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Since I started working in tech, one goal that kept coming up was workflow automation. Whether automating a report or setting up retraining pipelines for machine learning models, the idea was always the same: do less manual work and get more consistent results. But automation isnt just for analytics. RevOps teams want to streamline processes… Read more The post Best Automation Tools In 2025 for Data Pipelines, Integrations, and More appeared first on Seattle Data Guy.
Despite the best efforts of many ML teams, most models still never make it to production due to disparate tooling, which often leads to fragmented data and ML pipelines and complex infrastructure management. Snowflake has continuously focused on making it easier and faster for customers to bring advanced models into production. In 2024, we launched over 200 AI features, including a full suite of end-to-end ML features in Snowflake ML , our integrated set of capabilities for machine learning mode
The database landscape has reached 394 ranked systems across multiple categoriesrelational, document, key-value, graph, search engine, time series, and the rapidly emerging vector databases. As AI applications multiply quickly, vector technologies have become a frontier that data engineers must explore. The essential questions to be answered are: When should you choose specialized vector solutions like Pinecone, Weaviate, or Qdrant over adding vector extensions to established databases like Post
By Ko-Jen Hsiao , Yesu Feng and Sudarshan Lamkhede Motivation Netflixs personalized recommender system is a complex system, boasting a variety of specialized machine learned models each catering to distinct needs including Continue Watching and Todays Top Picks for You. (Refer to our recent overview for more details). However, as we expanded our set of personalization algorithms to meet increasing business needs, maintenance of the recommender system became quite costly.
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
Open source has played an essential role in the tech industry and beyond. Whether in the AI/ML, web, or mobile space, our open source community grew and evolved while connecting people worldwide. At Meta Open Source , 2024 was a year of growth and transformation. Our open source initiatives addressed the evolving needs and challenges of developerspowering breakthroughs in AI and enabling the creation of innovative, user-focused applications and experiences.
While the technology continues to be a male-dominated industry, more women are pursuing careers in the space, driving meaningful change and innovation. At Precisely, recognizing the impact that women have in tech and championing their contributions is a top priority. To support this, the Precisely Women in Technology (PWIT) network, was created as a dedicated place for women to connect, share experiences, and learn from one another.
While the technology continues to be a male-dominated industry, more women are pursuing careers in the space, driving meaningful change and innovation. At Precisely, recognizing the impact that women have in tech and championing their contributions is a top priority. To support this, the Precisely Women in Technology (PWIT) network, was created as a dedicated place for women to connect, share experiences, and learn from one another.
Learn model serving, CI/CD, ML orchestration, model deployment, local AI, and Docker to streamline ML workflows, automate pipelines, and deploy scalable, portable AI solutions effectively.
Introducing Apache Airflow® 3.0 Be among the first to see Airflow 3.0 in action and get your questions answered directly by the Astronomer team. You won't want to miss this live event on April 23rd! Save Your Spot → Editor’s Note: Data Council 2025, Apr 22-24, Oakland, CA Data Council has always been one of my favorite events to connect with and learn from the data engineering community.
During the InferESG project we made a pivotal decision to create an alternative architecture, one that sits parallel to the agentic framework used for the conversational part of the system. This decision came about from discussions with the client, and their needs to analyse and process company sustainability reports, evaluate them and compare them to relevant materiality topics.
Read Time: 3 Minute, 9 Second Snowpark Magic: Auto-Create Tables from S3 Folders In modern data lakes, its common for departments like Finance, Marketing, Sales, etc., to continuously drop data files into their respective folders within an S3 bucket. These files often arrive in CSV format, and over time, teams request new folders or refresh their data.
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
Unleashing GenAIEnsuring Data Quality at Scale (Part2) Transitioning from individual repository source systems to consolidated AI LLM pipelines, the importance of automated checks, end-to-end observability, and compliance with enterprise businessrules. T Introduction There are several opportunities (and needs!) to improve operational effectiveness and analytical capacity when integrating data repository systems for AI Large Language Model (LLM) pipelines.
Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew. The data warehouse solved for performance and scale but, much like the databases that preceded it, relied on proprietary formats to build vertically integrated systems.
AI can do a lot these days. At this very moment, an army of SaaS companies are hard at work infusing AI assistants and copilots into every horizontal B2B workflow currently known to humankind. ChatGPT can summarize the web to help sales prospects. Gemini can polish Google documents for research teams. GitHub copilot can even code alongside you like your own pocket-sized Steve Wozniak.
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!
In today’s fast-paced digital world, maintaining high standards and addressing contemporary requirements is crucial for any company. One of our customers, a leading automotive manufacturer, relies on the IBM Z for its computing power and rock-solid reliability. However, they faced a growing challenge: integrating and accessing data across a complex environment.
Unleashing GenAIEnsuring Data Quality at Scale (Part1) Transitioning from isolated repository systems to consolidated AI LLM pipelines Photo by Joshua Sortino on Unsplash Introduction This blog is based on insights from articles in Database Trends and Applications, Feb/Mar 2025 ( DBTA Journal ). Across these informative articles, one message rings loud and clear: Artificial intelligence (AI)and large language models (LLMs) in particularrequires relentless attention to dataquality.
CycleGAN is a powerful Generative Adversarial Network (GAN) optimized for unpaired image-to-image translation. CycleGAN, unlike traditional GANs, does not require paired datasets, in which each image in one domain corresponds to an image in another. This makes it extremely useful for tasks that require collecting paired data, which can be difficult or impossible.
This guide provides a detailed, step-by-step explanation of how to connect ChatGPT with Google Sheets, along with practical examples and advanced features to make the most of this integration.
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.
Running dbt on Databricks has never been easier. The integration between dbtcore and Databricks could not be more simple to set up and run. Wondering how to approach running dbt models on Databricks with SparkSQL? Watch the tutorial below. The post dbt on Databricks appeared first on Confessions of a Data Guy.
Databricks enables organizations to securely share data, AI models, and analytics across teams, partners, and platforms without duplication or vendor lock-in. With Delta Sharing, Databricks.
What is Steganography? It is the practice of concealing information within ordinary files or media, making the hidden data undetectable to anyone unaware of its presence. From ancient techniques like invisible ink to modern digital methods that embed messages in images, audio, or network traffic, it has evolved significantly. While it is widely used for data protection and digital watermarking, cybercriminals also exploit it to hide malware and evade detection.
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?
Let's discover what small language models (SLMs) are, how they can be used in RAG systems and applications, and when to use them over their large language counterparts.
Singapore's Tech Acceleration Lab helps other government agencies modernize by onboarding them to a streaming-capable government cloud, which features Confluent as a key vendor.
Apache Airflow® is the open-source standard to manage workflows as code. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries. Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering.
Masked Language Models, also called MLMs, have truly emerged as a revolution in the Natural Language Processing (NLP) paradigm. They allow machines to achieve near-human performance in understanding and functioning in human language. They do this by masking certain words in a sentence and training the models to predict these missing words, thereby modeling the contextual relationships between words for a richer understanding of language.
Artificial intelligence (AI) and machine learning (ML) are transforming the way the world works by enabling smarter, faster, and more automated decision-making. However, one of the challenges that have emerged as AI systems evolve is the issue of AI/ML hallucinationsoutputs generated by models that are plausible but incorrect, which can undermine the reliability of AI systems.
Qwen models, developed by Alibaba, have shown strong performance in both code completion and instruction tasks. In this blog, well show how you can register.
Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali
As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.
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