Artificial Intelligence in Big Data

AI is only as powerful as the data it learns from. But raw data alone isn’t enough—it needs to be collected, processed, structured, and analyzed before it can drive meaningful AI applications.  How does data transform into AI-driven insights? Here’s the data journey that powers modern AI and analytics:  1. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – AI models need diverse inputs: structured data (databases, spreadsheets) and unstructured data (text, images, audio, IoT streams). The challenge is managing high-volume, high-velocity data efficiently.  2. 𝗦𝘁𝗼𝗿𝗲 𝗗𝗮𝘁𝗮 – AI thrives on accessibility. Whether on AWS, Azure, PostgreSQL, MySQL, or Amazon S3, scalable storage ensures real-time access to training and inference data.  3. 𝗘𝗧𝗟 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺, 𝗟𝗼𝗮𝗱) – Dirty data leads to bad AI decisions. Data engineers build ETL pipelines that clean, integrate, and optimize datasets before feeding them into AI and machine learning models.  4. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – Data lakes and warehouses such as Snowflake, BigQuery, and Redshift prepare and stage data, making it easier for AI to recognize patterns and generate predictions.  5. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – AI doesn’t work in silos. Well-structured dimension tables, fact tables, and Elasticube models help establish relationships between data points, enhancing model accuracy.  6. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 – The final step is turning data into intelligent, real-time business decisions with BI dashboards, NLP, machine learning, and augmented analytics.  AI without the right data strategy is like a high-performance engine without fuel. A well-structured data pipeline enhances model performance, ensures accuracy, and drives automation at scale.  How are you optimizing your data pipeline for AI? What challenges do you face when integrating AI into your business? Let’s discuss.  

I’ve worked in data science for a decade, and I’ve seen the field evolve a lot. But nothing compares to what’s happened in the last three. Generative AI has completely reshaped our workflows. What used to take weeks of manual data prep and iteration now happens in days or even hours. The role of a data scientist is shifting fast: less about repetitive coding, more about designing intelligent workflows that solve real business problems. I recently came across Google's new Practical Guide to Data Science, and here are a few insights that stood out for me: ➝ The agentic shift Most of a data scientist’s day used to be cleaning data, tuning models, and writing the same pipelines again and again. Now AI agents automate those parts. The value we bring is moving to analysis, interpretation, and driving business outcomes. ➝ Multimodal data For years, our work was limited to structured tables. But most enterprise data is unstructured like images, PDFs, audio, and free text. With BigQuery, you can now analyze this directly with SQL. That means questions that used to be impossible, like combining sales data with call transcripts, are finally within reach. ➝ Blending external intelligence with enterprise data Foundation models bring real-world knowledge into the enterprise stack. Instead of writing rules for every scenario, you can ask nuanced questions like: Which of our products show high satisfaction based on quality? This type of reasoning used to take months of manual analysis. ➝ AI as a feature engineering engine Instead of just running basic sentiment analysis, you can extract structured insights at scale. For example, pulling out sentiment specifically around “battery life” or “user interface” and joining it with sales data. Raw text turns into powerful features that drive models. ➝ In-place model development Moving data around used to be the bottleneck. With BigQuery ML, you can now train and deploy models right where the data lives. Teams have seen deployment times cut by 10x, shifting the focus from infrastructure to speed of insight. ➝ Vector embeddings and semantic search Vector search used to mean adding another system. Now it’s built into BigQuery. That means semantic product discovery, document retrieval, and multimodal analysis all within your data warehouse. Data scientists role is changing, and now it's less about syntax, more about strategy. Less about writing every line of code, more about designing AI-powered workflows. If you want to dive deeper, I recommend checking out the full guide. It’s packed with practical examples that show just how much the landscape has shifted

Introducing a System of Action with #BigQuery For the last decade, data platforms were built for human scale. We built dashboards, wrote SQL, and looked backwards to figure out what happened. But the era of reactive intelligence is over. The future belongs to proactive AI agents. And here is the reality: you cannot run an autonomous AI agent on a passive, legacy data warehouse. Agents don't just need raw tables, they need semantic knowledge, deterministic business logic, and the ability to reason across both structured and unstructured data instantly. To power this #SystemOfAction, your data platform has to evolve. Today, we are announcing a massive leap forward: #BigQuery is officially an autonomous data-to-AI platform built for the agentic era. Here is how we are rewiring BigQuery for the future: 🧠 Graph-Based Reasoning: We are introducing BigQuery #Graph. By mapping entities, relationships, and business metrics natively in the data layer, we are transforming raw data into a deterministic "business map." This grounds your AI agents in governed reality, allowing them to accurately solve complex, multi-hop operational problems. 🌍 The True Cross-Cloud Lakehouse: Intelligence shouldn't be trapped behind cloud boundaries. We are bringing #BigQueryAI and analytics directly to your Amazon Web Services (AWS) and Microsoft Azure data. Combined with Managed #Iceberg tables and zero-copy catalog federation (spanning Databricks, Snowflake, S3 Glue, and more), we are shattering the walled gardens. 📄 Unlocking Unstructured Data: Agents demand a multimodal reality. With new native AI processing, like AI.PARSE_DOCUMENT and BigQuery-native Gemma embeddings, you can now process complex documents, images, and text right alongside your structured data. No data movement. No complex pipelines. ⚡ Proactive Agentic Workflows: We are moving past simple Q&A. BigQuery now autonomously detects metric shifts, performs automated root-cause analysis to explain why it happened, and delivers heavily researched briefings directly to your teams. BigQuery isn’t just where your data lives anymore, it’s where your enterprise thinks, reasons, and acts. The agentic era is here. Dive into the massive new capabilities we launched today: https://lnkd.in/gQ9i87Dy #BigQuery #GoogleCloud #DataEngineering #AgenticAI #DataLakehouse #AIArchitecture Google Cloud

Is AI your next Data Scientist? I’ve been running AI at data problems for weeks. It keeps winning. Apify = the open web, unlocked. Plug it in and AI reaches what used to take scrapers and hours: Zillow, Redfin, Realtor. Google Maps and Places. Amazon pricing. LinkedIn. X, Instagram, TikTok, YouTube. Crunchbase, Yelp, TripAdvisor, Booking. “Find me these houses in my area.” Done — portals scraped, map pulled, listings reviewed, interactive map returned. Same trick works for cars or lead lists. Big data? No problem. I handed AI a GitHub repo of massive public datasets and asked one narrow question: US corn production, 2015. The file only had grid-cell averages. AI noticed the gap, went out to the open web on its own, found the production numbers, reconciled, merged it all back in. That’s the shift: AI knows when it doesn’t have enough — and goes hunting by itself. Old project, rebuilt in a day. Years back I built a disease-vs-insurance model across US ZIP codes. This time: short description, dispatched to a sandbox, I went and did other work. AI pulled from 80+ public databases it found, scored, clustered, and returned an interactive chart. See something odd? Just ask — new visualization, reasoning, or driver explained. Output: a differentiated go-to-market per ZIP code. Day job. Excel financials in. Analysis, labeled gaps, and benchmark or competitor numbers. Not a summary — a briefing that changes how I walk into the meeting. The pattern: AI finds the data, builds the model, scores it, charts it. All at your fingertips. The data scientist role isn’t going away. But the leverage just moved. #AI #DataScience #AIAgents #GenerativeAI #DataAnalytics #FutureOfWork #AIinPractice

AI is reshaping data analytics, but is it a game-changer or just another tool? bigbasket, India’s leading online grocery platform, has cracked the code by combining AI with human expertise to optimize operations and enhance customer experience. How BigBasket Uses AI + Human Intelligence ✅ Smarter Demand Prediction – AI forecasts demand, cutting stockouts and overstock. Humans refine insights for local trends. ✅ Better Inventory Management – AI automates restocking, but human oversight ensures real-world factors aren’t missed. ✅ Personalized Shopping – AI suggests products, but human curation makes recommendations feel more relevant. ✅ Targeted Marketing – AI segments audiences, but human creativity crafts compelling messaging. The Verdict? AI + Humans = A Winning Formula BigBasket shows that AI isn’t replacing human analysts—it’s amplifying them. While AI crunches numbers, humans bring strategy, intuition, and creativity. What Do You Think? 𝑾𝒊𝒍𝒍 𝑨𝑰 𝒆𝒗𝒆𝒓 𝒓𝒆𝒑𝒍𝒂𝒄𝒆 𝒕𝒉𝒆 𝒊𝒏𝒕𝒖𝒊𝒕𝒊𝒐𝒏 𝒐𝒇 𝒉𝒖𝒎𝒂𝒏 𝒂𝒏𝒂𝒍𝒚𝒔𝒕𝒔, 𝒐𝒓 𝒘𝒊𝒍𝒍 𝒊𝒕 𝒂𝒍𝒘𝒂𝒚𝒔 𝒃𝒆 𝒂 𝒕𝒐𝒐𝒍 𝒕𝒐 𝒆𝒏𝒉𝒂𝒏𝒄𝒆 𝒉𝒖𝒎𝒂𝒏 𝒆𝒙𝒑𝒆𝒓𝒕𝒊𝒔𝒆? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔! #BigBasket #AIinAnalytics #DataDrivenDecisionMaking #BusinessIntelligence #RetailTech 

Businesses leveraging AI-powered data analytics, including the latest advancements, are projected to see a 40% increase in operational efficiency. 🤯 In today's hyper-competitive landscape, the lag time between data generation and actionable insights can be the difference between thriving and just surviving. Traditional data analysis often involves manual, time-consuming processes, hindering agility and the ability to capitalize on emerging opportunities. The Autonomous Data & AI Revolution is Here! Google's Data & AI Cloud continues to evolve, and at #GoogleCloudNext #2025, they unveiled groundbreaking features that bring us closer to truly autonomous data operations. Imagine AI not just assisting, but proactively working with your data. 💡 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 3 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐝: 𝐀. 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐑𝐨𝐥𝐞: Google is embedding intelligent agents directly into BigQuery and Looker, tailored to specific user needs. 1. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭 (𝐆𝐀): Automates tedious tasks like data preparation, transformation, enrichment, anomaly detection, and metadata generation within BigQuery pipelines. This means data engineers can focus on building robust and trusted data foundations instead of manual cleaning. 2. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐀𝐠𝐞𝐧𝐭 (𝐆𝐀): Integrated within Colab notebooks, this agent streamlines the entire model development lifecycle, from automated feature engineering and intelligent model selection to scalable training. Data scientists can accelerate their experimentation and focus on advanced modeling. 3. 𝐋𝐨𝐨𝐤𝐞𝐫 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 (Preview): Empowers all users to interact with data using natural language. Developed with DeepMind, it provides advanced analysis and transparent explanations, ensuring accuracy through Looker's semantic layer. A conversational analytics API is also in preview for embedding this capability into applications. 𝐁. 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐄𝐧𝐠𝐢𝐧𝐞 (Preview): This leverages the power of Gemini to understand your data context deeply. It analyzes schema relationships, table descriptions, and query histories to generate metadata on the fly, model data relationships, and recommend business glossary terms. 𝐂. 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐚𝐧𝐝 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐞𝐚𝐫𝐜𝐡 (𝐆𝐀) 𝐢𝐧 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲: Building on the Knowledge Engine, this feature allows users to uncover hidden insights and search for data using natural language. This makes data exploration more intuitive and accessible to a wider range of users. By embedding AI directly into the data lifecycle, organizations can achieve unprecedented levels of efficiency, agility, and insight generation. Follow Omkar Sawant for more! More details in the comments. #DataAnalytics #AI #GoogleCloudNext #Autonomous #Data #BigQuery #Looker #AI #LifeAtGoogle

AI is reshaping the future of work faster than many expected and it’s not just about automation, but fundamentally changing how knowledge workers operate. The recent BBC article (link in comments) highlights a growing reality, AI tools like ChatGPT and others are already transforming roles across industries, from drafting reports to coding and customer service. For data leaders and CIOs, this means a critical question: Are you ready to harness AI to amplify your team’s impact, rather than be disrupted by it? Here’s what I’m seeing with Microsoft Fabric, Power BI, and Azure AI in the field: - AI accelerates routine analysis and report generation, freeing analysts for higher-value insights. - Copilot and generative AI can speed up report creation, but only when paired with strong data governance and trusted semantic models. - The biggest wins come from integrating AI into decision workflows, not just automating tasks. The challenge? Many organisations are stuck in pilot purgatory, investing heavily but struggling to show measurable ROI or scale adoption. If you’re leading data and digital transformation, the time to act is now: - Build a clear AI + data strategy aligned to business outcomes. - Invest in governance and semantic models that enable trustworthy AI. - Enable your teams with training and a culture that embraces AI as a force multiplier. What’s your biggest AI opportunity or challenge right now? Let’s discuss. #DataStrategy #CIO #HeadOfData #OnyxData

❄️ Building AI Apps on a Solid Data Foundation with Iceberg 🚀 A strong data foundation is critical for building scalable, efficient, and reliable AI applications. Here’s a breakdown of a robust architecture for AI-driven workflows: 🔄 Data Capture: Debezium Server captures real-time change data from MySQL to ensure up-to-date information. Kafka streams this data seamlessly for further processing. ⚙️ Data Processing: Spark Streaming applies real-time Fraud Detection Logic, ensuring security and reliability. The processed data is funneled into a Data Science Bucket for advanced analytics and model development. 📦 Data Storage: Redis handles high-speed storage for real-time user data. PostgreSQL stores user probabilities and predictions for long-term analysis. 🔍 Advanced Querying: Tools like Trino provide efficient querying on the processed data, enabling seamless insights for decision-making. 💡 Why It Matters: This architecture ensures data consistency, supports real-time analytics, and provides a scalable foundation for AI applications. 👉 What tools or frameworks do you use for your AI data workflows? Let’s discuss in the comments! #DataArchitecture #AIApps #BigData #SparkStreaming #Kafka #MachineLearning