Modern data engineering demands robust, real-time pipelines with dbt & Spark for scalable lakehouse architectures. Get faster, accurate insights! What's your biggest data challenge? #DataEngineering #ModernDataStack

Modern data engineering means real-time insights & trusted data. Leverage Lakehouses, Streaming, dbt, & Spark for robust pipelines. What's your biggest data challenge? #DataEngineering #ModernDataStack

If you found this useful, follow @_ShahKruti for more threads on underrated infrastructure plays. I cover the companies quietly powering the AI era — one thread at a time. 🔁 #Fivetran #DataEngineering #ModernDataStack #AIInfrastructure #TechInvesting 12/12

I charged $15K for a 6-week project last month. The client said it was the difference between a failed AI initiative and one that actually shipped. Here's exactly what I built: The problem: A Series B company (80 employees) had data scattered across 8 different tools. No single source of truth. Two AI initiatives had already stalled because nobody trusted the underlying data. The CEO wanted AI agents. The foundation wasn't there. Week 1: AI Readiness Diagnostic. Scored their current state across 5 dimensions. Data foundation: 3/10. Data quality: 4/10. Infrastructure: 6/10. Team readiness: 7/10. Use case clarity: 9/10. They knew exactly what they wanted to build. The data underneath it was a disaster. Week 2-4: Built the foundation from scratch. → Terraform-managed infrastructure on GCP → dbt data warehouse unifying all 8 source systems → Tested models for every data source → CI/CD pipeline so their team could maintain it without me → Complete runbooks their engineers could follow Week 5-6: Handoff and documentation. Walked the team through every system. Made sure they could extend and maintain it. The result: Clean, tested data foundation covering 8 unified sources. The data team stopped firefighting. Within 3 months of the foundation going live, they deployed 2 AI agents: → Customer health scoring agent → Automated support triage agent Both went to production on the first attempt. Not because the models were perfect. Because the data was trustworthy. Same company that failed at AI twice. Same CEO who wanted agents. Different foundation. That's what $15K buys. Not the AI. The foundation that makes the AI actually work. Full case study → mldeep.io/case-study #DataEngineering #AIAgents #SaaSFounders #SeriesB #DataFoundation #dbt #ModernDataStack #AIStrategy #DataPipeline #BuildInPublic

Gleanだけ初見でしたが、他は全てModernDataStack時代からの主要プレイヤーですね。いわゆるデータ基盤を構成するツール達です。 以前はデータのアウトプット先がBI、もしくはデータ加工した上でSalesforceなどにアプリケーションに戻すReverseETLなどだったものが、AIがアウトプット先に加わった事で新しい価値が出て、新しい要素も増えたという事かなと思います。 全く新しいものではなく、従来のデータ基盤にレイヤーが上乗せさせるようなイメージをしています。（全然違ったらすいませんｗ） もしかしたら進化しているかもしれませんが、Databricks以外は1つで全てをカバーできないと思うので、今まで通りETL DWH dbt Atlanみたいな構成はやはり作る必要があると思う。（dbtでデータカタログできたっけかな、できればAtlanなしでもいけそう） 逆にそこなしで「コンテキストレイヤーだ！」みたいな事だけを取り組もうとしても全然ダメなので、結局基礎ちゃんとしようぜって話にはなるはず。 なので、一旦は難しく考えずにシンプルなデータ基盤は作ろう。昔よりもAIが賢いので、多少データが汚いのはたぶんどうにかなる。のでまずは一箇所に集めて、整理できるようにしよう。が一丁目一番地。 で、何が問題かというとそこのデータ基盤を作る難易度はあんまり変わってなくて、AIにサクッとお願いしてできましたにはなくて、割とちゃんとデータエンジニアの方にお願いする必要がる。 それだといつまで経ってもデータ活用が進まないので、死ぬほど簡単にできないかなと思って開発したのはMagicSuccessの前身のサービスのDataMageだった。ノーコードデータ基盤で、fivetran BigQuery dbtをオールインワンかつノーコードで使えるようにした感じのサービス。 そこにアプリケーションレイヤーを乗せてカスタマーサクセス分野に特化させたのがMagicSuccess。 なのでMagicSuccessは従来のfivetran BigQuery dbt CRM AIエージェントをオールインワンで使えるようになっていて、だからこそエンジニアリソースを使わなくてもビジネスサイドがAI活用まで最短でたどり着けるようになってるし、どう活用するかに集中できるようになってる。 日本はグローバルに比べてデータエンジニアやOpsの人口が圧倒的に少ないからデータ基盤の構築もなかなか進まない、だからこそ作ったサービスなので、こんなサービス世界中どこ探しても無いぜｗ基本はパーツで分かれてるからね。 という事で、これまで粛々とやってきた点が線になろうとしているのを感じている今日このごろ。楽しくなってきたぜ！

As a data engineer, I see the recent developments with Anthropic and Elon Musk as a significant indicator of the shifting landsca… michael.business/en/news/the… #DataPlatform #AI #ModernDataStack #DataEngineering

The @CollateData Summit '26 speaker lineup is something else. Bonnie Xu — @OpenAI Amy Forest — @Yelp Lukas Patzke — @Airbus Angelita Frozza Sanches — @Scout24 Muqtafi Akhmad — @Rakuten Andrew Ford — @AO Dan Kostecki — @AmbryGenetics Jeppe Johansen — @Unity Cirene Simbahan — @unionbankph Suresh Srinivas Sriharsha Chintalapani — @CollateData Practitioners. Not analysts. They're sharing what production actually looks like. June 10. Free. Virtual. 🔗 buff.ly/HcxGymJ #CollateSummit26 #OpenMetadata #DataGovernance #DataEngineering #ModernDataStack #DataCommunity #AIReadiness #VirtualEvent

Data infrastructure has never been faster. Yet, decision making speed remains largely unchanged. CEO Soham's latest article in @BigDATAwire gets into why. Read it now: hpcwire.com/bigdatawire/2026… #DataInfrastructure #ModernDataStack #WisdomAI

The Modern Data Stack Explained (2025–2026 Edition) The Modern Data Stack (MDS) is a cloud-native, modular ecosystem designed to manage the entire data lifecycle—from extraction to insights to real-world action. Unlike legacy monolithic systems, MDS is flexible, composable, and scalable, allowing teams to swap tools as needs evolve. In this video, we break down each layer of the modern data stack and explain how organizations are building reliable, analytics-ready, and AI-powered data platforms in 2025–2026. 📌 What You’ll Learn: ✅ Why companies shifted from ETL to ELT ✅ How Lakehouse architecture is reshaping data storage ✅ The role of dbt, orchestration, and analytics engineering ✅ How Reverse ETL activates data in operational tools ✅ Why data observability & governance are critical ✅ How AI agents are transforming analytics and productivity 🧱 Modern Data Stack Layers Covered: 1️⃣ Data Ingestion & Integration – Fivetran, Airbyte 2️⃣ Storage & Lakehouse Architecture – Snowflake, BigQuery, Databricks 3️⃣ Transformation & Modeling – dbt, Dataform 4️⃣ Orchestration – Airflow, Dagster, Prefect 5️⃣ BI & Analytics – Tableau, Power BI, Looker Studio 6️⃣ Reverse ETL & Activation – Hightouch, Census, Weld 7️⃣ Observability & Monitoring – Monte Carlo, Bigeye, Soda 8️⃣ Governance & Discovery – Alation, Atlan, Collibra 9️⃣ Semantic Layer – dbt Semantic Layer, Cube, LookML 🔟 AI Agents & Automation – The future of analytics in 2026 🎯 Who This Video Is For: Data Analysts & Analytics Engineers Data Engineers & Platform Architects Product Managers & Founders Anyone learning modern data architecture 👍 If you found this helpful, like, subscribe, and share it with your data team! 💬 Drop a comment if you want deep dives on any specific layer. #ModernDataStack #DataEngineering #AnalyticsEngineering #DataArchitecture #ELT #Lakehouse #dbt #ReverseETL #DataObservability #AIinAnalytics #BigData #CloudData #Data2026 The Modern Data Ecosystem youtu.be/qODGMMo2luE?si=4WOS… via @YouTube

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝐒𝐭𝐚𝐜𝐤 — 𝐋𝐚𝐲𝐞𝐫 𝐛𝐲 𝐋𝐚𝐲𝐞𝐫 The way organizations handle data has evolved dramatically. Gone are the days of monolithic ETL systems — today’s Modern Data Stack is modular, scalable, and cloud-native. 𝐋𝐞𝐭’𝐬 𝐛𝐫𝐞𝐚𝐤 𝐢𝐭 𝐝𝐨𝐰𝐧: 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 Tools like Fivetran and Airbyte simplify data movement by pulling data from multiple source systems (SaaS apps, databases, APIs) into your warehouse with minimal setup. 𝐅𝐨𝐜𝐮𝐬: Reliability & low-maintenance pipelines 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐋𝐚𝐲𝐞𝐫 Cloud data warehouses such as Snowflake, Google BigQuery, and Amazon Redshift provide scalable, high-performance storage and compute. 𝐅𝐨𝐜𝐮𝐬: Separation of compute & storage, elasticity 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 With dbt (data build tool), teams can transform raw data into analytics-ready models using SQL — with built-in testing and documentation. 𝐅𝐨𝐜𝐮𝐬: Data modeling, testing, version control 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 Workflow tools like Apache Airflow and Prefect manage dependencies, scheduling, and execution of pipelines. 𝐅𝐨𝐜𝐮𝐬: Automation & pipeline reliability 𝐒𝐞𝐫𝐯𝐢𝐧𝐠 𝐋𝐚𝐲𝐞𝐫 Finally, data is consumed via BI tools (like dashboards) and reverse ETL platforms that push insights back into operational systems. 𝐅𝐨𝐜𝐮𝐬: Democratizing data & enabling action 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬? This layered architecture enables teams to be more agile, choose best-in-class tools, and scale independently across each stage of the data lifecycle. 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭: Faster insights, better decisions, and a truly data-driven organization. #DataEngineering #ModernDataStack #DataArchitecture #Analytics #BigData #CloudComputing #DataOps

Happy Easter weekend. One copy of data > 10 duplicated datasets Open formats > vendor lock-in Governance > chaos That’s what actually makes AI work. #AIReady #DataStrategy #DataGovernance #DataEngineering #ModernDataStack

Legacy data pipelines = bottlenecks 🚫 Batch-heavy ⚠️ Hard-coded ⚠️ Not scalable ⚠️ Not AI-ready ⚠️ More data shouldn’t mean more complexity. 🚀 Time to modernize with NAM INFERYX #DataPipelines #BigData #AI #DataEngineering #Scalability #ModernDataStack #ETL #Cloud #DataOps #NAM #Inferyx

DataOS: Unified DataOps and the Data Developer Platform Architecture Data is no longer just storage—it’s a product. In this video, we break down how modern enterprises are moving from messy, monolithic data systems to powerful Unified DataOps platforms. Discover: ✅ Data Mesh vs Data Fabric vs Data OS (explained simply) ✅ How platforms like Microsoft Fabric are changing the game ✅ Why self-serve data infrastructure is the future ✅ The role of orchestration tools like Airflow If you're building data platforms, working in AI/ML, or scaling analytics—this video will change how you think about data. 💡 The future of data is decentralized, product-driven, and developer-first. #DataOps #DataEngineering #DataMesh #DataFabric #MicrosoftFabric #BigData #AI #DataArchitecture #ApacheAirflow #ModernDataStack youtu.be/yrmLow4XqWA?si=LrFE… via @YouTube

🔷 Medallion Architecture Explained | Bronze, Silver & Gold Layers in a Modern Data Lakehouse In this video, we break down the Medallion Architecture—a powerful, multi‑layered data design pattern used in modern data lakehouse systems. Whether you're building scalable analytics, preparing data for BI dashboards, or enabling AI/ML workflows, this is a must‑know architecture for every data engineer. 🌟 What You’ll Learn ✔ What is Medallion Architecture? ✔ The role of Bronze, Silver, Gold layers ✔ How data quality improves across each stage ✔ Why organizations use this multi‑hop design ✔ Technologies behind the architecture (Delta Lake, Apache Spark, Unity Catalog) ✔ How it enables governance, ACID reliability & single source of truth ✔ Real-world applications in finance, healthcare & AI workloads 🧱 Layer Breakdown 🔶 Bronze Layer: Raw, ingested data from diverse sources ⚪ Silver Layer: Cleaned, filtered, joined, and enriched datasets 🟡 Gold Layer: Business-ready aggregates & curated datasets for analytics This structured approach transforms chaotic “data swamps” into scalable, governed, and insight-driven data assets, making it foundational for modern data platforms. 🔔 Subscribe for More If you’re passionate about data engineering, lakehouse architectures, and enterprise analytics, hit the subscribe button and join the DatahubHouse community! #MedallionArchitecture #Lakehouse #DataEngineering #DeltaLake #ApacheSpark #DataPipeline #ModernDataStack #DataGovernance #BigData #AIAnalytics youtu.be/-esiZ98uG2o?i=crnCn…

🚀 What Exactly Is a Data OS? Most companies want to be “data‑driven,” but they’re stuck with fragmented tools, siloed datasets, and slow analytics. That’s why the industry is moving toward a Data OS (Data Operating System) — the abstraction layer that unifies your entire data ecosystem. Think of it as the operating system for your company’s data, just like Windows or macOS is for your applications. 🔍 What a Data OS Does A Data OS provides: Unified interoperability across data warehouses, lakes, BI tools, and apps. Open table format support (Iceberg / Delta / Hudi). Strong metadata, governance, and lineage. Semantic layer metrics standardization. Automated DataOps orchestration. AI/ML readiness at enterprise scale. This means faster analytics, consistent definitions, automated governance, and dramatically lower operational complexity. Real Data OS Tools Exist Today ⭐ 1. DataOS — The Modern Data Company (Enterprise-Grade) A full data product platform supporting lifecycle management and self‑service consumption. Offers a semantic layer, data quality, governance, observability, and standardized metrics. Built on Apache Iceberg for open, portable data architecture and zero vendor lock‑in. Strong market presence and recognition on Gartner Peer Insights. ⭐ 2. Gaio DataOS — All‑in‑One AI‑Driven Data Platform Replaces Databricks, PowerBI, n8n, and KNIME in a single unified platform Includes ETL, Big Data, ML, LLMs, Dashboards, and Workflow Automation Features natural language querying, predictive analytics, and AI‑powered insights Built for both technical and non‑technical teams ⭐ 3. Shakudo — A Modern Data OS to Streamline Workflows A backbone for managing and integrating complex data sources Centralized automation for reducing data engineering workload Enables real-time insight extraction and unified data operations 🧠 Why This Matters As AI becomes the default interface to data: Companies without a Data OS will struggle with inconsistencies and ops overhead Companies adopting a Data OS gain speed, trust, interoperability, and AI readiness A Data OS is not the future — it’s already here. #DataOS #ModernDataStack #DataEngineering #Lakehouse #ApacheIceberg #DataProducts #DataGovernance #AIReadyData #DataPlatforms #DataOps #MetadataManagement #OpenDataFormats #EnterpriseAI #DataActivation #DatahubHouse

In a world of fragmented BI stacks and rising infrastructure costs, a universal semantic layer isn’t just governance. It’s financial performance. Announced at SKO as a major milestone for the team, this study reflects insights from real enterprise customers across retail, telecom, and financial services. Read the full report here: 🔗 ow.ly/hbhS50YohkJ #SemanticLayer #EnterpriseAI #DataGovernance #BusinessIntelligence #ModernDataStack

📈 Les données de votre entreprise sont un moteur de croissance. À condition de les activer. ERP, CRM, SaaS… Les entreprises disposent d’un volume massif de données. Mais accumuler ne crée pas de valeur. La différence se fait dans leur activation : des données fiables, centralisées, prêtes à accélérer la décision. Trop d’organisations restent en mode "bricolage" : pipelines fragiles, exports manuels… Résultat : lenteur et opportunités manquées. L’enjeu n’est plus de collecter, mais d’industrialiser l’intégration pour centraliser, fiabiliser et transformer la donnée en insights activables. 🎙 Comme le rappelle @VirginieBrard, VP France & Benelux chez @fivetran : une donnée non activée est un coût. Une donnée activée devient un avantage concurrentiel. #data #IA #transformationdigitale #datadriven #ModernDataStack

📊 Data pros, meet your ultimate playbook: semantic layers, dbt Mesh, CI/CD strategies, and more. Build maintainable, scalable models in SQL & Python while orchestrating decentralized workflows with confidence. #ModernDataStack #dbt #DataTransformation 🔗 ow.ly/RNVn50XrVnO

📊 Data pros, meet your ultimate playbook: semantic layers, dbt Mesh, CI/CD strategies, and more. Build maintainable, scalable models in SQL & Python while orchestrating decentralized workflows with confidence. #ModernDataStack #dbt #DataTransformation 🔗 ow.ly/RNVn50XrVnO

Switching between Slack and your data catalog to verify sources, check data quality, or find asset owners? There's a better way. AskCollate brings conversational AI directly into your Slack workspace, giving teams instant access to metadata, data health metrics, and governance insights without leaving their daily communication hub. Here's what's possible: → Query data using natural language ("Summarize eco-friendly product performance using Snowflake data") → Verify data trustworthiness with instant health checks and test results → Identify asset owners and trace lineage in seconds → Generate executive summaries for stakeholder meetings on the fly In our latest demo, a data leader analyzes product performance, validates data quality across multiple tables, identifies ownership, and creates a board-ready summary - all within minutes from a Slack channel. This is data governance reimagined. No more context switching. No more hunting through multiple tools. Just natural conversations that deliver the insights you need, when you need them. Part of Collate v1.11's suite of AI-powered tools designed to help teams manage, understand, and govern data more efficiently. 🎥 Watch here: youtu.be/2f5R13Jf78Q #DataGovernance #ConversationalAI #SlackIntegration #MetadataManagement #DataQuality #ModernDataStack #AIForData