Module Name

Choose from the list of modules

String

Yes

One of the seven on the website

Entity Name

Choose from the list of entities

String

Yes

Only appears after the module name is selected

Entity Description

The description of the respective entity

String

Yes

Entity Business Owner Name

First name + Last name of Entity Business Owner

String

Yes

Entity Business Owner Email

The Business Owner email Id

String

Yes

should have @

Entity Data Owner Name

The Data Owner Name of Entity

String

Yes

Entity Data Owner Email

The Data Owner Email of Entity

String

Yes

should have @

Entity It Owner Name

The IT owner name of an entity

String

Yes

Entity It Owner Email

The IT owner email of the entity

String

Yes

should have @

Vendor Support Name

The vendor support name of the entity. Default to support@sprngy.com, non-editable

String

Auto-Fill

Vendor Support Email

The vendor support name of the entity. Default to support@sprngy.com, non-editable

String

Auto-Fill

Rule Name

QUERY_IMPORT_PROCESSOR/ IN_MEMORY_IMPORT_PROCESSOR_NAME

Processor/Rule name to run

Import Model description -
Rule Name

Process Rule Use Ind

ENDOFDAY/INTRADAY

Process Rule Use indicator for the given entity and rulename (ex. If it’s ENDOFDAY it’s going to run import pipeline in endofday mode. and If it’s INTRADAY then it’s going to run in intraday mode.

Import Model description -
Process Rule Use Ind

Connection URL

jdbc:mysql://localhost/schema_name?serverTimezone=UTC

JDBC Connection URL from which data needs to be imported i.e. MySQL, Oracle, MySQL Server, Azure MySQL etc.

Import Model description -
Connection URL

SQL Query

(select t1.* from (select t.*, ROW_NUMBER() OVER() row_id from (select * from schema_name.table_name where 1=1) t) t1) t2

SQL Query to run on the given JDBC connection to import data

Import Model description -
SQL Query

Split by Column Name

row_id

The unique integer column name base on which partition is going to take place in order to optimise the query

Import Model description -
Split by Column Name

Num Mappers

1/4/8/11 etc.

The number of partitions required for the given driver table to make import more efficient,

Import Model description -
Num Mappers

Target Directory

/BigAnalytixsPlatform/modulename/Entityname/SDL/Land

The HDFS directory where imported data should be overwritten

Import Model description -
Target Directory

Driver Table

select count(*) as COUNT from Databasename.Tablename

The main driver table of JDBC connection based on which the the count of rows and columns is going to fetch

Import Model description -
Driver Table

Memory Table Name

Tablename

After reading data from JDBC connection in SPARK, this is the name of that spark memory table.

Import Model description -
Memory Table Name

Coalesce Ind

YES/NO

Whether to coalesce data or not when writing data to HDFS

Import Model description -
Coalesce Ind

Step Number

1(2,3,4)

The step number to assign based on the other information provided.

Import Model description -
Step Number

Step Sequence

1/2/3/4

The step sequence / sub steps.

Import Model description - Step Sequence

Fetch Size

100000/250000/500000

The fetch size to import those many number of rows from JDBC connection

Import Model description -
Fetch Size

On Failure

STOP/CONTINUE

If the current step fails then whether to stop the pipeline or continue

Import Model description -
On Failure

Data Write Mode

overwrite/append

When writing data, whether to append or overwrite the data to the provided target directory.

Import Model description -
Data Write Mode

Retry Count

Any number (count)

When reading data from RDBMS using spark, if there is any failure then this feature indicate the number of times it should retry. (For QUERY_IMPORT_PROCESSOR)

Import Model Description - Load In Memory

Retry Interval

Any number (seconds)

When reading data from RDBMS using spark, if there is any failure then this feature indicate the time/interval it should retry in context of retry count.(For QUERY_IMPORT_PROCESSOR)

Import Model Description - Load In Memory

Load In Memory

YES/NO

When reading data, whether to load/cache data in memory.

Import Model Description - Load In Memory

Bound Query

select 1 as LOWERBOUND, count(*) as UPPERBOUND from <schema_name>.<table_name>

When reading data from RDBMS, this feature describes to get the upper and lowerbound for partitions to optimize the read operation in spark.

Import Model Description - Load In Memory