Learn the fundamentals of SAP Enterprise Project and Portfolio management Project Systems (PS), Portfolio and Project Management (PPM) and Commercial Project Management (CPM) and their integration with other SAP modules. This book covers various business scenarios from different industries including the public sector, engineering and construction, professional services, telecom, mining, chemical, and pharmaceutical. Author Joseph Alexander Soosaimuthu will help you understand common business challenges and pain areas faced in portfolio, program and project management, and will provide suitable recommendations to overcome these challenges. This book not only suggests solutions within SAP, but also provides workarounds or integrations with third-party tools based on various Industry-specific business requirements. SAP Portfolio and Project Management addresses commonly asked questions regarding SAP EPPM implementation and deployment, and conveys a framework to facilitate engagement and discussion with key stakeholders. This provides coverage of SAP on-premise solutions with ECC 6.08 and SAP PPM 6.1 deployed on the same client, as well as S/4 HANA On-Premise 2020 with integration to BPC and BI/W systems. Interface with other third-party schedule management, estimation, costing and forecasting applications are also covered in this book. After completing SAP Portfolio and Project Management, you will be able to implement SAP Enterprise Portfolio and Project Management based on industry best practices. For your reference, you’ll also gain a list of development objects and a functionality list by Industry, and a Fiori apps list for Enterprise Portfolio and Project Management (EPPM). What You Will Learn Understand the fundamentals of project, program and portfolio management within SAP EPPM Master the art of project forecasting and scheduling integrations with other SAP modules Gainknowledge of the different interface options for scheduling, estimation, costing and forecasting third party applications Learn EPPM industry best practices, and how to address industry-specific business challenges Leverage operational and strategic reporting within EPPM Who This Book For Functional consultants and business analysts who are involved in SAP EPPM (PS, PPM and CPM) deployment and clients who are interested and are in the process of having SAP EPPM deployed for their Enterprise.

This IBM® Redbooks Product Guide provides an overview of the features and functions that are available with the IBM DS8900F models that run microcode Release 9.2 (Bundle 89.20 / Licensed Machine Code 7.9.20). As of August 2021, the DS8900F with DS8000 Release 9.2 is the latest addition. The DS8900F is an all-flash system exclusively, and it offers three classes: IBM DS8980F: Analytic Class: The DS8980F Analytic Class offers best performance for organizations that want to expand their workload possibilities to artificial intelligence (AI), Business Intelligence, and Machine Learning. IBM DS8950F: Agility Class: The agility class is efficiently designed to consolidate all your mission-critical workloads for IBM Z, IBM LinuxONE, IBM Power Systems, and distributed environments under a single all-flash storage solution.. IBM DS8910F: Flexibility Class: The flexibility class delivers significant performance for midrange organizations that are looking to meet storage challenges with advanced functionality delivered as a single rack solution.

Gain practical guidance for implementing data models on the SAP BW/4HANA platform using modern modeling concepts. You will walk through the various modeling scenarios such as exposing HANA tables and views through BW/4HANA, creating virtual and hybrid data models, and integrating SAP and non-SAP data into a single data model. Data Modeling with SAP BW/4HANA 2.0 gives you the skills you need to use the new SAP BW/HANA features and objects, covers modern modelling concepts, and equips you with the practical knowledge of how to use the best of the HANA and BW/4HANA worlds. What You Will Learn Discover the new modeling features in SAP BW/4HANA Combine SAP HANA and SAP BW/4HANA artifacts Leverage virtualization when designing and building data models Build hybrid data models combining InfoObject, OpenODS, and a field-based approach Integrate SAP and non-SAP data into single model Who This Book Is For BI consultants, architects, developers, and analysts working in the SAP BW/4HANA environment.

Data Modeling for Azure Data Services is an essential guide that delves into the intricacies of designing, provisioning, and implementing robust data solutions within the Azure ecosystem. Through practical examples and hands-on exercises, this book equips you with the knowledge to create scalable, performant, and adaptable database designs tailored to your business needs. What this Book will help me do Understand and apply normalization, dimensional modeling, and data vault modeling for relational databases. Learn to provision and implement scalable solutions like Azure SQL DB and Azure Synapse SQL Pool. Master how to design and model a Data Lake using Azure Storage efficiently. Gain expertise in NoSQL database modeling and implementing solutions using Azure Cosmos DB. Develop ETL/ELT processes effectively using Azure Data Factory to support data integration workflows. Author(s) None Braake brings a wealth of expertise as a data architect and cloud solutions builder specializing in Azure's data services. With hands-on experience in projects requiring sophisticated data modeling and optimization, None crafts detailed learning material to help professionals level up their database design and Azure deployment skills. Dedicated to explaining complex topics with clarity and approachable language, None ensures that the learners gain not just knowledge but applied competence. Who is it for? This book is a valuable resource for business intelligence developers, data architects, and consultants aiming to refine their skills in data modeling within modern cloud ecosystems, particularly Microsoft Azure. Whether you're a beginner with some foundational cloud data management knowledge or an experienced professional seeking to deepen your Azure data services proficiency, this book caters to your learning needs.

Learn about business intelligence (BI) features in T-SQL and how they can help you with data science and analytics efforts without the need to bring in other languages such as R and Python. This book shows you how to compute statistical measures using your existing skills in T-SQL. You will learn how to calculate descriptive statistics, including centers, spreads, skewness, and kurtosis of distributions. You will also learn to find associations between pairs of variables, including calculating linear regression formulas and confidence levels with definite integration. No analysis is good without data quality. Advanced Analytics with Transact-SQL introduces data quality issues and shows you how to check for completeness and accuracy, and measure improvements in data quality over time. The book also explains how to optimize queries involving temporal data, such as when you search for overlapping intervals. More advanced time-oriented information in the book includes hazard and survival analysis. Forecasting with exponential moving averages and autoregression is covered as well. Every web/retail shop wants to know the products customers tend to buy together. Trying to predict the target discrete or continuous variable with few input variables is important for practically every type of business. This book helps you understand data science and the advanced algorithms use to analyze data, and terms such as data mining, machine learning, and text mining. Key to many of the solutions in this book are T-SQL window functions. Author Dejan Sarka demonstrates efficient statistical queries that are based on window functions and optimized through algorithms built using mathematical knowledge and creativity. The formulas and usage of those statistical procedures are explained so you can understand and modify the techniques presented. T-SQL is supported in SQL Server,Azure SQL Database, and in Azure Synapse Analytics. There are so many BI features in T-SQL that it might become your primary analytic database language. If you want to learn how to get information from your data with the T-SQL language that you already are familiar with, then this is the book for you. What You Will Learn Describe distribution of variables with statistical measures Find associations between pairs of variables Evaluate the quality of the data you are analyzing Perform time-series analysis on your data Forecast values of a continuous variable Perform market-basket analysis to predict customer purchasing patterns Predict target variable outcomes from one or more input variables Categorize passages of text by extracting and analyzing keywords Who This Book Is For Database developers and database administrators who want to translate their T-SQL skills into the world of business intelligence (BI) and data science. For readers who want to analyze large amounts of data efficiently by using their existing knowledge of T-SQL and Microsoft’s various database platforms such as SQL Server and Azure SQL Database. Also for readers who want to improve their querying by learning new and original optimization techniques.

Take a dive into data lakes “Data lakes” is the latest buzz word in the world of data storage, management, and analysis. Data Lakes For Dummies decodes and demystifies the concept and helps you get a straightforward answer the question: “What exactly is a data lake and do I need one for my business?” Written for an audience of technology decision makers tasked with keeping up with the latest and greatest data options, this book provides the perfect introductory survey of these novel and growing features of the information landscape. It explains how they can help your business, what they can (and can’t) achieve, and what you need to do to create the lake that best suits your particular needs. With a minimum of jargon, prolific tech author and business intelligence consultant Alan Simon explains how data lakes differ from other data storage paradigms. Once you’ve got the background picture, he maps out ways you can add a data lake to your business systems; migrate existing information and switch on the fresh data supply; clean up the product; and open channels to the best intelligence software for to interpreting what you’ve stored. Understand and build data lake architecture Store, clean, and synchronize new and existing data Compare the best data lake vendors Structure raw data and produce usable analytics Whatever your business, data lakes are going to form ever more prominent parts of the information universe every business should have access to. Dive into this book to start exploring the deep competitive advantage they make possible—and make sure your business isn’t left standing on the shore.

Imagine you are a business user, consultant, or developer about to enter an SAP S/4HANA implementation project. You are well-versed with SAP’s product portfolio and you know that the preferred reporting option in S/4HANA is embedded analytics. But what exactly is embedded analytics? And how can it be implemented? And who can do it: a business user, a functional consultant specialized in financial or logistics processes? Or does a business intelligence expert or a programmer need to be involved? Good questions! This book will answer these questions, one by one. It will also take you on the same journey that the implementation team needs to follow for every reporting requirement that pops up: start with assessing a more standard option and only move on to a less standard option if the requirement cannot be fulfilled. In consecutive chapters, analytical apps delivered by SAP, apps created using Smart Business Services, and Analytical Queries developed either using tiles or in adevelopment environment are explained in detail with practical examples. The book also explains which option is preferred in which situation. The book covers topics such as in-memory computing, cloud, UX, OData, agile development, and more.Author Freek Keijzer writes from the perspective of an implementation consultant, focusing on functionality that has proven itself useful in the field. Practical examples are abundant, ranging from “codeless” to “hardcore coding.” What You Will Learn Know the difference between static reporting and interactive querying on real-time data Understand which options are available for analytics in SAP S/4HANA Understand which option to choose in which situation Know how to implement these options Who This Book is For SAP power users, functional consultants, developers

A revolution is occurring in data management regarding how data is collected, stored, processed, governed, managed, and provided to decision makers. The data lake is a popular approach that harnesses the power of big data and marries it with the agility of self-service. With this report, IT executives and data architects will focus on the technical aspects of building a data lake for your organization. Alex Gorelik from Facebook explains the requirements for building a successful data lake that business users can easily access whenever they have a need. You'll learn the phases of data lake maturity, common mistakes that lead to data swamps, and the importance of aligning data with your company's business strategy and gaining executive sponsorship. You'll explore: The ingredients of modern data lakes, such as the use of different ingestion methods for different data formats, and the importance of the three Vs: volume, variety, and velocity Building blocks of successful data lakes, including data ingestion, integration, persistence, data governance, and business intelligence and self-service analytics State-of-the-art data lake architectures offered by Amazon Web Services, Microsoft Azure, and Google Cloud

"Hands-On SQL Server 2019 Analysis Services" is a comprehensive guide to mastering data analysis using SQL Server Analysis Services (SSAS). This book provides you with step-by-step directions on creating and deploying tabular and multi-dimensional models, as well as using tools like MDX and DAX to query and analyze data. By the end, you'll be confident in designing effective data models for business analytics. What this Book will help me do Understand how to create and optimize both tabular and multi-dimensional models with SQL Server Analysis Services. Learn to use MDX and DAX to query and manipulate your data for enhanced insights. Integrate SSAS models with visualization tools like Excel and Power BI for effective decision-making. Implement robust security measures to safeguard data within your SSAS deployments. Master scaling and optimizing best practices to ensure high-performance analytical models. Author(s) Steven Hughes is a data analytics expert with extensive experience in business intelligence and SQL Server technologies. With years of practical experience in using SSAS and teaching data professionals, Steven has a knack for breaking down complex concepts into actionable knowledge. His approach to writing involves combining clear explanations with real-world examples. Who is it for? This book is intended for BI professionals, data analysts, and database developers who want to gain hands-on expertise with SQL Server 2019 Analysis Services. Ideal readers should have familiarity with database querying and a basic understanding of business intelligence tools like Power BI and Excel. It's perfect for those aiming to refine their skills in modeling and deploying robust analytics solutions.

Discover the capabilities and features of SAP Analytics Cloud to draw actionable insights from a variety of data, as well as the functionality that enables you to meet typical business challenges. With this book, you will work with SAC and enable key decision makers within your enterprise to deliver crucial business decisions driven by data and key performance indicators. Along the way you’ll see how SAP has built a strong repertoire of analytics products and how SAC helps you analyze data to derive better business solutions. This book begins by covering the current trends in analytics and how SAP is re-shaping its solutions. Next, you will learn to analyze a typical business scenario and map expectations to the analytics solution including delivery via a single platform. Further, you will see how SAC as a solution meets each of the user expectations, starting with creation of a platform for sourcing data from multiple sources, enabling self-service for a spectrum of business roles, across time zones and devices. There’s a chapter on advanced capabilities of predictive analytics and custom analytical applications. Later there are chapters explaining the security aspects and their technical features before concluding with a chapter on SAP’s roadmap for SAC. Empower Decision Makers with SAP Analytics Cloud takes a unique approach of facilitating learning SAP Analytics Cloud by resolving the typical business challenges of an enterprise. These business expectations are mapped to specific features and capabilities of SAC, while covering its technical architecture block by block. What You Will Learn Work with the features and capabilities of SAP Analytics Cloud Analyze the requirements of a modern decision-support system Use the features of SAC that make it a single platform for decision support in a modern enterprise. See how SAC provides a secure and scalable platform hosted on the cloud Who This Book Is For Enterprise architects, SAP BI analytic solution architects, and developers.

Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will Learn Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it wererelational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments

Explore the modern market of data analytics platforms and the benefits of using Snowflake computing, the data warehouse built for the cloud. With the rise of cloud technologies, organizations prefer to deploy their analytics using cloud providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Cloud vendors are offering modern data platforms for building cloud analytics solutions to collect data and consolidate into single storage solutions that provide insights for business users. The core of any analytics framework is the data warehouse, and previously customers did not have many choices of platform to use. Snowflake was built specifically for the cloud and it is a true game changer for the analytics market. This book will help onboard you to Snowflake, present best practices to deploy, and use the Snowflake data warehouse. In addition, it covers modern analytics architecture and use cases. It provides use cases of integration with leading analytics software such as Matillion ETL, Tableau, and Databricks. Finally, it covers migration scenarios for on-premise legacy data warehouses. What You Will Learn Know the key functionalities of Snowflake Set up security and access with cluster Bulk load data into Snowflake using the COPY command Migrate from a legacy data warehouse to Snowflake integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools Who This Book Is For Those working with data warehouse and business intelligence (BI) technologies, and existing and potential Snowflake users

Get a head-start on learning one of SQL Server 2019’s latest and most impactful features—Big Data Clusters—that combines large volumes of non-relational data for analysis along with data stored relationally inside a SQL Server database. This book provides a first look at Big Data Clusters based upon SQL Server 2019 Release Candidate 1. Start now and get a jump on your competition in learning this important new feature. Big Data Clusters is a feature set covering data virtualization, distributed computing, and relational databases and provides a complete AI platform across the entire cluster environment. This book shows you how to deploy, manage, and use Big Data Clusters. For example, you will learn how to combine data stored on the HDFS file system together with data stored inside the SQL Server instances that make up the Big Data Cluster. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019 using Release Candidate 1. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will Learn Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For For data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environment

Use window functions to write simpler, better, more efficient T-SQL queries Most T-SQL developers recognize the value of window functions for data analysis calculations. But they can do far more, and recent optimizations make them even more powerful. In T-SQL Window Functions, renowned T-SQL expert Itzik Ben-Gan introduces breakthrough techniques for using them to handle many common T-SQL querying tasks with unprecedented elegance and power. Using extensive code examples, he guides you through window aggregate, ranking, distribution, offset, and ordered set functions. You'll find a detailed section on optimization, plus an extensive collection of business solutions — including novel techniques available in no other book. Microsoft MVP Itzik Ben-Gan shows how to: • Use window functions to improve queries you previously built with predicates • Master essential SQL windowing concepts, and efficiently design window functions • Effectively utilize partitioning, ordering, and framing • Gain practical in-depth insight into window aggregate, ranking, offset, and statistical functions • Understand how the SQL standard supports ordered set functions, and find working solutions for functions not yet available in the language • Preview advanced Row Pattern Recognition (RPR) data analysis techniques • Optimize window functions in SQL Server and Azure SQL Database, making the most of indexing, parallelism, and more • Discover a full library of window function solutions for common business problems About This Book • For developers, DBAs, data analysts, data scientists, BI professionals, and power users familiar with T-SQL queries • Addresses any edition of the SQL Server 2019 database engine or later, as well as Azure SQL Database Get all code samples at: MicrosoftPressStore.com/TSQLWindowFunctions/downloads

Become an expert who can use window functions to solve T-SQL query problems. Replace slow cursors and self-joins with queries that are easy to write and perform better. This new edition provides expanded examples, including a chapter from the world of sports, and covers the latest performance enhancements through SQL Server 2019. Window functions are useful in analytics and business intelligence reporting. They came into full blossom with SQL Server 2012, yet they are not as well known and used as often as they ought to be. This group of functions is one of the most notable developments in SQL, and this book shows how every developer and DBA can benefit from their expressive power in solving day-to-day business problems. Once you begin using window functions, such as ROW_NUMBER and LAG, you will discover many ways to use them. You will approach SQL Server queries in a different way, thinking about sets of data instead of individual rows. Your querieswill run faster, be easier to write, and easier to deconstruct, maintain, and enhance in the future. Just knowing and using these functions is not enough. You also need to understand how to tune the queries. Expert T-SQL Window Functions in SQL Server clearly explains how to get the best performance. The book also covers the rare cases when older techniques are the best bet. What You Will Learn Solve complex query problems without cumbersome self-joins that run slowly and are difficult to read Create sliding windows in a result set for computing such as running totals and moving averages Return aggregate and detail data simultaneously from the same SELECT statement Compute lag and lead and other values that access data from multiple rows in a result set Understand the OVER clause syntax and how to control the window Avoid framing errors that can lead to unexpected results Who This Book Is For Anyone who writes T-SQL queries, including database administrators, developers, business analysts, and data scientists. Before reading this book, you should understand how to join tables, write WHERE clauses, and build aggregate queries.

Business Intelligence (BI) has emerged as a field which seeks to support managers in decision-making. It encompasses the techniques, methods and tools for conducting analytically-based IT solutions, which are referred to as OLAP (OnLine Analytical Processing). Within this field, SQL has a role as a leader and is continuously evolving to cover both transactional and analytical data management. This book discusses the functions provided by Microsoft® SQL Server 2014/2016 in terms of business intelligence. The analytic functions are considered as an enrichment of the SQL language. They combine a series of practical functions to answer complex analysis requests with all the simplicity, elegance and acquired performance of the SQL language. Drawing on the wide experience of the author in teaching and research, as well as insights from contacts in the industry, this book focuses on the issues and difficulties faced by academics (students and teachers) and professionals engaged in data analysis with the SQL Server 2014/2016 database management system.

Leverage the power of SQL Server 2017 Integration Services to build data integration solutions with ease Key Features Work with temporal tables to access information stored in a table at any time Get familiar with the latest features in SQL Server 2017 Integration Services Program and extend your packages to enhance their functionality Book Description Microsoft SQL Server 2017 uses the power of R and Python for machine learning and containerization-based deployment on Windows and Linux. By learning how to use the features of SQL Server 2017 effectively, you can build scalable apps and easily perform data integration and transformation. You'll start by brushing up on the features of SQL Server 2017. This Learning Path will then demonstrate how you can use Query Store, columnstore indexes, and In-Memory OLTP in your apps. You'll also learn to integrate Python code in SQL Server and graph database implementations for development and testing. Next, you'll get up to speed with designing and building SQL Server Integration Services (SSIS) data warehouse packages using SQL server data tools. Toward the concluding chapters, you'll discover how to develop SSIS packages designed to maintain a data warehouse using the data flow and other control flow tasks. By the end of this Learning Path, you'll be equipped with the skills you need to design efficient, high-performance database applications with confidence. This Learning Path includes content from the following Packt books: SQL Server 2017 Developer's Guide by Milos Radivojevic, Dejan Sarka, et. al SQL Server 2017 Integration Services Cookbook by Christian Cote, Dejan Sarka, et. al What you will learn Use columnstore indexes to make storage and performance improvements Extend database design solutions using temporal tables Exchange JSON data between applications and SQL Server Migrate historical data to Microsoft Azure by using Stretch Database Design the architecture of a modern Extract, Transform, and Load (ETL) solution Implement ETL solutions using Integration Services for both on-premise and Azure data Who this book is for This Learning Path is for database developers and solution architects looking to develop ETL solutions with SSIS, and explore the new features in SSIS 2017. Advanced analysis practitioners, business intelligence developers, and database consultants dealing with performance tuning will also find this book useful. Basic understanding of database concepts and T-SQL is required to get the best out of this Learning Path.

Take a deep dive into the many uses of dynamic SQL in Microsoft SQL Server. This edition has been updated to use the newest features in SQL Server 2016 and SQL Server 2017 as well as incorporating the changing landscape of analytics and database administration. Code examples have been updated with new system objects and functions to improve efficiency and maintainability. Executing dynamic SQL is key to large-scale searching based on user-entered criteria. Dynamic SQL can generate lists of values and even code with minimal impact on performance. Dynamic SQL enables dynamic pivoting of data for business intelligence solutions as well as customizing of database objects. Yet dynamic SQL is feared by many due to concerns over SQL injection or code maintainability. Dynamic SQL: Applications, Performance, and Security in Microsoft SQL Server helps you bring the productivity and user-satisfaction of flexible and responsive applications to your organization safely and securely. Your organization’s increased ability to respond to rapidly changing business scenarios will build competitive advantage in an increasingly crowded and competitive global marketplace. With a focus on new applications and modern database architecture, this edition illustrates that dynamic SQL continues to evolve and be a valuable tool for administration, performance optimization, and analytics. What You'ill Learn Build flexible applications that respond to changing business needs Take advantage of creative, innovative, and productive uses of dynamic SQL Know about SQL injection and be confident in your defenses against it Address performance concerns in stored procedures and dynamic SQL Troubleshoot and debug dynamic SQL to ensure correct results Automate your administration of features within SQL Server Who This Book is For Developers and database administrators looking to hone and build their T-SQL coding skills. The book is ideal for developers wanting to plumb the depths of application flexibility and troubleshoot performance issues involving dynamic SQL. The book is also ideal for programmers wanting to learn what dynamic SQL is about and how it can help them deliver competitive advantage to their organizations.

Apache Superset Quick Start Guide teaches you how to leverage Apache Superset to create interactive and insightful data visualizations. With this book, you'll understand how to integrate Superset with popular databases and build user-friendly dashboards tailored for business intelligence needs. What this Book will help me do Set up and configure Apache Superset for data visualization tasks. Integrate data from SQL databases into Superset for dashboards. Design dashboards tailored to represent business metrics and insights. Use Superset's visualization techniques to explore and present various datasets. Understand and apply user role management and security features in Superset. Author(s) None Shekhar is an experienced data visualization and business intelligence specialist with years of experience in working with Apache Superset. They have written several guides on utilizing open-source tools for enterprise needs. Their technical expertise and approachable writing style make this guide practical and engaging. Who is it for? This book is geared towards data analysts, business intelligence professionals, and developers. Beginners to Superset can quickly grasp the fundamentals, while those with prior experience in data visualization will appreciate the advanced techniques. It's perfect for anyone looking to enhance their data storytelling and dashboard design skills.

This book, Hands-On Big Data Modeling, provides you with practical guidance on data modeling techniques, focusing particularly on the challenges of big data. You will learn the concepts behind various data models, explore tools and platforms for efficient data management, and gain hands-on experience with structured and unstructured data. What this Book will help me do Master the fundamental concepts of big data and its challenges. Explore advanced data modeling techniques using SQL, Python, and R. Design effective models for structured, semi-structured, and unstructured data types. Apply data modeling to real-world datasets like social media and sensor data. Optimize data models for performance and scalability in various big data platforms. Author(s) The authors of this book are experienced data architects and engineers with a strong background in developing scalable data solutions. They bring their collective expertise to simplify complex concepts in big data modeling, ensuring readers can effectively apply these techniques in their projects. Who is it for? This book is intended for data architects, business intelligence professionals, and any programmer interested in understanding and applying big data modeling concepts. If you are already familiar with basic data management principles and want to enhance your skills, this book is perfect for you. You will learn to tackle real-world datasets and create scalable models. Additionally, it is suitable for professionals transitioning to working with big data frameworks.