![]() Q3: How do we convert data from a Database to a Data Warehouse?Ī: In a database, we have data organized as tables having rows and columns. It maintains the relationships between the data in a different way.īoth database and data warehouse have data physically stored in rows and columns, but the purpose and hence the logical structure of each is different. Hence to separate the transactional and analytical environment, we need a Data Warehouse.Ī Data Warehouse serves more of a reading purpose for insights and analytics, hence optimized in a way where we can read the data quickly. Though it is normalized to optimize queries, if it serves analytical queries too there will be more query blocks, locks, and deadlocks. Q2: Why do we need a Data Warehouse if we have a Database?Ī: A Database has more of a reading and writing purpose for transactions. Hence it is partially normalized since the main focus is only on SELECT or read queries, not insertion and update queries. The data is read on a regular basis but is loaded on a periodic basis- weekly, monthly, or yearly. It is normalized to reduce redundancies and optimize transactional queries (insert, update, delete)Ī data warehouse on the other hand is used for storing historical and analytical data to obtain business insights. ![]() Q1: What is a Database and a Data Warehouse?Ī: A database is used for storing transactional data, hence the data in it gets updated very quickly. This will give us a better understanding of Azure Services that provide similar resources in the Cloud. To get started with the first Module of Azure Data Engineer, it is important to clarify the data types and uses of various data storage services that we use on-premise. So, here we discuss some Q/A’s asked during the Live session from Module 1: Explore compute and storage options for data engineering workloads >Basic Data Terminologies Out of which, in the Day 1 Live Session of the Microsoft Azure Data Engineer Training Program, we covered the concepts of Compute and Storage options for Data Engineering workloads like Azure Synapse Analytics, Azure DataBricks, Azure Data Lake Storage, Azure Delta Lake Architecture. In this post, we will be sharing the Day 1 live session review with the FAQs of Azure Data Engineering Day 1 Training which will help you in understanding some basic concepts. We have recently started our Azure Data Engineer Training Program. They also manage, monitor, and ensure the security and privacy of data using Azure services like Azure synapse analytics and Azure Data Lake.
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