Courses

Develop and Implement a Data Warehouse Solution Step by step What you'll learn: Design a data warehouseImplement a data warehouseCreate ETL Solution with SSISTroubleshoot and Debug SSIS packageDeploying SSIS SolutionEnforce Data QualityCleanse dataExploring Data SourcesExtracting modified dataLoading and modifying dataConsuming data in Data WarehouseGenerate Reports using SSRSAnalyse data with SSASInteract with Data using T-SQL This course describes how to design and implement a data warehouse solution. students will learn how to create a data warehouse with Microsoft SQL Server implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services. The Primary responsibilities of a data warehouse developer include: Implementing a data warehouse. Developing SSIS packages for data extraction, transformation, and loading. Enforcing data integrity by using Master Data Services. Cleansing data by using Data Quality Services. Prerequisites :Experience of working with relational databases, including: Designing a normalized database. Creating tables and relationships. Querying with Transact-SQL. Some exposure to basic programming constructs (such as looping and branching). An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable. Students will learn how to :••Deploy and Configure SSIS packages. ••Download and installing SQL Server ••Download and attaching AdventureworksDW database ••Download and installing SSDT ••Download and installing Visual studio ••Describe data warehouse concepts and architecture considerations. ••Select an appropriate hardware platform for a data warehouse. ••Design and implement a data warehouse. ••Implement Data Flow in an SSIS Package. ••Implement Control Flow in an SSIS Package. ••Debug and Troubleshoot SSIS packages. ••Implement an ETL solution that supports incremental data extraction. ••Implement an ETL solution that supports incremental data loading. ••Implement data cleansing by using Microsoft Data Quality Services. ••Implement Master Data Services to enforce data integrity. ••Extend SSIS with custom scripts and components. ••Databases vs. Data warehouses ••Choose between star and snowflake design schemas ••Explore source data ••Implement data flow ••Debug an SSIS package ••Extract and load modified data ••Enforce data quality ••Consume data in a data warehouseThe volume of data available is huge and increasing daily. Structured Query Language -SQL (pronounced as sequel) is the standard language used to communicate and interact with data stored in relational management database systems like Microsoft SQL Server Oracle, PostgreSQL,MySQL etc.Different database management systems have their own proprietary version of the SQL language but they all conform to using some commands in SQL the same way. Microsoft SQL Server's version of SQL is known as Transact-SQL (T-SQL).You will learn the basics of the SQL language and Transact-SQL since both use certain commands in the same way. What You will learn includes: Installing SQL ServerInstall SSMSBasic Database ConceptsCreating DatabaseCreating TableCreating ViewsCreating stored proceduresReading data from a databaseUpdating database recordsBacking up databaseDeleting RecordsTruncating TableDropping TableDropping DatabaseRestore Database

Whether you’re an aspiring data engineer, data architect, business analyst, or data scientist, strong data warehousing skills are a must. With the hands-on experience and competencies, you gain on this course, your resume will catch the eye of employers and power up your career opportunities. A data warehouse centralizes and organizes data from disparate sources into a single repository, making it easier for data professionals to access, clean, and analyze integrated data efficiently. This course teaches you how to design, deploy, load, manage, and query data warehouses, data marts, and data lakes. You’ll dive into designing, modeling, and implementing data warehouses, and explore data warehousing architectures like star and snowflake schemas. You’ll master techniques for populating data warehouses through ETL and ELT processes, and hone your skills in verifying and querying data, and utilizing concepts like cubes, rollups, and materialized views/tables. Additionally, you’ll gain valuable practical experience working on hands-on labs, where you’ll apply your knowledge to real data warehousing tasks. You’ll work with repositories like PostgreSQL and IBM Db2, and complete a project that you can refer to in interviews.

Data Warehouse Engineers and Business Analysts are in high demand as organizations become increasingly dependent on data to support their operations. Data warehousing has transformed the way organizations perform business analysis and make strategic decisions. Massive amounts of data from multiple sources can be easily accessed using SQL and formatted for analysis, reporting and Business Intelligence for organizations to gain deeper business insights. The Data Warehouse Engineer Professional Certificate provides you the skills and knowledge to design, deploy and manage Enterprise Data Warehouses (EDW) and utilize Business Intelligence tools to analyze and extract insights using reports and dashboards. Upon completing this program, you’ll gain practical experience to work with Relational Database Management Systems (RDBMS), query data using SQL statements, utilize Linux/UNIX shell scripts to automate repetitive tasks, and build data pipelines using Apache Airflow and Kafka to Extract, Transform and Load (ETL) data. You’ll also acquire the skills to build and operationalize Data Warehouses and conduct data analysis. Within each course you’ll practice your skills with numerous hands-on labs and multiple projects to add to your portfolio for launching your career. To get started, all you need is basic computer literacy and the desire to learn and practice new skills.

This Professional Certificate is intended to help you develop the job-ready skills and portfolio for an entry-level Business Intelligence (BI) or Data Warehousing Engineering position. Throughout the online courses in this program, you will immerse yourself in the in-demand role of a Data Warehouse Engineer and acquire the essential skills you need to work with a range of tools and databases to design, deploy, operationalize and manage Enterprise Data Warehouses (EDW). By the end of this Professional Certificate, you will be able to perform the key tasks required in a data warehouse engineering role. You will work with Relational Database Management Systems (RDBMS) and query data using SQL statements. You will use Linux/UNIX shell scripts to automate repetitive tasks, and build data pipelines with tools like Apache Airflow and Kafka to Extract, Transform and Load (ETL) data. You will gain experience with managing databases and data warehouses. Finally, you will design and populate data warehouse systems and utilize Business Intelligence tools to analyze and extract insights using reports and dashboards. This program is suitable for anyone with a passion for learning and is suitable for you whether you have a college degree or not and does not require any prior data engineering, or programming experience.

Specific aspects of Data Warehouse Development Process What you'll learn: Understand various stages in Data Warehouse development processVarious processes like Waterfall model, V model and Agile methodsSpecific aspects of Data Warehouse development processImportance of the various phases and the practicality of each phaseOverview of various issues and Project Management issues to be considered in the Data warehouse and Business Intelligence projects Data is the new asset for the enterprises. And, Data Warehouse store the data for better insights and knowledge using Business Intelligence. Development of an Enterprise Data Warehouse has more challenges compared to any other software projects because of the Challenges with data structuresThe way data is evaluated for it's qualityComplex business rules/validationsDifferent development methods (various SDLC models like Water Fall model, V model, Agile Model, Incremental model, Iterative model)Regulatory requirements for various domains like finance, telecom, insurance, Retail and IMECompliance from third party governing bodiesExtracting data for various visualization purposesIn this course, we talk about the specific aspects of the Data Warehouse Development process taking real time practicalsituations and how to handle them better using best practices for sustainable, scalable and robust implementations.

Prepare for a career in the field of data warehousing. In this program, you’ll learn in-demand skills like SQL, Linux, and database architecture to get job-ready in less than 3 months. Data warehouse engineers design and build large databases called data warehouses, used for data and business analytics. They work closely with data analysts, data scientists, and project management to power analysis that enable insights and inform decision-making. This program will teach you the foundational data warehousing skills employers are seeking for entry level data warehouse roles. This program will not only help you start your career in data warehousing, but also provides a strong foundation for future career development in other paths such as Business Intelligence (BI) roles. You’ll learn the latest tools used by professional data warehouse engineers including Relational Database Management Systems (RDBMS), PostgreSql, and MySQL. Alongside these tools, learn how to use Linux/UNIX shell scripts to automate repetitive tasks and build data pipelines and Extract, Transform and Load (ETL) data. You’ll also work with data warehouses and query them using SQL and BI tools. When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support.

Explore the evolution of data warehousing in this 57-minute conference talk from PASS Data Community Summit. Learn about Microsoft's approach to adapting existing data warehouse infrastructure to handle the explosive growth in data volume, variety, and velocity. Discover how to scale out relational data to petabytes using Parallel Data Warehouse technologies, and non-relational data with HDInsight. Understand the benefits of in-memory columnstore for enhanced performance and how to manage mixed workloads effectively. Delve into big data concepts, including Hadoop and its various offerings for on-premise and cloud deployments. Examine the integration of relational data and Hadoop using PolyBase, and explore the flexibility of deployment options and hybrid solutions in modern data warehousing.

In this course, you’ll learn to create cloud-based data warehouses. You’ll sharpen your data warehousing skills, deepen your understanding of data infrastructure, and be introduced to data engineering on the cloud using Amazon Web Services (AWS).

Explore fundamental shifts in data warehousing architecture and data processing design principles in this 42-minute conference talk by Maria Zakourdaev. Gain modern data warehouse modelling insights to build future-ready data infrastructures. Learn about evolving trends, best practices, and innovative approaches that are shaping the next generation of data warehouses. Discover how to adapt traditional methodologies to meet the demands of today's data-driven organizations and prepare for tomorrow's challenges in data management and analytics.

Explore Microsoft's modern data warehouse architecture in this comprehensive conference talk from PASS Data Community Summit. Dive into the rebranding of Azure SQL Data Warehouse to Azure Synapse Analytics and discover how it goes beyond a traditional relational data warehouse. Learn about Microsoft's vision for modern analytics data architecture, encompassing traditional datasets, big data, and real-time streaming analytics under a unified interface. Gain insights into the architectural overview of the platform, current features, upcoming developments, and how this evolution impacts analytics approaches. Understand the implications of Azure Synapse Analytics for data professionals and organizations seeking to leverage advanced analytics capabilities.

Best Practices and Concepts for Architecture and Dimensional Design What you'll learn: Master the techniques needed to build a data warehouse for your organization.Determine your options for the architecture of your data warehousing environment.Apply the key design principles of dimensional data modeling.Combine various models and approaches to unify and load data within your data warehouse. If you are a current or aspiring IT professional in search of sound, practical techniques to plan, design, and build a data warehouse or data mart, this is the course for you.During the course, you’ll put what you learn to work and define sample data warehousing architectures and dimensional data structures to help emphasize the best practices and techniques covered in this course. Each section has either scenario based quiz questions or hands on assignments that emphasizes key learning objectives for that section’s material. This way, you can be confident as you move through the course that you’re picking up the key points about data warehousing.To build this course, I drew from more than 30 years of my own data warehousing work on more than 40 client projects and engagements. I’ve been a thought leader in the discipline of data warehousing since the early 1990s when modern data warehousing came onto the scene. I’ve literally seen it all...and written about the discipline of data warehousing in books such as the original Data Warehousing For Dummies ® , along with articles, white papers, and as a monthly data warehousing columnist. I’ve led global consulting practices delivering data warehousing (and its related discipline, business intelligence) to some of the most recognizable brand name customers, along with smaller-sized organizations and governmental agencies. My own consulting firm, Thinking Helmet, Inc., specializes in data warehousing, business intelligence, and related disciplines. I’ve rolled up my sleeves and personally tackled every aspect of what you’ll learn in this course. I’ve even learned a few painful lessons, and have built a healthy share of “lessons learned” into the course material.In this course, I take you from the fundamentals and concepts of data warehousing all the way through best practices for the architecture, dimensional design, and data interchange that you’ll need to implement data warehousing in your organization. You’ll find many examples that clearly demonstrate the key concepts and techniques covered throughout the course. By the end of the course, you’ll be all set to not only put these principles to work, but also to make the key architecture and design decisions required by the “art” of data warehousing that transcend the nuts-and-bolts techniques and design patterns.Specifically, this course will cover:Foundational data warehousing concepts and fundamentalsThe symbiotic relationship between data warehousing and business intelligenceHow data warehousing co-exists with data lakes and data virtualizationYour many architectural alternatives, from highly centralized approaches to numerous multi-component alternativesThe fundamentals of dimensional analysis and modelingThe key relational database capabilities that you will put to work to build your dimensional data modelsDifferent alternatives for handling changing data history within your environment, and how to decide which approaches to apply in various situationsHow to organize and design your Extraction, Transformation, and Loading (ETL) capabilities to keep your data warehouse up to date Data warehousing is both an art and a science. While we have developed a large body of best practices over the years, we still have to make this-or-that types of decisions from the earliest stages of a data warehousing project all the way through architecture, design, and implementation. That’s what I’ve instilled into this course: the fusion of data warehousing art and science that you can bring to your organization and your own work. So come join me on this journey through the world of data warehousing!

Discover how to streamline data warehouse development and management in this 45-minute conference talk from SQLBits. Learn to leverage metadata for automating repetitive tasks, reducing pipeline clutter, and accelerating data warehouse implementation. Explore innovative approaches to Insert/Update/Delete operations and efficient data loading techniques. Gain insights from experienced speakers Barney Lawrence, Emma Dolling, and Ruth Pearson as they share their expertise in Microsoft data platforms, business intelligence, and modern analytics. Delve into topics such as SQL 2019, Power BI, data modeling, and platform-agnostic solutions. Enhance your skills in developing, modeling, and visualizing data for more effective data warehouse management.

AWS SimuLearn is an online learning experience that pairs generative AI-powered simulations with hands-on practice to help individuals learn how to translate business problems into technical solutions through the simulation of dialog between a customer and a technology professional. AWS SimuLearn: Cloud Data Warehouse In this AWS SimuLearn assignment, you will review a real-world scenario helping a fictional customer design a solution on AWS. After the design is complete, you will build the proposed solution in a guided lab within a live AWS Console environment. You will gain hands-on experience working with AWS services, using the same tools technology professionals use to construct AWS solutions. How does it work? AWS SimuLearn is powered by generative AI that enables you to have life-like conversations with AI customers. Your responses to the AI are evaluated to help develop your soft skills like communication. and problem solving. As you provide the correct responses, a quiz agent with test you and you can also get help from Dr. Newton, the helper agent when you get stuck. Once you provide all of the correct responses for the solution you will move to building it and validating in a live AWS Console environment. AWS SimuLearn: Cloud Data Warehouse - Services Used Cloud Data Warehouse AWS Glue, Amazon Redshift, Amazon S3

Master Data Warehousing, Dimensional Modeling & ETL process What you'll learn: Architect & implement a professional data warehouse end-to-endYou will learn the principles of Data Warehouse DesignYou will master ETL process in both theory & practiseYou will implement in a case study your own data warehouse & ETL processYou will learn the modern architecture of a Data WarehouseDimensional Modeling in a professional way Master Data Warehousing, Dimensional Modeling & ETL processDo you want to learn how to implement a data warehouse in a modern way?This is the only course you need to master architecting and implementing a data warehouse end-to-end! Data Modeling and data warehousing is one of the most important skills in Business Intelligence &Data Engineering!This is the most comprehensive &most modern course you can find on data warehousing. Here is why:Most comprehenisve course with 9 hours video lecturesLearn from a real expert - crystal clear & straight-forwardMaster theory &practice - hands-on demonstrations, assignments &quizzesWe will implement a complete data warehouse - end-to-endUnderstand everything step by step from the absolute basics to the advanced topicsLearn the practical steps and the important theory to upskill your careerThis course will take you all the way to being able to architect and implement a data warehouse in a company in a professional manner. Here is what you'll learn:Data Warehouse BasicsData Warehouse architectureData Warehouse infrastructureData ModelingSetting up an ETLprocess Dimensional Modeling:Facts &DimensionsImplementing a comeplete data warehouse hands-onSlowly Changing DimensionsUnderstanding ETLtoolsELTvs. ETLAdvanced topics like:Columnar storage, OLAPCubes, In-memory databases, massive parallel processing &cloud data warehousesOptimizing a data warehouse using indexes (B-tree indexes &Bitmap indexes)Practically using and connecting a data warehouse By the end of this course you will be able to design &build a complete data warehouse from the ground up. You will have the knowledge, the practical skills and the confidence to implement a modern data warehouse professionally.Everything you need to be a highly proficient data architect, data engineer, data analyst or Business Intelligence expert! Join now to get instant & lifetime access - of course backed by the no-questions-asked 30 days money back guarantee!

Discover the basics of data warehouses, how they function and differ from other solutions, and how to implement them in your current or future role.

Explore the latest developments in data lakehouse querying and learn how to maximize the potential of your data lakehouse in this 20-minute conference talk. Discover why proprietary data warehouses may not be the best solution for accelerating queries and delve into cutting-edge technical advancements in query engines. Gain insights into Coinbase's data architecture, which utilizes Databricks Lakehouse and StarRocks. Understand the challenges of achieving flexibility, scalability, and cost-effectiveness in data lakehouses, and learn how to overcome them without resorting to complex and costly ingestion pipelines that compromise data governance and freshness. Presented by Eric Sun, Senior Engineering Manager at Coinbase, and Sida Shen, Product Manager at CelerData, this talk offers valuable knowledge for data professionals seeking to optimize their data lakehouse performance.

This is the second course in the Data Warehousing for Business Intelligence specialization. Ideally, the courses should be taken in sequence. In this course, you will learn exciting concepts and skills for designing data warehouses and creating data integration workflows. These are fundamental skills for data warehouse developers and administrators. You will have hands-on experience for data warehouse design and use open source products for manipulating pivot tables and creating data integration workflows. In the data integration assignment, you can use either Oracle, MySQL, or PostgreSQL databases. You will also gain conceptual background about maturity models, architectures, multidimensional models, and management practices, providing an organizational perspective about data warehouse development. If you are currently a business or information technology professional and want to become a data warehouse designer or administrator, this course will give you the knowledge and skills to do that. By the end of the course, you will have the design experience, software background, and organizational context that prepares you to succeed with data warehouse development projects. In this course, you will create data warehouse designs and data integration workflows that satisfy the business intelligence needs of organizations. When you’re done with this course, you’ll be able to: * Evaluate an organization for data warehouse maturity and business architecture alignment; * Create a data warehouse design and reflect on alternative design methodologies and design goals; * Create data integration workflows using prominent open source software; * Reflect on the role of change data, refresh constraints, refresh frequency trade-offs, and data quality goals in data integration process design; and * Perform operations on pivot tables to satisfy typical business analysis requests using prominent open source software

Course covers the basics and fundamentals of Data Vault 2.0 along with Agile Methodology and Big Data What you'll learn: Get understanding about Traditional Data warehouse conceptsLearn fundamentals of the Data Vault modeling approachUnderstand principles of Agile Project Management in Data Vault 2.0Learn Big Data Platforms Integration with Data Vault 2.0Understand Data Vault architecture and layersLearn Business and Information VaultUnderstand advanced data modeling techniquesDesign a Data Vault practical scenario from scratchConvert 3NF & Dimensional Model to Data VaultUnderstand Dimensional model principles in Data Vault Data Vault is an innovative modeling technique invented by Dan Linstedt to simplify data integration from multiple sources, offers auditability and design flexibility to cope with data from the heterogeneous information systems which supports most business demands todayIt is designed to deliver an Enterprise Data Warehouse while solving many of the drawbacks of the 3NF (Inmon) and Dimensional Modelling(Kimball).In this course, you will Learn the basics of Data Modelling to become familiar with core conceptsUnderstand the fundamentals of traditional Data Warehouse approachesLearn many of today’s Data Warehousing problems and issues with 3NF or Star SchemaUnderstand how Data Vault addresses these challenges and provide an innovative approachLearn the fundamentals of the Data Vault modeling approach from core concepts to advanced, and from architecture to key benefits Learn how to effectively model Hubs, Links and SatellitesUnderstand DV Modeling constructs in detailUnderstand the different architectural and modeling layers of DV 2.0Learn Business Vault, Information Vault and significance of Dimensional LayerUnderstand where to use 3NF, Dimensional Model or Data VaultUnderstand loading patterns and architectureLearn how to handle schema and grain changes on the Data Vault modelLearn why Agile Methodology is important for scalable Data Warehouses Get familiar with Big Data Terminologies along with Data Vault Methodology It also contains a hands-on case study to get participants familiar with the principles and concepts Footnote: Automatically created subtitles are corrected!

This course brings ABAP programming and SAP Business Warehouse together. Learn ABAP development practices that will bring balance, quality, and performance to your projects. Business Warehouse Developers need to model and develop many complex data flows and queries. Learn ABAP to add an indispensable tool into your toolset and to solve complex problems in the most important areas of BW like transformation, DTP, InfoPackage, and query variables. Besides learning ABAP syntax, you will also gain insight into a programming style that keeps the performance, maintainability, and readability in focus.

Dive into a comprehensive 90-minute webinar on designing, building, and operating Modern Data Warehouse (MDW) solutions in Azure. Explore key considerations for successful MDW projects, including architecture, security, business continuity, governance, and monitoring. Learn about data lakes, infrastructure as code, DataOps, and large-scale data copy pipelines. Discover best practices for data governance, monitoring, and security in MDW environments. Gain insights into Azure Synapse Analytics and its role in modern data warehousing. Benefit from expert knowledge shared by Microsoft FastTrack and Cloud Solution Architects, based on experiences from hundreds of organizations. Follow along with provided Microsoft Learn resources to deepen your understanding and accelerate your next MDW project.