2025 Operational Data Platform Trends

We’re nearly two-thirds of the way through 2025, and the pace of consolidation in the data and analytics space is just heating up. One thing is clear, the modern data stack is collapsing into unified platforms. Just a few of the headline moves so far: Redis + Decodable: bringing real-time streaming pipelines into the real-time database, a powerful foundation for AI agents. Fivetran: from movement to full lifecycle with Census (Reverse ETL) and Tobiko Data (advanced transformations). Datadog + Metaplane: extending observability from infrastructure into data pipelines Coalesce + CastorDoc: blending transformation workflows with data cataloging and governance. IBM: a historic consolidator, with Hakkoda Inc. (data/AI consulting) and DataStax (Apache Casandra). Salesforce’s $8B bet on Informatica and ServiceNow + data.world: embedding integration and governance directly into enterprise platforms. Snowflake + Crunchy Data vs. Databricks + Neon: rival moves to bring Postgres into their clouds as a foundation for AI-native apps. Qlik + Upsolver and Boomi + Rivery: shoring up ingestion, ELT, and CDC capabilities. dbt Labs + SDF Labs: a very technical bet on faster and safer developer experience No surprise, but every acquisition appears to be framed by how it accelerates AI readiness. At Coginiti we’ve always believed in being more than a “point tool”. It will be interesting to see how many of these can be integrated to feel like a unified whole.

Why 2025 will redefine data #infrastructure: 11 expert insights on #sovereignclouds, exploding #data, #PaaS and more ~Shabham Sharma VentureBeat "If 2023 was all about generative AI-powered chatbots and search, 2024 introduced agentic #AI — tools capable of planning and executing multi-step actions across digital environments. From Devin’s engineering breakthroughs to Microsoft’s early trials with Copilot Vision, the innovations were diverse, but one constant remained: the need to keep data infrastructure organized and reliable. As enterprises leaned into advanced AI initiatives, several trends reshaped how data is managed, secured and used. Businesses increasingly adopted #multicloud, open data, and open governance strategies to avoid vendor lock-in and gain more flexibility. They also focused on unstructured data, transforming data marketplaces into hubs providing pre-trained AI models with proprietary datasets and apps. Simultaneously, progress in vector and graph databases added new possibilities, setting the foundation for what’s next. 1. Real-time multimodal data will fuel intelligent data flywheel 2. Chill factor: Liquid-cooled #datacenter 3. Global data explosion to create storage shortage  4. AI factories will evolve to PaaS 5. Companies will use their massive datasets but demand reliability 6. #Enterprise agents will devour communications data 7. Data #governance and quality will be biggest barriers to successful and ethical AI adoption 8. Unified data #observability platforms will emerge as essential tools 9. All hail the sovereign cloud 10. Rise of data processing at the #edge 11. Protection of unstructured data will become more urgent To sum up, 2025 promises significant advancements in enterprise data infrastructure, ranging from multimodal data flywheels to sovereign clouds. However, challenges such as data governance and storage shortages will persist. Success in this dynamic space will depend on balancing innovation with trust and sustainability, turning data into a lasting competitive advantage."

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 𝐚𝐧�� 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

It's 2025, and the data industry is already off to a whirlwind start. At the end of last year, Databricks secured one of the largest funding rounds in history. Rivery was acquired by Boomi, and we can't forget the massive buyout of Tabular. And that's without even mentioning AI. For me, the real question for 2025 isn't just which tools will dominate the market but what outcomes they will deliver. As organizations face increasing pressure to maximize the return on their data investments, they'll need to be more strategic. In turn, here's what you can expect to see shaping the data world this year. 1️⃣ Iceberg Will Cement Its Stronghold—Sort Of If you're in the data space, you've probably heard plenty about Iceberg by now— especially since Databricks acquired Tabular. So, does that mean we're all standardizing on Iceberg moving forward? In a perfect world, having a universal standard for data storage would reduce complexity and make interoperability across tools far easier. But here's the thing…plenty of companies don't want to spend their days stitching together five or more tools just to generate reports. For example, I once had a director of engineering say pretty plainly, "I like skiing on weekends." Their point? They wanted something that works with minimal involvement. So, this will come down to implementation. 2️⃣ SQL Isn't Going Anywhere When I landed my first data job, the industry was all about building data lakes. The dream? A massive, unstructured data store where you could query anything and instantly gain insights. Ok, that's a massive reduction in the actual structure and goal of a data lake. But we did end up with a lot of data swamps. At the same time, alternate query languages seemed to pop up everywhere, and you couldn't go a week without seeing another "SQL is dead" hot take. Yet here we are, and SQL is still standing strong. But I fear someone is going to suggest we try Data Lakes 1.0 again with LLMs. 3️⃣ AI—From Press Release Driven Initiatives To Real Life The data world continues to push for AI. I've had companies ask me directly where they can implement it in their workflows. What they're usually referring to, though, are large language models (LLMs) or AI agents—a very narrow slice of what AI actually encompasses. Still, the AI push isn't slowing down anytime soon. Plenty of companies stand to profit, so expect the hype to keep rolling. But where is it actually headed? I believe we’ll see even more creative AI use cases emerge in 2025—and likely really hit in 2027, especially for those companies that have built a solid data foundation. Now I do have several other predictions which you can read about here -  https://lnkd.in/gBy8X5hq Image credit Isaac Faber Ph.D.

O'Reilly's Technology Trends for 2025 report, published today, is based on analyzed data from 2.8 million users on its learning platform, and giving insights into the most popular technology topics consumed - identifying emerging trends that could influence business decisions in the year ahead. The outlook for AI technologies is marked by dramatic growth in key areas. The percentages describe the growth in interest or usage of specific areas within the field: Prompt Engineering surged by 456%, AI Principles by 386%, and Generative AI by 289%. Additionally, the use of GitHub Copilot skyrocketed by 471%, highlighting a robust interest in tools that boost productivity. In terms of security, there was a significant 44% increase in interest in governance, risk, and compliance, accompanied by heightened attention to application security and the zero trust model. While traditional programming languages such as Python and Java experienced declines, data engineering skills witnessed a 29% increase, underscoring their essential role in powering AI applications. * * * Based on these numbers, the report analyses the Technology Trends for 2025 in the field of AI: I. Diverse AI Models: Unlike previous years when ChatGPT dominated, the field now includes a variety of strong contenders like Claude, Google’s Gemini, and Llama. These models have broadened the AI landscape and are each finding their niches within different user bases. II. Skill Growth: There has been a significant increase in interest and development in AI skills, notably in Machine Learning, Artificial Intelligence, Natural Language Processing, Generative AI, AI Principles, and Prompt Engineering. These skills are seeing varying levels of growth, with Prompt Engineering experiencing the most substantial surge. III. Shift in Platform Focus: Interest in GPT has declined as the industry moves away from platform-specific knowledge towards more generalized, foundational AI understanding. This shift reflects a maturation in the industry as developers seek capabilities that are applicable across various models. IV. Future Trends: The report anticipates potential disillusionment with AI, a phenomenon more sociological than technical, often due to overhyped expectations. Nonetheless, advancements continue, particularly in making AI interactions more intuitive and reducing the need for complex prompts. V. Development Tools and Data Engineering: Tools like LangChain and retrieval-augmented generation (RAG) are highlighted as key to building more sophisticated AI applications that can handle private data more securely and efficiently. Moreover, the importance of data engineering skills is underscored, supporting AI applications with robust data infrastructure. * * * The insights of the report can guide strategic planning, investment decisions, and curriculum development, and overall, offer a valuable snapshot of the technology landscape.

The data world is shifting fast. These are three trends that are rewriting the rules of the game: consolidation, unification, and operationalization. 1️⃣ From the modern data stack → modern consolidation We’ve gone full circle. First, we broke down legacy monoliths to embrace the “best-of-breed” approach. Every team built a super modular stack: 10+ specialized tools, each solving one problem perfectly. But now, the market (and teams) are realizing: maintaining that level of fragmentation is unsustainable. We’re seeing a wave of platform consolidation. It may not be perfect, but it gets the job done. 2️⃣ From structured data → all data “Data” used to mean rows and columns. Now it’s everything: text, docs, sharepoint, images, unstructured content of every kind. This shift is forcing teams to think differently about storage, governance, and discovery. 3️⃣ From analytics → analytics + operations The conversation is moving from time-to-insight to time-to-action. Dashboards are only valuable if they drive change in real operations. The future lies in connecting insights directly back to business operations, closing the loop between what we see and what we do. This is part of the talk "Why Data Teams Must Lead the Next AI Revolution" that Tim Gasper and I presented at Big Data LDN 👉 What other shifts are you seeing in data world?