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We're here to help you find itThe "Data Fundamentals with SQL on Azure" course is designed to provide learners with a comprehensive understanding of data management concepts and the practical skills needed to work with data on the Azure platform. It covers core data principles, relational and non-relational data systems, and modern data warehouse analytics, all within the context of Azure's services and tools.
Module 1 lays the foundation by discussing core data concepts, the roles within data management, and the differences between relational and non-relational data, as well as data analytics. Module 2 and Module 3 delve into Azure's relational and non-relational data offerings respectively, teaching how to provision, deploy, query, and manage data stores. Module 4 explores the components of a modern data warehouse including data ingestion and processing, and introduces Power BI for data visualization. Finally, Module 5 is a SQL Workshop that offers hands-on experience with Microsoft SQL Server, instructing on how to write queries, modify data, and perform database definition language (DDL) operations.
This course is ideal for individuals looking to gain a solid grounding in data fundamentals, enabling them to effectively leverage SQL and Azure services in their data-driven roles.
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
To ensure that participants can successfully engage with and benefit from the Data Fundamentals With SQL on Azure course offered by Koenig Solutions, the following minimum prerequisites are recommended:
Note: The course is designed to introduce these concepts and does not require extensive prior experience or technical expertise in databases or Azure services. The aim is to provide foundational knowledge and skills that can be built upon through practice and further learning.
"Koenig Solutions' Data Fundamentals with SQL on Azure course provides a comprehensive overview of data management in the cloud for aspiring IT professionals."
Gain foundational knowledge in data concepts, explore Azure's data services, and develop SQL querying skills to manage and analyze relational and non-relational data effectively.
Core data concepts involve understanding how data is structured, stored, and accessed. In databases like Azure SQL, data is organized into tables with rows and columns, facilitating efficient storage and retrieval. Fundamental operations on data include creating, reading, updating, and deleting records. Indexing is used to speed up data retrieval, while normalization reduces data redundancy and improves integrity. Data security, backup, and recovery are also crucial to protect and maintain data availability. Understanding these basics is essential for managing and utilizing data effectively in any database system.
Relational data refers to the way data is structured and organized in a database using tables. Each table, which stores data about a particular type of entity, contains rows and columns. Rows represent individual records, and columns represent attributes of those records. The relational model uses primary keys (a unique identifier for each row) and foreign keys (references to a primary key in another table) to link tables together. This structured approach facilitates data management and querying, making it easier to retrieve, insert, update, or delete data effectively and efficiently.
Non-relational data, also known as NoSQL, refers to a type of database that stores and retrieves data in formats other than the traditional row-and-column tables of relational databases. This allows for the storage of unstructured and semi-structured data such as documents, graphs, and key-value pairs. Non-relational databases are designed to scale more efficiently and handle large volumes of rapidly changing data, making them ideal for big data applications, real-time web apps, and content management. They provide flexibility, allowing developers to adapt their data structures as needs evolve.
Data analytics involves examining raw data to make conclusions about that information. Techniques and processes are automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics enable more informed business decisions and can analyze customer trends and satisfaction, which can lead to new – and better – products and services. The field combines multiple data systems, including Azure SQL, which is used to process large volumes of data. Understanding data analytics can dramatically impact business strategies by utilizing data effectively to enhance operational efficiency and predict industry trends.
A modern data warehouse is a data storage system that allows for the efficient collection, storage, and analysis of data from various sources. It supports advanced analytics capabilities, enabling businesses to convert raw data into actionable insights using tools for data processing, querying, and reporting. It's designed to handle large volumes of data in diverse formats, making it essential for businesses looking to leverage big data for strategic advantages. Features like scalability, high performance, and the integration of different data types and sources are crucial in a modern data warehouse setup, accommodating evolving data needs in a digital landscape.
Data ingestion is the process of obtaining and importing data for immediate use or storage in a database. It involves the transfer of data from various sources to a location where it can be accessed, used, and analyzed by an organization. This is crucial for businesses to make informed decisions based on timely and accurate data analysis. Effective data ingestion helps in consolidating different data types from multiple sources, thereby streamlining data processing and accessibility. This process supports various formats and sources, ensuring that data is ready for analysis, which is essential for operational intelligence and strategic decision-making.
Power BI is a data analysis tool developed by Microsoft that helps professionals visualize information and share insights across an organization or embedded in an app or website. It connects to a wide range of data sources, enabling users to create interactive reports and dashboards. Through user-friendly interfaces, it allows you to transform raw data into compelling and easy-to-understand visual representations, enhancing decision-making processes. This tool supports a collaborative environment where reports and findings can easily be shared with others, improving the overall efficiency and intelligence of business operations.
SQL, or Structured Query Language, is a programming language used to manage and manipulate databases. It allows you to access, create, edit, and organize data stored in a structured format. For instance, SQL can be used to retrieve specific information from a database, update records, or create new data entries. It operates through simple, readable commands like SELECT, INSERT, DELETE, and UPDATE. SQL is essential for database management in various applications, ranging from small-scale to large enterprise environments. It is particularly central in systems like Azure SQL, which integrates SQL's capabilities with Azure cloud services.
Microsoft SQL Server is a database management system developed by Microsoft. It's designed to handle a wide range of data processing applications in enterprise environments. SQL Server supports various data types and applications, allowing organizations to manage databases, execute queries, and design complex business solutions. Its capabilities include transaction management, data analysis, and robust security features. Users can run it on-site, while also having the option to integrate with Azure, Microsoft's cloud platform, for enhanced flexibility and scalability. This integration with Azure helps in providing cost-effective and efficient solutions tailored for diverse business needs.
Database Definition Language (DDL) is a set of SQL commands used to define and modify the structure of database objects like tables, schemas, and indexes. Common DDL operations include CREATE, to build new database elements; ALTER, to modify existing database structures; and DROP, to delete elements from the database. These commands help manage and organize the data environment, ensuring databases are set up effectively to store and retrieve data as needed. DDL does not handle data manipulation or data querying, which are handled by other subsets of SQL commands.
"Koenig Solutions' Data Fundamentals with SQL on Azure course provides a comprehensive overview of data management in the cloud for aspiring IT professionals."
Gain foundational knowledge in data concepts, explore Azure's data services, and develop SQL querying skills to manage and analyze relational and non-relational data effectively.
