Trends in Data Infrastructure Development

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

As enterprises accelerate their deployment of GenAI agents and applications, data leaders must ensure their data pipelines are ready to meet the demands of real-time AI. When your chatbot needs to provide personalized responses or your recommendation engine needs to adapt to current user behavior, traditional batch processing simply isn't enough. We’re seeing three critical requirements emerge for AI-ready data infrastructure. We call them the 3 Rs: 1️⃣ Real-time: The era of batch processing is ending. When a customer interacts with your AI agent, it needs immediate access to their current context. Knowing what products they browsed six hours ago isn't good enough. AI applications need to understand and respond to customer behavior as it happens. 2️⃣ Reliable: Pipeline reliability has taken on new urgency. While a delayed BI dashboard update might have been inconvenient, AI application downtime directly impacts revenue and customer experience. When your website chatbot can't access customer data, it's not just an engineering problem. It's a business crisis. 3️⃣ Regulatory compliance: AI applications have raised the stakes for data compliance. Your chatbot might be capable of delivering highly personalized recommendations, but what if the customer has opted out of tracking? Privacy regulations aren't just about data collection anymore—they're about how AI systems use that data in real-time. Leading companies are already adapting their data infrastructure to meet these requirements. They're moving beyond traditional ETL to streaming architectures, implementing robust monitoring and failover systems, and building compliance checks directly into their data pipelines. The question for data leaders isn't whether to make these changes, but how quickly they can implement them. As AI becomes central to customer experience, the competitive advantage will go to companies with AI-ready data infrastructure. What challenges are you facing in preparing your data pipelines for AI? Share your experiences in the comments 👇 #DataEngineering #ArtificialIntelligence #DataInfrastructure #Innovation #Tech #RudderStack

This year, the State of Data and AI Engineering report has been marked by consolidation, innovation and strategic shifts across the data infrastructure landscape. I identified 5 key trends that are defining a data engineering ecosystem that is increasingly AI-driven, performance-focused and strategically realigned. Here's a sneak peek at what the report covers: - The Diminishing MLOps Landscape: As the standalone MLOps space is rapidly consolidating, capabilities are being absorbed into broader platforms, signaling a shift toward unified, end-to-end AI systems. - LLM Accuracy, Monitoring & Performance is Blooming: Following 2024's shift toward LLM accuracy monitoring, ensuring the reliability of generative AI models has moved from "nice-to-have" to business-critical. - AWS Glue and Catalog Vendor Lock-in: While Snowflake just announced read/write support for federated Iceberg REST catalogs, finally loosening its catalog grip, AWS Glue is already offering full read/write federation, and is therefore the neutral catalog of choice for teams avoiding vendor lock-in. - Storage Providers Are Prioritizing Performance: in line with the growing demand for low-latency storage, we see a broader trend in which cloud providers are racing to meet the storage needs of AI and real-time analytics workloads. - BigQuery's Ascent in the Data Warehouse Wars: with 5x the number of customers of both Snowflake and Databricks combined, BigQuery is solidifying its role as a cornerstone of Google Cloud’s data and AI stack. These trends highlight how data engineering is evolving at an unprecedented pace to meet the demands of a rapidly changing technological landscape. Want to dive deeper into these critical insights and understand their implications for your data strategy? Read the full report here: https://lnkd.in/dPCYrgg6 #DataEngineering #AI #DataStrategy #TechTrends #DataInfrastructure #GenerativeAI #DataQuality #MLOps

Is your data architecture keeping up with the pace of innovation? Modern Data Engineering is revolutionizing how we architect, process, and deliver insights. No longer shackled to monolithic systems, companies are embracing hybrid architectures that blend cloud-native solutions, real-time processing, and AI-driven analytics. For data engineers, staying ahead means expanding horizons and mastering the evolution of data architectures in the 21st century: 🔹 Data Warehouse – The traditional backbone of BI, built for structured data and fast querying. ✅ Schema-on-write, optimized for reporting ⚠️ Less flexible for unstructured data, costly to scale 🔹 Data Lake – A flexible approach to handle raw, unstructured data at scale. ✅ Schema-on-read, native format storage ⚠️ Risk of becoming a “data swamp” without governance 🔹 Data Lakehouse – The best of both worlds, combining structured analytics with data lake flexibility. ✅ Unified platform for SQL + ML workloads ⚠️ Still evolving, may require reskilling teams 🔹 Data Mesh – A paradigm shift that treats data as a product and decentralizes ownership. ✅ Domain-oriented data governance, self-serve infrastructure ⚠️ Requires cultural and organizational changes ⚡️ Making the transition? Key considerations: 🔹 Assess your current architecture and gaps 🔹 Define clear objectives aligned with business goals 🔹 Understand data sources (volume, variety, velocity) 🔹 Evaluate your team’s skills and reskilling needs 🔹 Start small, scale fast – iterate and expand 🔹 Embrace hybrid architectures for flexibility Companies leading this transformation are accelerating by 3-5x while slashing infrastructure costs by 40-60%! Have you encountered challenges or unlocked innovative solutions in your data journey? Let’s discuss! ⬇️ 🔗 Image Credits: lakeFS #data #dataengineering #cloud #analytics

A nice morning coffee read ☕ • CIOs are doubling down on their investments in #data and #AI. Faced with increasing audience expectations, new competitive pressures, a challenging economic backdrop, and an unprecedented speed of innovation, technology leaders need their data and AI assets to deliver more growth to the business than ever before. They are investing to secure this future: every organization surveyed will boost its spending on modernizing data infrastructure and adopting AI during the next year, and for nearly half (46%), the increase will exceed 25%. • Executives expect AI adoption to be transformative in the short term. Eighty-one percent of survey respondents expect AI to boost efficiency in their industry by at least 25% in the next two years. One-third say the gain will be at least 50%. • As generative AI spreads, flexible approaches are favored. Eighty-eight percent of organizations are using generative AI, with one-quarter (26%) investing in and adopting it and another 62% experimenting with it. • Lakehouse has become the data architecture of choice for the era of generative AI. Nearly three- quarters of surveyed organizations have adopted a lakehouse architecture, and almost all of the rest expect to do so in the next three years. Survey respondents say they need their data architecture to support streaming data workloads for real-time analytics (a capability deemed “very important” by 72%), easy integration of emerging technologies (66%), and sharing of live data across platforms (64%). Ninety-nine percent of lakehouse adopters say the architecture is helping them achieve their data and AI goals, and 74% say the help is “significant.” #digitaltechnology #innovation #dataanalytics

What is coming next for LLM makers? Hiring insights from job openings data for the LLM makers reveal where they're placing bets (and where strategies differ)... 🧱 Infrastructure Investments: All major players are investing heavily in infrastructure. These companies are building multi-gigawatt data centers and developing specialized hardware optimization capabilities, signaling that proprietary infrastructure is viewed as a critical competitive advantage. 🤑 Enterprise GTM: Companies across the board are aggressively building out enterprise sales capabilities, with a common pattern of establishing specialized roles for vertical markets, solutions architects, and customer success teams. This indicates a clear industry-wide shift toward enterprise monetization. 🤖 Agent Development: Multiple companies are investing in agent technology that can use tools, control operating systems, and perform complex tasks with minimal oversight. This represents the next frontier beyond foundation models. 📽️ Multimodal Capabilities: Every major player is expanding beyond text into video, audio, and image processing capabilities, suggesting multimodal AI is becoming table stakes in the industry. 🛡️ Safety & Security: Demonstrates varying levels of investment in safety, from Anthropic's intensive biosecurity and red-teaming efforts to more standard safeguards at other companies. Hiring trends collectively signal an industry-wide shift from sole focus on R&D to commercial deployment and scale, with a growing emphasis on enterprise-grade features, safety, and specialized infrastructure. Drop a comment below for *free* access to the detailed hiring insights for each of the LLM makers. P.S. Is anyone surprised that I'm back to hiring data and insights? More to come from CB Insights' new job openings and hiring insights data.

Enterprise data infrastructure is simultaneously a call and response to every technological shift — it both enables new products and businesses, while simultaneously evolving to support the demands created by these same innovations. Over the last fifty years, we’ve progressed from traditional on-premise data warehouses to cloud-native data warehouses and data lakes. Today, we’re at an exciting inflection point for the landscape as we’re evolving quickly past the modern data stack due to multiple catalysts that are ushering in a post-modern Data 3.0 era. For one, as we noted last year, AI’s proliferation has led to profound changes within the AI infrastructure landscape. But in the midst of this major technological shift, another tectonic transformation is afoot. The very core of enterprise data infrastructure is being reimagined due to the impact of a revolutionary architectural paradigm—the data lakehouse—which supports multiple use cases, from analytics to AI workloads, in a powerful, interoperable platform. The lakehouse paradigm doesn’t just represent a marginal improvement to the architectures that came before it. Rather, it is a radical transformation that will bring forth an era of unprecedented interoperability and set the stage for the next wave of multi-billion-dollar data infrastructure giants to emerge. Our full Data 3.0 roadmap here including 4 thesis areas that Lauri J. Moore and I are tracking closely: https://lnkd.in/gAkxqb5b

It’s prediction season, and one trend is clear—AI, edge computing and network transformation are no longer experiments: they’re business imperatives. Leaders aren’t chasing hype. They demand real outcomes. At GTT, we’re focused on delivering networking and security solutions that fuel growth, resilience and innovation. In 2025, we expect: - Real-time AI-powered security and Zero Trust frameworks to become essential. The C-suite will see security not just for compliance, but as a strategic business enabler—prioritizing proactive, adaptive resilience over reactive defenses. - DeepSeek to represent a seismic shift in how AI is consumed. With AI requiring less cost and energy, distributed enterprises will double down on AI that optimizes network performance, proactively detects and mitigates threats, cuts operational costs and enhances experiences—moving beyond experimental use cases to measurable outcomes. - Network-as-a-Service (NaaS) to become a strategic imperative. NaaS will evolve beyond on-demand models, shifting the burden of capital investments to providers and leveraging more cost-effective shared infrastructure. Businesses will rapidly adopt truly flexible, on-demand networking and security services to gain greater agility, scalability and cost efficiency via a dynamic OpEx model. The C-suite will increasingly favor this approach to thrive in today’s fast changing markets. - Edge, satellite, 5G and local compute to drive real-time business innovation. AI, IoT and distributed workforces will require ultra-reliable, low-latency networks that extend to the edge. By processing data closer to users and apps—with built-in security and seamless cloud integration—enterprises can unlock automation, react faster with real-time insights and introduce new business models. While the required apps remain uncertain, the need for an adaptable edge infrastructure is undeniable—all AI and data-driven innovation will rely on it. To stay ahead, businesses will future-enable their infrastructure, preparing for the unknown opportunities and demands of tomorrow. - Telcos to retrench and reinvent. Legacy providers will continue to retreat, divesting non-core assets and cutting costs, while others follow the path already paved by providers that have long recognized that connectivity alone isn’t enough—doubling down on cloud, security, and AI-driven services. The winners will be those that have built integrated, platform-based offerings, and are already delivering the secure, high-performance networks businesses need with corresponding robust, in-house technical support and professional services. Looking back at 2024, I’m proud to say we helped businesses stay connected and secure in an increasingly complex world. In 2025, we’ll continue delivering intelligent, high-performance networks that make innovation possible. Here’s to another year of connection, achievement and Greater Technology Together. #AI #Predictions #NaaS

Bloomberg recently featured some of our firm's data center research in this article: https://lnkd.in/eCkbqawk Some insights: AI is reshaping the world’s digital backbone — and the data centers of 2035 won’t look anything like today’s. In this week’s newsletter, we explore the future trajectory of data centers as utilities, and the tech that’s quietly redefining value for investors, operators, and occupiers: - Quantum and fusion tech could fracture traditional data models - Modular deployment is emerging as the fastest route to AI-edge infrastructure - GPU-led computing is driving both innovation and obsolescence - Energy management is no longer optional — it’s a differentiator - Stakeholder roles are converging around shared outcomes: resilience, yield, and performance New power, new processors, new protocols. The data center of tomorrow isn’t just about uptime — it’s about strategic advantage. Explore more future-focused research: https://lnkd.in/eEAe-ARN At The Proptech Connection, we’re working with clients across the ecosystem to make sense of this shift and capture opportunities in an increasingly fragmented landscape. 56,129+ subscribers already follow us for insights like these — join them here: https://lnkd.in/g_e4QVDH DM me if you’d like to explore how we can work together, and we'll schedule a discovery meeting. Alongside: Stephen Macdonald CA, Stuart Daun