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Microsoft Fabric introduces MCP support for Real-Time Intelligence (RTI)Become an AI Generalist that makes $100K (in 16 hours)AI isn’t the future — it’s the present, quietly reshaping work, money, and opportunity. McKinsey says AI is set to add $13Trillion to the economy by 2030 — but also replace millions of jobs. Will you use it to get ahead, or get left behind? Don’t worry here’s exactly what you need: Join the World’s First 16-Hour LIVE AI Mastermind for professionals, founders, consultants & business owners like you.Rated 4.9/5 by 150,000 global learners – this will truly make you an AI Generalist that can build, solve & work on anything with AI.In just 16 hours & 5 sessions, you will:✅ Learn the basics of LLMs and how they work.✅ Master prompt engineering for precise AI outputs.✅ Build custom GPT bots and AI agents that save you 20+ hours weekly.✅ Create high-quality images and videos for content, marketing, and branding.✅ Automate tasks and turn your AI skills into a profitable career or business.All by global experts from companies like Amazon, Microsoft, SamurAI and more. And it’s ALL. FOR. FREE. Join now and get $5100+ in additional bonuses: 🔥$5,000+ worth of AI tools across 3 days — Day 1: 3000+ Prompt Bible, Day 2: $10K/month AI roadmap, Day 3: Personalized automation toolkit.Attend all 3 days to unlock the cherry on top — lifetime access to our private AI Slack community!Register Now (free only for the next 72 hours)SponsoredSubscribe|Submit a tip|Advertise with usWelcome to BIPro #103, your weekly digest of the top breakthroughs, practical insights, and real-world applications driving business intelligence forward. This edition covers everything from real-time AI integrations to SQL tuning tips, with direct links to help you dive deeper.Key Highlights:🔹 [Microsoft Fabric RTI gets MCP Support] – Bring AI to real-time dashboards with natural language access to live data and open APIs.🔹 [Detecting Fraud with Amazon Neptune + GraphStorm] – Combine graph analytics with GNNs for smarter fraud prevention.🔹 [BigQuery Enhanced Vectorization (Preview)] – Achieve 21× faster queries with SIMD and smart encoding, coming soon with Parquet support.🔹 [Inline Scalar UDFs in Microsoft Fabric Warehouse] – Reduce execution time by designing UDFs for automatic inlining.🔹 [Looker CI Preview] – Integrate LookML validations into pull requests to ship trusted models faster.🔹 [Tradeshift's Embedded Analytics with Amazon QuickSight + Q] – Empower users with natural language analytics in a unified B2B experience.🔹 [ANTLR-Powered SQL to DataFrame Converter] – Automate SQL-to-Spark/Pandas translations with grammar-driven conversion logic.🧠 Stay sharp, stay connected. BIPro brings you the trends that matter and the tools to act on them.✉️ Have tips or tools to share? Reply and contribute to our next edition.Cheers,Merlyn ShelleyGrowth Lead, PacktJoin us on July 19 for a 150-minute interactive MCP Workshop. Go beyond theory and learn how to build and ship real-world MCP solutions. Limited spots available! Reserve your seat today.Use Code EARLY35 for 35% off📊 Data Viz Trends Shaping the Future of Insights🔶 Introducing MCP Support for Real-Time Intelligence (RTI): Microsoft Fabric RTI now supports Model Context Protocol (MCP), enabling AI agents to access real-time data, run natural language queries, and integrate easily via open APIs. With schema discovery, plug-and-play setup, and future expansions like digital twins and proactive insights, MCP brings powerful, AI-driven analytics to real-time intelligence workflows.🔶 Use Graph Machine Learning to detect fraud with Amazon Neptune Analytics and GraphStorm: Amazon Neptune Analytics and GraphStorm empower fraud detection by combining scalable graph analytics with graph machine learning. Using Graph Neural Networks, the solution uncovers hidden fraud networks, predicts risk scores, and enriches transaction graphs with embeddings. This pipeline, deployable via SageMaker, enables real-time, dynamic analysis of evolving fraud behaviors.🔶 Understanding BigQuery enhanced vectorization: Google BigQuery’s enhanced vectorization, now in preview, redefines query performance by leveraging SIMD processing, advanced data encodings, and parallel execution. Tight integration with Capacitor enables faster joins, aggregations, and filter pushdowns. Real-world benchmarks show up to 21× speedups, delivering major gains in efficiency. Full rollout and Parquet support coming soon!📈 Dive into Databases: SQL Essentials🔶 How to make your SQL scalar user-defined function (UDF) inlineable in Microsoft Fabric Warehouse? Microsoft Fabric now supports inline scalar UDFs in Warehouse preview, improving performance by reducing execution overhead. This post highlights common inlining blockers like non-deterministic functions, complex query usage, and multiple return statements, with guidance on how to refactor them. Broaden SQL compatibility and efficiency through inline-friendly UDF design practices.🔶 Introducing Continuous Integration for Looker: Looker now offers Continuous Integration (CI) in preview, giving developers tools to improve speed, accuracy, and confidence in deploying LookML code. CI validates LookML, detects SQL and content issues early, and integrates with pull requests or schedules. This helps teams catch errors before production, ensuring reliable and consistent data experiences.🔶 Recovering data in SQL Server without full backup: When full backups fail, SQL Server's transaction logs can help recover deleted data. This guide walks through using fn_dump_dblog to extract deleted rows from .trn files, even without a full backup. It’s a complex but powerful recovery method that reveals deep insights into SQL Server internals and storage behavior.🔄 Real-World Transformation: How Gen BI Made Data Work🔶 Fabric Eventhouse now supports Eventstream Derived Streams in Direct Ingestion mode (Preview): Microsoft Fabric now supports direct ingestion of derived streams into Eventhouse, enabling seamless, no-code real-time data routing. Users can configure this from the Eventstream canvas, Embedded Real-Time Hub, or Eventhouse Get Data Wizard. Derived streams, created through transformations, can now be ingested directly into KQL tables. This feature is currently in Preview.🔶 The Hidden Cost of MAXDOP: CPU Spikes, Query Slowdowns, and What We Learned. Hardcoding MAXDOP 4 in queries boosted individual performance but caused CPU contention and system-wide slowdowns. Testing revealed that higher MAXDOP reduces concurrency on limited-core systems. The real fix came from proper indexing and statistics updates, cutting query time from 20 to 1 second. Always tune first before applying parallelism.🔶 From code to community: The collective effort behind SQL Server 2025. SQL Server 2025 is now in public preview, bringing AI integration, vector search, zero-ETL analytics via Fabric mirroring, and powerful developer features like JSON and regex support. Designed for cloud agility and built through deep community collaboration, it’s the most transformative SQL Server release in over a decade.⚡ Quick Wins: BI Hacks for Instant Impact🔶 Transforming B2B intelligence: Tradeshift’s journey with Amazon QuickSight and Amazon Q.Tradeshift transformed its B2B analytics by embedding Amazon QuickSight and Amazon Q into its platform, enabling scalable, AI-powered insights for buyers and sellers. This shift replaced a limited in-house tool, reduced engineering overhead, and empowered users to explore data through natural language, redefining how intelligence is delivered across their global e-invoicing network.🔶 Building an SQL to DataFrame Converter With ANTLR: ANTLR enables seamless SQL-to-DataFrame translation by parsing SQL syntax into structured trees, which can be programmatically converted into Pandas or Spark code. This approach tackles key challenges like syntax differences, execution models, and feature gaps. With proper grammar, visitors, and dialect handling, developers can automate and scale query conversion across frameworks.🔶 PostgreSQL: pgSCV 0.14.1 released! This PostgreSQL monitoring agent adds support for pg_stat_ssl, pg_stat_subscription, and new pg_stat_io metrics. It also updates Grafana dashboards, Go, and Alpine Linux. Designed for Prometheus compatibility, pgSCV continues to evolve as a unified exporter for PostgreSQL environments. Full details on GitHub.🔶 MySQL Orchestrator Failover Behavior During Replication Lag: Managing MySQL replication failovers becomes more resilient with orchestrator settings like FailMasterPromotionIfSQLThreadNotUpToDate, DelayMasterPromotionIfSQLThreadNotUpToDate, and FailMasterPromotionOnLagMinutes. These options offer fine-grained control over failover behavior, ensuring consistency by preventing or delaying master promotions when replicas are lagging, ultimately balancing availability and data integrity during outages or transitions.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Statsig’s Experimentation Analytics in Microsoft Fabric, Gemini CLI for PostgreSQL Snyk body { margin: 0; padding: 0; -webkit-text-size-adjust: 100% !important; -ms-text-size-adjust: 100% !important; -webkit-font-smoothing: antialiased !important; } img { border: 0 !important; outline: none !important; } p { Margin: 0px !important; Padding: 0px !important; } table { border-collapse: collapse; mso-table-lspace: 0px; mso-table-rspace: 0px; } td, a, span { border-collapse: collapse; mso-line-height-rule: exactly; } .buttontext { text-transform: inherit } .ExternalClass * { line-height: 100%; } .em_defaultlink a { color: inherit; text-decoration: none; } .em_footer a { color: #979797; text-decoration: underline; } .em_purple a { color: #8a2ac2 !important; text-decoration: underline !important; } .em_g_img+div { display: none; } a[x-apple-data-detectors], u+.em_body a, #MessageViewBody a { color: inherit; text-decoration: none; font-size: inherit !important; font-family: inherit !important; font-weight: inherit !important; line-height: inherit; } @media only screen and (max-width: 100%; } .em_wrapper { width: 100%; } .em_hide { display: none !important; } .em_full_img img { width: 100%; height: auto !important; max-width: 100%; } .em_center { text-align: center !important; } .em_side15 { width: 100%; } .em_ptop { padding-top: 20px !important; } .em_pbottom { padding-bottom: 20px !important; } .em_h20 { height: 20px !important; font-size: 1px !important; line-height: 1px !important; } .em_hauto { height: auto !important; } u+.em_body .em_full_wrap { width: 100%; width: 100%; } .em_pad { padding: 20px 15px !important; } .em_ptb { padding: 20px 0px 20px !important; } .em_pad1 { padding: 20px 15px 10px !important; } .em_pad2 { padding: 10px 15px 20px !important; } .em_ptb1 { padding: 30px 0px 20px !important; } .em_plrb { padding: 0px 15px 20px !important; } .em_h10 { height: 10px !important; line-height: 0px !important; font-size: 0px !important; } .em_wrap_50 { width: 100%; } } @media screen and (max-width: 100%; height: auto !important; } .em_img_1 img { width: 100%; height: auto !important; } .em_img_2 { width: 100%; height: auto !important; } .em_img_2 img { width: 100%; height: auto !important; } .em_img_3 { width: 100%; height: auto !important; } .em_img_3 img { width: 100%; height: auto !important; } .em_img_4 { width: 100%; height: auto !important; } .em_img_4 img { width: 100%; height: auto !important; } } The future of secure AI-driven development is here, and DevSecCon25 is leading the conversation! Join us on October 22, 2025 for this one-day event to hear from leading experts in AI and security from Qodo, Ragie.ai, Casco, Arcade.dev, and more! The full agenda includes: Mainstage - Hear inspiring keynotes from leaders in AI and cybersecurity. Expect forward-looking insights, industry thought leadership, and a vision of what’s next in the world of secure AI. AI demos track - Bring your laptop and join us for interactive, hands-on demos under the theme “Build and Secure with AI.” You'll leave with skills you can immediately apply. AI security track - Cutting-edge talks exploring the evolving security challenges of the AI era. Discover how to safeguard AI-driven applications, gain visibility into models, and secure agents across the SDLC. Snyk innovation track - Experience the latest advancements from Snyk in this dynamic track featuring live product demos, major announcements, and customer success stories. Don't miss this opportunity to gain the knowledge and strategies needed to embrace the AI revolution securely. Save your Spot Manage Preferences | Book a Demo| Contact Us| Community SponsoredSubscribe|Submit a tip|Advertise with UsWelcome to BIPro Expert Insights #115We’re excited to bring you another packed edition full of deep dives, practical tutorials, and cutting-edge updates in Data Management & BI. This week, we’re thrilled to welcome Nishant Arora, Solutions Architect at AWS, to our newsletter portfolio, who will be sharing deep-dive insights on how AI is reshaping industries.Nishant takes us into the world oftrustworthy AI in automotive and manufacturing, where safety, explainability, and regulatory readiness are non-negotiable for ML systems driving the future of mobility.Alongside this deep dive, here are the key highlights making waves in BI and data this week:Power BI September Updates➖Copilot default-on, smarter DAX, new visuals, and Teams integration.Fabric September Updates➖Governance tools, Python notebooks GA, Fabric CLI open-sourced, and more.Fabric Data Factory➖Stronger security and compliance for enterprise-scale data integration.Statsig in Fabric➖Native experimentation analytics for A/B testing and feature insights.BigQuery Upgrades➖Conversational insights andTimesFM-powered forecasting without extra ML setup.Gemini CLI for PostgreSQL➖Use plain English for queries, schema management, and extensions.Case Study: SSENSE➖Migrated 600 dashboards toQuickSight, cutting costs by two-thirds.Case Study: PayNet➖Modernized BI with near real-time analytics and natural language queries.From safer AI in cars to smarter analytics in the enterprise, this week’s stories all point toward one theme:building trust while pushing innovation forward.Let’sdive in.Cheers,Merlyn ShelleyGrowth Lead, PacktTrustworthy Machine Learning in Automotive: Safety, Explainability, and Regulation Readiness– Written by Nishant Arora, Solutions Architect at AWSArtificial intelligence (AI) and machine learning (ML) are redefining the automotive industry. Cars are no longer just mechanical systems; they are intelligent, adaptive, and connected machines. Advanced driver-assistance systems (ADAS), predictive maintenance tools, and self-driving algorithms promise safer and more efficient transportation. Yet, the integration of ML also raises pressing concerns:can we guarantee these systems behave safely, explain their choices, andcomply withstrict automotive standards?Unlike recommendation systems or digital assistants, automotive MLoperatesinlife-critical environments. A single wrong decisionidentifyinga pedestrian, miscalculating braking distance, orfailing to detectsensor faults could have irreversible consequences. This is whytrustworthinessis not just a desirable property, but apreconditionfor adoption at scale.Safety as the Core of TrustIn safety-critical applications, evaluating ML performance goes beyond accuracy. What matters is whether the system preserves safe operation under all circumstances. A useful framing is:P (Safe|Model Decision)This probability expresses the likelihood that, given a model’s action, the outcome is safe. Accuracy alone does not guarantee that the rare but dangerous cases are adequately addressed.Equally important is theability to measure uncertainty. For example, an object recognition system in an autonomous car must know when it is unsure if a shadow is a pedestrian or just road texture. This can be modeled as predictive variance:Var(y∣x,θ)whereyis the outcome for inputxunder model parameters θ. Systems that quantify uncertainty allow safer fallback strategies such as driver takeover or conservative control.Safety can also be built directly into model training. A combinedobjectivefunction might looklike: L=Laccuracy+λ⋅LsafetywhereLaccuracyreflects predictive performance andLsafetypenalizes unsafe decisions, weighted by factorλ. In this way, the model learns not only to be correct, but also to respect predefined safety boundaries.Finally,confidence calibrationis vital. Regulators often require that predicted probabilities align with actual outcomes, ensuring that an ML model’s confidence is trustworthy:E[∣y^−y∣]≤εwhereεrepresentsthe maximum allowable deviation. Poor calibration can create dangerous overconfidence even when classification accuracy is high.Explainability: Building Human TrustEven a safe system will not be widely adopted if engineers, regulators, and customers cannot understand how it works. This is whereexplainable ML (XAI)becomes indispensable.Some prominent methods include:>> Feature attribution tools(e.g., SHAP, LIME) that show which sensor inputs or environmental factors most influenced a model’s decision.>> Surrogate models, such as simple decision trees approximating a deep neural network, whichmake the decision boundary more interpretable.>> Rule-based explanations, translating complex outputs into understandable logic:“if road is slippery and braking distance exceeds threshold, reduce speed.”Such techniques allow developers to debug failures, give regulators evidence for certification, and help buildpublic confidencein ML-driven cars.Regulation and Safety StandardsTraditional automotive safety is governed by standards likeISO 26262, which defines processes and Automotive Safety Integrity Levels (ASILs). These were designed for deterministic, rule-based software. ML, by contrast, is probabilistic and data-driven, creating new challenges for compliance.To bridge this gap, companies are adoptingverification and validation (V&V) frameworkstailored for ML. These include large-scale simulation testing, corner-case scenario generation, and monitoring model drift once systems are deployed. The aim is not just to test for accuracy, but to produceaudittrailsand evidence of robustness that regulators can certify.Looking ahead, standardswilllikely evolveto explicitly account for ML, requiring documentation of uncertainty estimates, explainability reports, and continuous monitoring logs.Emerging Pathways to Safer MLSeveral technological approaches show promise in making automotive ML more trustworthy:Cloud-NativeMLOpsCloud platforms now allow continuous retraining and redeployment of ML models asconditions shift (e.g., new road layouts or changing weather patterns). With automated testing pipelines, everynew versioncan be checked against safety and compliance metrics before deployment.Digital Twins and Safety-Constrained Reinforcement LearningDigital replicas of cars and environments enable billions of simulated test miles without real-world risk. Reinforcement learning agents can be trained with explicit safety constraints, ensuring that unsafe behaviors are never reinforced.Self-Monitoring Agentic AIFuture systems may integrate agentic AI that audits its own behavior in real-time. Such systems could flag potential regulatory violations, halt unsafe actions, or escalate control to human operators. Thisrepresentsa step toward vehicles thatself-enforce compliancerather than relying solely on external oversight.Conclusion: Toward a Trustworthy FutureAI in automotive promises safer roads, lower maintenance costs, and smarter mobility. But none of this progress matters unless these systems areprovablysafe, transparent, and regulationready.Automakers must embed safetyobjectivesdirectly into training and evaluation. Regulators must expand standards like ISO 26262 to incorporate probabilistic models. Cloud providers and technology partners must deliver the infrastructure for continuous monitoring and compliance assurance.The next era of mobility will not be defined merely by how advanced ML models become, but by how muchtrustsociety places in them. Only when AI systems are demonstrably safe, explainable, and aligned with regulatory frameworks will we see widespread adoption of truly autonomous and intelligent vehicles.References➖ ISO 26262:2018. Road Vehicles – Functional Safety.International Organization for Standardization.➖ Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete Problems in AI Safety.arXivpreprint arXiv:1606.06565.➖ Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning.arXivpreprint arXiv:1702.08608.➖ Kendall, A., & Gal, Y. (2017). What uncertainties do we need in Bayesian deep learning for computer vision?Advances in Neural Information Processing Systems (NeurIPS).➖ Shapley, L. S. (1953). A value forn-person games.Contributions to the Theory of Games, 2(28), 307–317. (Basis for SHAP explainability methods).➖ National Highway Traffic Safety Administration (NHTSA). (2020). Automated Vehicles 4.0: Preparing for the Future of Transportation.U.S. Department of Transportation.Highlights in BI & Data Management⬛Power BI September 2025 Feature Summary:ThePower BI September 2025 Feature Summarycoincides withFabConViennaand delivers major updates. Highlights include Copilot default-on with AI enhancements, improved DAX time intelligence, live Direct Lake semantic model editing, TMDL view GA, advanced visualization options, and mobile NFC support. Users can alsobenefitfrom Fabric certifications, championships, and enhanced report-sharing in Teams.⬛Power BI in Teams – Content Shared in Teams Chats Now Opens a Dedicated Separate Window Within Teams:Power BI now makes collaboration inTeamssmoother. Reports shared in chats or channels open in aseparate window, keeping the original conversation in a collapsible side panel. This lets you explore data while continuing the chat, switch between Teams apps without disruption, and avoid multiple pop-ups, streamlining multitasking andmaintainingworkflow flow.⬛Fabric September 2025 Feature Summary:TheFabric September 2025 Feature Summaryhighlights new certifications,FabConVienna’s Power BI DataViz World Championships, and major platform upgrades. Key releases include theGovern TabandDomains Public APIs(GA), expandedMicrosoft Purview policies, enhancedDataflow Gen2 Copilot features,Python notebooks GA,Fabric CLI open-sourced, and newmirroring/connectors. These updates strengthen governance, extensibility, and developer productivity across Fabric.⬛Mission-Critical Data Integration: What’s New in Fabric Data Factory?This is aboutnew mission-critical security and compliance features in Microsoft Fabric Data Factory. The update highlights how Fabric now supports enterprise-grade data integration by strengtheningauthentication, isolation, secret management, gateway controls, and automation. The goal is to help organizations handle sensitive, large-scale workloads across hybrid and cloud environments withgreater security, resilience, and governance.⬛Statsig Experimentation Analytics (Preview):This is aboutStatsig’sExperimentation Analytics becoming available in Microsoft Fabric. It introduces a new workload in theFabric Workload Hubthat lets product teams run experiments, define custom metrics, analyze user behavior, and measure feature impact, all directly on data inOneLake. The integrationeliminatesdata movement, streamlines A/B testing, and enables faster, data-driven product innovation within the Fabric ecosystem.⬛AI-based forecasting and analytics in BigQuery via MCP and ADK:Google has expanded AI agent capabilities inBigQuerywith two new tools:ask_data_insightsfor conversational analytics andBigQueryForecastfor time-series predictions. Agents can now answer complex questions in plain English, provide transparent reasoning, and generate forecasts using theTimesFMmodel, all without moving data or setting up ML infrastructure, streamlining enterprise-scale data analysis and prediction workflows.⬛Gemini CLI for PostgreSQL in action:TheGemini CLI extension for PostgreSQLsimplifies database tasks by letting developers useplain English commandsinstead of switching between tools. It canidentifyand install extensions likepg_trgmfor fuzzy search, recommend performance optimizations, and generate queries automatically. Beyond search, it supports schema exploration, lifecycle management, and code generation, turning the CLI into a truedatabase assistant.⬛How SSENSE modernized their analytics platform with Amazon QuickSight?SSENSE, a global luxury e-commerce platform, migratednearly 600dashboards from its legacy BI system toAmazonQuickSightin just 8 months. The move reduced analytics costs by two-thirds, cut dashboard maintenance by 80%, and improved Athena data availability by 95%. With seamless AWS integration, AI-driven features, and broad user adoption, SSENSE achieved scalable, efficient self-service analytics.⬛How PayNet enhanced payment analytics with Amazon QuickSight?Payments Network Malaysia (PayNet), the country’s national payments backbone, modernized its BI by migrating toAmazonQuickSight. LeveragingAthena, S3, and AWS Glue,PayNetenabled near real-time transaction analytics, cross-border payment insights, and secure SSO via IAM Identity Center. WithAmazon Q, users now query data in natural language for defect monitoring, benchmarking, and operational visibility, boosting efficiency, security, and collaboration.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} body { margin: 0; padding: 0; -webkit-text-size-adjust: 100% !important; -ms-text-size-adjust: 100% !important; -webkit-font-smoothing: antialiased !important; } img { border: 0 !important; outline: none !important; } p { Margin: 0px !important; Padding: 0px !important; } table { border-collapse: collapse; mso-table-lspace: 0px; mso-table-rspace: 0px; } td, a, span { border-collapse: collapse; mso-line-height-rule: exactly; } .buttontext { text-transform: inherit } .ExternalClass * { line-height: 100%; } .em_defaultlink a { color: inherit; text-decoration: none; } .em_footer a { color: #979797; text-decoration: underline; } .em_purple a { color: #8a2ac2 !important; text-decoration: underline !important; } .em_g_img+div { display: none; } a[x-apple-data-detectors], u+.em_body a, #MessageViewBody a { color: inherit; text-decoration: none; font-size: inherit !important; font-family: inherit !important; font-weight: inherit !important; line-height: inherit; } @media only screen and (max-width: 100%; } .em_wrapper { width: 100%; } .em_hide { display: none !important; } .em_full_img img { width: 100%; height: auto !important; max-width: 100%; } .em_center { text-align: center !important; } .em_side15 { width: 100%; } .em_ptop { padding-top: 20px !important; } .em_pbottom { padding-bottom: 20px !important; } .em_h20 { height: 20px !important; font-size: 1px !important; line-height: 1px !important; } .em_hauto { height: auto !important; } u+.em_body .em_full_wrap { width: 100%; width: 100%; } .em_pad { padding: 20px 15px !important; } .em_ptb { padding: 20px 0px 20px !important; } .em_pad1 { padding: 20px 15px 10px !important; } .em_pad2 { padding: 10px 15px 20px !important; } .em_ptb1 { padding: 30px 0px 20px !important; } .em_plrb { padding: 0px 15px 20px !important; } .em_h10 { height: 10px !important; line-height: 0px !important; font-size: 0px !important; } .em_wrap_50 { width: 100%; } } @media screen and (max-width: 100%; height: auto !important; } .em_img_1 img { width: 100%; height: auto !important; } .em_img_2 { width: 100%; height: auto !important; } .em_img_2 img { width: 100%; height: auto !important; } .em_img_3 { width: 100%; height: auto !important; } .em_img_3 img { width: 100%; height: auto !important; } .em_img_4 { width: 100%; height: auto !important; } .em_img_4 img { width: 100%; height: auto !important; } }

Local Debugging for Fabric User Data Functions, BigQuery Workload Management UpgradesYour Exclusive Invite for the World’s first 2-day AI Challenge (usually $895, but $0 today)51% of companies have started using AITech giants have cut over 53,000 jobs in 2025 itselfAnd 40% of professionals fear that AI will take away their job.But here’s the real picture - companies aren't simply eliminating roles, they're hiring people who are AI-skilled, understand AI, can use AI & even build with AI. Join the online 2-Day LIVE AI Mastermind by Outskill - a hands-on bootcamp designed to make you an AI-powered professional in just 16 hours. Usually $895, but for the next 48 hours you can get in for completely FREE!In just 16 hours & 5 sessions, you will:Learn the basics of LLMs and how they workMaster prompt engineering for precise AI outputsBuild custom GPT bots and AI agents that save you 20+ hours weeklyCreate high-quality images and videos for content, marketing, and brandingAutomate tasks and turn your AI skills into a profitable career or business📅Kick off Call & Session 1- Friday (10am EST- 1pm EST)🧠Sessions 2-5:🕜Saturday 11 AM to 7 PM EST; Sunday 11AM EST to 7PM ESTAll by global experts from companies like Amazon, Microsoft, SamurAI and more. And it’s ALL. FOR. FREE. You will also unlock $3,000+ in AI bonuses: Slack community access, Your Personalised AI tool kit, and Extensive Prompt Library with 3000+ ready-to-use prompts - all free when you attend!Join in now, we have limited free seats!SponsoredSubscribe|Submit a tip|Advertise with usWelcome to BIPro #102 – Smarter, Sharper, AI-First BIFrom zero-ETL breakthroughs to smarter debugging in Fabric, this issue is packed with forward-looking updates that make your BI practice leaner and more intelligent. Whether you're exploring transactional BigQuery features, debugging user data functions locally, or scaling agentic AI, these updates give you the tools to stay ahead in an increasingly AI-driven analytics landscape.Key Highlights:Free Chapter: Learning Tableau 2025Explore Tableau’s latest AI-powered features with a hands-on chapter from the bestselling series by Joshua Milligan.BigQuery Workload Management UpgradesBoost efficiency with smarter slot allocation, autoscaling improvements, and predictable performance.Zero-ETL from DynamoDB to SageMaker LakehouseEnable real-time analytics without pipelines using no-code CDC-based integration.Local Debugging for Fabric User Data FunctionsTest and troubleshoot Python-based SQL code safely in VS Code—no production risks.Best Practices for Purview DLP in FabricSecure sensitive data in OneLake with Microsoft’s enhanced DLP policies.Azure Data Factory vs Synapse vs Fabric PipelinesGet clarity on core differences in tools, billing, and connection management.SponsoredCloudVRM slashes vendor review and audit time by connecting directly to cloud environments, no spreadsheets, no forms, just real-time compliance, 24/7. Watch the demo.💡 Whether you’re scaling AI in your stack or still fighting dashboard fatigue, this week’s BIPro is packed with insights to move your strategy forward.Helping you close the gap between data and action.Cheers,Merlyn ShelleyGrowth Lead, PacktGet Chapter 1 of Learning Tableau 2025 – Free!Explore Tableau’s newest AI-powered capabilities with a free PDF of Chapter 1 from the latest edition of the bestselling series, Learning Tableau 2025.Written by Tableau Visionary Joshua Milligan, this hands-on guide helps you build smarter dashboards, master data prep, and apply AI-driven insights.Sign up to download your free chapter!Grab Your Free Chapter Now!📊 Data Viz Trends Shaping the Future of Insights🔷 Understanding updates to BigQuery workload management: Boost BigQuery Efficiency. Google’s June 2025 update enhances BigQuery workload management with fairer slot allocation, reservation predictability, autoscaler granularity, runtime reservation targeting, and detailed cost labeling. These updates improve performance, cost control, and scalability, empowering BI teams to manage compute resources more intelligently for consistent, optimized data analytics at scale.🔷 BigQuery features for transactional data management: BigQuery Evolves Beyond Analytics. Google’s latest update brings transactional power to BigQuery. Highlights include fine-grained DML for efficient data edits, CHANGES TVF for row-level change tracking, and real-time DML on streaming data. 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Whether you're onboarding teammates or migrating workloads, these insights help you navigate ETL/ELT orchestration and cost-efficient pipeline development across Microsoft's evolving data platforms.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Behind the Book: Eric Narro on Bringing Python Models to Life with TaipyMaster AI in 16 hours & Become Irreplaceable before 2025 ends! 🚀The spooky season is here—candies, costumes, and fear is everywhere.But the real nightmare? It's not ghosts—it's job lossOver 71% of people believe AI will take their jobs by 2025. The anxiety is real: Are you good enough? Fast enough? Smart enough?But here's your treat this Halloween—a real solution to end the fearJoin the online 2-Day LIVE AI MASTERMIND by Outskill - a hands-on intensive training designed to make you an AI powered professional who can learn, earn and Build with AI.Usually $395, but as a part of their halloween sale 🎃, you can get in for completely FREE!Rated 9.8/10 by Trustpilot– an opportunity that makes you an AI Generalist who can build, solve & work on anything with AI, instead of fearing it.In just 16 hours & 5 sessions, you will:✅ Build AI agents that save up to 20+ hours weekly and turn time into money✅ Master 10+ AI tools that professionals charge $150/hour to implement✅ Launch your $10K+ AI consulting business in 90 days or less✅ Automate 80% of your workload and scale your income without working more hoursLearn strategies used by the biggest giants like Google, Amazon, Microsoft from their practitioners 🚀🔥🧠Live sessions- Saturday and Sunday🕜10 AM EST to 7 PM EST🎁 You will also unlock $5000+ in AI bonuses: prompt bibles 📚, roadmap to monetize AI 💰 and your personalised AI toolkit builder ⚙️️ — all free when you attend!Join in now, (we have limited free seats!)SponsoredSubscribe|Submit a tip|Advertise with UsWelcome to BIPro Expert Insights #116!Behind the Book: Eric Narro and Getting Started with TaipyIn this week’s Expert Edition, we’re excited to feature Eric Narro, Analytics Engineer and author of Getting Started with Taipy. Eric brings a refreshing perspective on how to move from time series to chatbots—and how to bring your Python models to life with Taipy, a tool purpose-built for taking data apps from prototype to production.For those unfamiliar, Taipy is a pure-Python application builder designed to help you deploy scalable, interactive data applications in real production environments. It’s ideal for turning your analytics, models, and algorithms into end-user experiences, whether dashboards, optimization tools, or AI-powered chatbots.This week, Eric not only shares his technical insights in the article From Time Series to Chatbots: Bring Your Python Models to Life with Taipy, but also takes us behind the scenes of his author journey in How I Got to Write a Book with Packt, a personal story about curiosity, persistence, and how a chance encounter at PyCon France sparked a book deal.And here’s a bonus: for one week only, you can grab Getting Started with Taipy at 30% off (ebook) and 10% off (print), the perfect time to dive in and start building production-ready Python applications.Let’s explore both the tech and the story that made it possible.Cheers,Merlyn ShelleyGrowth Lead, PacktMeet Taipy: A Pure-Python, Fast, and Scalable Application BuilderBy Eric NarroFrom Time Series to Chatbots: Bring your Python Models to Life with TaipyTaipy is a Python application builder with one clear promise:deploy your data applications in real production environments. It’s the ideal tool for creating scalable, interactive apps that bring your models, analytics, and algorithms to life. Whether you’re building dashboards, optimization tools, or AI-powered chatbots, Taipy helps data professionals turn prototypes into powerful, end-user applications. WithGetting Started with Taipy, you’ll learn how to build complete applications from the ground up, deploy them confidently, and explore real-world examples and advanced use cases that showcase Taipy’s full potential.Python has long been the go-to language for data professionals, not because they’re developers, butbecause Python makes complex work accessible.Analysts, data scientists, and AI engineers use it to model data, run analytics, and visualize results.But when it comes to turning those models into real applications for end users, things get tricky. Building a web app the traditional way, with backend frameworks, databases, and front-end stacks, is often out of reach for data teams. It demands skills, time, and coordination that slow everything down and increase costs.Tools like Power BI or Tableau help visualize data, but they can’t trulyrunPython code or offer the flexibility of a full application. Python frameworks like Streamlit, Dash, Panel, or Gradio solve the problem partially. Each has trade-offs. To give an example, Streamlit is a great library for prototyping: it’s very easy to learn, and you can create demos in no time. While you can take Streamlit applications to production, they are harder to scale because they don’t optimize the way code runs, and they run on their own server (you can’t run them in a WSGI server). What this means is you can create useful applications for end users if they make limited use of the app, or if you don’t need to process large amounts of data.That’s where Taipy comes in!Taipy lets you create scalable, production-grade applications directly in Python.Whether for time series, optimization, geospatial analysis, or even LLM chatbots, Taipy is designed for performance and scalability.You can deploy Taipy apps on WSGI servers, handle multiple users efficiently, and still build everything using pure Python.Continue reading the full article on our Packt Medium Handle here.How I Got to Write a Book with Packt | My Personal Experience by Erric NarroWould you have told me, 4 years ago: “Eric, you’re going to write a book about computer science or data topics, and you’ll actually be apublished author,” I’d have laughed and said, “Come on, stop lying to me!”But here we are.In this article, I want to sharehow I ended up writing a book: the story behind the opportunity, the twists and turns that led me there, and the lessons I picked up along the way. In a follow-up post, I’ll dive deeper into what it was really like to go through the writing and publishing process.The motivation for this article is to eventually motivate anyone reading this to take action and work hard towards their goals, whatever they are. A second motivation is to show how working towards your goals may end up giving you unexpected results (I never thought of writing a book before I was asked to do it!)I’m 38 as I write these lines. I guess any story behind any personal outcome could be traced back to birth, but don’t worry, I won’t torture you with a detailed overview of my past! Still, there’s a wholewaterfall of eventsthat led me here, and that’s what I want to write about.How I Became a Data AnalystFor a living, I have a job. I’m a data analyst. Although, to be honest, I actually do more of a data engineering role these days (with ETL tasks, integrating data into databases, and so on, it’s quite diversified).I’ve written before aboutthe path I took to becoming a data analyst, but to summarize: I was a vineyard technician for 8 years, I learned Python to build my own tools, eventually I learned programming more extensively in college with distance studies and through a number of Coursera courses, and I effectively changed careers after sometime programming both at my work, or by doing personal projects.It took me a long time to take the step of changing careers, in part because I started to learn programming (and program effectively) as a way to improve and automate tasks at my former job (which I didn’t dislike); also, at some point, there was some lack of confidence to make the switch. But over time, I realized that I loved programming and working with data even more than being a vineyard technician, and that gave me the push I needed.Before officially changing careers, I had already learnedversion control, built a small GitHubportfolio(with README files and documentation about them). I also gained a foundation inSQL. I dabbled in web development withPHPandMySQL. I knew how to deal withLinux systems. During college, I had programmed inC, Bash, LISP, JavaScript, and Prolog, and I knew my way around Boolean logic, encoding, and binary calculations. I also knew some about statistics, analytical workflows, and data warehousing.The reason I mention all this is: it was quite odd that I became a data analyst in the first place;I found that passion along the way. But at the same time, it wasn’t a miracle either;it was the result of hard work, and ultimately, it was the result of other people giving me a chance, based on what I was able to share with them. That is why it’s important tojust do things. Efforts will end up paying in one way or another. Byjust doing things, you’ll eventually end up doing too many things, and then you’ll have to choose which one… but until then, just start doing something you like!And well, I also mention this because there’s no way I would have found out about Taipy, which is a Python library, had I not been proficient with Python!Continue reading the full article on our Packt Medium Handle here.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Go behind the scenes of how Eric Narro turned his Taipy expertise into a published Packt book.👋 Hello ,Welcome to BIPro #117 - Behind the Book, Part 2: Eric Narro’s Journey from Idea to PublicationThis week, we take you inside the creative process of Eric Narro, author of Getting Started with Taipy,the definitive guide to building production-ready Python applications. In Part 2 of our Behind the Book series, Eric shares what it was like to turn his expertise into a full-length Packt publication, from his first LinkedIn message with our team to collaborating with the Taipy developers and shaping his experience into a book that empowers Python builders worldwide.Knowledge Partner Spotlight: OutskillAt Packt, we’ve partnered with Outskill to help readers gain practical exposure to AI tools through free workshops, complementing the deeper, hands-on, expert-led experiences offered through Packt Virtual Conferences.If you're interested in enhancing your AI skills, Outskill’s LIVE 2-Day AI Mastermind offers a 16-hour training on AI tools, automations, and agent-building. This weekend’s sessions (Saturday and Sunday, 10 AM–7 PM EST) are available at no cost as part of their Black Friday Sale, providing a great opportunity to elevate your knowledge in just two days.Learn AI tools, agents & automations in just 16 hoursJoin now, limited free seats available!Cheers,Merlyn ShelleyGrowth Lead, PacktSponsored:🔸82% of data breaches happen in the cloud. Join Rubrik’s Cloud Resilience Summit to learn how to recover faster and keep your business running strong. [Save Your Spot]🔸Build your next app on HubSpot’s all-new Developer Platform,the flexible, AI-ready foundation to create, extend, and scale your integrations with confidence. [Start Building Today]Subscribe|Submit a tip|Advertise with UsMy Experience Writing a Book with Packt by Eric NarroI wrote a book with Packt, it’s called “Getting Started with Taipy”. Taipy is a Python library that lets you create data applications. I’ve writtenlots of articles about it.I recently wrote abouthow I got to write a book with Packt. In this article, I’ll write about the process of writing it. I’ll describe the steps involved, but I’ll also share what I experienced during the writing process. Each section of this article represents one part of the process, although they all overlapped to some extent. I’ll also discuss everything I’ve gained from this experience.How I Got the OfferIt all started on June 13th, 2024. Pratik, someone from Packt, contacted me on LinkedIn. Packt was developing a book about Taipy, and they were looking for an author with expertise in the area.I was able to recover the LinkedIn messages and read them again. This first contact was followed by two online meetings, the latest one on June 25th.What I remember from this period of time is having mixed feelings. I first thought it could be a scam. I checked about other people having an experience writing for Packt, I checked about the people that contacted me, and their contacts as well; it all looked legit (and ultimately, it sure was!) I remember being excited, and also a bit worried: if I agreed to continue this, would I be able to actually write the book?What I remember from this period is having mixed feelings. At first, I thought it could be a scam. I checked whether other people had written for Packt, looked into the people who contacted me, and even checked their contacts — everything looked legit (and ultimately, it sure was!). I remember being excited but also a bit worried: if I agreed to continue, would I actually be able to write the book?When you get such an offer, the process doesn’t start right away, although it doesn’t take long to get started either! There’s a round of questions and explanations. The team at Packt presented their workflow. This is also when Nilesh joined in! Nilesh was on board from the beginning until the end of the project, and it was fantastic working with him all along!I also asked a few questions. The one that intrigued me most was:was this book commissioned by the Taipy team?I didn’t know if that was something Packt did. It turned out that the book wasn’t a Taipy initiative; Packt has a current awareness team that tracks trends. They detected growing interest around Taipy and identified a need for a book in the library.Even though the book wasn’t initiated by the Taipy team, I wanted to reach out to them to present the project. I knew them a little from being a project contributor, and I thought informing them was the least I could do. I also wanted to make sure there were no trademark or other legal issues, even though Packt has a dedicated legal team to ensure its books comply with the law. I also asked if they’d be willing to answer my questions when they arose and to review the chapters. They agreed, and I can’t thank them enough for that! Florian, Alexandre, and Rym were my main contacts at Taipy: thanks to each of them individually as well!While these preliminary steps might seem unimportant at first glance, they were moments of extreme excitement, maybe because this was my first book! All these conversations lasted for about a month, but they also overlapped with the process of writing the outline, which I’ll talk about next.The OutlineWriting an outline is the first part of the process. This is something you start as soon as you sign the book’s contract (which is done electronically and is a process without much interest).Writing the outline, along with some other documents such as the product description, was definitely the part I liked the least about the process! But it needs to be done.This process took about two weeks, with several exchanges with the team at Packt. In the outline, you have to describe the main parts of the book and what each chapter will cover. This is a difficult task (at least for a first-timer — I think I’d do a better job if I had to go through this process again). What’s hard about this exercise is calibrating the real size of each chapter.For example, Chapter 2 is about Taipy’s visual elements. The subject is huge, and it ended up being a mega-chapter that covers lots of concepts. Conceptually, it makes sense to have a dedicated chapter for this, but I could have imagined a way to split it into two. However, Chapters 3, 4, and 5 cover distinct aspects of Scenario Management (a Taipy-related concept) and ended up having a proper size. Doing a well-calibrated outline is hard!Another challenge in creating the outline was imagining some of the examples I’d include in the book. Part 2 of the book is a set of tutorials that demonstrate how to create Taipy applications close to real enterprise use cases. I wanted this set of examples to show different areas of Taipy (or how to use Taipy in different ways) and also demonstrate how it can be applied in different industries, to solve problems of various types (optimization, reporting, forecasting, LLM apps, and so on). This “imagination work” takes time!Ultimately, I had to change some of the applications I had in mind. For example, I initially wanted to create an app that uses satellite images to monitor farmland. In my head, this was great… until I thought:Which particular piece of land am I going to use?Because, you know… all those farmlands are private properties, and I didn’t want any problems (nor did I want to ask for permission). I changed the app to use public parks in Paris, which was a good compromise. This approach had pros and cons: on one hand, I had more data about the parks that I could display in the app; on the other, the size of the parks and the type of land made the app less useful. But overall, I found it to be a good compromise.Writing the first draft of the outline was quick, but finalizing it tookquite some time— about two months. Of course, that doesn’t mean I spent two months writing nonstop; it was mostly small improvements and changes following reviews by different stakeholders. The last messages I can find in my emails regarding the outline date are from late August. By that point, I was already writing the book!Speaking of writing the book, let’s see how that went!The Writing ProcessThe writing process is the longest part. For each chapter, the process includes:Writing the chapter…It was reviewed by a person at TaipyIt was then reviewed for editingIt was then reviewed by technical reviewers (not affiliated with Taipy)It was then reviewed for copy editingIt underwent a final revision by the authorIf you consider that “writing the chapter” also involved reviewing what I had just written — several times — this means I had to re-read each chapter ten times or more. And honestly, reviewing a text — whatever text it may be — so many times is emotionally difficult. But I did it!Let’s go back to the beginning of writing a chapter. The process starts with creating drafts and sketching the concepts I’ll write about. But if you think that actual writing is what takes the most time… you’re not even close to the truth. Writing takes some time, yes, but there’swaymore to it!Since the book is about a Python library (Taipy) that lets you create data applications, almost every chapter includes an app coded specifically for it. This means that for most chapters, I had to build a demonstration app, and coding apps takes time!When starting a chapter, I’d have to decide exactly what application I was going to code. This included finding a dataset that made sense for the app and that had a license I could use in the book. Just imagining all this was already a time-consuming process!The next step was usually creating the application while writing the key parts of the process as it was being built. This took a lot of time, especially for Chapters 7 to 14. The reason is that, while Taipy helps you create apps quickly, what actually takes time is coding all the other parts (the non-Taipy parts, if you will, such as machine learning pipelines or geographic information programs).Continue reading the full article on our Medium handle.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Practical strategies for shipping ML systems that work, not just demos.Subscribe|Submit a tip|Advertise with usWelcome to BIPro 106.The Machine Learning Summit opened yesterday by tackling the question a lot of teams are quietly asking:Why do so many GenAI projects stall out after the pilot phase?Stephen Klein (Curiouser.ai founder, AI ethics expert) laid it out clearly:Most failures aren’t technical. They happen because of misaligned goals, poor handoffs, and internal gridlock.The real differentiator isn’t just model performance. It’s how clearly your team can think, how quickly they can act, and how much your organization actually trusts the output.His line summed it up: “We can’t automate trust. We can’t automate imagination. That’s where the value is.”This week’s sessions are built to help you avoid those traps. Ticket holders get full access to all session replays and condensed highlights.Register for the ML SummitHere’s what’s coming up:Day 2 (today): Practical playbooks for scaling ML beyond the prototype: LLM fine-tuning, tabular and time series use cases that actually deliver value, and getting model interpretability right.Day 3: How to operationalize GenAI properly: LLMOps, causal AI, synthetic data pipelines, plus a hands-on workshop to build working AI agents.If you want frameworks and examples you can apply directly, especially for getting past stalled pilots and messy deployments, this is worth your time.Register for the ML SummitCheers,Merlyn ShelleyGrowth Lead, Packt*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}