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Show sample rows of each step

Set breakpoint

Show the optimized execution plan

Show performance-related statistics

Define additional filters

Is is rerun with the first failed row.

It is rerun from the beginning and the partially loaded data is always handled automanticaly.

It is rerun only if the dataflow is part of a recovery unit.

It is rerun from the beginning and the desing of the data flow must deal with partially loaded data.

Aggregation operations used with a group by statement

join operations between a file and a database table

Load operations that contain trigger

Join operations between sources that are on the same database servers

Match_Pattern

Decode

Literal

Match_regex

count_distinct(Account_TYPE)

Count(*)

count(Account_type)

Gen_Row_Num()

IS

null

?

CA

To define a connection to SAP HANA

To reduce the memory consumption in the target database.

To reduce the memory consumption in the source database.

To enhance processing performance of datadlows used in real time jobs.

Create an auditing rule that raises an exception

Raise an exception in a Conditional connected to the target table.

Set a breakpoint on the line connected to the target table.

Use the raise_exception function in the validation transform.

database name

database server name

Datastore name

user name and password

database version

To count the number of rows processed at user defined points to collect runtime statistics

To define rules based on the number of records processed overall once the dataflow is finished

to define rules that each record processed by the dataflow has to comply with

To view the data as it is processed by the dataflow in order to ensure its correctness

View and modify the overall optimization plan of a data services engine

View and modify the SQL to improve performance

View and modify the SQL and adjust the dataflow to maximize push-down operations.

View and modify the database execution plan within the Data Services Designer

Run the nightly job with the enable recovery flag turned on.

Design the dataflow to ensure a second run does not result in duplicate

add a try block before each dataflow and a Catch block after each dataflow

Merge the dataflows from the job and rerun it.