Category: Azure SQL Managed Instance
Extract, Transform, and Load (ETL) – Core Data Concepts
Extract, Transform, and Load (ETL) ETL pipelines process data in a linear fashion with a different step for each phase. They first collect data from different sources, transform the data to remove dirty data and conform to business rules, and load the processed data into a destination data store. This approach has been used in …
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Deploying through the Azure Portal – Relational Databases in Azure
Deploying through the Azure Portal Deploying services in Azure can be done manually on the Azure Portal or automated using a scripting language (e.g., PowerShell or Bash) or an Infrastructure as Code template. SQL Server on Azure VMs are no different than any other service in this aspect, providing users multiple options for managing the …
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Load – Core Data Concepts
Load The last phase of an ETL process involves loading the transformed data to a destination data model. This data model can be a data warehouse such as Azure Synapse Analytics or Azure SQL Database, a database such as Azure Cosmos DB that serves highly distributed web applications, or an object store such as ADLS …
Control Flows and Data Flows – Core Data Concepts
Control Flows and Data Flows Many ETL tools employ two methods for orchestrating data pipelines. Tasks that ensure the orderly processing of data processing activities are known as control flows. Data processing activities are referred to as data flows and can be executed in sequence from a control flow. Data engineers that use ADF to …
Transform – Core Data Concepts
Transform The second phase of an ETL process involves transforming the extracted data that is cleansed and meets a set of business requirements. Data is scrubbed of dirty data and prepared so that it fits the schema of the destination data model. Transformations are split into multiple activities for optimal data pipeline performance. This modular …
Diagnostic – Core Data Concepts
Diagnostic Diagnostic analytics use historical data to answer questions about why different events have happened. While descriptive analytics use historical data to display past results, diagnostic analytics take this a step further by determining the root cause behind those results. This is the first technique that leverages machine learning to provide insights. Examples of diagnostic …
Prescriptive – Core Data Concepts
Prescriptive Prescriptive analytics solutions are a step up from predictive analytics as they not only predict outcomes, but they also advise organizations on how to reach a desired outcome. These solutions use findings from descriptive, diagnostic, and predictive analytics techniques to answer questions about what actions should be taken to achieve a particular goal. Combinations …
Line Chart – Core Data Concepts
Line Chart Line charts represent how a series of values change over time. Power BI enhances line charts by including a tooltip that provides more granular information for each data point on the x-axis. This is helpful if you are trying to prove a correlation between data points. Tooltips can be displayed by hovering your …
Summary – Core Data Concepts
Summary The concepts included in this chapter cover the different categories of data, storage options, and processing patterns. This chapter also covered common analysis techniques and when to use different visualizations depending on what business questions you are trying to answer. Understanding these core definitions will help you design data solutions in Azure for any …
Relational Data Structures – Relational Databases in Azure
Relational Data Structures Relational databases are composed of several different components. Take an OLTP database that powers a retail company’s POS for example. This database probably has a customer table that contains rows for every customer that has made a purchase. The table can include columns for each customer’s first name, last name, phone number, …
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