Newly Released Microsoft DP-203 Exam PDF

The following query returns the transaction log size on each distribution. If one of the log files is reaching 160 GB, you should consider scaling up your instance or limiting your transaction size.

-- Transaction log size

SELECT

instance_name as distribution_db,

cntr_value*1.0/1048576 as log_file_size_used_GB,

pdw_node_id

FROM sys.dm_pdw_nodes_os_performance_counters

WHERE

instance_name like 'Distribution_%'

AND counter_name = 'Log File(s) Used Size (KB)'

References:

https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-manage-monitor

The conditional split transformation routes data rows to different streams based on matching conditions. The conditional split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified stream.

Box 1: dept=='ecommerce', dept=='retail', dept=='wholesale'

First we put the condition. The order must match the stream labeling we define in Box 3.

Syntax:

split(

disjoint: {true | false}

) ~> @(stream1, stream2, ..., )

Box 2: discount : false

disjoint is false because the data goes to the first matching condition. All remaining rows matching the third condition go to output stream all.

Box 3: ecommerce, retail, wholesale, all

Label the streams

If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity, not a mapping flow,5 with your own data processing logic and use the activity in the pipeline. You can create a custom activity to run R scripts on your HDInsight cluster with R installed.

Box 1: openrowset

The easiest way to see to the content of your CSV file is to provide file URL to OPENROWSET function, specify csv FORMAT.

Example:

SELECT *

FROM OPENROWSET(

BULK 'csv/population/population.csv',

DATA_SOURCE = 'SqlOnDemandDemo',

FORMAT = 'CSV', PARSER_VERSION = '2.0',

FIELDTERMINATOR =',',

ROWTERMINATOR = '\n'

Box 2: openjson

You can access your JSON files from the Azure File Storage share by using the mapped drive, as shown in the following example:

SELECT book.* FROM

OPENROWSET(BULK N't:\books\books.json', SINGLE_CLOB) AS json

CROSS APPLY OPENJSON(BulkColumn)

WITH( id nvarchar(100), name nvarchar(100), price float,

pages_i int, author nvarchar(100)) AS book