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We're here to help you find itEnhance your data management skills with our course on Implementing a SQL 2016 Data Warehouse (SSIS). This training focuses on essential concepts such as data warehousing, ETL processes, and how to leverage Microsoft SQL Server SSIS for effective data extraction and transformation.
Students will explore topics like designing fact and dimension tables, implementing columnstore indexes, and utilizing Azure SQL Data Warehouse. Practical labs ensure hands-on experience, allowing learners to deploy and configure SSIS packages efficiently.
By the end of this course, you will be equipped to build robust data warehouse solutions, ensuring high data quality and seamless integration, making you a valuable asset in any data-driven organization.
Successfully delivered 139 sessions for over 210 professionals
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| Course Fee | 2,500 |
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Total Fees |
2,500 (USD) |
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
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Prerequisites for the Implementing a SQL 2016 Data Warehouse (SSIS) Course (Course Code: 20767)
To successfully undertake the Implementing a SQL 2016 Data Warehouse (SSIS) course, it is recommended that students have the following minimum knowledge and skills:
This foundational knowledge will help students grasp the concepts presented in the course and engage more effectively with the hands-on labs and practical applications.
Implementing a SQL 2016 Data Warehouse (SSIS) equips professionals with essential skills for effectively designing and managing data warehouses, focusing on SQL Server and Azure solutions.
The Implementing a SQL 2016 Data Warehouse (SSIS) course equips participants with the essential skills and knowledge to design, implement, and manage data warehouse solutions using SQL Server Integration Services (SSIS) and Azure SQL Data Warehouse.
Columnstore indexes optimize the storage and querying of data in databases, particularly useful for large data sets used in analytics and reporting. Instead of storing data row-by-row as in traditional databases, columnstore indexes store data column-wise. This organization allows for faster retrieval of data columns, compresses data efficiently, and significantly improves query performance. By focusing on columnar storage, you can accelerate data analysis and operational reporting, making it an excellent choice for Microsoft SQL Server environments focused on heavy read operations, such as big data analysis and business intelligence applications.
Azure SQL Data Warehouse is a cloud-based data warehousing service from Microsoft that leverages the power of SQL Server. It's designed to process large volumes of data quickly by scaling compute resources, allowing for high-performance analytics. This service integrates with various Microsoft tools, including SSIS (SQL Server Integration Services) for data integration, SSAS (SQL Server Analysis Services) for data analysis, and SSRS (SQL Server Reporting Services) for reporting. Azure SQL Data Warehouse is ideal for businesses looking to manage, analyze, and report on big data with the flexibility of cloud scalability and the integration of various Microsoft technologies.
SQL Server Integration Services (SSIS) is a component of Microsoft SQL Server used for data integration and workflow applications. You can customize SSIS by writing your own scripts and creating unique components. This customization allows you to extend its functionality to meet specific requirements not covered by the pre-existing tasks and transformations within SSIS. Essentially, through custom scripts and components, SSIS can be tailored to perform complex data manipulation, conversion, and integration tasks more effectively, enhancing its utility in data management and analysis projects.
SQL Server Data Quality Services (DQS) is a feature of Microsoft SQL Server that helps to ensure the quality of your data. DQS enables you to cleanse, match, and manage data efficiently. It lets you build knowledge bases and define rules to identify incomplete, incorrect, or inaccurate data, improving the reliability of reports and decisions. By integrating with Microsoft SSIS (SQL Server Integration Services), it enhances data integration and workflow solutions, supporting data integrity across different systems and platforms. This service is crucial for maintaining accurate and dependable data in businesses of all sizes.
SQL Server Master Data Services (MDS) is a feature of Microsoft SQL Server, used for managing the critical data within an organization. It ensures the accuracy, uniformity, and consistency of critical data, known as master data, across different systems in an organization. MDS facilitates creating a central repository that provides a single, comprehensive view of business-critical information. Through MDS, data stewards can define and manage rules, hierarchies, and relationships of the data, helping in governance and compliance. It simplifies data management and helps in making informed decisions based on reliable and accurate data.
A data warehouse is a centralized system used for storing, analyzing, and securely handling large amounts of business data. It allows organizations to consolidate information from multiple sources so it can be accessed and analyzed from one place. This enables more informed decision-making and strategic business insights. Typically, data warehouses support tools and components like Microsoft SSIS (SQL Server Integration Services) for efficient data integration, SSAS (SQL Server Analysis Services) for data analysis, and SSRS (SQL Server Reporting Services) for generating comprehensive reports, enhancing the overall utility and performance of the data warehouse.
Infrastructure planning is the process of organizing and defining the hardware, software, and systems necessary to build and support a technology environment. It involves assessing current infrastructure, defining technology requirements, managing resources, and designing a scalable and practical solution. Effective planning must prioritize reliability, efficiency, and security to support organizational goals. It typically includes considerations for data management tools like Microsoft SQL Server SSIS (SQL Server Integration Services), which is crucial for data integration and workflow applications. Proper planning ensures that IT infrastructure meets current and future needs while optimizing cost and performance.
Designing and implementing a data warehouse involves creating a central repository where data from various sources is consolidated to support business decision-making. The process includes data extraction, transformation, and loading (ETL), often using tools like Microsoft SSIS (SQL Server Integration Services). This setup allows for effective data management and easy access to processed data through tools like SSAS (SQL Server Analysis Services) and SSRS (SQL Server Reporting Services). Training or certification in Microsoft SSIS and understanding SQL Server can significantly enhance the efficiency and robustness of a data warehouse system.
Implementing a SQL 2016 Data Warehouse (SSIS) equips professionals with essential skills for effectively designing and managing data warehouses, focusing on SQL Server and Azure solutions.
The Implementing a SQL 2016 Data Warehouse (SSIS) course equips participants with the essential skills and knowledge to design, implement, and manage data warehouse solutions using SQL Server Integration Services (SSIS) and Azure SQL Data Warehouse.
