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This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?
👋 Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. We cover one out of five topics in today’s subscriber-only The Scoop issue. To get this newsletter every week, subscribe. Pollen, the events festival tech startup, went bankrupt in August after raising more than $200M in venture funding. In an exclusive investigative article , I covered the events and details leading up this bankruptcy.
Will Rust kill Python for Data Engineers? If you only came here to know this, my answer is no. Betteridge’s Law strikes again! But then again, you have to ask: was Python made for Data Engineering in the first place? Rust may not replace Python outright, but it has consumed more and more of JavaScript tooling and there are increasingly many projects trying to do the same with Python/Data Engineering.
I have a calendar reminder that tells me when I founded Big Data Institute. It just told me I founded the company eight years ago. The reminder is called “Independent Anniversary.” It’s the day I split off and executed my vision for an independent, big data consulting company. Independence has all sorts of manifestations. For you, it’s an independent look at technology and vendors from someone who’s worked at a vendor (Cloudera) and worked in distributed systems for even longer.
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
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.
Summary The "data lakehouse" architecture balances the scalability and flexibility of data lakes with the ease of use and transaction support of data warehouses. Dremio is one of the companies leading the development of products and services that support the open lakehouse. In this episode Jason Hughes explains what it means for a lakehouse to be "open" and describes the different components that the Dremio team build and contribute to.
Will Rust kill Python for Data Engineers? If you only came here to know this, my answer is no. Betteridge’s Law strikes again! But then again, you have to ask: was Python made for Data Engineering in the first place? Rust may not replace Python outright, but it has consumed more and more of JavaScript tooling and there are increasingly many projects trying to do the same with Python/Data Engineering.
Will Rust kill Python for Data Engineers? If you only came here to know this, my answer is no. Betteridge’s Law strikes again! But then again, you have to ask: was Python made for Data Engineering in the first place? Rust may not replace Python outright, but it has consumed more and more of JavaScript tooling and there are increasingly many projects trying to do the same with Python/Data Engineering.
As we wrap up Hispanic Heritage month this #ClouderaLife Spotlight features Elias Avila, senior staff proactive support engineer for Cloudera. In this spotlight, we talk about his career in technology and his philosophy for getting the most out of work in terms of satisfaction and advancement. We also talk about his upbringing in the primarily Mexican American community of Salinas, California, and the important role Hispanics play in California’s Central Valley. .
Summary Logistics and supply chains are under increased stress and scrutiny in recent years. In order to stay ahead of customer demands, businesses need to be able to react quickly and intelligently to changes, which requires fast and accurate insights into their operations. Pathway is a streaming database engine that embeds artificial intelligence into the storage, with functionality designed to support the spatiotemporal data that is crucial for shipping and logistics.
We say ‘xerox’ speaking of any photocopy, whether or not it was created by a machine from the Xerox corporation. We describe information search on the Internet with just one word — ‘google’. We ‘photoshop pictures’ instead of editing them on the computer. And COVID-19 made ‘zoom’ a synonym for a videoconference. Kafka can continue the list of brand names that became generic terms for the entire type of technology.
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.
Like all of our customers, Cloudera depends on the Cloudera Data Platform (CDP) to manage our day-to-day analytics and operational insights. Many aspects of our business live within this modern data architecture, providing all Clouderans the ability to ask, and answer, important questions for the business. Clouderans continuously push for improvements in the system, with the goal of driving up confidence in the data.
by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations. A large number of batch workflows run daily to serve various business needs.
Real-time customer 360 applications are essential in allowing departments within a company to have reliable and consistent data on how a customer has engaged with the product and services. Ideally, when someone from a department has engaged with a customer, you want up-to-date information so the customer doesn’t get frustrated and repeat the same information multiple times to different people.
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.
The telecommunications industry continues to develop hybrid data architectures to support data workload virtualization and cloud migration. However, while the promise of the cloud remains essential — not just for data workloads but also for network virtualisation and B2B offerings — the sheer volume and scale of data in the industry require careful management of the “journey to the cloud.”.
The complexity of modern vehicles means that spotting root-causes that prevent them from working is difficult. Mechanics, operators & OEMs must step into a new era of digital data-based diagnostics.
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
Information technology has been at the heart of governments around the world, enabling them to deliver vital citizen services, such as healthcare, transportation, employment, and national security. All of these functions rest on technology and share a valuable commodity: data. . Data is produced and consumed in ever-increasing amounts and therefore must be protected.
Introduction . When we hear the word ‘hypothesis,’ the first thing that comes to our mind is a kind of theory. Assuming and explaining theories is a fundamental part of Business Analytics. In the past few years, the field of Business Analytics has proliferated and made several advancements. As the number of people interested in its statistical applications in business has increased, the concept of hypothesis testing has grabbed everyone’s attention.
React enables much of the modern web you’re familiar with: fluid, responsive, and animation-rich websites. It’s no wonder that React.js is the most used JavsScript framework for web development, according to the 2021 State of JavaScript survey.
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 this post I will demonstrate how Kafka Connect is integrated in the Cloudera Data Platform (CDP), allowing users to manage and monitor their connectors in Streams Messaging Manager while also touching on security features such as role-based access control and sensitive information handling. If you are a developer moving data in or out of Kafka, an administrator, or a security expert this post is for you.
Introduction . The primary goal of data collection is to gather high-quality information that aims to provide responses to all of the open-ended questions. Businesses and management can obtain high-quality information by collecting data that is necessary for making educated decisions. . It is necessary to gather data to draw conclusions and decide what is factual to increase the quality of the information. .
The Apache Hop team just released version 2.1.0. This new release is the result of four and a half months of work on over 200 tickets and comes packed with new functionality, bug fixes and improvements.
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!
PostgreSQL and MySQL are among the most popular open-source relational database management systems (RDMS) worldwide. Both RDMS enable businesses to organize and interlink large amounts of data, allowing for effective data management. For all of their similarities, PostgreSQL and MySQL differ from one another in many ways. In this PostgreSQL vs. MySQL comparison, we analyze crucial differences between the two database management systems to discover how they work and when to use them.
The Center for Business Analytics at the University of Cincinnati will present its annual Data Science Symposium 2022 on November 8. This all day in-person event will have three featured speakers and two tech talk tracks with four concurrent presentations in each track. The event, held at the Lindner College of Business, is open to all.
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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