Explanation:
The ideal solution for hierarchical data is a graph database. Although our solution does not require a database management system, Azure SQL Database is the best choice for create, read, update, and delete (CRUD) activities and utilizes the least amount of storage space (DBMS). Hierarchical databases are not the ideal choice for file storage; instead, use object storage. File storage should not be done on a table.
Explanation:
A view is a fictitious table constructed from SELECT query results. SQL statements that can be executed immediately are defined by a stored procedure. You may find information in a table by using the index. An association between two tables is a relationship.
Explanation:
Any kind of business logic that may be reused across the program can be contained in a stored procedure. A stored procedure has the ability to both update and add new data to tables. Both an application and the server can execute a stored procedure.
Because an inline function cannot alter or create objects, it cannot be utilized to finish the task. A database query can be made using it. Because a view cannot alter or create objects, it cannot be utilized to fulfill the task. A database query can be made using it. The goal cannot be accomplished by a table-valued function since it is unable to alter or create objects. A database query can be made using it.
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Explanation:
In a relational database, each entry in a table has the same amount of columns, is required to have a data type, and can have a primary key built on several columns. Relational databases do not mandate that a table have a single foreign key or that all of its columns have values.
Explanation:
Binary files, documents, photos, music, and video are all examples of unstructured data.
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Explanation:
Columnar data formats include ORC and Parquet. Row-based Avro and delimited data are both stored in CSV.
Explanation:
Data duplication is reduced by the process of normalization. This can be accomplished by dividing entities into separate tables and creating connections between the tables.
Explanation:
Based on a shared key, JOIN is used to merge data from two tables. Content from a GROUP BY command is filtered using the HAVING command. UNION does not use a shared key and instead presents the contents of two groups of columns from two different tables. Only values present in both tables are displayed by INTERSECT.
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Explanation:
Workloads for analytical data process historical data, prioritize read operations, and accumulate business metrics. Transactional data workloads are optimized for read and write operations.
Explanation:
For snowflake schemas with historical data, OLAP databases are employed. This kind of data is not appropriate for table storage. Hierarchical data, such organizational charts with nodes and edges, is stored in graph databases. OLTP databases are not utilized for historical data in a snowflake schema and are used for create, read, update, and delete (CRUD) activities. They also consume the least amount of storage space.
Explanation:
A table is inserted using the SELECT - INTO command. A table's columns' values can be changed using UPDATE. With INSERT - VALUES, values are added to a single row. Data is filtered by SELECT - HAVING.
Explanation:
Sequential data is kept in time series databases. Time series are not suitable for table storage. Hierarchical data, such organizational charts with nodes and edges, is stored in graph databases. The best option for creation, read, update, and delete (CRUD) activities is Azure SQL Database, which also utilizes the least amount of storage space and is not time series-optimized.
Explanation:
Columns are given datatypes to constrain the kinds of data that can be stored. For instance, you may use an integer datatype for a column that needs to only hold whole integers, like a quantity column.
Explanation:
You are able to store data as a graph database using Azure Cosmos DB. SQL Managed Instance, Azure SQL Database, and Azure Synapse Analytics do not.
Explanation:
Large volumes of aggregated data are stored in highly denormalized analytical databases that are read-focused. Workloads for time series do not store a lot of aggregated data. Hierarchical data is stored by graph workloads. Create, read, update, and delete (CRUD) activities are optimized for transactional workloads.
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Explanation:
High levels of normalization and CRUD operation optimization are features of transactional databases.
