Ryan Neal, JD MBA’s Post

With the 'Intelligent Org Mapping' feature in Blueprints 'Lakehouse Optimizer', FinOps leaders, data scientists, and IT teams can work smarter—not harder—to make informed decisions, reduce waste, and reinvest savings where they drive the most value from their Databricks investments.

🚀 Intelligent Org Mapping: A Smarter Way to Optimize Your Data Ecosystem 🌟 At Blueprint, we’re committed to helping organizations uncover untapped potential within their data operations - allowing you to do more with more. This feature in the Lakehouse Optimizer, Intelligent Org Mapping, takes this mission to a new level. 💡 What is Intelligent Org Mapping? It’s a game-changer for teams using Databricks. By mapping cost, performance, and governance insights across your organization, this feature empowers decision-makers to: ✅ Understand how data usage and costs break down by team, department, or project ✅ Identify inefficiencies and opportunities for collaboration ✅ Ensure every dollar spent aligns with your organization’s priorities 🎯 Why It Matters: With Intelligent Org Mapping, FinOps leaders, data scientists, and IT teams can work smarter—not harder—to make informed decisions, reduce waste, and reinvest savings where they drive the most value. 🌟Did you know? Every organization that has embarked on a free trial of the Lakehouse Optimizer has uncovered cost management optimizations— *every single one.* Ready to get ahead for 2025? Start your free trial today and see the difference for yourself! https://lnkd.in/gNTmMdTz #Databricks #LakehouseOptimizer #DataGovernance #FinOps #AIInnovation

Reflections leverages its partnership with Databricks to enhance data-driven decision-making across various industries. Check out this great writeup from Uthara Prakash and Gokulavasan S from our reflections team covering: 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞: Reflections leverages Databricks to orchestrate data pipelines and streamline processing, enhancing data-driven decision-making across industries like healthcare, retail, and finance. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐌𝐋: Utilizing Databricks' capabilities, the team develops bespoke models and analytics to provide actionable insights, supported by tools like MLFlow and the Databricks Feature Store. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Reflections employs Databricks to ensure scalable data workflows and efficient metadata management, significantly reducing model training times through parallel processing. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠: Integration with visualization tools like Power BI enables the creation of intuitive dashboards for better decision-making.

In the dynamic landscape of data-driven decision-making, Reflections stands as a beacon of innovation, leveraging cutting-edge technologies such as Databricks to unlock the full potential of data. With our deep expertise in Databricks combined with our dedicated team of professionals, we stand as a trusted partner in driving innovation and delivering tangible results. Uthara Prakash Gokulavasan S #databricks #innovation #accuratedecisions #thoughtleadership #reflector #reflectionsinfosystems #reflectionsglobal #reflections

I've seen an uptick in the number of engineers who’ve needed to untangle the growing mess of their data lake processes. They've shared stories of struggling to scale their operations, and coming to terms with the absence of any sort of framework for their team to deploy their workflows. In some of the worst scenarios, their data engineers resorted to individually scheduling lambda function after lambda function just to get their pipelines out the door. This spiraled into hundreds of lambdas (aka, “lambdas hell”🔥) and effectively created an ungovernable nightmare. Managing a data lake shouldn’t feel like trying to control a storm, and engineers shouldn’t have to make peace with failure and wasted resources. After speaking with many Prefect users during my tenure in product advocacy, this is how I think about resilient data lakes—and how Prefect empowers teams to achieve them: → Scheduling static babysitter workflows to monitor data lakes for changes is a dated and wasteful practice. Prefect has an events system which can be used to design event-driven pipelines that fire off only when necessary. → Data lake operations should scale with demand, not fold under pressure. With Prefect’s cloud-agnostic design, teams can leverage their preferred flavor of container orchestration to scale their processes on their terms. → Data Governance shouldn’t be an afterthought. Prefect offers a framework that democratizes workflow authoring and deployment, while providing a single pane of glass for visibility across all data operations. Curious to see how all of this works in practice? Check out my article, From Chaos to Clarity: Managing Data Lakes with Prefect. It’s a deep dive into using Prefect to automate data lake operations and unlock the full potential of your analytics workflows. 👇

Day 2 of 30: Breaking Down Data Silos with Data Mesh Architecture! Ever felt stifled by the rigid walls of data silos? You're not alone! The quest for a more dynamic and efficient way to manage data has led us to an exciting frontier: Data Mesh Architecture. Imagine transforming a traditional library, where books are locked away in separate rooms, into a vibrant open-space where knowledge flows freely and is easily accessible. That's the essence of Data Mesh – it's about breaking down those data 'rooms' and fostering a culture where data is shared and utilized seamlessly across the organization. Data Mesh pivots on four core principles: Domain-Oriented Decentralized Data Ownership: Data ownership is distributed across self-service, domain-driven teams, fostering accountability and agility. Data as a Product: Data is treated as a product, with clear ownership, governance, and consumption models. Self-Serve Data Infrastructure: Teams have access to the tools and resources they need to manage their data independently. Federated Computational Governance: Governance remains centralized, but computational resources are distributed for optimal performance. These principles not only democratize data access but also empower teams to take ownership, enhancing agility and scalability. For data engineering teams, this means an end to the bottleneck of data silos. It's about improved data discoverability, enhanced collaboration, and the ability to scale innovations rapidly. Data Mesh isn't just a new architecture; it's a new ethos that can turbocharge our data practices. Curious about bringing Data Mesh into your organization? It's a journey that involves not just technological shifts but also a cultural transformation towards shared data responsibility. As we delve deeper into the transformative trends in data engineering, Data Mesh stands out as a beacon for the future, guiding us towards a more interconnected and democratized data landscape. Stay tuned for more insights as we continue our 30-day journey. #DataEngineering #DataMesh #DataManagement #Innovation #CommunityLearning #BigData #DataDemocratization #CloudArchitecture #APIOps #DataGovernance #DataPrivacy #FutureofData #Day2of30

𝙄𝙣𝙨𝙞𝙜𝙝𝙩 𝙑𝙚𝙡𝙤𝙘𝙞𝙩𝙮 In today’s fast-moving business landscape, 𝘃𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗲𝗱𝗴𝗲—𝗶𝘁’𝘀 𝗮 𝗻𝗲𝗰𝗲𝘀𝘀𝗶𝘁𝘆. Rapid adaptation driven by real-time data is reshaping industries, as 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝘀𝗽𝗲𝗲𝗱 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴𝗹𝘆 𝗱𝗲𝗳𝗶𝗻𝗲𝘀 𝗽𝗿𝗼𝗳𝗶𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗴𝗿𝗼𝘄𝘁𝗵. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗔𝗰𝗵𝗶𝗲𝘃𝗶𝗻𝗴 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Real-time data unlocks faster, smarter decisions, but many organizations face challenges: • 𝘿𝙖𝙩𝙖 𝙇𝙖𝙩𝙚𝙣𝙘𝙮 Delays in collection and processing limit timely action. • 𝙋𝙧𝙤𝙘𝙚𝙨𝙨𝙞𝙣𝙜 𝙎𝙥𝙚𝙚𝙙 Legacy systems can’t keep up with modern data demands. • 𝙄𝙣𝙩𝙚𝙜𝙧𝙖𝙩𝙞𝙤𝙣 𝙂𝙖𝙥𝙨 Fragmented systems hinder a unified view of operations. 𝟰 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗳𝗼𝗿 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗮𝘁𝗮  Organizations can overcome these hurdles with: • 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 for immediate responses to changes. • 𝗔𝗴𝗶𝗹𝗲 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 to streamline pipelines. • 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Machine learning tools enable predictive and prescriptive insights in near real-time. • 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 to grow with data needs. Key Tools and Technologies like streaming platforms (e.g., Apache Kafka) and cloud analytics solutions enable real-time capabilities, but Enterprise Architecture (EA) is the linchpin. EA provides the roadmap to strategize, integrate, and deliver these technologies, ensuring alignment with business goals and delivering timely insights to drive profitability and growth. In a world where 𝗮𝗴𝗶𝗹𝗶𝘁𝘆 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗿𝗻𝗲𝗿𝘀𝘁𝗼𝗻𝗲 𝗼𝗳 𝘀𝘂𝗰𝗰𝗲𝘀𝘀, real-time data powers organizations to adapt faster, innovate smarter, and stay ahead. _ 👍 Like if you enjoyed this. ♻️ Repost for your network.  ➕ Follow @Kevin Donovan 🔔 _ 🚀 Join Architects' Hub!  Sign up for our newsletter. Connect with a community that gets it. Improve skills, meet peers, and elevate your career! Subscribe 👉 https://lnkd.in/dgmQqfu2 Photo by Orian Lev Ari #RealTime #Agility #EnterpriseArchitecture #DataDriven #DigitalTransformation

In the age of real-time data, staying ahead of the curve is key to driving impactful decisions. This article takes you through an important journey of automating data ingestion and monitoring using Snowpipe. By adopting this technology, businesses can streamline their data operations and focus on deriving insights rather than getting bogged down by the data collection process. The practical steps outlined here highlight how you can implement Snowpipe efficiently, making it easier to harness the power of data. I encourage everyone to reflect on their data strategies and consider what innovations can pave the way for success in your projects. Sharing our experiences can foster greater community growth, so feel free to share your own insights and stories about data automation in the comments! #Snowflake #DataIngestion #RealTimeData #Snowpipe #DataOperations #CloudTechnologies #Collaboration https://lnkd.in/gdf5iwus

Data chaos doesn’t fix itself—and throwing tools at it won’t help either. I’ve got a game plan for that. If you’ve been enjoying The Data Leader’s Blueprint here on LinkedIn, you’re in the right place for quick wins: Short reads. Potent knowledge nuggets. Power-ups for your day. But for the deep dives—the frameworks, tools, and real solutions—you’ll want to check out my Substack: The Six-Pack Series: Bite-sized frameworks to tackle AI readiness, metadata messes, and more. Red Insights: Bold, opinionated strategies for leaders who need the full playbook, not just the highlights. 📍 Think of LinkedIn as your quick recharge. Substack? That’s your data-first deep dive toolkit. 👉 If you’re ready to tackle data challenges head-on, the full playbook is here: https://lnkd.in/dYPgxMnN "Let’s make data chaos a thing of the past. Who’s with me?"

❓ How much 💰 is bad data costing your business? Poor data quality can lead to significant financial losses, inefficiencies, and missed opportunities. Even minor data errors can ripple through decision-making processes, resulting in flawed insights and costly mistakes. In DataOps, the stakes are higher than in traditional DevOps—because when your data is wrong, the consequences are immediate and impactful. Want to learn how to avoid costly data problems? Check out Reece’s recent blog below. Recce CL Kao Karen Hsieh #DataQuality #DataOps #BusinessInsights #DataDriven #DigitalTransformation #Analytics