This page provides a guide to Establishing a Connection to Azure Analysis Services in CData Cloud, as well as information on the available resources, and a reference to the available connection properties.

Connecting to Azure Analysis Services

Establishing a Connection shows how to authenticate to Azure Analysis Services and configure any necessary connection properties to create a database in CData Cloud

Accessing Data from CData Cloud Services

Accessing data from Azure Analysis Services through the available standard services and CData Cloud administration is documented in further details in the CData Cloud Documentation.

Connect to Azure Analysis Services by selecting the corresponding icon in the Database tab. Required properties are listed under Settings. The Advanced tab lists connection properties that are not typically required.

Connecting to Azure Analysis Services

To connect, set the URL property to a valid Azure Analysis Services server, for instance, asazure://southcentralus.asazure.windows.net/server, in addition to authenticating.

Optionally, set Database to distinguish which Azure database on the server to connect to.

Authenticating to Azure Analysis Services

Entra ID (Azure AD)

Note: Microsoft has rebranded Azure AD as Entra ID. In topics that require the user to interact with the Entra ID Admin site, we use the same names Microsoft does. However, there are still CData connection properties whose names or values reference "Azure AD".

Microsoft Entra ID is a multi-tenant, cloud-based identity and access management platform. It supports OAuth-based authentication flows that enable the Cloud to access Azure Analysis Services endpoints securely.

Authentication to Entra ID via a web application always requires that you first create and register a custom OAuth application. This enables your application to define its own redirect URI, manage credential scope, and comply with organization-specific security policies.

For full instructions on how to create and register a custom OAuth application, see Creating an Entra ID (Azure AD) Application.

After setting AuthScheme to AzureAD, the steps to authenticate vary, depending on the environment. For details on how to connect from desktop applications, web-based workflows, or headless systems, see the following sections.

Azure Service Principal

Note: Microsoft has rebranded Azure AD as Entra ID. In topics that require the user to interact with the Entra ID Admin site, we use the same names Microsoft does. However, there are still CData connection properties whose names or values reference "Azure AD".

Azure Service Principal is role-based application-based authentication. This means that authentication is done per application, rather than per user. All tasks taken on by the application are executed without a default user context, but based on the assigned roles. The application access to the resources is controlled through the assigned roles' permissions.

For information about how to set up Azure Service Principal authentication, see Creating a Service Principal App in Entra ID (Azure AD).

Azure Password

To authenticate directly to Azure Active Directory using your Azure credentials, specify the following connection properties:

• AuthScheme: AzurePassword.
• User: The account for the authenticating Azure user.
• Password: The authenticating Azure user's password.
• AzureTenant: The Directory (tenant) ID of the custom Azure AD application used to authenticate to Azure Analysis Services on Azure. To find this, go to the custom application's Overview page.

Managed Service Identity (MSI)

If you are running Azure Analysis Services on an Azure VM and want to automatically obtain Managed Service Identity (MSI) credentials to connect, set AuthScheme to AzureMSI.

User-Managed Identities

If your VM has multiple user-assigned managed identities, you must also specify OAuthClientId.

Creating an Entra ID (Azure AD) Application

Note: Microsoft has rebranded Azure AD as Entra ID. In topics that require the user to interact with the Entra ID Admin site, we use the same names Microsoft does. However, there are still CData connection properties whose names or values reference "Azure AD".

Azure Analysis Services supports OAuth-based authentication using Microsoft Entra ID. If you will connect via a web application and want to authenticate via Entra ID, you must first register a custom OAuth application in the Entra Admin Center, as described below.

If you will connect via a desktop application or headless machine, you can authenticate using Azure Analysis Services's built-in embedded application credentials, which use CData branding. However, custom OAuth applications are also compatible with desktop and headless authentication flows, and may be preferable for production deployments or environments requiring strict policy control.

Registering the Application

To register an OAuth application in Microsoft Entra ID, follow these steps:

1. Go to https://portal.azure.com.
2. In the left-hand navigation pane, select Microsoft Entra ID > App registrations.
3. Click New registration.
4. Enter a name for the application.
5. Specify the types of accounts this application should support:
   • For private-use applications, select Accounts in this organization directory only.
   • For distributed applications, select one of the multi-tenant options.

   Note: If you select Accounts in this organizational directory only when you connect with the CData Cloud, you must set AzureTenant to the tenant's ID (either GUID or verified domain). Otherwise, authentication will fail.

6. Set Select a platform to Web, and set the redirect URI to http://localhost:33333 (default), or use another URI appropriate for your deployment. When using a custom redirect URI set the CallbackURL connection property to match this URI exactly.
7. Click Register. The application management screen opens. Record these values for later use:
   • Application (client) ID is used for OAuthClientId
   • Directory (tenant) ID is used for AzureTenant
8. Go to Certificates & Secrets. Click New Client Secret, set the desired expiration, and save the generated value. This value will only be shown once — record it to use with OAuthClientSecret.

9. To confirm, click Add permissions.

Azure Analysis Services is an OLAP database that exposes data as cubes, which you query with MDX (multidimensional expressions). The Cloud models these cubes in relational views that you can query with SQL-92. The following mapping is for the layout of the model:

• Catalog - Displayed in the Cloud as a Catalog.
• Cube - Displayed in the Cloud as a Schema.
• Measure - Available in the Cloud under the special Measures view.
• Dimension - Each dimension is exposed as a view.
• Level - Each individual level of a hierarchy is exposed as a column on the appropriate dimension view.

Joining Measures and Dimensions

In order to retrieve measures per specific level value, issue a join between the Measure view and any Dimension or set of dimensions. For example, issuing the following retrieves the number of customers in each city:

SELECT m.[Customer Count], c.[City] 
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m

Note that there is no ON condition necessary. That is because tables are already related appropriately in Azure Analysis Services. If you are using a tool that requires ON conditions, set IncludeJoinColumns to true. This appends a number of foreign key columns to each view, relating them to one another. These columns do not return data on their own, but may be picked up on automatically with tools to construct the ON conditions for joins where needed.

Aggregating Data

Data stored in Azure Analysis Services is already aggregated. In many cases, attempting to retrieve an aggregate may be syntactically equivalent to not specifying anything. For example, the following query returns the exact same data as the previous:

SELECT SUM(m.[Customer Count]), c.[City] 
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m
GROUP BY c.[City]

The exception to this rule is when an aggregation of filtered results is requested. In such cases, a calculation is requested from Azure Analysis Services. For example, to calculate the sum and average of customers in France and Germany:

SELECT SUM(m.[Customer Count]), AVG(m.[Customer Count]), c.[Country] 
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m
WHERE c.[Country] IN ('France', 'Germany')
GROUP BY c.[Country]

Selecting Multiple Hierarchies

In Azure Analysis Services, individual dimensions are made up of hierarchies which may have one or more levels. For instance, the AdventureWorks Customers table has City, Country, and Gender. City and Country are part of the same hierarchy while Gender is its own hierarchy.

When selecting multiple hierarchies, the method to support this is to cross join the values in MDX. While not obvious from a relational table model of the data as the Cloud presents, this can cause very expensive queries to be executed. For example, executing the following:

SELECT c.[Country], m.[Customer Count]
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m

The query results in 6 rows. However, selecting Gender as well:

SELECT c.[Country], c.[Gender], m.[Customer Count]
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m

The query now results in 12 rows. This is because Gender and Country are on different hierarchies, thus a crossjoin is required in order to return both together. Each additional hierarchy added to the SELECT multiplies the total results by the number of available values in that hierarchy. Thus to get a count of how many rows to expect, one can execute the following:

SELECT (Count(c.[Country])*Count(c.[Gender])) AS totalrows
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 

Response Row Limit

Due to how selecting multiple hierarchies multiplies the total number of result rows, it is possible to balloon the number of response rows very quickly, which results in timeouts. In order to try to give some visibility into which queries are very expensive, the ResponseRowLimit connection property has been added as a mechanism to try and guide users into better practices. When set, it calculates how many rows to expect before any query is executed. If the number of predicted rows exceeds the limit, an error is thrown indicating how many rows to expect back with the query.

It is recommended to select only the columns required or to apply a WHERE criteria. Both can significantly reduce the number of response rows, which has a huge impact on performance. If you are already familiar with the Cloud and what queries may be expensive, ResponseRowLimit may be disabled by setting it to 0.

DAX Mode (Tabular Models)

For tabular data models, the Cloud supports an alternative DAX-based query mode. Set QueryMode to DAX to enable this mode.

In DAX mode, the Cloud translates SQL queries into DAX (Data Analysis Expressions) instead of MDX. The data model exposes tables and columns directly from the tabular model, with a single global "Measures" view containing all model-defined measures:

• Table - Each table in the tabular model is exposed as a view with its native columns.
• Measure - All measures are grouped in the global Measures view, reflecting their model-wide scope.

Querying Measures in DAX Mode

To retrieve measure values sliced by dimension columns, use a CROSS JOIN (or INNER JOIN) between the dimension and the Measures view:

SELECT c.Country, m.[Total Sales]
FROM Customers c CROSS JOIN Measures m

Inline Aggregations in DAX Mode

DAX mode supports natural inline aggregations grouped by dimension columns:

SELECT c.Country, SUM(s.Amount) AS TotalSales
FROM Sales s INNER JOIN Customers c ON s.CustomerID = c.CustomerID
GROUP BY c.Country

Advantages of DAX Mode

DAX mode is recommended for tabular models because it offers simpler data type mapping, efficient semantic JOINs via SUMMARIZECOLUMNS, natural support for DISTINCTCOUNT and SUMX, and no hierarchy cross-join multiplication.

When QueryMode is set to MDX, the Cloud translates SQL queries into MDX (Multidimensional Expressions) for execution against OLAP cube models. This page is a comprehensive guide to how SQL constructs map to MDX queries.

SELECT Queries

SELECT * FROM [Adventure Works].Measures

SELECT
    {
        [Measures].[Average Sales Amount],
        [Measures].[Customer Count],
        ...
    } ON 0
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT [City], [Country]
FROM [Adventure Works].Customer

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            CROSSJOIN(
                [Customer].[Customer Geography].[City].ALLMEMBERS,
                [Customer].[Customer Geography].[Country].ALLMEMBERS
            ),
            { [Measures].[Microsoft.Mashup.Engine.One] }
        )
    ,@__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT [Education]
FROM [Adventure Works].Customer

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            [Customer].[Education].[Education].ALLMEMBERS,
            { [Measures].[Microsoft.Mashup.Engine.One] }
        )
    ,@__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

Note: When querying only dimension columns, the Cloud creates a placeholder measure ([Microsoft.Mashup.Engine.One]) to enable row generation. The ALLMEMBERS function returns all members at the specified hierarchy level.

WHERE Filters

SELECT [City]
FROM [Adventure Works].Customer
WHERE [Country] = 'United States'

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            ADDCALCULATEDMEMBERS(
                DESCENDANTS( 
                    { [Customer].[Customer Geography].[Country].&[United States] },
                    [Customer].[Customer Geography].[City],
                    LEAVES
                )
            ),
            { [Measures].[Microsoft.Mashup.Engine.One] }
        )
    ,@__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT [Education]
FROM [Adventure Works].Customer
WHERE [Country] IN ('Australia', 'United States')

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            [Customer].[Education].[Education].ALLMEMBERS,
            { [Measures].[Microsoft.Mashup.Engine.One] }
        )
    ,@__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
WHERE
    {
        [Customer].[Customer Geography].[Country].&[Australia],
        [Customer].[Customer Geography].[Country].&[United States]
    }
CELL PROPERTIES VALUE

SELECT [Education]
FROM [Adventure Works].Customer
WHERE [Country] = 'Australia'
  AND [Gender] = 'Male'

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            [Customer].[Education].[Education].ALLMEMBERS,
            { [Measures].[Microsoft.Mashup.Engine.One] }
        ),
        @__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
WHERE
    CROSSJOIN(
        { [Customer].[Customer Geography].[Country].&[Australia] },
        { [Customer].[Gender].&[Male] }
    )
CELL PROPERTIES VALUE

Note: Filters on different dimensions use CROSSJOIN in the MDX WHERE clause to create an AND condition.

Measure Queries

SELECT m.[Customer Count]
FROM [Adventure Works].Measures AS m

SELECT
    { [Measures].[Customer Count] } ON 0
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT c.[City], c.[Country], m.[Customer Count]
FROM [Adventure Works].Customer AS c
INNER JOIN [Adventure Works].Measures AS m

SELECT
    { [Measures].[Customer Count] } ON 0,
    SUBSET(
        NONEMPTY(
                ADDCALCULATEDMEMBERS(
                    DESCENDANTS( 
                        { [Customer].[Customer Geography].[Country].ALLMEMBERS },
                        [Customer].[Customer Geography].[City],
                        LEAVES
                    )
                ),
            { [Measures].[Customer Count] }
        ),
        @__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT c.[City], c.[Gender], p.[Class],
       m.[Average Sales Amount], m.[Average Unit Price]
FROM [Adventure Works].Customer AS c
INNER JOIN [Adventure Works].Product AS p
INNER JOIN [Adventure Works].Measures AS m

SELECT
    {
        [Measures].[Average Sales Amount],
        [Measures].[Average Unit Price]
    } ON 0,
    SUBSET(
        NONEMPTY(
            CROSSJOIN(
                CROSSJOIN(
                    ADDCALCULATEDMEMBERS(
                        DESCENDANTS( 
                            { [Customer].[Customer Geography].[Country].ALLMEMBERS },
                            [Customer].[Customer Geography].[City],
                            LEAVES
                        )
                    ),
                    [Customer].[Gender].[Gender].ALLMEMBERS
                ),
                [Product].[Class].[Class].ALLMEMBERS
            ),
            { [Measures].[Average Sales Amount],
              [Measures].[Average Unit Price] }
        ),
        @__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

Many BI tools require explicit JOIN syntax with an ON clause. The Cloud adds a virtual MeasuresId column to every table for this purpose. An INNER JOIN on MeasuresId is treated identically to a CROSS JOIN with the Measures table:

SELECT c.[Country], m.[Customer Count]
FROM [Adventure Works].Customer AS c
INNER JOIN [Adventure Works].Measures AS m
  ON c.MeasuresId = m.MeasuresId

SELECT
    { [Measures].[Customer Count] } ON 0,
    SUBSET(
        NONEMPTY(
            [Customer].[Customer Geography].[Country].ALLMEMBERS,
            { [Measures].[Customer Count] }
        ),
        @__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

Aggregations and GROUP BY

SELECT MAX(m.[Customer Count]) AS mymax
FROM [Adventure Works].Customer AS c
INNER JOIN [Adventure Works].Measures AS m

WITH MEMBER Measures.[mymax] AS ([Measures].[Customer Count])

SELECT
    {
        [Measures].[mymax]
    } ON 0
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT SUM(m.[Customer Count]) AS sum
FROM [Adventure Works].Customer AS c
INNER JOIN [Adventure Works].Measures AS m
WHERE c.[Country] IN ('United States', 'Australia')
GROUP BY c.[Country]

WITH MEMBER Measures.[sum] AS ([Measures].[Customer Count])

SELECT
    { [Measures].[sum] } ON 0,
    SUBSET(
        NONEMPTY(
            { [Customer].[Customer Geography].[Country].&[Australia],
              [Customer].[Customer Geography].[Country].&[United States] },
            { [Measures].[sum] }
        ),
        @__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT COUNT(c.[Country]) AS count
FROM [Adventure Works].Customer AS c

WITH MEMBER Measures.[count] AS COUNT (
    ADDCALCULATEDMEMBERS(
        DESCENDANTS( 
            { [Customer].[Customer Geography].CurrentMember },
            [Customer].[Customer Geography].[Country],
            SELF
        )
    )
)

SELECT
    { [Measures].[count] } ON 0
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT COUNT(c.[City]) AS count
FROM [Adventure Works].Customer AS c
WHERE c.Country = 'Australia'

WITH MEMBER Measures.[count] AS COUNT (
    ADDCALCULATEDMEMBERS(
        DESCENDANTS( 
            { [Customer].[Customer Geography].[Country].&[Australia] },
            [Customer].[Customer Geography].[City],
            SELF
        )
    )
)

SELECT
    { [Measures].[count] } ON 0
FROM [Adventure Works]
CELL PROPERTIES VALUE

ORDER BY

SELECT [Education]
FROM [Adventure Works].Customer
ORDER BY [Education] ASC

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        ORDER(
            NONEMPTY(
                [Customer].[Education].[Education].ALLMEMBERS,
                { [Measures].[Microsoft.Mashup.Engine.One] }
            ),
            [Customer].[Education].CurrentMember.MEMBER_CAPTION, BASC
        )
    ,@__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

SELECT c.[City], c.[Country], m.[Customer Count]
FROM [Adventure Works].Customer AS c
INNER JOIN [Adventure Works].Measures AS m
WHERE m.[Customer Count] > 0
ORDER BY m.[Customer Count]

SELECT
    {
        [Measures].[Customer Count]
    } ON 0,
    SUBSET(
        ORDER(
            NONEMPTY(
                ADDCALCULATEDMEMBERS(
                    DESCENDANTS( 
                        {
                            [Customer].[Customer Geography].[Country].ALLMEMBERS
                        },
                        [Customer].[Customer Geography].[City],
                        LEAVES
                    )
                ),
                {
                    [Measures].[Customer Count]
                }
            ),
            [Measures].[Customer Count], BASC
        )
    ,@__CDATA_OFFSET)
    HAVING [Measures].[Customer Count] > 0        PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

The ORDER function uses these sort flags:

LIMIT and OFFSET

SELECT [City], [Country]
FROM [Adventure Works].Customer
WHERE [Country] = 'United States'
LIMIT 20 OFFSET 10

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            ADDCALCULATEDMEMBERS(
                DESCENDANTS( 
                    { [Customer].[Customer Geography].[Country].&[United States] },
                    [Customer].[Customer Geography].[City],
                    LEAVES
                )
            ),
            { [Measures].[Microsoft.Mashup.Engine.One] }
        )
    ,@__CDATA_OFFSET,@__CDATA_LIMIT)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

The SUBSET function signature is SUBSET(set, start_index, [count]). The @__CDATA_OFFSET and @__CDATA_LIMIT parameters are replaced at execution time.

CASE Expressions

SELECT CASE [Country]
    WHEN 'United States' THEN 'States'
    ELSE 'Not States'
END AS StatesOrNoStates
FROM [Adventure Works].Customer

WITH MEMBER Measures.[StatesOrNoStates] AS 
  CASE [Customer].[Customer Geography].CurrentMember.MEMBER_CAPTION 
  WHEN  'United States' THEN 'States' 
  ELSE  'Not States' 
  END 

SELECT
    { [Measures].[StatesOrNoStates] } ON 0,
    SUBSET(
        NONEMPTY(
            [Customer].[Customer Geography].[Country].ALLMEMBERS,
            { [Measures].[StatesOrNoStates] }
        ),
        @__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

HAVING Clause

SELECT m.[Customer Count]
FROM [Adventure Works].Customer AS c
INNER JOIN [Adventure Works].Measures AS m
WHERE m.[Customer Count] > 100
  AND c.[Country] = 'Australia'
GROUP BY c.[City]

SELECT
    {
        [Measures].[Customer Count]
    } ON 0,
    SUBSET(
        NONEMPTY(
            ADDCALCULATEDMEMBERS(
                DESCENDANTS( 
                    {
                        [Customer].[Customer Geography].[Country].&[Australia]
                    },
                    [Customer].[Customer Geography].[City],
                    LEAVES
                )
            ),
            {
                [Measures].[Customer Count]
            }
        )
    ,@__CDATA_OFFSET)
    HAVING [Measures].[Customer Count] > 100        PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

DISTINCT

SELECT DISTINCT [Country]
FROM [Adventure Works].Customer

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            [Customer].[Customer Geography].[Country].ALLMEMBERS,
            { [Measures].[Microsoft.Mashup.Engine.One] }
        )
    ,@__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

COUNT(DISTINCT)

SELECT COUNT(DISTINCT c.[City]) AS citycount,
       COUNT(DISTINCT c.[Country]) AS countrycount
FROM [Adventure Works].Customer AS c

WITH MEMBER Measures.[citycount] AS COUNT (
    ADDCALCULATEDMEMBERS(
        DESCENDANTS( 
            { [Customer].[Customer Geography].CurrentMember },
            [Customer].[Customer Geography].[City],
            SELF
        )
    )
)
MEMBER Measures.[countrycount] AS COUNT (
    ADDCALCULATEDMEMBERS(
        DESCENDANTS( 
            { [Customer].[Customer Geography].CurrentMember },
            [Customer].[Customer Geography].[Country],
            SELF
        )
    )
)

SELECT
    { [Measures].[citycount], [Measures].[countrycount] } ON 0
FROM [Adventure Works]
CELL PROPERTIES VALUE

LIKE Filters

LIKE filters are not natively supported in MDX. The Cloud retrieves all members at the relevant hierarchy level (using ALLMEMBERS) and applies the LIKE pattern on the client side.

SELECT [Country]
FROM [Adventure Works].Customer
WHERE [Country] LIKE 'United%'

WITH MEMBER [Measures].[Microsoft.Mashup.Engine.One] AS 1

SELECT
    { [Measures].[Microsoft.Mashup.Engine.One] } ON 0,
    SUBSET(
        NONEMPTY(
            [Customer].[Customer Geography].[Country].ALLMEMBERS,
            { [Measures].[Microsoft.Mashup.Engine.One] }
        )
    ,@__CDATA_OFFSET)
    PROPERTIES MEMBER_CAPTION ON 1
FROM [Adventure Works]
CELL PROPERTIES VALUE

MDX Function Reference

The following table summarizes key MDX functions used in query translation:

Hierarchy Navigation

MDX uses DESCENDANTS to navigate parent-child relationships in hierarchies:

When QueryMode is set to DAX, the Cloud translates SQL queries into DAX (Data Analysis Expressions) for execution against tabular data models. This page provides a comprehensive reference for how SQL constructs map to DAX queries.

SELECT Queries

SELECT * FROM Sales

EVALUATE 'Sales'

SELECT EmpName, Title
FROM Employees

EVALUATE
SELECTCOLUMNS(
    'Employees',
    "EmpName", 'Employees'[EmpName],
    "Title", 'Employees'[Title]
)

SELECT EmpName AS Name, Title AS Role
FROM Employees

EVALUATE
SELECTCOLUMNS(
    'Employees',
    "Name", 'Employees'[EmpName],
    "Role", 'Employees'[Title]
)

WHERE Filters

SELECT EmpName
FROM Employees
WHERE Title = 'Rep'

EVALUATE
SELECTCOLUMNS(
    FILTER('Employees',
        'Employees'[Title] = "Rep"),
    "EmpName", 'Employees'[EmpName]
)

SELECT * FROM Sales
WHERE Amount > 100

EVALUATE
SELECTCOLUMNS(
FILTER('Sales', 'Sales'[Amount] > 100),
    "col1", 'Sales'[col1],
	"coln", 'Sales'[coln],	
)

SELECT City FROM Stores
WHERE Country = 'USA'
   OR Country = 'UK'

EVALUATE
SELECTCOLUMNS(
    FILTER('Stores',
        'Stores'[Country] = "USA"
        || 'Stores'[Country] = "UK"),
    "City", 'Stores'[City]
)

SELECT City FROM Stores
WHERE City IN ('NYC', 'London')

EVALUATE
SELECTCOLUMNS(
    FILTER('Stores',
        'Stores'[City] IN {"NYC", "London"}),
    "City", 'Stores'[City]
)

SELECT City FROM Stores
WHERE Country NOT IN ('UK')

EVALUATE
SELECTCOLUMNS(
    FILTER('Stores',
        NOT('Stores'[Country] IN {"UK"})),
    "City", 'Stores'[City]
)

SELECT EmpName FROM Employees
WHERE Title IS NOT NULL

EVALUATE
SELECTCOLUMNS(
    FILTER('Employees',
        'Employees'[Title] <> BLANK()),
    "EmpName", 'Employees'[EmpName]
)

Note: LIKE filters are not natively supported in DAX. The Cloud retrieves all rows and applies LIKE filtering on the client side.

Measure Queries

SELECT m.[Total Amount]
FROM Measures m

EVALUATE
SUMMARIZECOLUMNS(
    "Total Amount", [Total Amount]
)

SELECT s.Country, m.[Total Amount]
FROM Stores s
CROSS JOIN Measures m

EVALUATE
SUMMARIZECOLUMNS(
    'Stores'[Country],
    "Total Amount", [Total Amount]
)

SELECT e.Title, s.Country,
       m.[Total Amount], m.[Total Units]
FROM Employees e
CROSS JOIN Stores s
CROSS JOIN Measures m

EVALUATE
SUMMARIZECOLUMNS(
    'Employees'[Title],
    'Stores'[Country],
    "Total Amount", [Total Amount],
    "Total Units", [Total Units]
)

SELECT e.EmpName, m.[Total Amount]
FROM Employees e
CROSS JOIN Measures m
WHERE e.Title = 'Rep'

EVALUATE
SUMMARIZECOLUMNS(
    'Employees'[EmpName],
    FILTER('Employees',
        'Employees'[Title] = "Rep"),
    "Total Amount", [Total Amount]
)

Many BI tools require explicit JOIN syntax with an ON clause. The Cloud adds a virtual MeasuresId column to every table for this purpose. An INNER JOIN on MeasuresId is treated identically to a CROSS JOIN with the Measures table:

SELECT s.Country, m.[Total Amount]
FROM Stores s
INNER JOIN Measures m
  ON s.MeasuresId = m.MeasuresId

EVALUATE
SUMMARIZECOLUMNS(
    'Stores'[Country],
    "Total Amount", [Total Amount]
)

Implicit Aggregations

SELECT SUM(Amount) FROM Sales

EVALUATE
ROW("SUM", SUM('Sales'[Amount]))

SELECT COUNT(*) FROM Sales

EVALUATE
ROW("COUNT", COUNTROWS('Sales'))

SELECT COUNT(Amount) FROM Sales

EVALUATE
ROW("COUNT", COUNT('Sales'[Amount]))

SELECT s.Country, SUM(sa.Amount)
FROM Stores s
INNER JOIN Sales sa
  ON s.StoreID = sa.StoreID
GROUP BY s.Country

EVALUATE
SUMMARIZECOLUMNS(
    'Stores'[Country],
    "SUM", SUM('Sales'[Amount])
)

SELECT s.Country,
       AVG(sa.Amount),
       MIN(sa.Units),
       MAX(sa.Amount)
FROM Stores s
INNER JOIN Sales sa
  ON s.StoreID = sa.StoreID
GROUP BY s.Country

EVALUATE
SUMMARIZECOLUMNS(
    'Stores'[Country],
    "AVG", AVERAGE('Sales'[Amount]),
    "MIN", MIN('Sales'[Units]),
    "MAX", MAX('Sales'[Amount])
)

When the SUM aggregate function contains an arithmetic expression rather than a single column, the Cloud uses the SUMX function:

SELECT e.EmpName,
       SUM(sa.Amount * sa.Units)
FROM Employees e
INNER JOIN Sales sa
  ON e.EmpID = sa.EmpID
GROUP BY e.EmpName

EVALUATE
SUMMARIZECOLUMNS(
    'Employees'[EmpName],
    "SUM", SUMX('Sales',
        'Sales'[Amount] * 'Sales'[Units])
)

The following table summarizes the SQL-to-DAX aggregate function mapping:

SUM(col)

SUM('Table'[col])

COUNT(*)

COUNTROWS('Table')

COUNT(col)

COUNT('Table'[col])

COUNT(DISTINCT col)

DISTINCTCOUNT('Table'[col])

AVG(col)

AVERAGE('Table'[col])

MIN(col)

MIN('Table'[col])

MAX(col)

MAX('Table'[col])

SUM(expr)

SUMX('Table', expr)

JOIN Queries

INNER JOIN

SELECT e.EmpName, sa.Amount
FROM Employees e
INNER JOIN Sales sa
  ON e.EmpID = sa.EmpID

EVALUATE
SELECTCOLUMNS(
    NATURALINNERJOIN('Employees', 'Sales'),
    "EmpName", 'Employees'[EmpName],
    "Amount", 'Sales'[Amount]
)

SELECT e.EmpName, s.City, sa.Amount
FROM Employees e
INNER JOIN Sales sa
  ON e.EmpID = sa.EmpID
INNER JOIN Stores s
  ON sa.StoreID = s.StoreID

EVALUATE
SELECTCOLUMNS(
    NATURALINNERJOIN(
        NATURALINNERJOIN(
            'Employees', 'Sales'),
        'Stores'),
    "EmpName", 'Employees'[EmpName],
    "City", 'Stores'[City],
    "Amount", 'Sales'[Amount]
)

LEFT JOIN

SELECT e.EmpName, SUM(sa.Amount)
FROM Employees e
LEFT JOIN Sales sa
  ON e.EmpID = sa.EmpID
GROUP BY e.EmpName

EVALUATE
ADDCOLUMNS(
    VALUES('Employees'[EmpName]),
    "SUM", CALCULATE(SUM('Sales'[Amount]))
)

CROSS JOIN

SELECT e.EmpName, s.City
FROM Employees e
CROSS JOIN Stores s

EVALUATE
GENERATE(
    SELECTCOLUMNS('Employees',
        "EmpName", 'Employees'[EmpName]),
    SELECTCOLUMNS('Stores',
        "City", 'Stores'[City])
)

Note: RIGHT JOIN and FULL OUTER JOIN are not supported in DAX mode. The Cloud returns an error if these join types are used.

HAVING Clause

SELECT s.Country, m.[Total Amount]
FROM Stores s
CROSS JOIN Measures m
HAVING m.[Total Amount] > 500

EVALUATE
FILTER(
    SUMMARIZECOLUMNS(
        'Stores'[Country],
        "Total Amount", [Total Amount]
    ),
    [Total Amount] > 500
)

SELECT s.City, SUM(sa.Amount)
FROM Stores s
INNER JOIN Sales sa
  ON s.StoreID = sa.StoreID
GROUP BY s.City
HAVING SUM(sa.Amount) > 500
   AND COUNT(sa.Units) < 10

EVALUATE
FILTER(
    SUMMARIZECOLUMNS(
        'Stores'[City],
        "SUM", SUM('Sales'[Amount])
    ),
    ([SUM] > 500 && COUNT('Sales'[Units]) < 10)
)

ORDER BY

SELECT EmpName, Title
FROM Employees
ORDER BY Title ASC

EVALUATE
SELECTCOLUMNS(
    'Employees',
    "EmpName", 'Employees'[EmpName],
    "Title", 'Employees'[Title]
)
ORDER BY [Title] ASC

SELECT e.EmpName, m.[Total Amount]
FROM Employees e
CROSS JOIN Measures m
ORDER BY m.[Total Amount] DESC

EVALUATE
SUMMARIZECOLUMNS(
    'Employees'[EmpName],
    "Total Amount", [Total Amount]
)
ORDER BY [Total Amount] DESC

If the ORDER BY column is not in the SELECT list, the Cloud automatically includes it in the DAX projection so it is available for sorting.

LIMIT and OFFSET

SELECT EmpName FROM Employees
ORDER BY EmpName DESC
LIMIT 2

EVALUATE
TOPN(2,
    SELECTCOLUMNS(
        'Employees',
        "EmpName", 'Employees'[EmpName]
    ),
    [EmpName], DESC
)

SELECT EmpID FROM Employees
LIMIT 2

EVALUATE
TOPN(2,
  SELECTCOLUMNS(
    'Employees',
    "EmpID", 'Employees'[EmpID]
  )
)

DAX does not have a native OFFSET. The Cloud handles offset by requesting LIMIT + OFFSET rows via TOPN and discarding the initial OFFSET rows before returning results.

DISTINCT

SELECT DISTINCT Title
FROM Employees

EVALUATE
SUMMARIZECOLUMNS(
    'Employees'[Title]
)

Queries that use SUMMARIZECOLUMNS (such as measure queries or GROUP BY) already return distinct results, so DISTINCT is not applied redundantly in those cases.

Subquery IN Clause

SELECT e.EmpName
FROM Employees e
WHERE e.EmpID IN (
    SELECT EmpID FROM Sales
    WHERE Amount > 300
)

EVALUATE
SELECTCOLUMNS(
    FILTER('Employees',
        'Employees'[EmpID] IN
            SELECTCOLUMNS(
                FILTER('Sales',
                    'Sales'[Amount] > 300),
                "EmpID", 'Sales'[EmpID]
            )
    ),
    "EmpName", 'Employees'[EmpName]
)

COUNT(DISTINCT)

SELECT e.Title,
       COUNT(DISTINCT sa.EmpID)
FROM Employees e
INNER JOIN Sales sa
  ON e.EmpID = sa.EmpID
GROUP BY e.Title

EVALUATE
SUMMARIZECOLUMNS(
    'Employees'[Title],
    "COUNT", DISTINCTCOUNT('Sales'[EmpID])
)

Fine Tuning Data Access

Setting the following other properties may also be useful in certain situations:

• QueryPassthrough: Indicates if MDX queries are being submitted. By default this is false, which causes the driver to accept only SQL-92 compliant queries.

  Setting this property to true causes all queries to be passed through directly to Azure Analysis Services.

• ExtraProperties: Additional properties to submit along with an MDX query.
• IncludeJoinColumns: Boolean indicating if extra columns used to make ON conditions with joins should be added.

  These do not come back with any values - they are added purely to enable tools that require them in order to automatically set up relationships between tables when creating joins.

• ResponseRowLimit: Sets a calculated limit on the number of rows to allow the user to select before returning an error.

  Because queries are being translated to MDX, selecting only a few columns may exponentially multiply the number of expected results.

  For this reason, ResponseRowLimit is available to try to give some guidance on what types of queries are likely to result in a Timeout. May be disabled by setting to 0.

Customizing the SSL Configuration

To enable TLS, set the following:

• URL: Prefix the connection string with https://

With this configuration, the Cloud attempts to negotiate TLS with the server. The server certificate is validated against the default system trusted certificate store. You can override how the certificate gets validated using the SSLServerCert connection property.

To specify another certificate, see the SSLServerCert connection property.

Client SSL Certificates

The Azure Analysis Services Cloud also supports setting client certificates. Set the following to connect using a client certificate.

• SSLClientCert: The name of the certificate store for the client certificate.
• SSLClientCertType: The type of key store containing the TLS/SSL client certificate.
• SSLClientCertPassword: The password for the TLS/SSL client certificate.
• SSLClientCertSubject: The subject of the TLS/SSL client certificate.

Connecting Through a Firewall or Proxy

HTTP Proxies

To authenticate to an HTTP proxy, set the following:

• ProxyServer: the hostname or IP address of the proxy server that you want to route HTTP traffic through.
• ProxyPort: the TCP port that the proxy server is running on.
• ProxyAuthScheme: the authentication method the Cloud uses when authenticating to the proxy server.
• ProxyUser: the username of a user account registered with the proxy server.
• ProxyPassword: the password associated with the ProxyUser.

Other Proxies

Set the following properties:

• To use a proxy-based firewall, set FirewallType, FirewallServer, and FirewallPort.
• To tunnel the connection, set FirewallType to TUNNEL.
• To authenticate, specify FirewallUser and FirewallPassword.
• To authenticate to a SOCKS proxy, additionally set FirewallType to SOCKS5.

Azure Analysis Services is an OLAP database that exposes data as cubes, which you query with MDX (multidimensional expressions). The Cloud models these cubes in relational views that you can query with SQL-92. The following mapping is for the layout of the model:

• Catalog - Displayed in the Cloud as a Catalog.
• Cube - Displayed in the Cloud as a Schema.
• Measure - Available in the Cloud under the special Measures view.
• Dimension - Each dimension is exposed as a view.
• Level - Each individual level of a hierarchy is exposed as a column on the appropriate dimension view.

MDX Data Model (Default)

In the default MDX mode (QueryMode = MDX), the Cloud models the data as follows:

Measure Attributes

By default, all measure attributes are listed in a 'Measures' view. However, you can set SplitMeasures to 'true' to split the measures view; the result is each measure attribute is included in its respective view based on the Measure Group value. Further classification based on 'Measure Directories' is not included.

DAX Data Model

When QueryMode is set to DAX, the Cloud uses DAX metadata functions (INFO.TABLES, INFO.VIEW.COLUMNS, INFO.MEASURES) to discover the tabular model structure. This mode is designed for tabular data models and provides a simpler relational representation.

Model Layout

The DAX data model exposes the following objects:

• Catalog - The database (same as MDX mode).
• Schema - Represents the schema of the tabular model.
• Table - Each table in the tabular model is exposed as a view with its native columns and data types.
• Measures - By default, all model-defined measures are grouped in a single global "Measures" view. Use SplitMeasures to distribute measures across tables (see below).
• MeasuresId - A virtual join column automatically added to every table, enabling BI tools to construct joins between tables and the Measures view. This is included for compatibility with visual query builders that require an explicit ON clause.

Measure Organization

By default, all measures are grouped in a single "Measures" view. You can use SplitMeasures and SplitMeasuresOn to control how measures are organized:

• SplitMeasures = false (default) - All measures are placed in a single "Measures" view.
• SplitMeasures = true, SplitMeasuresOn = MeasureGroup - Measures are grouped with the fact table they belong to (their home table). Tables that contain both columns and measures behave as unified entities.
• SplitMeasures = true, SplitMeasuresOn = DisplayFolder - Measures are grouped into tables that match the name of their DisplayFolder metadata. Measures without a DisplayFolder go into the shared "Measures" view.
• SplitMeasures = true, SplitMeasuresOn = DisplayFolder,MeasureGroup - Measures are grouped by DisplayFolder first; if a measure has no DisplayFolder, it falls back to its MeasureGroup (home table).

If a DisplayFolder name conflicts with an existing table name, the generated table is prefixed with "Measures_" to avoid ambiguity (e.g., "Measures_Sales").

Querying with DAX Mode

DAX mode translates SQL queries into DAX expressions. The following SQL patterns are supported:

Implicit Aggregation

Aggregate functions including SUM, AVG, MIN, MAX, COUNT, and DISTINCTCOUNT are supported and translate directly to their DAX equivalents. For example:

SELECT City, SUM(Amount) FROM Sales GROUP BY City

Complex arithmetic expressions within aggregates are also supported. For example,

SUM(Price * Quantity)

Joining Tables with Measures

To retrieve measures sliced by dimension values, join a table with the Measures view using CROSS JOIN or INNER JOIN:

SELECT s.City, m.[Total Amount]
FROM Stores s
CROSS JOIN Measures m

The Cloud translates this into a DAX SUMMARIZECOLUMNS expression that leverages the model's built-in relationships for efficient evaluation.

INNER JOIN and LEFT JOIN between physical tables are also supported and use DAX NATURALINNERJOIN and ADDCOLUMNS patterns respectively.

Filtering, Sorting, and Pagination

WHERE clauses (including IN lists and IS NULL checks), ORDER BY, DISTINCT, LIMIT, and HAVING are all supported. LIMIT translates to DAX TOPN for server-side row limiting.

Joining Measures and Dimensions

In order to retrieve measures per specific level value, issue a join between the Measure view and any Dimension or set of dimensions. For example, issuing the following retrieves the number of customers in each city:

SELECT m.[Customer Count], c.[City] 
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m

Note that there is no ON condition necessary. That is because tables are already related appropriately in Azure Analysis Services. If you are using a tool that requires ON conditions, set IncludeJoinColumns to true. This appends a number of foreign key columns to each view, relating them to one another. These columns do not return data on their own, but may be picked up on automatically with tools to construct the ON conditions for joins where needed.

Aggregating Data

Data stored in Azure Analysis Services is already aggregated. In many cases, attempting to retrieve an aggregate may be syntactically equivalent to not specifying anything. For example, the following query returns the exact same data as the previous:

SELECT SUM(m.[Customer Count]), c.[City] 
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m
GROUP BY c.[City]

The exception to this rule is when an aggregation of filtered results is requested. In such cases, a calculation is requested from Azure Analysis Services. For example, to calculate the sum and average of customers in France and Germany:

SELECT SUM(m.[Customer Count]), AVG(m.[Customer Count]), c.[Country] 
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m
WHERE c.[Country] IN ('France', 'Germany')
GROUP BY c.[Country]

Selecting Multiple Hierarchies

In Azure Analysis Services, individual dimensions are made up of hierarchies which may have one or more levels. For instance, the AdventureWorks Customers table has City, Country, and Gender. City and Country are part of the same hierarchy while Gender is its own hierarchy.

When selecting multiple hierarchies, the method to support this is to cross join the values in MDX. While not obvious from a relational table model of the data as the Cloud presents, this can cause very expensive queries to be executed. For example, executing the following:

SELECT c.[Country], m.[Customer Count]
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m

The query results in 6 rows. However, selecting Gender as well:

SELECT c.[Country], c.[Gender], m.[Customer Count]
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 
INNER JOIN [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Measures AS m

The query now results in 12 rows. This is because Gender and Country are on different hierarchies, thus a crossjoin is required in order to return both together. Each additional hierarchy added to the SELECT multiplies the total results by the number of available values in that hierarchy. Thus to get a count of how many rows to expect, one can execute the following:

SELECT (COUNT(c.[Country])*COUNT(c.[Gender])) AS totalrows
FROM [AdventureWorksDW2012Multidimensional-SE].[Adventure Works].Customer AS c 

Response Row Limit

Due to how selecting multiple hierarchies multiplies the total number of result rows, it is possible to balloon the number of response rows very quickly, which results in timeouts. In order to try to give some visibility into which queries are very expensive, the ResponseRowLimit connection property has been added as a mechanism to try and guide users into better practices. When set, it calculates how many rows to expect before any query is executed. If the number of predicted rows exceeds the limit, an error is thrown indicating how many rows to expect back with the query.

It is recommended to select only the columns required or to apply a WHERE criteria. Both can significantly reduce the number of response columns, which has a huge impact on performance. If you are already familiar with the Cloud and what queries may be expensive, ResponseRowLimit may be disabled by setting it to 0.

Dynamic Management Views

Dynamic Management Views (DMVs) are queries that return information about model objects, server operations, and the server health of your Azure Analysis Services instance. These objects can be queried through the Cloud by setting QueryPassthrough to 'true' and issuing the desired query:

SELECT * FROM $System.DBSchema_Tables   
WHERE TABLE_TYPE = 'SCHEMA'   
ORDER BY TABLE_NAME ASC  

You can query the system tables described in this section to access schema information, information on data source functionality, and batch operation statistics.

Schema Tables

The following tables return database metadata for Azure Analysis Services:

• sys_catalogs: Lists the available databases.
• sys_schemas: Lists the available schemas.
• sys_tables: Lists the available tables and views.
• sys_tablecolumns: Describes the columns of the available tables and views.
• sys_procedures: Describes the available stored procedures.
• sys_procedureparameters: Describes stored procedure parameters.
• sys_keycolumns: Describes the primary and foreign keys.
• sys_indexes: Describes the available indexes.

Data Source Tables

The following tables return information about how to connect to and query the data source:

• sys_connection_props: Returns information on the available connection properties.
• sys_sqlinfo: Describes the SELECT queries that the Cloud can offload to the data source.

Query Information Tables

The following table returns query statistics for data modification queries

• sys_identity: Returns information about batch operations or single updates.

Lists the available databases.

The following query retrieves all databases determined by the connection string:

SELECT * FROM sys_catalogs

Columns

Lists the available schemas.

The following query retrieves all available schemas:

          SELECT * FROM sys_schemas
          

Columns

Lists the available tables.

The following query retrieves the available tables and views:

          SELECT * FROM sys_tables
          

Columns

Describes the columns of the available tables and views.

The following query returns the columns and data types for the [adventureworks].[Model].Customer table:

SELECT ColumnName, DataTypeName FROM sys_tablecolumns WHERE TableName = 'Customer' AND CatalogName = 'adventureworks' AND SchemaName = 'Model'

Columns

Lists the available stored procedures.

The following query retrieves the available stored procedures:

          SELECT * FROM sys_procedures
          

Columns

Describes stored procedure parameters.

The following query returns information about all of the input parameters for the SelectEntries stored procedure:

SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'SelectEntries' AND Direction = 1 OR Direction = 2

To include result set columns in addition to the parameters, set the IncludeResultColumns pseudo column to True:

SELECT * FROM sys_procedureparameters WHERE ProcedureName = 'SelectEntries' AND IncludeResultColumns='True'

Columns

Pseudocolumns

Describes the primary and foreign keys.

The following query retrieves the primary key for the [adventureworks].[Model].Customer table:

         SELECT * FROM sys_keycolumns WHERE IsKey='True' AND TableName='Customer' AND CatalogName='adventureworks' AND SchemaName='Model'
          

Columns

Describes the foreign keys.

The following query retrieves all foreign keys which refer to other tables:

         SELECT * FROM sys_foreignkeys WHERE ForeignKeyType = 'FOREIGNKEY_TYPE_IMPORT'
          

Columns

Describes the primary keys.

The following query retrieves the primary keys from all tables and views:

         SELECT * FROM sys_primarykeys
          

Columns

Describes the available indexes. By filtering on indexes, you can write more selective queries with faster query response times.

The following query retrieves all indexes that are not primary keys:

          SELECT * FROM sys_indexes WHERE IsPrimary='false'
          

Columns

Returns information on the available connection properties and those set in the connection string.

The following query retrieves all connection properties that have been set in the connection string or set through a default value:

SELECT * FROM sys_connection_props WHERE Value <> ''

Columns

Describes the SELECT query processing that the Cloud can offload to the data source.

See SQL Compliance for SQL syntax details.

Discovering the Data Source's SELECT Capabilities

Below is an example data set of SQL capabilities. Some aspects of SELECT functionality are returned in a comma-separated list if supported; otherwise, the column contains NO.

The following query retrieves the operators that can be used in the WHERE clause:

SELECT * FROM sys_sqlinfo WHERE Name = 'SUPPORTED_OPERATORS'

Columns

Returns information about attempted modifications.

The following query retrieves the Ids of the modified rows in a batch operation:

         SELECT * FROM sys_identity
          

Columns

Describes the available system information.

The following query retrieves all columns:

SELECT * FROM sys_information

Columns

Stored procedures are function-like interfaces that extend the functionality of the Cloud beyond simple SELECT operations with Azure Analysis Services.

Stored procedures accept a list of parameters, perform their intended function, and then return any relevant response data from Azure Analysis Services, along with an indication of whether the procedure succeeded or failed.

CData Cloud - Azure Analysis Services Stored Procedures

Gets the admin consent URL that must be opened separately by an admin of a given domain to grant access to your application. Only needed when using custom OAuth credentials.

Input

Result Set Columns

The connection string properties are the various options that can be used to establish a connection. This section provides a complete list of the options you can configure in the connection string for this provider. Click the links for further details.

Authentication

Azure Authentication

OAuth

JWT OAuth

SSL

Logging

Schema

Miscellaneous

This section provides a complete list of the Authentication properties you can configure in the connection string for this provider.

The type of authentication to use when connecting to Azure Analysis Services.

Possible Values

Data Type

string

Default Value

"AzureAD"

Remarks

• AzureAD: Set this to perform Azure Active Directory OAuth authentication.
• AzureMSI: Set this to automatically obtain Managed Service Identity credentials when running on an Azure VM.
• AzurePassword: Set this to submit User and Password to Azure. Under the hood, an OAuth process still takes place, but user/password credentials are submitted, which avoids a browser window that requests authorization.
• AzureServicePrincipal: Set this to authenticate as an Azure Service Principal using client credentials.
• AzureServicePrincipalCert: Set this to authenticate as an Azure Service Principal using a certificate.

The URL used to connect to the Azure Analysis Services.

Data Type

string

Default Value

""

Remarks

The HTTP or HTTPS URL used to connect to the Azure Analysis Services in the format asazure://[region].asazure.windows.net/[server]. You can obtain the server name in the Azure portal -> Overview -> Server name.

The query language used to translate SQL queries when interacting with Azure Analysis Services. Select MDX or DAX to determine the query mode used.

Possible Values

Data Type

string

Default Value

"MDX"

Remarks

This property controls whether the Cloud translates SQL queries into MDX (Multidimensional Expressions) or DAX (Data Analysis Expressions) when executing queries against Azure Analysis Services.

Available values are:

• MDX (default) - Uses MDX for query translation. MDX is compatible with both multidimensional and tabular models and is the traditional query language for OLAP cubes. The data model exposes dimensions, hierarchies, and levels as views.
• DAX - Uses DAX for query translation. DAX is optimized for tabular data models and provides simpler data type mapping, more natural aggregation support, and improved JOIN handling. The data model exposes tables and columns directly from the tabular model.

Interaction with QueryPassthrough

The QueryPassthrough property can be used for query passthrough regardless of this setting. When QueryPassthrough is true, raw queries (MDX or DAX) are sent directly to Azure Analysis Services without SQL translation.

Specifies the authenticating user's user ID.

Data Type

string

Default Value

""

Remarks

The authenticating server requires both User and Password to validate the user's identity.

Specifies the authenticating user's password.

Data Type

string

Default Value

""

Remarks

The authenticating server requires both User and Password to validate the user's identity.

This section provides a complete list of the Azure Authentication properties you can configure in the connection string for this provider.

Identifies the Azure Analysis Services tenant being used to access data. Accepts either the tenant's domain name (for example, contoso.onmicrosoft.com ) or its directory (tenant) ID.

Data Type

string

Default Value

""

Remarks

A tenant is a digital container for your organization's users and resources, managed through Microsoft Entra ID (formerly Azure AD). Each tenant is associated with a unique directory ID, and often with a custom domain (for example, microsoft.com or contoso.onmicrosoft.com).

To find the directory (tenant) ID in the Microsoft Entra Admin Center, navigate to Microsoft Entra ID > Properties and copy the value labeled "Directory (tenant) ID".

This property is required in the following cases:

• When AuthScheme is set to AzureServicePrincipal or AzureServicePrincipalCert
• When AuthScheme is AzureAD and the user account belongs to multiple tenants

You can provide the tenant value in one of two formats:

• A domain name (for example, contoso.onmicrosoft.com)
• A directory (tenant) ID in GUID format (for example, c9d7b8e4-1234-4f90-bc1a-2a28e0f9e9e0)

Specifying the tenant explicitly ensures that the authentication request is routed to the correct directory, which is especially important when a user belongs to multiple tenants or when using service principal–based authentication.

If this value is omitted when required, authentication may fail or connect to the wrong tenant. This can result in errors such as unauthorized or resource not found.

Specifies the Azure network environment to which you will connect. Must be the same network to which your Azure account was added.

Possible Values

Data Type

string

Default Value

"GLOBAL"

Remarks

Required if your Azure account is part of a different network than the Global network, such as China, USGOVT, or USGOVTDOD.

This section provides a complete list of the OAuth properties you can configure in the connection string for this provider.

Specifies the client ID (also known as the consumer key) assigned to your custom OAuth application. This ID is required to identify the application to the OAuth authorization server during authentication.

Data Type

string

Default Value

""

Remarks

This property is required in two cases:

• When using a custom OAuth application, such as in web-based authentication flows, service-based authentication, or certificate-based flows that require application registration.
• If the driver does not provide embedded OAuth credentials.

(When the driver provides embedded OAuth credentials, this value may already be provided by the Cloud and thus not require manual entry.)

OAuthClientId is generally used alongside other OAuth-related properties such as OAuthClientSecret and OAuthSettingsLocation when configuring an authenticated connection.

OAuthClientId is one of the key connection parameters that need to be set before users can authenticate via OAuth. You can usually find this value in your identity provider’s application registration settings. Look for a field labeled Client ID, Application ID, or Consumer Key.

While the client ID is not considered a confidential value like a client secret, it is still part of your application's identity and should be handled carefully. Avoid exposing it in public repositories or shared configuration files.

For more information on how this property is used when configuring a connection, see Establishing a Connection.

Specifies the client secret assigned to your custom OAuth application. This confidential value is used to authenticate the application to the OAuth authorization server. (Custom OAuth applications only.).

Data Type

string

Default Value

""

Remarks

This property (sometimes called the application secret or consumer secret) is required when using a custom OAuth application in any flow that requires secure client authentication, such as web-based OAuth, service-based connections, or certificate-based authorization flows. It is not required when using an embedded OAuth application.

The client secret is used during the token exchange step of the OAuth flow, when the driver requests an access token from the authorization server. If this value is missing or incorrect, authentication fails with either an invalid_client or an unauthorized_client error.

OAuthClientSecret is one of the key connection parameters that need to be set before users can authenticate via OAuth. You can obtain this value from your identity provider when registering the OAuth application.

Notes:

• This value should be stored securely and never exposed in public repositories, scripts, or unsecured environments.
• Client secrets may also expire after a set period. Be sure to monitor expiration dates and rotate secrets as needed to maintain uninterrupted access.

For more information on how this property is used when configuring a connection, see Establishing a Connection

A space-separated list of scopes.

Data Type

string

Default Value

""

Remarks

Scopes are set to define what kind of access the authenticating user will have; for example, read, read and write, restricted access to sensitive information. System administrators can use scopes to selectively enable access by functionality or security clearance.

When InitiateOAuth is set to GETANDREFRESH, you must use this property if you want to change which scopes are requested.

When InitiateOAuth is set to either REFRESH or OFF, you can change which scopes are requested using either this property or the Scope input.

This connection property specifies the list of OAuth scopes to be requested.

When left empty, the driver automatically selects the appropriate Azure Analysis Services scope based on the AzureEnvironment and the authentication flow in use.

This section provides a complete list of the JWT OAuth properties you can configure in the connection string for this provider.

Supplies the name of the client certificate's JWT Certificate store.

Data Type

string

Default Value

""

Remarks

The OAuthJWTCertType field specifies the type of the certificate store specified in OAuthJWTCert. If the store is password-protected, use OAuthJWTCertPassword to supply the password..

OAuthJWTCert is used in conjunction with the OAuthJWTCertSubject field in order to specify client certificates. If OAuthJWTCert has a value, and OAuthJWTCertSubject is set, the CData Cloud initiates a search for a certificate. For further information, see OAuthJWTCertSubject.

Designations of certificate stores are platform-dependent.

Notes

• The most common User and Machine certificate stores in Windows include:
  • MY: A certificate store holding personal certificates with their associated private keys.
  • CA: Certifying authority certificates.
  • ROOT: Root certificates.
  • SPC: Software publisher certificates.
• In Java, the certificate store normally is a file containing certificates and optional private keys.
• When the certificate store type is PFXFile, this property must be set to the name of the file.
• When the type is PFXBlob, the property must be set to the binary contents of a PFX file (i.e. PKCS12 certificate store).

Identifies the type of key store containing the JWT Certificate.

Possible Values

Data Type

string

Default Value

"PEMKEY_BLOB"

Remarks

Provides the password for the OAuth JWT certificate used to access a password-protected certificate store. If the certificate store does not require a password, leave this property blank.

Data Type

string

Default Value

""

Remarks

This property specifies the password needed to open a password-protected certificate store. To determine if a password is necessary, refer to the documentation or configuration for your specific certificate store.

Identifies the subject of the OAuth JWT certificate used to locate a matching certificate in the store. Supports partial matches and the wildcard '*' to select the first certificate.

Data Type

string

Default Value

"*"

Remarks

The value of this property is used to locate a matching certificate in the store. The search process works as follows:

• If an exact match for the subject is found, the corresponding certificate is selected.
• If no exact match is found, the store is searched for certificates whose subjects contain the property value.
• If no match is found, no certificate is selected.

You can set the value to '*' to automatically select the first certificate in the store. The certificate subject is a comma-separated list of distinguished name fields and values. For example: CN=www.server.com, OU=test, C=US, [email protected].

Common fields include:

If a field value contains a comma, enclose it in quotes. For example: "O=ACME, Inc.".

This section provides a complete list of the SSL properties you can configure in the connection string for this provider.

Specifies the TLS/SSL client certificate store for SSL Client Authentication (2-way SSL). This property works in conjunction with other SSL-related properties to establish a secure connection.

Data Type

string

Default Value

""

Remarks

This property specifies the client certificate store for SSL Client Authentication. Use this property alongside SSLClientCertType, which defines the type of the certificate store, and SSLClientCertPassword, which specifies the password for password-protected stores. When SSLClientCert is set and SSLClientCertSubject is configured, the driver searches for a certificate matching the specified subject.

Certificate store designations vary by platform. On Windows, certificate stores are identified by names such as MY (personal certificates), while in Java, the certificate store is typically a file containing certificates and optional private keys.

The following are designations of the most common User and Machine certificate stores in Windows:

For PFXFile types, set this property to the filename. For PFXBlob types, set this property to the binary contents of the file in PKCS12 format.

Specifies the type of key store containing the TLS/SSL client certificate for SSL Client Authentication. Choose from a variety of key store formats depending on your platform and certificate source.

Possible Values

Data Type

string

Default Value

"PEMKEY_BLOB"

Remarks

This property determines the format and location of the key store used to provide the client certificate. Supported values include platform-specific and universal key store formats. The available values and their usage are:

Specifes the password required to access the TLS/SSL client certificate store. Use this property if the selected certificate store type requires a password for access.

Data Type

string

Default Value

""

Remarks

This property provides the password needed to open a password-protected certificate store. This property is necessary when using certificate stores that require a password for decryption, as is often recommended for PFX or JKS type stores.

If the certificate store type does not require a password, for example USER or MACHINE on Windows, this property can be left blank. Ensure that the password matches the one associated with the specified certificate store to avoid authentication errors.

Specifes the subject of the TLS/SSL client certificate to locate it in the certificate store. Use a comma-separated list of distinguished name fields, such as CN=www.server.com, C=US. The wildcard * selects the first certificate in the store.

Data Type

string

Default Value

"*"

Remarks

This property determines which client certificate to load based on its subject. The Cloud searches for a certificate that exactly matches the specified subject. If no exact match is found, the Cloud looks for certificates containing the value of the subject. If no match is found, no certificate is selected.

The subject should follow the standard format of a comma-separated list of distinguished name fields and values. For example, CN=www.server.com, OU=Test, C=US. Common fields include the following:

Note: If any field contains special characters, such as commas, the value must be quoted. For example: CN="Example, Inc.", C=US.

Specifies the certificate to be accepted from the server when connecting using TLS/SSL.

Data Type

string

Default Value

""

Remarks

If you are using a TLS/SSL connection, use this property to specify the TLS/SSL certificate to be accepted from the server. If you specify a value for this property, all other certificates that are not trusted by the machine are rejected.

This property can take the following forms:

Note: It is possible to use '*' to signify that all certificates should be accepted, but due to security concerns this is not recommended.

This section provides a complete list of the Logging properties you can configure in the connection string for this provider.

Specifies the verbosity level of the log file, which controls the amount of detail logged. Supported values range from 1 to 5.

Data Type

string

Default Value

"1"

Remarks

This property defines the level of detail the Cloud includes in the log file. Higher verbosity levels increase the detail of the logged information, but may also result in larger log files and slower performance due to the additional data being captured.

The default verbosity level is 1, which is recommended for regular operation. Higher verbosity levels are primarily intended for debugging purposes. For more information on each level, refer to Logging.

When combined with the LogModules property, Verbosity can refine logging to specific categories of information.

This section provides a complete list of the Schema properties you can configure in the connection string for this provider.

Optional setting that restricts the schemas reported to a subset of all available schemas. For example, BrowsableSchemas=SchemaA,SchemaB,SchemaC .

Data Type

string

Default Value

""

Remarks

Listing all available database schemas can take extra time, thus degrading performance. Providing a list of schemas in the connection string saves time and improves performance.

The Analysis Services catalog to use. This may also be known as a Database from within Analysis Services.

Data Type

string

Default Value

""

Remarks

This input is optional as catalogs are displayed by default. However, if you have QueryPassthrough set to true, this value ensures the MDX queries are submitted to the correct catalog.

Set this to true to include extra join columns on each table.

Data Type

bool

Default Value

false

Remarks

Some tools may require an ON condition (or generate them automatically) based on foreign key references. By setting IncludeJoinColumns to true, every table includes a foreign key reference to the other tables. These columns do not return any data and are not useful for anything other than passing as ON conditions to perform joins upon.

In Azure Analysis Services, the dimensions and measures making up the tables are already related naturally. There is no context on which to join them provided. Therefore, the CData Cloud supports joining without specifying an ON condition, so they are optional to specify.

This section provides a complete list of the Miscellaneous properties you can configure in the connection string for this provider.

Specifies additional HTTP headers to append to the request headers created from other properties, such as ContentType and From. Use this property to customize requests for specialized or nonstandard APIs.

Data Type

string

Default Value

""

Remarks

Use this property to add custom headers to HTTP requests sent by the Cloud.

This property is useful when fine-tuning requests to interact with APIs that require additional or nonstandard headers. Headers must follow the format "header: value" as described in the HTTP specifications and each header line must be separated by the carriage return and line feed (CRLF) characters. Important: Use caution when setting this property. Supplying invalid headers may cause HTTP requests to fail.

Determines if each level should be converted into a measure, allowing calculations to be performed on the measure.

Data Type

bool

Default Value

false

Remarks

By default, all levels are of type String. Enabling this option allows a level to be resolved down to its key property, creating a measure that has the level's DBType data type. Calculations can then be performed on the measure.

Set this to true to report expressions as part of the description on measure columns.

Data Type

bool

Default Value

false

Remarks

The Cloud reports the remarks for several types of entities (dimensions, measures, measure groups, and hierarchies) as table and column descriptions. By default, the Cloud includes only the remarks in measure column descriptions.

If this option is enabled, then the measure expression is included in the measure column description, along with the remarks. The descriptions on other types of entities are not affected.

Additional properties to submit on each MDX request to Azure Analysis Services.

Data Type

string

Default Value

""

Remarks

You can use the ExtraProperties connection property to add extra values to the PropertiesList in the XMLA request. Use name=value pairs separated by a semicolon to submit the properties. For example, Catalog=MyCatalog;Cube=MyCube;.

A list of properties may be found by executing SELECT * FROM $System.DISCOVER_PROPERTIES.

Specifies the maximum number of rows returned for queries that do not include either aggregation or GROUP BY.

Data Type

int

Default Value

-1

Remarks

The default value for this property, -1, means that no row limit is enforced unless the query explicitly includes a LIMIT clause. (When a query includes a LIMIT clause, the value specified in the query takes precedence over the MaxRows setting.)

Setting MaxRows to a whole number greater than 0 ensures that queries do not return excessively large result sets by default.

This property is useful for optimizing performance and preventing excessive resource consumption when executing queries that could otherwise return very large datasets.

The number of response rows to allow before erroring. Set to 0 for no limit.

Data Type

int

Default Value

100000

Remarks

Selecting a lot of columns results in a number of crossjoins occurring under the hood when translated to something that is acceptable for Azure Analysis Services. This is not intuitive if you are not familiar with MDX. It can easily result in very large responses that time out. The ResponseRowLimit is designed to try to alert the user to understand what can be very expensive requests.

Set this to true to include hidden dimensions, measures, and levels.

Data Type

bool

Default Value

false

Remarks

By default the Cloud does not report entities that Azure Analysis Services marks as hidden. Enabling this option allows you to query them.

Set this to true to split Measures table into individual tables.

Data Type

bool

Default Value

false

Remarks

All measures are currently grouped into a single table 'Measures'. Set this to true to split Measures table into individual tables (if a table only contains measures) and include measures in their respective dimension tables.

Use this property in conjunction with SplitMeasures to set the priority for how measures should be organized into tables.

Data Type

string

Default Value

"MeasureGroup"

Remarks

This property controls the order in which measure storage attributes are used to sort them into subtables when SplitMeasures is true. Provide a comma-delimited list of storage methods in the order they should be prioritized. Available values are:

• MeasureGroup
• DisplayFolder

Specifies whether or not throws an exception if there is no rows updated.

Data Type

bool

Default Value

false

Remarks

Specifies whether or not throws an exception if there is no rows updated.

Specifies the maximum time, in seconds, that the provider waits for a server response before throwing a timeout error.

Data Type

int

Default Value

60

Remarks

The timeout applies to each individual communication with the server rather than the entire query or operation. For example, a query could continue running beyond 60 seconds if each paging call completes within the timeout limit.

Timeout is set to 60 seconds by default. To disable timeouts, set this property to 0.

Disabling the timeout allows operations to run indefinitely until they succeed or fail due to other conditions such as server-side timeouts, network interruptions, or resource limits on the server.

Note: Use this property cautiously to avoid long-running operations that could degrade performance or result in unresponsive behavior.

LZMA from 7Zip LZMA SDK

LZMA SDK is placed in the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or distribute the original LZMA SDK code, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means.

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