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🗞️Welcome to BIPro#90 – Your Weekly Business Intelligence Boost! 🚀

Another week, another round of exciting updates in the world of data and BI! This time, we’re exploring SQL Database Project in Azure Data Studio, handling high-volume data in Azure Synapse, and unlocking the power of Key Vault services in Azure.

We’ve also got some cool insights on Memorystore Cluster Autoscaler now on GitHub, Threads in OpenAI Assistants API, and how SQL Dynamic Data Masking helps with privacy and compliance. And if you're into Spring Data Neo4j, we've got something for you too!

Plus, check out the latest BI book releases and top highlights to keep you ahead in this data-driven world. Let’s get into it! 👇

📚 New Releases You Can't Miss:

✦ Causal Inference in R

✦ Python Feature Engineering Cookbook

✦ Quantum Machine Learning and Optimisation in Finance

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

Cheers,

Merlyn Shelley

Growth Lead, Packt

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❯❯❯❯ 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.

❯❯❯❯ SQL Database Project in Azure Data Studio: This article explains how to use the Azure Data Studio extension for managing SQL Database projects. It covers installation, project creation from existing databases or from scratch, adding tables, creating views, and stored procedures. The guide also emphasizes version control in Visual Studio and simplifies publishing changes.

❯❯❯❯ An Effective Approach for High Volume Data in Azure Synapse: This article outlines an efficient approach for handling high-volume data in Azure Synapse Analytics. It covers parallel data loading using the COPY INTO command, leveraging Parquet files for efficiency, and implementing dynamic partitioning in fact tables. The method ensures optimal query performance by maintaining balanced distributions and sufficient row counts per partition.

❯❯❯❯ JSON in Microsoft SQL Server: A Comprehensive Guide: This article explores handling JSON data in Microsoft SQL Server, covering storage, retrieval, validation, querying, modification, and performance optimization. It demonstrates using built-in functions like JSON_VALUE, JSON_QUERY, OPENJSON, and JSON_MODIFY, while ensuring data integrity with ISJSON() constraints. Best practices include indexing computed columns, schema validation with stored procedures, and error handling to maintain efficient and secure JSON operations in SQL Server.

❯❯❯❯ Creating a Linked Server in Amazon RDS for SQL Server: A Step-by-Step Guide. This guide explains how to create and configure a linked server in Amazon RDS for SQL Server using SQL commands. It covers prerequisites, authentication setup, testing, and advanced configurations like timeout settings and remote procedure calls. Best practices include using linked servers sparingly, securing connections, and optimizing queries for performance.

❯❯❯❯ Using Key Vault services in Azure Ecosystem: This guide explains how to use Azure Key Vault to securely store and manage secrets like passwords and access keys. It covers creating a Key Vault, storing secrets, and setting up access permissions using Access Control (IAM) and Access Policies. Applications can retrieve secrets securely, reducing the need to store sensitive information in code.

❯❯❯❯ Software Deployment Strategies: This article explores software deployment strategies, focusing on Canary and Blue-Green deployments. Canary deployment gradually releases updates to a small group of users, ensuring stability before a full rollout. Blue-Green deployment runs two environments in parallel, enabling instant rollback if needed. Both strategies minimize downtime and risks, with trade-offs in complexity and cost.

❯❯❯❯ Support Vector Machines: A Progression of Algorithms. This article explains the progression of Support Vector Machines (SVMs) from Maximal Margin Classifier (MMC) to Support Vector Classifier (SVC) and finally to full SVM. MMC finds a strict linear boundary, SVC allows some misclassification, and SVM extends this by using kernel functions to classify non-linear data efficiently.

❯❯❯❯ Accelerate migration from traditional BI tools to Amazon QuickSight with generative AI and Storm Reply. This article details BMW Group's migration from on-premises BI tools to Amazon QuickSight, leveraging automation and generative AI. The project streamlined dashboard conversions, reducing manual effort by 80% while maintaining 90% data accuracy. The approach improved scalability, simplified BI processes, and demonstrated the potential of AI-driven cloud BI modernization.

❯❯❯❯ Deep Dive into WebSockets and Their Role in Client-Server Communication. This blog thoroughly examines real-time communication methods, focusing on WebSockets and their role in enabling two-way interactions. It explains how WebSockets differ from traditional HTTP approaches, outlines design challenges for messaging apps, and discusses scaling strategies, reliability, and best practices.

❯❯❯❯ Amazon Redshift Serverless adds higher base capacity of up to 1024 RPUs. This blog explains how Amazon Redshift Serverless transforms data warehousing by scaling compute resources with a new 1024 RPU capacity. It compares performance against 512 RPUs for complex queries, data ingestion, and analytics, emphasizing cost efficiency and faster execution times.

❯❯❯❯ Governing the ML lifecycle at scale, Part 4: Scaling MLOps with security and governance controls. This blog outlines building and governing a multi-account machine learning platform for streamlined model deployment. It describes roles, standardized templates, secure provisioning, and automation that empower data science teams to transition models into production efficiently while ensuring governance and collaboration.

❯❯❯❯ Handle errors in Apache Flink applications on AWS. This blog explains error handling in streaming applications using Apache Flink. It details proven strategies for managing errors through retries and dead letter queues. The post shows how asynchronous I/O and side outputs effectively preserve data integrity and boost reliability.

❯❯❯❯ Memorystore Cluster Autoscaler now on GitHub. This article is about the open-source Memorystore Cluster Autoscaler for Redis on Google Cloud. It explains how the tool automatically scales Redis clusters, adjusting shard count based on CPU and memory usage, to optimize performance and manage costs. The article details its architecture, deployment options via Cloud Run or GKE, and various configuration scenarios for different workload patterns.

❯❯❯❯ New query insights capabilities for Cloud SQL Enterprise Plus. This article introduces the new query insights enhancements for Cloud SQL Enterprise Plus edition. It explains how detailed telemetry, 30-day query plans, wait event analysis, index recommendations, and an AI-powered chat interface empower developers and DBAs to quickly diagnose and optimize high-performance databases on Google Cloud.

❯❯❯❯ Spectra Logic Offers 24G Optical SAS Switch to Transform Data Center Tape Storage. This blog introduces Spectra Logic's OSW-2400 Optical SAS Switch, a new solution that transforms tape storage connectivity in data centers. It explains how active optical cables extend connection distances up to 100 meters, enabling flexible deployments, improved performance, and significant cost savings by reducing the need for expensive Fibre Channel infrastructure.

❯❯❯❯ A Guide to Using Amazon Bedrock Prompts for LLM Integration: This blog introduces Amazon Bedrock, a fully managed service that simplifies integrating large language models into applications. It outlines key benefits like access to diverse models, enhanced security, and serverless operation, while providing hands-on Python examples, prompt management strategies, and best practices for production usage.

❯❯❯❯ An In-Depth Guide to Threads in OpenAI Assistants API: This blog compares the limitations of standard chat completion models with the enhanced capabilities of the Assistance API. It explains how the Assistance API overcomes issues like lack of memory, computational limitations, and synchronous processing by supporting features such as persistent threads, code interpretation, file retrieval, function calling, and asynchronous workflows. The post includes Python code examples demonstrating how to create, list, retrieve, modify, and delete threads and messages, helping developers manage conversation context more effectively.

❯❯❯❯ Indexed View for Aggregating Metrics: This blog explores using Microsoft Azure SQL for storing and querying daily user metrics in web applications. It demonstrates how to aggregate data, such as user activity from a hotel booking site, over daily, weekly, or monthly intervals, and highlights the performance benefits of using indexed views for real-time analytics on large datasets.

❯❯❯❯ Spring Data Neo4j: How to Update an Entity: This blog explores various methods for updating entities in Spring Data Neo4j. It highlights the limitations of the default save () method, which can inadvertently overwrite existing values with null, and demonstrates alternative approaches such as PATCH methods, custom Cypher queries, and DTO-based projections to update only specific properties while preserving existing data.

❯❯❯❯ SQL Dynamic Data Masking for Privacy and Compliance: This blog explains SQL Server Dynamic Data Masking, a feature that obscures sensitive data from non-privileged users to enhance security and compliance. It covers when and why to use masking (e.g., in development environments, for third-party access, and to meet regulatory requirements), outlines prerequisites and masking functions, and provides step-by-step examples for applying and testing masking rules. The post also discusses how dynamic masking supports data minimization, audit readiness, and scalability, ensuring only authorized users see full data while others view masked values.

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