artificial intelligence that leverages vast amounts of data to analyze and produce r
Pride Month is underway and we at Cloudera are looking forward to joining the global celebration of diversity, equity and the ongoing effort for LGBTQ+ ( L esbian, G ay, B isexual, T ransgender, Q ueer/ Q uestioning) rights and recognition. Pride Month serves as a reminder that the fight for equality and equity for members of the LGBTQ+ community is not over.
In the insurance sector, customers demand personalized, fast, and efficient service that addresses their needs. Meanwhile, insurance agents must access a large amount.
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.
In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.
Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. Learn More → Databricks: Open Sourcing Unity Catalog This week brought many exciting developments, with Snowflake and Databricks announcing open-source catalogs.
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
Snowflake’s mission is to mobilize the entire world’s data, and there are millions of data scientists and developers who don’t have access to full-stack engineering teams. It’s been our endeavor to bring the power of the AI Data Cloud to every individual developer, data scientist and machine learning engineer, so that they can build and share world-class data apps — all by themselves.
We’re introducing parameter vulnerability factor (PVF) , a novel metric for understanding and measuring AI systems’ vulnerability against silent data corruptions (SDCs) in model parameters. PVF can be tailored to different AI models and tasks, adapted to different hardware faults, and even extended to the training phase of AI models. We’re sharing results of our own case studies using PVF to measure the impact of SDCs in model parameters, as well as potential methods of identifying SDCs in model
Juneteenth holds profound significance in the history of freedom and equality for Black Americans. Also known as Freedom Day or Emancipation Day, Juneteenth commemorates the anniversary of June 19, 1865, when news of the Emancipation Proclamation reached Galveston, Texas, finally declaring freedom for enslaved Americans held in the Confederacy–more than two years after the proclamation was issued in on January 1, 1863.
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
Welcome to Snowflake’s Startup Spotlight, where we ask startups about the problems they’re solving, the apps they’re building and the lessons they’ve learned during their startup journey. In this edition, we’ll learn why Terence Bennett, CEO of DreamFactory , and his team are championing a new way to think about API integrations. What was the genesis of DreamFactory?
A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale. Netflix is not the only place where data engineers are solving challenging problems with creative solutions.
Discover what's new in ArcGIS Pro 3.3 for CAD and BIM workflows, allowing you to directly read datasets from Autodesk Revit, Civil 3D, and Industry Foundation Classes.
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!
Recent Snowflake workshops and roundtables have started with the question: “Does your data strategy match your AI ambition?” It certainly sparks customer engagement, but is that the right question to ask? Right now, it seems appropriate with all of the interest — dare I say “hype” — around AI. But it merely reflects the current darling of the tech world, focusing on the technology itself, rather than the ultimate goal.
The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models. Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies.
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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