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📬Welcome to BIPro #88 – Your Weekly Business Intelligence Boost! 🚀

Get ready to explore the latest breakthroughs in AI-powered analytics, cloud data solutions, and next-gen BI tools! This week, we’re diving into OpenAI’s Deep Research Agent, Microsoft Fabric Copilot for DAX, and Striim’s AI-driven mirroring for operational data. Plus, don’t miss our expert insights on data readiness, visualization enhancements, and seamless cloud migrations.

Check out our top highlights and latest BI book releases to stay ahead in the data-driven world! Let’s dive in 👇

📚 New Releases You Can't Miss:

✦ Causal Inference in R

✦ Python Feature Engineering Cookbook

✦ Quantum Machine Learning and Optimisation in Finance

🧮 This week’s highlights:

❯ MicroStrategy Offers Personalized Experiences with AI in Latest MicroStrategy ONE Release

❯ Building your first RAG pipeline with RedisVL

❯ Microsoft Fabric Copilot to write DAX queries in Power BI update

❯ What OpenAI’s Deep Research Means for the Future of Data Science

❯ Mirroring operational data for the AI era with Striim and Microsoft Fabric

❯ Tips for migrating Oracle-based applications to Google Cloud

❯ An Effective Approach for High Volume Data in Azure Synapse

Dive in and let this week’s insights supercharge your BI journey! 🚀

Cheers,

Merlyn Shelley

Growth Lead, Packt

❯❯❯❯ Causal Inference in R: Written by Subhajit Das, this book offers a deep dive into causal inference using R, guiding readers through foundational concepts and advanced techniques like propensity score matching and instrumental variables.

It helps you develop skills to construct and interpret causal models, address challenges in controlled experiments, and apply doubly robust estimation. With real-world case studies and hands-on examples, the book empowers readers to make informed, data-driven decisions by understanding and establishing causal relationships with precision.

❯❯❯❯ Python Feature Engineering Cookbook: Written by Soledad Galli, this third edition of the Python Feature Engineering Cookbook provides a complete guide to crafting powerful features for machine learning models. It covers practical solutions for common challenges, such as imputing missing values and encoding categorical variables, while optimizing data transformation processes.

The book explores advanced techniques like feature extraction from dates, times, text, and time series data, as well as using tools like Featuretools and tsfresh. With step-by-step instructions and real-world examples, it helps readers build reproducible feature engineering pipelines, ultimately enhancing machine learning model performance.

❯❯❯❯ Quantum Machine Learning and Optimisation in Finance: Written by Antoine Jacquier and Oleksiy Kondratyev, this second edition of Quantum Machine Learning and Optimisation in Finance explores how quantum algorithms enhance financial modeling and decision-making. The book focuses on quantum machine learning (QML) and optimization algorithms, with an emphasis on near-term applications using NISQ systems.

It offers practical insights into hybrid quantum-classical computational protocols and addresses the limitations of current quantum hardware. The authors provide an accessible yet rigorous approach to QML, covering topics like quantum neural networks, quantum annealing, and variational algorithms, equipping readers with the knowledge to apply quantum techniques in financial innovation.

❯❯❯❯ An Effective Approach for High Volume Data in Azure Synapse: Azure Synapse Analytics, an MPP database, enables efficient high-volume data loading using the COPY INTO command. Data ingestion leverages Parquet files for performance. Fact tables use hash-distributed dynamic partitioning for scalability. Monthly partitions optimize query performance, ensuring balanced data distribution and compression.

❯❯❯❯ MicroStrategy Offers Personalized Experiences with AI in Latest MicroStrategy ONE Release: MicroStrategy ONE’s latest update focuses on enhancing AI-powered business intelligence by improving the Auto AI bot’s conversational abilities, personalization, and contextual understanding. It introduces new chart types, user feedback integration, and better AI deployment controls, making AI-driven analytics more intuitive and adaptable.

❯❯❯❯ Using Blue/Green Deployment For (near) Zero-Downtime Primary Key Updates in RDS MySQL: This blog explains how Amazon RDS Blue/Green deployment enables modifying large tables using asynchronous replication, minimizing downtime. It covers creating a Green environment, altering table structures, restarting replication, and switching over. The process ensures a smooth transition while keeping the database synchronized and minimizing disruption to applications.

❯❯❯❯ Building your first RAG pipeline with RedisVL: This blog details the journey of building a Retrieval Augmented Generation (RAG) pipeline using the Redis Vector Library. It covers setting up Redis, processing data with vector embeddings, designing a schema, performing semantic searches, and creating an AI assistant that retrieves context-aware insights from financial documents.

❯❯❯❯ What is content-based filtering? This blog explores content-based filtering in recommender systems, explaining its machine learning techniques, advantages, and limitations. It compares content-based vs. collaborative filtering, highlighting their trade-offs. The blog also provides a Redis-powered tutorial on building a movie recommendation system using vector embeddings, semantic search, and metadata-driven filtering for personalized suggestions.

❯❯❯❯ Deep Dive into WebSockets and Their Role in Client-Server Communication: This blog explores WebSockets and real-time communication, comparing them with polling, webhooks, and Server-Sent Events (SSE). It explains how WebSockets enable bidirectional, persistent connections ideal for chat apps, gaming, and live notifications. The blog details WebSocket handshakes, connection setup, efficiency benefits, and practical use cases for interactive, low-latency applications.

❯❯❯❯ How to Share a Secret: Shamir’s Secret Sharing: This blog explains secret sharing and explores Shamir’s Secret Sharing, a cryptographic technique for securely distributing secrets among multiple parties. It covers how polynomial-based secret sharing works, its security properties, real-world applications (e.g., medical research, finance), advantages, limitations, and implementation details, ensuring data privacy while enabling controlled access.

❯❯❯❯ Analyze Tornado Data with Python and GeoPandas: This blog explores tornado data analysis using NOAA’s public-domain database from 1950–2023. It details data retrieval, filtering, geospatial mapping with GeoPandas, and visualizing tornado occurrences. The project highlights regional tornado trends, the expansion of ‘Dixie Alley,’ and improvements in detection due to Doppler radar advancements, revealing shifting tornado patterns over time.

❯❯❯❯ How to do Date calculations in DAX: This blog explores date calculations in DAX, focusing on the DATEADD() function for time-based analysis. It explains shifting dates by days, months, and years, handling weeks with alternative methods, and using TREATAS() and CALCULATETABLE() for dynamic filtering. Practical examples demonstrate how to apply these techniques in real-world data models.

❯❯❯❯ How to Implement Guardrails for Your AI Agents with CrewAI: This blog explores implementing guardrails for AI agents using CrewAI, ensuring controlled, safe, and reliable outputs. It covers LLM safety concerns, CrewAI’s agent-task separation, workflow management with Flows, and real-time content verification. A practical example demonstrates multi-agent coordination, iterative text validation, and mitigating risks in AI-powered applications.

❯❯❯❯ Mirroring operational data for the AI era with Striim and Microsoft Fabric: This blog explores Striim’s partnership with Microsoft Fabric to enable real-time data integration and AI-driven analytics. It introduces SQL2Fabric-Mirroring, a low-latency, scalable solution for replicating on-premises SQL data to Microsoft Fabric OneLake, supporting AI, analytics, and decision-making. The blog highlights Change Data Capture (CDC), automated synchronization, and seamless cloud integration.

❯❯❯❯ Microsoft Fabric January 2025 update: This blog highlights Microsoft Fabric’s latest updates, including NotebookUtils session management, enhanced COPY INTO permissions, Fabric REST APIs, and ALM improvements. It announces FabCon 2025, Power BI DataViz Championships, free DP-700 certification training, and Copilot AI enhancements. Key updates span Power BI, OneLake, Data Engineering, Data Warehouse, and Real-Time Intelligence innovations.

❯❯❯❯ Private Preview of Migration assistant for Fabric Data Warehouse: This blog introduces Microsoft Fabric’s Migration Assistant, designed to streamline SQL Server and Synapse migrations to Fabric Data Warehouse. Currently in Private Preview, it offers schema conversion, data migration, and AI-powered assistance. Organizations can join the preview, provide feedback, and collaborate with the product team before the public release.

❯❯❯❯ Power BI January 2025 Feature Summary: The January 2025 Power BI update brings exciting new features to enhance data exploration and visualization. Users can now quickly analyze data with the “Explore this data” option and improved Treemap tiling methods. Updates include semantic model version history tracking, TMDL scripting (preview), and enhanced PowerPoint storytelling tools. AI-driven Copilot enhancements provide suggested questions for deeper insights. A new Snowflake connector and advanced visualizations like Lollipop Charts expand analytics capabilities. Additionally, Microsoft Fabric Conference 2025 registration is open, and the Fabric Data Engineer Certification (DP-700) is now available.

❯❯❯❯ Microsoft Fabric Copilot to write DAX queries in Power BI update: Microsoft Fabric Copilot now enhances DAX query writing in Power BI with semantic model descriptions, synonyms, and sample values. This update improves query accuracy by leveraging metadata from tables, columns, and measures. Users can define descriptions for clarity, add synonyms for flexibility, and utilize sample values for context, streamlining data insights.

❯❯❯❯ Gather organization-wide Amazon RDS orphan snapshot insights using AWS Step Functions and Amazon QuickSight: AWS customers can now automate orphaned RDS snapshot identification across accounts and regions using AWS Step Functions, Lambda, Glue, and QuickSight. This solution enhances visibility, optimizes cloud spend, and streamlines snapshot management with centralized insights. It leverages AWS Organizations, Athena, and S3, offering flexible deployment and automated monitoring via EventBridge.

❯❯❯❯ The Apiphani Data Pipeline and AWS Services Industrialize Data Delivery for BI, ML, and AI: This blog explores how Apiphani, an AWS Partner, helps organizations industrialize data delivery and maximize the value of BI, ML, AI, and digital products through scalable, reusable data pipelines. It covers technology, operational models, and cultural transformation, demonstrating how businesses can accelerate data-driven decision-making, reduce costs, and improve governance.

❯❯❯❯ Hybrid big data analytics with Amazon EMR on AWS Outposts: This blog explores Amazon EMR on AWS Outposts, a hybrid big data analytics solution that brings the power of Amazon EMR to on-premises environments. It details how businesses can process petabyte-scale data while meeting data residency, compliance, and latency requirements. The blog also covers deployment architecture, data integration with Amazon S3, network optimization with AWS Direct Connect, and secure data access using AWS Glue and Lake Formation.

❯❯❯❯ February 2025 Amazon QuickSight events: This blog highlights upcoming Amazon QuickSight events for February 2025, showcasing the latest advancements in BI and generative BI. Attendees can explore industry use cases, new features like Amazon Q, advanced visualizations, and prompted reports. The blog also provides details on virtual learning sessions, in-person meetups, and user groups, helping organizations stay updated on QuickSight innovations and best practices.

❯❯❯❯ What OpenAI’s Deep Research Means for the Future of Data Science: This blog introduces OpenAI’s Deep Research Agent, a revolutionary tool that automates multi-step research, synthesizes diverse data sources, and delivers verified insights for data scientists. It highlights how Deep Research accelerates problem-solving in AI, healthcare, and finance, ensuring accuracy, efficiency, and scalability in tackling complex, domain-specific challenges with real-time, transparent data synthesis.

❯❯❯❯ Tips for migrating Oracle-based applications to Google Cloud: This blog explores the Google Cloud-Oracle partnership, enabling businesses to migrate and modernize Oracle databases and applications on Google Cloud. It details migration paths, containerization with GKE and Cloud Run, Exadata integration, and Java optimization with GraalVM. Businesses benefit from scalability, security, and flexibility, accelerating cloud transformation, DevOps integration, and cost efficiency while leveraging Google’s high-performance infrastructure.

❯❯❯❯ Open Mirroring for SAP sources – dab and Simplement: This blog highlights Fabric Mirroring, a data replication feature in Microsoft Fabric that ensures seamless synchronization of source data into Fabric OneLake. It introduces Open Mirroring, an extensible replication platform, now supporting SAP data integration. Partners like dab Nexus and Simplement Roundhouse enable efficient SAP data replication, enhancing data accessibility, analytics, and integration across Fabric workloads.

❯❯❯❯ Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data. This blog explores data-centric AI and data pruning to improve model efficiency and accuracy. It demonstrates how the "furthest-from-centroid" selection strategy on MNIST achieves 98.73% accuracy using just 50% of the dataset. Key insights include reducing redundancy, enhancing decision boundaries, and optimizing dataset curation, challenging the assumption that more data always improves AI models.

We’ve got more great things coming your way, see you soon!