The top 20 Data Warehouse courses you need to take

1. IBM Data Warehouse Engineer

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.\n\nData 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.\n\nThis 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.\n\nYou’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.\n\nWhen 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...

2. Data Warehouse Concepts, Design, and Data Integration

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...

3. Data Warehouse Development Process

4.3

(790)

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 purposes In this course, we talk about the specific aspects of the Data Warehouse Development process taking real time practical situations and how to handle them better using best practices for sustainable, scalable and robust implementations...

4. Modern Data Warehouse Concepts

4

(88)

The objective of this course is to learn/know the fundamentals of the Modern Data Warehouse and what strategies can be used move from a traditional Data Warehouse in combination of Big Data Technologies, Data Lakes and Data Visualization. A Modern Data Warehouse gives us flexibility to analyze the data we need, in the format we need it by using familiar tools , technologies, concepts like Big Data, Hadoop, Cloud Ecosystems, BI tools and SQL/NoSQL. A Modern Data Warehouse will be able to consume and process variety of data formats like the semi structured, unstructured and multi structured. These data sets come in multiple formats and are generated from a non-transactional systems such as machines, sensors, and customer interaction streams. These systems are not only varied but also, they're producing data at volumes, varieties, and velocities like we've never seen before. This kind of data is not new. We've actually had multi-structured data for a long time, but very few organizations could work with it before because it is so expensive to store and so hard to connect or link them using the traditional data warehouse architectures or models...

5. Cloud Data Warehouse Concepts

4.2

(129)

'The Cloud' or 'Cloud computing' is one of the hottest buzzwords in technology. It appears more than 48 million times on the Internet search every day. Cloud computing and software-as-a-service (SaaS) have been around for quite some time now and we have been using it via multiple applications both at work and personally via mobile apps. But when it comes to the data warehouse on a cloud, the concept or the idea has recently emerged as an alternative to conventional or traditional, on-premises data warehousing and similar types of solutions which we have been working on. When choosing a DW solution for the first time, the very first consideration is typically one between an on-prem DW or a cloud-based one. And while a lot of folks new to the Data Warehouse domain go straight to the cloud these days because it is faster, easier and pay as you go or use method of pricing, the scalable features and the quick turnaround time on multiple aspects. Not that the Cloud is one stop solution for all Data Warehouse needs and there are still many reasons why an organization might want to choose an on-prem solution. Even now there are a lot of projects/implementations which are maintaining and enhancing the traditional data warehouses on a daily basis. And, lot of projects and team members are also dealing with issues with different kinds of sources, the increase in volumes of data and the outburst of new requirements from business and analytics to see the real value of the unstructured formats of data. I'm here to help you on your journey to understand the basics of 'Cloud' and the Cloud Data Warehouse. We would take little baby steps and go slow and easy, so you can learn more about what the cloud Data Warehouse really is and make sure that you'll understand the cloud Data Warehouse by the time you finish with this course. We will take examples of our day to day use of applications like Facebook, Netflix, Google Maps etc to learn more and understand better...

6. Relational Database Support for Data Warehouses

Relational Database Support for Data Warehouses is the third course in the Data Warehousing for Business Intelligence specialization. In this course, you'll use analytical elements of SQL for answering business intelligence questions. You'll learn features of relational database management systems for managing summary data commonly used in business intelligence reporting. Because of the importance and difficulty of managing implementations of data warehouses, we'll also delve into storage architectures, scalable parallel processing, data governance, and big data impacts. In the assignments in this course, you can use either Oracle or PostgreSQL...

7. Data Warehouse Fundamentals for Beginners

4.5

(21,823)

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 dateData 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!...

8. Modernizing Data Lakes and Data Warehouses with Google Cloud

The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course...

9. Modern Data Warehouse Analytics in Microsoft Azure

In this course, you will learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build your foundational knowledge of cloud data services within Microsoft Azure. You will explore the processing options available for building data analytics solutions in Azure. You will explore Azure Synapse Analytics, Azure Databricks, and Azure HDInsight. This is the fourth course in a program of five courses to help prepare you to take the Exam DP-900: Microsoft Azure Data Fundamentals. so that you can demonstrate that you have a foundational knowledge of the core database concepts in a cloud environment. This course is ideal for IT professionals who want to learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build their foundational knowledge of cloud data services within Microsoft Azure with a view to taking up roles as Data Engineers and Database Administrators. It is also suitable for working database professionals looking for additional skills or credentials to showcase expertise in a cloud environment and IT professionals looking to specialize in the specific area of Azure data. To be successful in this course, you need to have basic computer literacy and proficiency in the English language. Successful Azure Data Fundamentals students start with some basic awareness of computing and Internet concepts, and an interest in extracting insights from data. It is an advantage to have experience using a web browser, familiarity with basic data-related concepts, such as working with tables of data in a spreadsheet, and visualizing data using charts...

10. Modeling Data Warehouse with Data Vault 2.0

4.5

(2,342)

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!...

11. Data Warehouse - The Ultimate Guide

4.6

(4,246)

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 ETL process Dimensional Modeling: Facts & DimensionsImplementing a comeplete data warehouse hands-onSlowly Changing DimensionsUnderstanding ETL toolsELT vs. ETLAdvanced topics like: Columnar storage, OLAP Cubes, 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 warehouseBy 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!...

12. ETL Framework for Data Warehouse Environments

3.9

(594)

This course provides a high level approach to implement an ETL framework in any typical Data Warehouse environments. The practical approaches can be used for a new application that needs to design and implement ETL solution which is highly reusable with different data loading strategies, error/exception handling, audit balance and control handling, a bit of job scheduling and the restartability features and also to any existing ETL implementations. For existing implementations this framework needs to be embedded into the existing environment, jobs and business requirements and it might also go to a level of redesigning the whole mapping/mapplets and the workflows (ETL jobs) from scratch, which is definitely a good decision considering the benefits for the environment with high re-usability and improved design standards.  This course is a combination of standard and practical approaches of designing and implementing a complete ETL solution which details the guidelines, standards, developer/architect checklist and the benefits of the reusable code. And, this course also teaches you the Best practices and standards to be followed in implementing ETL solution.  Though this course, covers the ETL design principles and solutions based on Informatica 10x, Oracle 11g, these can be incorporated to any of the ETL tools in the market like IBM DataStage, Pentaho, Talend, Ab-intio etc.  Multiple reusable code bundles from the marketplace, checklists and the material required to get started on UNIX for basic commands and Shell Scripting will be provided...

13. Creating a Data Warehouse Through Joins and Unions

This is a self-paced lab that takes place in the Google Cloud console. This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs...

14. Introduction to Data Warehouse and Teradata Basics

4.4

(72)

This tutorial is intended to give an overview on, What is Data warehouse, Why we need Data Warehouse, Components of Data Warehouse, Difference between Operational Database & Data Warehouse DataBase, Difference between Data Warehouse DataBase & DataMarts. Next it focus on Teradata Database, Where does Teradata come in Data Warehouse Architecture, Who use Teradata. Later in the tutorial we will cover details on Teradata Developer roles and responsibilities. And Classification of Teradata Database Administration along with Roles and Responsibilities of Application DBA & System DBA. Important Note: Full, free lifetime access All future extra lectures and upgrades to this course is always for free. Course is charged with very very minimal amount, And its charged to meet the expenses of this course production. After course completion if you think that my course content is not worth of what I have charged then you can always ask for full refund within 30 days...

15. Data Warehouse Concepts: Basic to Advanced concepts

3.8

(2,771)

In this course, you will learn all the concepts and terminologies related to the Data Warehouse , such as the OLTP, OLAP, Dimensions, Facts and much more, along with other concepts related to it such as what is meant by Start Schema, Snow flake Schema, other options available and their differences. It also explains how the data is managed with in the Data Warehouse and explains the process of reading and writing data onto the Warehouse. Later in the course you would also learn the basics of Data Modelling and how to start with it logically and physically. You would also learn all the concepts related to Facts, Dimensions, Aggregations and commonly used techniques of ETL. Upon completion of this course, you would have a clear idea about, all the concepts related to the Data Warehouse, that should be sufficient to help you start off with the next step of becoming an ETL developer or Administering the Data warehouse environment with the help of various tools. All the Best and Happy Learning!...

16. Azure SQL Data Warehouse Synapse Analytics Service

3.8

(1,332)

Why Azure Synapse Analytics Service  (formerly Azure SQL Data Warehouse)Azure Synapse Analytics truly is a game-changer in Data processing and Analytics. In the most recent study conducted by GigaOm in January 2019 for the TPC-H benchmark report shows that Synapse Analytics is 14 times fast and still 94% cheaper than any other leading service in the market. And that's why clients are shifting to Azure and Azure is growing at nearly twice the rate of Amazon Web Services. According to a 2019 Dice report, there was an 88% year-over-year growth in job postings for data engineers, which was the highest growth rate among all technology jobs. If you are interested in this domain, there cannot be a better time to start learning Azure Synapse Analytics Data Warehouse then now. Expected OutcomesIn this courseYou will learn the difference between Traditional vs Modern vs Synapse Data warehouse architectureYou will learn why Microsoft Synapse Analytics service is going to be a game-changer in the Data AnalyticsYou will learn how to provision, configure and scale Azure Synapse Analytics serviceYou will learn Cloud Data Warehouse MPP architecture, table types, partitioning, distribution key, and many other important concepts. You will learn different migration techniques and advantage of PolyBase over other techniques with lots of DemosYou will learn Security, Configuration, backups, monitoring and other important topics with lots of DemosBy the end of this course, you will have a fairly good understanding of Synapse Service and you can directly start working in a Production environment.100% Syllabus covered for DP200 and DP201 certification exam for Azure Data warehouse (Synapse)What if I am new in Data Warehouse?I have included a module on Data Warehouse Basics (Crash course to speed up with Cloud warehousing)LevelBeginners & intermediateIntended AudienceBeginners in Azure PlatformData Warehouse developers/ adminsDatabase and BI developersDatabase AdministratorsData EngineersData ScientistData Analyst or similar profilesOn-Premises Database related profiles who want to learn how to implement these technologies in Azure Cloud. Anyone who is looking forward to starting his career as an Azure Data Engineer. PrerequisitesBasic T-SQL and Database conceptsAzure Free trial SubscriptionLanguageEnglishIf you are not comfortable in English, please do not take the course, captions are not good enough to understand the course. What's insideVideo lectures, PPTs, Demo Resources, Quiz, Assignment, other important linksFull lifetime access with all future updatesCertificate of course completion30-Day Money-Back GuaranteeCourse In DetailIntroductionMicrosoft has recently released this brand new service, which is a big success for the data team at Microsoft. Synapse contained very rich features, not only to the engine itself to increase performance, but also to add new functionality in providing a unified analytics experience for diff data teams. In the most recent study conducted by GigaOm in January 2019 for the TPC-H benchmark report shows that Synapse Analytics is 14 times fast and still 94% cheaper than any other leading service in the market. And that's why clients are shifting to Azure and Azure is growing at nearly twice the rate of Amazon Web Services. Azure Synapse Analytics truly is a game-changer in Data processing and Analytics. According to a 2019 Dice report, there was an 88% year-over-year growth in job postings for data engineers, which was the highest growth rate among all technology jobs. If you are interested in this domain, there cannot be a better time to start learning Azure Synapse Analytics Data Warehouse then now. I hope you will join me on this exciting journey of learning this technology. Azure Synapse Analytics ServiceWhy we should consider warehousing solutions in the cloud?And then we'll discuss Microsoft's brand new Azure Synapse analytics service, and how this service brings together enterprise data warehousing and Big Data analytics, and provide a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs. Advantages of Synapse analytics service over other cloud-based analytics services. We will discuss the difference between Traditional vs Modern vs Synapse Architecture. You will also see Azure Synapse studio which provides a unified experience for all Data Professionals. So, whether you are Data Engineer, Data Scientists, Database administrator, business analysts or any other IT professional, you will find your space in Synapse studio. And then finally in Demo, we'll provision new Azure Synapse Analytics Service, we will see how to pause or resume compute node which is very important, and how to set firewall rules and connect with SQL Server Management studioInternal and ArchitectureIn this module where you are going to learn Azure Data Warehouse famous MPP or Massive parallel processing architecture. And then we'll discuss various cloud data warehousing internal but important concepts like storage and data distribution through Hash, round-robin and replicated tablesWe will learn not only different Data types and table types like columnstore, heap and Clustered B-tree index, but we will also learn best practices around, how to partition our data into these diff table types. We will discuss in detail about distribution key and how to analyses the table to find the best distribution key according to partition. Then we'll discuss how to apply these concepts in dimensional modeling. And then finally we'll take a case of Microsoft's famous Adventure works DW, we will download and restore it in our on-premises management studio, and we will analyze the distribution and the data types for Data Warehouse and we will prepare it to migrate to Cloud data warehousing. Data MigrationIn this module, we will learn about data loading or data migration in the Azure Data warehouse serviceWe'll start with learning the best practices of loading data in MPP architectureThen we'll learn about different loading methods and we will learn the difference between Single client loading methods and Parallel readers loading methodsWe will see specifically the difference between SSIS and PolyBase loading methods, and why PolyBase is preferred for large tables. We will learn the PolyBase process in detail and go through all steps to set up the PolyBase environment. SecurityIn this module, we will take a look at how we can secure our azure SQL data warehouseActually, without security, nothing else really matters and that's why Microsoft Azure provides 5 layers of defense to secure your Azure SQL DatabaseFirst is Threat Protection - This is a most outer layer of security, and in this Microsoft Azure constantly monitor the traffic to your Azure SQL Database and look for suspicious patterns. Then the next comes to Network Security - Network security is to make sure only requests which are coming from valid IP addresses can access your database. Authentication and Access control are part of Access ManagementAuthentication - Authentication is about validating your credentials like User Name/User ID and password to verify your identity. And Access control or Authorization determines what kind of access the authenticated user has over particular resources. And finally comes the Data Protection layer - Microsoft provides different information protection and encryption technologies to protect our data in your Azure SQL Database. Configuration and OptimizationIn this module, we'll examine configuration settings and common tasks that are available to us inside the Microsoft Azure Synapse Analytics service portal. We will see it is how incredibly easy to backup and restore data warehouse in the Synapse Analytics service portal. Then, we will take a look at price optimization and will see how you evaluate different configuration suits best for your needs. We will learn about Managing workload, which helps solution Architects to ensure that data warehouses always have enough resources to hit SLA for classic data warehousing activities like loading, transforming and querying data. We will learn different monitoring tools like query activity, alerts, metrics, diagnostic settings and resource health provided by Synapse portal. We will also look at the option to submit a support ticket to Microsoft in case you are not able to resolve the issue at your end. And Finally, we are going to delete all the resources we created during Demo, this is very important to avoid any charges when you are not using the system. Data Warehouse Crash CourseIn this module, you will learn, what is Data Warehouse, Why we need it and how it is different from the traditional transactional database. We will learn the concept of dimensional modeling which is a database design method optimized for data warehouse solutions. Then I will explain what we mean when we say facts and their corresponding fact tables. What are dimensions and their corresponding dimension tableshow are these special kinds of tables joined together to form a star schema or snowflake schema. This section will establish the foundation before you start my course on Azure Synapse Analytics or formally known as Azure SQL Data Warehouse. Some students Feedback (from other courses)One of the most amazing courses i have ever taken on Udemy. Please don't hesitate to take this course. The instructor is really professional and has a great experience about the subject of the course. - Khadija BadaryVery nicely explained most of the concepts. a must have course for beginners - Manoranjan SwainI appreciate this course explaining everything in great detail for a beginner. This will assist me in overcoming challenges at my work - Benjamin CurtisGood course for Beginners. Labs are really helpful to grasp the concept. Thank you - SapnaTopics touched in this courseMicrosoft SQL Server, Azure SQL Server, Azure SQL Data Warehouse, Data Factory, Data Lake, Azure Storage, Azure Synapse Analytics Service, PolyBase, Azure monitoring, Azure Security, Data Warehouse, SSIS...

17. Data Warehouse ETL Testing & Data Quality Management A-Z

4.4

(2,977)

Learn the essentials of ETL Data Warehouse Testing and Data Quality Management through this step-by-step tutorial. This course takes you through the basics of ETL testing, frequently used Data Quality queries, reporting and monitoring. In this tutorial we will learn how to build database views for Data Quality monitoring and build Data Quality visualizations and reports!.. Learn to build data quality dashboards from scratch!.. Learn some of the most common mistakes made when performing ETL/ELT tests.... Forget about manual ad-hoc ETL testing, learn more about automated ETL and data quality reportsThe course contains training materials, where you can practice, apply your knowledge and build an app from scratch. The training materials are provided in an Excel file that you can download to your computer. Each module also ends with a short quiz and there is a final quiz at the end of the course. After completion of this course, you will receive a certificate of completion. Good luck and hope you enjoy the course. Pre-requisites: Basic knowledge of SQL Some experience with Visualization tools would be helpful, but not required Basic setup of database (PostgreSQL, Oracle) and visualization tool (Qliksense) is recommendedCourse content: The course consists of the following modules: IntroductionWhat is ETL/ELT Testing and Data Quality Management?Build database views for Data Quality MonitoringBuild dashboards for ReportingExercisesFinal QuizWho should follow this course?Students that want to learn the basics of ETL/ELT testing and Data Quality ManagementBusiness Analysts and Data Analysts that would like to learn more about ETL/ELT testing, frequently used queries and practical examplesSoftware Engineers that would like to build an automated solution for ETL/ELT testing using database views/dashboardsData Stewards and Managers considering to apply data quality standards within their organization...

18. Implementing a Data Warehouse with SQL Server 2012

4.6

(2,088)

Adding to its data management system Microsoft has come up with a new Server, Microsoft SQL Server 2012 which familiarizes us with the construction and usage of databases in SQL Server platform. This course is the successor of Microsoft SQL Server 2012, a step higher into the administration of the data sytem. It is an excellent platform for students to build and implement a data warehouse. The course intends to target all data professionals including data analysts and other aspiring professionals who wants to get ready for exam 70-463, also known as Implementing a Data Warehouse with SQL Server 2012. Towards the end of this course our participants will have a thorough knowledge on data warehouses and the uses of dimensions. Apart from that our learner will also understand the importance of Fact Table along with the various concepts that are involved in the implementation of Data Warehouse with SQL Server 2012. This course also looks into the different elements of Control Flow and allows the learner to comprehend how to work with variables. In this course you will learn about the different types of Transforms available in SSIS, apart from how to deploy and manage packages. Finally you will understand how to debug and secure packages. This course is that is the basis for all other SQL Server-related disciplines-Database Development, Database Administration, and Business Intelligence. The main idea of this course is to make our students cognize SQL Server 2012 databases administration. You will be comprehending a lot about the various issues and other decisions that are part of SQL Server installation and configuration. SQL Server 2012 is a prevailing platform that is widely used in the enterprise and cloud. There are many critical systems based on it. This Exam 70-463 is also a part of the series of certifications to master this platform. Apart from this as a student you will keen to look into the various operations involved including building and managing data warehouse and architecturing and implementing dimensions. You will also find it both challenging and interesting to work with various variables. There will also be a discussion on some of the important topics namely, instance, database and object security strategies. You will be also interested in implementing and automating ETL Solution. Some of the high availability technologies will also be discussed as part of the training by looking into deploying and managing packages along with debugging and securing themOur training is broken down to 90 lecture sessions that will cover all objectives. As add ons, we are also providing demos on other major concepts so that participants understand how the steps learned are implemented in real time...

19. Implementing a Data Warehouse with Microsoft SQL Server

3.9

(333)

This course describes how to implement a data warehouse solution. students will learn how to create a data warehouse with Microsoft SQL Server 2014, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services. Target Audience: =>This course is intended for database professionals who need to create and support a data warehousing solution. Primary responsibilities 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 2014••Download and attaching Adventureworks2014 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 warehouse...