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Effective collaboration and scalability are essential for building efficient data pipelines. However, data modeling teams often face challenges with complex extract, transform, and load (ETL) tools, requiring programming expertise and a deep understanding of infrastructure. This complexity can lead to operational inefficiencies and challenges in maintaining data quality at scale. dbt addresses these challenges by providing a simpler approach where data teams can build robust data models using SQ
Data quality test coverage has become one of the most critical challenges facing modern data engineering teams, particularly as organizations adopt the increasingly popular Medallion data architecture. While this multi-layered approach to data processing offers significant advantages in organizing and refining data, it also introduces complexity that demands rigorous testing strategies to ensure data integrity across all layers.
One of the fastest-growing areas of technology is machine learning, but even seasoned professionals occasionally stumble over new terms and jargon. It is simple to get overwhelmed by the plethora of technical terms as research speeds up and new architectures, loss functions, and optimisation techniques appear. This blog article is your carefully chosen reference to […] The post 50+ Must-Know Machine Learning Terms You (Probably) Haven’t Heard Of appeared first on Analytics Vidhya.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter AI-Powered Feature Engineering with n8n: Scaling Data Science Intelligence Generate strategic feature engineering recommendations using AI-powered workflows in n8n.
Speaker: Dylan Secrest, Founder of Alamo Innovation and Construction Digital Transformation Consultant
Construction payment workflows are notoriously complex when you consider juggling multiple stakeholders, compliance requirements, and evolving project scopes. Delays in approvals or misaligned data between budgets, lien waivers, and pay applications can grind progress to a halt. The good news? It doesn't have to be this way! Join expert Dylan Secrest to discover how leading contractors are turning payment chaos into clarity using digital workflows, integrated systems, and automation strategies.
Understand vector indexes, explore database indexing methods (such as Flat, IVF, and HNSW), and see how they power faster search in AI and real-world apps.
Yanir Seroussi – AI/ML Engineering Consultant About Writing Speaking Consulting Beyond good vibes: Securing AI agents by design August 8, 2025 Amidst the buzz about new AI models and features, there’s also a constant stream of news about prompt injection attacks. And the victims of those attacks aren’t just scrappy new startups. Even established players like Microsoft and Atlassian have released vulnerable AI software in recent months.
I have written several times recently about the importance of data in supporting artificial intelligence (AI), including generative AI (GenAI) and agentic AI. From a data platforms perspective, this is most evident in the role that analytic data platforms play in supporting the training and fine-tuning of AI models. Operational data platforms also have a role to play in supporting enterprise AI initiatives, however: specifically, by providing the data processing and persistence capabilities to s
I have written several times recently about the importance of data in supporting artificial intelligence (AI), including generative AI (GenAI) and agentic AI. From a data platforms perspective, this is most evident in the role that analytic data platforms play in supporting the training and fine-tuning of AI models. Operational data platforms also have a role to play in supporting enterprise AI initiatives, however: specifically, by providing the data processing and persistence capabilities to s
Self-Serve, Augmented Analytics IS Suitable for Data Scientists The world of data scientists and business analysts is chock full of data and busier than you might expect – especially today! Businesses have discovered the value of data in decision-making and, as markets and competition shift and change, these businesses have come to depend on IT staff and on data scientists to provide data to make decisions at the department, divisional, operational and strategic level.
Explore 10 agentic AI terms and concepts that are key to understanding the latest AI paradigm everyone wants to talk about — but not everyone clearly understands.
Have you ever considered building tools powered by LLMs? These powerful predictive models can generate emails, write code, and answer complex questions, but they also come with risks. Without safeguards, LLMs can produce incorrect, biased, or even harmful outputs. That’s where guardrails come in. Guardrails ensure LLM security and responsible AI deployment by controlling outputs […] The post Can You Trust Your LLM?
In today’s data-driven business world, rapid, fact-based decision-making is a competitive necessity. Yet for most organizations, it continues to be a complex task requiring technical skills to access and understand business data. This is where conversational analytics and natural language processing (NLP) are revolutionizing the way decision-makers engage with data.
ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!
AI agentic systems are evolving faster than any technology in recent memory. Mastering them requires robust data infrastructure, clear ROI metrics, security and trust, and a forward-looking operating model. Starting now can help you leverage AI agents for genuine business transformation. And beyond productivity, they are increasingly viewed as catalysts for tangible ROI and market disruption.
Tired of spending hours on repetitive data tasks? These Python scripts can come in handy for the overworked data scientist looking to simplify daily workflows.
The Future of Work: Human + AI for Real-World Business Impact Discover how intelligent augmentation transforms organisations by combining human creativity with AI efficiency. Learn practical steps to build a resilient, hybrid workforce for measurable results. What Is Intelligent Augmentation in the Workplace? Intelligent augmentation (also called augmented intelligence) uses artificial intelligence (AI) and machine learning to extend—not replace—human capabilities.
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.
When I first noticed it, it was just a simple spreadsheet. I was in a sales meeting, watching colleagues discussing a set of customer leads, when someone mentioned a lead-scoring model that had been built right in Excel. Curiously, this spreadsheet wasn’t part of our official customer-relationship system. It turned out our sales department had quietly created its own lead-scoring tool because the approved system was too slow for the fast-moving market.
The critical importance of data is nothing new to CIOs. Digital leaders spend years collecting and collating enterprise information. This data is squirrelled away in in-house data centers and cloud-based infrastructures with the hope that one day it’ll be useful. Now, with the rise of AI, that time has come. CIO.com’s 2025 State of the CIO Survey found that researching and implementing data-enabled AI products and projects is the top CEO priority for IT.
Picture this: senior executives convened in the boardroom, engaging with sleek screens displaying colourful dashboards—sales trending upward, risk well-managed, growth projections looking promising. The atmosphere is focused and productive. These sessions deliver exactly what they’re designed for: clarity, direction, and confident decision-making.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Agentic AI Hands-On in Python: A Video Tutorial Introducing a four-hour video workshop on agentic AI engineering from Jon Krohn and Edward Donner.
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 use of AI is transforming age-old agricultural practices followed by Indian farmers. Here is how. The post Can AI Save Indian Farmers? appeared first on Analytics Vidhya.
From streamlining repetitive tasks like email scheduling and A/B testing to analyzing data in real time to uncover emerging trends, AI is transforming how marketers operate. In fact, in a survey of over 1,000 marketers , 51% reported they are already using generative AI, while another 22% plan to use it soon. With nearly three-quarters of marketers either leveraging or preparing to leverage these tools, many wonder: does AI require human oversight?
Like many of you, I’ve seen firsthand the energy AI pilots generate inside large enterprises. Copilots, LLMs, intelligent automation — all bursting with promise. Hackathons happen, demos wow leadership and buzzwords flood internal slide decks. But after the initial excitement fades, most of those pilots quietly stall. They remain trapped in silos. Untouched.
Metaverse Analytics: Turning Virtual Data into Measurable Business Value Metaverse Analytics for Business: Unlocking Value from Virtual Data Unlock the value of virtual data in the metaverse. Discover practical strategies, KPIs, and real-world lessons to help your organisation make the most of metaverse analytics for measurable business results. When thinking about your virtual data, it is important to remain focused on the business value for the end user or customer.
AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter Diffusion Models Demystified: Understanding the Tech Behind DALL-E and Midjourney Understand the technical aspects of one of the most popular image generation model architectures.
Is this invisible asset, your most valuable asset? Asset-intensive organisations are masters at managing their physical assets. But, what if there was another asset that could transform your business—growing in value every day—and all it needed was to be managed with the same discipline you give to your most prized investments? This asset is data. Not just raw numbers or records, but a living, compounding source of insight, innovation, and competitive advantage.
The agentic AI sector is booming, valued at over $5.2 billion and projected to reach $200 billion by 2034. We’re entering an era where AI will be as commonplace as the internet, but there’s a critical flaw in its foundation. Today’s AI revolution relies on massive, power-hungry LLMs – a problem that SLMs for Agentic AI are […] The post SLMs for Agentic AI: Why Small Language Models Outperform LLMs?
R.E.M.’s song “It’s the End of the World as We Know It (and I Feel Fine),” released in 1987, was about the chaos of the modern world. The song covered themes of information overload and anxiety over cultural and political chaos. Many CIOs are pondering a similar question about AI and how it will change IT’s mission and operating model. Is AI the next catalyst that will change everything we know about running IT departments, investing in technology platforms, and digitally transforming businesses
Speaker: Claire Grosjean, Global Finance & Operations Executive
Finance teams are drowning in data—but is it actually helping them spend smarter? Without the right approach, excess spending, inefficiencies, and missed opportunities continue to drain profitability. While analytics offers powerful insights, financial intelligence requires more than just numbers—it takes the right blend of automation, strategy, and human expertise.
Are you burning entire days wrestling with spreadsheets? If your team’s key decisions, reporting, or operations depend on a labyrinth of Excel files, you’re not alone—but you might be in what many professionals know as “Excel Hell.” The good news? There’s a smarter way forward. Let’s break down the classic warning signs that your business is ready for something better.
Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter How to Go From Text to SQL with LLMs This is a step-by-step guide to prompting LLMs in natural language and getting SQL code.
Editor’s Note: This article was originally authored by our colleague and BARC Fellow, Douglas Laney, and was first published on Forbes.com. We are republishing it with full permission, as we believe its insights are highly relevant to the topics we cover and valuable for our community. CEOs from Shopify and other companies are establishing AI as a core business strategy mandate and communicating this clearly to their organizations.
For the longest time, the default response to any serious AI work was “just use ChatGPT” or “go with Claude.” Closed-source giants had the edge in coding, reasoning, writing, and multimodal tasks, due to being early adopters of the technology and having sufficient data at their disposal. But that’s changed. Free open-source AI models have […] The post You Don’t Need Closed AI Models Anymore!
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.
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