Category: Describe Analytics Techniques
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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Network Isolation – Relational Databases in Azure
Network Isolation An Azure SQL MI is required to be placed inside a VNet upon creation. On top of this requirement, the subnet that the Azure SQL MI is deployed to must be dedicated to hosting one or more Azure SQL MIs. This requirement restricts access to databases hosted on the Azure SQL MI 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 …
Load and Transform – Core Data Concepts
Load and Transform The key to any ELT workflow is the destination data store’s ability to process data without needing to store it in-engine. MPP technologies do this by fitting a schema over one or more files that are stored in ADLS or Azure Blob Storage. The destination data store only manages the schema of …
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 …
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 …
Analytical Workload Design Considerations – Relational Databases in Azure
Analytical Workload Design Considerations Data warehouses and online analytical processing (OLAP) systems are optimally designed for read-heavy applications. While OLTP systems focus on storing current transactions, data warehouses and OLAP models focus on storing historical data that can be used to measure a business’s performance and predict what future actions it should take. Data warehouses …
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Views – Relational Databases in Azure
Views Views are virtual tables whose contents are defined by a query. The rows and columns of data in a view come from tables referenced in the query that define the view. They act as a virtual layer to filter and combine data from regularly queried tables. Users can simplify their queries since views handle …