Exploring the Varbinary Type

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MicroStrategy maps binary data types from databases to either the Binary or Varbin MicroStrategy data types. For example, some databases are listed below with their various binary data types and their MicroStrategy mapping:. To determine how and when to use binary data types in MicroStrategy, the following MicroStrategy features are supported for binary data types:. MicroStrategy documentation comments or suggestions Product enhancement suggestions. Exploring the varbinary type data and measurements Attributes: Context for your levels of data Attribute elements: Data level values Attribute relationships Hierarchies: Data relationship organization Sample data model Building a logical data model User requirements Existing source systems Converting source data to analytical data Logical data modeling conventions Unique identifiers Cardinalities and ratios Attribute forms Warehouse Structure for Your Logical Data Model Columns: Data identifiers and values Tables: Physical groupings of related data Uniquely identifying data in tables with key structures Lookup tables: Attribute storage Relate exploring the varbinary type A unique case for relating attributes Fact tables: Fact data and levels of aggregation Homogeneous versus heterogeneous column naming Schema types: Data retrieval performance versus redundant storage Highly normalized schema: Minimal storage space Moderately normalized schema: Balanced storage space and query performance Highly denormalized schema: Layers Creating and modifying facts Creating facts Creating and modifying multiple facts Creating and modifying attributes Creating attributes Creating and modifying multiple attributes Defining attribute relationships Automatically defining attribute relationships Creating and modifying user hierarchies Creating user hierarchies The Building Blocks of Business Data: Facts Exploring the varbinary type facts Simultaneously creating multiple, simple facts Creating and modifying simple and advanced facts The structure of facts How facts are defined Mapping physical columns to facts: Fact expressions Fact column names and data types: Column aliases Modifying the levels at which facts are reported: Level extensions Defining a join on fact tables using table relations Defining exploring the varbinary type join on fact tables using fact relations Forcing facts to relate to attributes: Using cross product joins Lowering the level of fact data: Attributes Overview of attributes Creating attributes Simultaneously creating multiple attributes Adding and exploring the varbinary type attributes Unique sets of attribute information: Attribute elements Supporting data internationalization for attribute elements Column data descriptions and identifiers: Attribute forms Displaying forms: Attribute form properties Attribute form expressions Modifying attribute data types: Column aliases Attribute forms versus separate attributes Attribute relationships Viewing and editing the parents and children of attributes Exploring the varbinary type many-to-many and joint child relationships Split hierarchy with many-to-many relationships Attributes that use the same lookup table: Attribute roles Specifying attribute roles Attributes with multiple ID columns: Exploring the varbinary type compound attributes Using exploring the varbinary type to browse and report on data Defining how attribute forms are displayed by default Optimizing and Maintaining Your Project Updating your MicroStrategy project schema Data warehouse and project interaction: Warehouse Exploring the varbinary type Before you begin using the Warehouse Catalog Accessing the Warehouse Catalog Adding and removing tables for a project Managing warehouse and project tables Modifying data warehouse connection and operation defaults Customizing catalog SQL statements Troubleshooting table and column messages Accessing multiple data sources in a project Connecting data sources to a project Adding data into a project Improving database insert performance: Aggregate tables When to use aggregate tables Determining the frequency of queries at a specific level Considering any related parent-child relationships Compression ratio Creating aggregate tables The size of tables in a project: Logical table size Dividing tables to increase performance: Partition mapping Server versus application partitioning Metadata partition mapping Warehouse partition mapping Metadata versus warehouse partition mapping Creating Hierarchies to Organize and Browse Attributes Creating user hierarchies Creating user hierarchies using Architect Types of hierarchies System hierarchy: Project schema definition User hierarchies: Logical business relationships Hierarchy organization Hierarchy structure Viewing hierarchies: Distinct attribute lookup table Business case 2: Attribute form expression across multiple tables Business case 3: Slowly changing dimensions Business case 4: One-to-many transformation tables Business case 5: Mapped to Binary Data Type.

Mapped to Varbin Data Type. MicroStrategy supports the following features for attributes that have an ID form mapped to a binary data type:. Exporting, which exports the binary data as a string of characters. MicroStrategy supports the following features for any attributes that have non-ID attribute forms that are mapped to a binary data type:. Exploring the varbinary type in data marts SQL Server only. Attribute form qualifications, excluding qualifications that use operators to compare characters such as Like or Contains.

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In this step, you explore the sample data and generate some plots. Later, you learn how to serialize graphics objects in Python, and then deserialize those objects and make plots. First, take a minute to browse the data schema, as we've made some changes to make it easier to use the NYC Taxi data. Each fare record includes payment information such as the payment type, total amount of payment, and the tip amount. The last three columns can be used for various machine learning tasks.

Developing a data science solution usually includes intensive data exploration and data visualization. Because visualization is such a powerful tool for understanding the distribution of the data and outliers, Python provides many packages for visualizing data. The matplotlib module is one of the more popular libraries for visualization, and includes many functions for creating histograms, scatter plots, box plots, and other data exploration graphs.

In this section, you learn how to work with plots using stored procedures. Rather than open the image on the server, you store the Python object plot as varbinary data, and then write that to a file that can be shared or viewed elsewhere. The stored procedure returns a serialized Python figure object as a stream of varbinary data. You cannot view the binary data directly, but you can use Python code on the client to deserialize and view the figures, and then save the image file on a client computer.

Create the stored procedure SerializePlots , if the PowerShell script did not already do so. Now run the stored procedure with no arguments to generate a plot from the data hard-coded as the input query. From a Python client, you can now connect to the SQL Server instance that generated the binary plot objects, and view the plots.

To do this, run the following Python code, replacing the server name, database name, and credentials as appropriate. Make sure the Python version is the same on the client and the server. Also make sure that the Python libraries on your client such as matplotlib are the same or higher version relative to the libraries installed on the server. The output file is created in the Python working directory.

To view the plot, locate the Python working directory, and open the file. The following image shows a plot saved on the client computer. Create data features using T-SQL. The feedback system for this content will be changing soon. Old comments will not be carried over. If content within a comment thread is important to you, please save a copy.

For more information on the upcoming change, we invite you to read our blog post. Review the data First, take a minute to browse the data schema, as we've made some changes to make it easier to use the NYC Taxi data The original dataset used separate files for the taxi identifiers and trip records. The original dataset spanned many files and was quite large.

The current data table has 1,, rows and 23 columns. Trip and fare records Each trip record includes the pickup and drop-off location and time, and the trip distance. The Python script is fairly simple: The Python graphics object is serialized to a pandas DataFrame for output. Using SQL Server authentication: The plots are saved in directory: Next step Step 4: