Category: Control Flows and Data Flows
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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Service Tiers – Relational Databases in Azure
Service Tiers There are two service tiers available for Azure SQL MI: Table 2.3 outlines some of the key differences between the two tiers. The descriptions listed are for the Gen5 hardware version of Azure SQL MI. TABLE 2.3 Azure SQL MI service tier characteristics Feature General Purpose Business Critical Number of vCores 4, 8, …
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 …
Extract, Load, and Transform (ELT) – Core Data Concepts
Extract, Load, and Transform (ELT) ELT workflows differ from ETL workflows solely in where the data transformation takes place. Instead of a separate transformation engine, the destination data store is used to load and transform data. This simplifies the design by removing extraneous components that would typically be used to transform data. Since the transformation …
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Describe Analytics Techniques – Core Data Concepts
Describe Analytics Techniques While it is important to spend considerable time planning and developing data processing pipelines, it is vital not to forget about the questions that drove the solution to be built in the first place. Being able to answer questions like the following is critical to the success of a business: What has …
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 …
Table – Core Data Concepts
Table A table is a grid that contains data that is ordered in rows and columns. Tables work well with quantitative comparisons where you are evaluating many values for a single category. Technologies such as Power BI Paginated Reports and SSRS format large tables to fit onto multiple pages make them easier to read. This …
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 …
Relational Database Features – Relational Databases in Azure
Relational Database Features Relational databases store data as collections of entities in the form of tables. In the context of data, entities can be described as nouns, such as persons, companies, countries, or products. Tables contain structured data that describes an entity and are composed of zero or more rows and one or more columns …
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