This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
What will data engineering look like in 2025? How will generative AI shape the tools and processes Data Engineers rely on today? As the field evolves, Data Engineers are stepping into a future where innovation and efficiency take center stage. GenAI is already transforming how data is managed, analyzed, and utilized, paving the way for […] The post Top 11 GenAI Powered Data Engineering Tools to Follow in 2025 appeared first on Analytics Vidhya.
As we approach the new year, it's time to gaze into the crystal ball and ponder the future. In this post, we delve into predictions for 2025, focusing on the transformative role of AI agents, workforce dynamics, and data platforms. Join Ananth Packkildurai, Ashwin Ashish, and Rajesh as they unravel the future and guide us through the fascinating changes ahead.
If you’re looking to pass hundreds of GBs of data quickly, you’re likely not going to use a REST API. That’s why every day, companies share data sets of users, patient claims, financial transactions, and more via SFTP. If youve been in the industry for a while, youve probably come across automated SFTP jobs that… Read more The post The Basics of SFTP: Authentication, Encryption, and File Management appeared first on Seattle Data Guy.
Snowflake leaders offer insight on AI, open source and cybersecurity development — and the fundamental leadership skills required — in the years ahead. As we come to the end of a calendar year, it’s natural to contemplate what the new year will hold for us. It’s an understatement to say that the future is very hard to predict, but it’s possible to both prepare for the likeliest outcomes and stay ready to adapt to the unexpected.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
If AI agents are going to deliver ROI, they need to move beyond chat and actually do things. But, turning a model into a reliable, secure workflow agent isn’t as simple as plugging in an API. In this new webinar, Alex Salazar and Nate Barbettini will break down the emerging AI architecture that makes action possible, and how it differs from traditional integration approaches.
This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. We kick off with a few topics focused on how were empowering Netflix to efficiently produce and effectively deliver high quality, actionable analytic insights across the company.
Agentic AI, small data, and the search for value in the age of the unstructured datastack. Image credit: MonteCarlo According to industry experts, 2024 was destined to be a banner year for generative AI. Operational use cases were rising to the surface, technology was reducing barriers to entry, and general artificial intelligence was obviously right around thecorner.
Well, everyone is abuzz with the recently announced S3 Tables that came out of AWS reinvent this year. I’m going to call fools gold on this one right out of the gate. I tried them out, in real life that is, not just some marketing buzz, and it will leave most people, not all, be […] The post AWS S3 Tables. Technical Introduction. appeared first on Confessions of a Data Guy.
Well, everyone is abuzz with the recently announced S3 Tables that came out of AWS reinvent this year. I’m going to call fools gold on this one right out of the gate. I tried them out, in real life that is, not just some marketing buzz, and it will leave most people, not all, be […] The post AWS S3 Tables. Technical Introduction. appeared first on Confessions of a Data Guy.
For more than a decade, Cloudera has been an ardent supporter and committee member of Apache NiFi, long recognizing its power and versatility for data ingestion, transformation, and delivery. Our customers rely on NiFi as well as the associated sub-projects (Apache MiNiFi and Registry) to connect to structured, unstructured, and multi-modal data from a variety of data sources – from edge devices to SaaS tools to server logs and change data capture streams.
As we turn the corner into 2025, were excited to announce that for the 7th quarter in a row, Monte Carlo has been named G2s #1 Data Observability Platform, as well as #1 in the Data Quality category. This recognition never gets old because G2 bases their rankings on feedback and insights from real customers who work in these tools every day to add value to their business.
Key Takeaways : The significance of using legacy systems like mainframes in modern AI. How mainframe data helps reduce bias in AI models. The challenges and solutions involved in integrating legacy data with modern AI systems. The potential benefits of these integrations. In todays rapidly evolving technological landscape, businesses across industries are constantly looking for ways to harness the power of artificial intelligence (AI) to drive better decision-making, enhance customer experiences
David J. Berg * , David Casler ^, Romain Cledat * , Qian Huang * , Rui Lin * , Nissan Pow * , Nurcan Sonmez * , Shashank Srikanth * , Chaoying Wang * , Regina Wang * , Darin Yu * *: Model Development Team, Machine Learning Platform ^: Content Demand ModelingTeam A month ago at QConSF, we showcased how Netflix utilizes Metaflow to power a diverse set of ML and AI use cases , managing thousands of unique Metaflow flows.
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
Three Zero-Cost Solutions That Take Hours, NotMonths A data quality certified pipeline. Source: unsplash.com In my career, data quality initiatives have usually meant big changes. From governance processes to costly tools to dbt implementationdata quality projects never seem to want to besmall. Whats more, fixing the data quality issues this way often leads to new problems.
A Drug Launch Case Study in the Amazing Efficiency of a Data Team Using DataOps How a Small Team Powered the Multi-Billion Dollar Acquisition of a Pharma Startup When launching a groundbreaking pharmaceutical product, the stakes and the rewards couldnt be higher. This blog dives into the remarkable journey of a data team that achieved unparalleled efficiency using DataOps principles and software that transformed their analytics and data teams into a hyper-efficient powerhouse.
Artificial Intelligence promises to transform lives and business as we know it. But what does that future look like? The AI Forecast: Data and AI in the Cloud Era , sponsored by Cloudera, aims to take an objective look at the impact of AI on business, industry, and the world at large. Hosted weekly by Paul Muller, The AI Forecast speaks to experts in the space to understand the ins and outs of AI in the enterprise, the kinds of data architectures and infrastructures that support it, the guardrai
Were explaining the end-to-end systems the Facebook app leverages to deliver relevant content to people. Learn about our video-unification efforts that have simplified our product experience and infrastructure, in-depth details around mobile delivery, and new features we are working on in our video-content delivery stack. The end-to-end delivery of highly relevant, personalized, timely, and responsive content comes with complex challenges.
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
Key Takeaways: Centralized visibility of data is key. Modern IT environments require comprehensive data for successful AIOps, that includes incorporating data from legacy systems like IBM i and IBM Z into ITOps platforms. Predictive of AIOps capabilities will revolutionize IT operations. The shift from reactive to proactive IT operations is driven by AI-powered analysis, automation and insights.
As we approach 2025, data teams find themselves at a pivotal juncture. The rapid evolution of technology and the increasing demand for data-driven insights have placed immense pressure on these teams. According to recent research, 95% of data teams are operating at or over capacity, highlighting the urgent need for strategic preparation. This isn’t just about keeping up; it’s about staying ahead so that data teams can deliver the data needed to fuel their organizations.
Still reeling from the anarchic introduction of generative AI , 2024 saw the beginnings of a tectonic shift in how we manage, enable, and activate our data for business users. And that left a lot of things to write about. For anyone following the data space over the last year, I shared quite a few articles on the hot-button topics that got me excited, from the evolution of data quality to the maturation of self-service architecturesand a whole lot of AI.
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!
Welcome to the first installment of a series of posts discussing the recently announced Cloudera AI Inference service. Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput.
Part 1: Understanding The Challenges By: VarunKhaitan With special thanks to my stunning colleagues: Mallika Rao , Esmir Mesic , HugoMarques Introduction At Netflix, we manage over a thousand global content launches each month, backed by billions of dollars in annual investment. Ensuring the success and discoverability of each title across our platform is a top priority, as we aim to connect every story with the right audience to delight our members.
Key Takeaways : Poor address data can lead to missed deliveries, incorrect customer information, and wasted resources negatively impacting overall customer satisfaction, operational efficiency, and profitability. Correcting bad addresses is just the beginning you need to then connect those clean addresses to other valuable data points to unlock real value.
We all know how it feels: staring at the terminal while your development server starts up, or watching your CI/CD pipeline crawl through yet another build process. For many React developers using Create React App (CRA), this waiting game has become an unwanted part of the daily routine. While CRA has been the go-to build tool for React applications for years, its aging architecture is increasingly becoming a bottleneck for developer productivity.
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.
If it seems like literally everyone and their CEO wants to build GenAI products, youre absolutely right. In our latest survey on the state of data reliability, nearly 100% of data leaders said they feel pressure from their own leadership to implement a GenAI strategy or deliver GenAI products. But data leaders understand something thats often lost on most C-Suites: GenAI products are only as valuable as the first-party data that powers it and that data is only as valuable as it is reliable.
Were thrilled to announce the release of a new Cloudera Accelerator for Machine Learning (ML) Projects (AMP): Summarization with Gemini from Vertex AI . An AMP is a pre-built, high-quality minimal viable product (MVP) for Artificial Intelligence (AI) use cases that can be deployed in a single-click from Cloudera AI (CAI). AMPs are all about helping you quickly build performant AI applications.
In 2024 , the global airline industry is projected to spend $291 billion on fuel, making it one of the most significant expenses for airlines. Inefficient fuel management not only drives up operational costs but also hampers environmental targets. However, optimizing fuel usage is complex, often hindered by limited real-time monitoring, which can lead to unnecessary waste due to inefficient routes, weather adjustments, excess weight, and outdated practices.
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?
Introduction In the Java ecosystem, dealing with null values has always been a source of confusion and bugs. A null value can represent various states: the absence of a value, an uninitialized object, or even an error. However, there has never been a consistent, standardized approach for annotating and ensuring null-safety at the language level. Nullability annotations like @Nullable and @NonNull are often used, but theyre not part of the core Java language, leading to inconsistencies across lib
2024 has been yet another groundbreaking year for AI, with major breakthroughs, industry shifts, and ethical challenges shaping its future. Let's uncover together the key moments that defined AI this year about to finalize.
Key Takeaways: Interest in data governance is on the rise 71% of organizations report that their organization has a data governance program, compared to 60% in 2023. Top reported benefits of data governance programs include improved quality of data analytics and insights (58%), improved data quality (58%), and increased collaboration (57%). Data governance is a top data integrity challenge, cited by 54% of organizations second only to data quality (56%).
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
We organize all of the trending information in your field so you don't have to. Join 37,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
145 
Let's personalize your content