Batch Data Sync Example

1. Overview

This example will take you through the creation and execution of a batch data sync where data will be loaded into the Cinchy via a CSV. In this example, we will be loading information into the People table in Cinchy. This is a self-contained example and can be replicated in any Cinchy environment without dependencies.

Example Use Case: You have historically maintained a record of all of your employees in a spreadsheet. Knowing that this significantly hinders your data and data management capabilities, you want to sync your file into Cinchy. Once synced, you can manage your employee information through the Cinchy data browser, instead of through a data silo.

You can review our documentation on Delimited File Sources here.

2. Sample Files and Code

This section contains:

The People Table XML schema. Alternatively, this can be manually created through the Cinchy Data Browser
A sample source CSV data file to load into Cinchy.

2.1 Cinchy Table XML

To create the People table used in this example, you can use the below is the XML Alternatively, you can create the table manually using the steps in section 2.1.1.

<?xml version="1.0" encoding="utf-16"?>
<Model xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" name="People Model" version="1.0.0" xmlns="http://www.cinchy.co">
  <Schema>
    <Tables>
      <Table name="People" domain="Sandbox" guid="d5e6409c-471e-4a19-ba9c-df6ff2b606aa" icon="fa fa-table" iconColour="#002060">
        <Columns>
          <Text name="Name" guid="d37935e7-0ebb-4267-ad10-758067221951" allowLinking="false" />
          <Text name="Title" guid="afebfe66-1d59-445d-9373-0f8bc2a5e804" allowLinking="false" />
          <Text name="Company" guid="aaa5aeb5-4be2-4dc7-89a6-2237ae5b9c74" allowLinking="false" />
        </Columns>
        <UniqueConstraints />
      </Table>
    </Tables>
  </Schema>
</Model>

2.1.1 Manual Table Creation

Log in to your Cinchy platform.
From under My Network, click the create button.
Select Table.
Select From Scratch.
Create a table with the following properties (Image 1):

Table Details

Values

Table Name

People

Icon + Colour

Default

Domain

Sandbox (if this domain does not exist please either create it or make sure to update this parameter where required during the data sync)

6. Click Columns in the left hand navigation to create the columns for the table.

7. Click the "Click Here to Add" button and aadd the following columns.

Column Details

Values

Column 1

Column Name: Name

Data Type: Text

Column 2

Column Name: Title

Data Type: Text

Column 3

Column Name: Company

Data Type: Text

9. Click the Save button to save your table.

2.2 Data File

The sample CSV file used in this example can be downloaded below.

Please note, if you are downloading this file to recreate this exercise the file path and the file name will need to be:

C:\Data\contacts.csv

Alternatively, you can update the path parameter in the data sync configuration to match the file path and name of your choosing.

The source file contains the following information which will be synced into our target Cinchy table (Image 2).

As we can see, the file has the following columns:

First Name
Last Name
Email Address
Title
Company

The People table created only has the following columns:

Name
Title
Company

When syncing the data from our source (CSV file) to our target (Cinchy People table) our batch data sync will need to take into consideration the following:

The first and last name from the source will need to end up merged into one column in our target (Name)
Email address from our sources is not a column in the target, therefore his column will not be synced into the target
The title column will be a one to one match from source to target
The company column will also be a one to one match from source to target

3. Creating the Data Sync

There are two options when you want to create a data sync in Cinchy.

You can input all of your necessary information through the intuitive Connections UI. Once saved, all of this data is uploaded as an XML into the Data Sync configurations table.
Or, you can bypass the UI and upload your XML config directly into the Data Sync configuration table yourself.

This example will walk you through option one.

3.1 Using the Connections UI

Within your Cinchy platform, navigate to the Connections Experience (Image 3).

In the Info tab, input the name of your data sync. For this example we are using "Contact Import" (Image 4).

Since this is a local file upload, we also need to set a Parameter. This value will be referenced in the "path" value of the Load Metadata box in step 5. For this example, we will set it to filepath (Image 5).

Navigate to the Source tab. As a reminder, we are using the .CSV file you downloaded at the beginning of this example as our source.
Under "Select a Source", choose Delimited File (Image 6).

The "Load Metadata" box will appear; this is where you will define some important values about your source needed for the data sync to execute. Using the below table as your guide, fill in your metadata parameters (Image 7):

Parameter

Description

Example

Source

The source location of your file. This can be either Local, S3, or Azure Blob Storage.

Local

Delimiter

The type of delimiter on your source file.

Since our file is a CSV, the delimiter is a comma, and we uses the ',' value.

Text Qualifier

A text qualifier is a character used to distinguish the point at which the contents of a text field should begin and end.

Header Rows to Ignore

The number of records from the top of the file to ignore before the data starts (includes column header).

Path

The path to your source file. In this case, it is the Parameter that was set in step 3.

@filepath

Choose File

This option will appear once you've correctly set your Path value.

Upload the sample CSV for this example.

Click Load.
In the Available Columns pop-up, select all of the columns that you want to import from the CSV. For this example, we will select them all (noting, however, that we will only map a few of them later) (Image 8).

Click Load.
Once you load your source, the schema section of the page will auto-populate with the columns that you selected in step 7 (Image 9). Review the schema to ensure it has the correct Name and Data Type. You may also choose to set any Aliases or add a Description.

Navigate to the Destination tab and select Cinchy Table from the drop down.
In the Load Metadata pop-up, input the Domain and Table name for your destination. In this example, we are using the Sandbox domain and the People table (Image 10).
Click Load.

Select the columns that you wish to use in your data sync (Image 11). These will be the columns that your source syncs to. In this example, we are using the Name, Title, and Company columns. Note that you will have many Cinchy system table available to use as well. Click Load.

The Connections experience will attempt to automatically map your source and destination columns based on matching names. In the below screenshot, it has been able to correctly match the "Company" and "Title" columns (Image 12). The "Name" target column is not a 1:1 match for any of our source columns, so we must match that one manually.

Select "First Name" from the Source Column drop down to finish mapping our data sync (Image 13).

Navigate to the Sync Behaviour tab. There are two options for data syncs: Full File and Delta. In this example, select Full File.

Full load processing means that the entire amount of data is imported iteratively the first time a data source is loaded into the data studio. Delta processing, on the other hand, means loading the data incrementally, loading the source data at specific pre-established intervals.

Read more about Sync Behaviour types here.

Set the following parameters (Image 14):

Parameter

Description

Example

Sync Key Column Reference

The SyncKey is used as a unique key reference when syncing the data from the data source into the Cinchy table. It is used to match data between the source and the target. This allows for updates to occur on changed records.

Name

New Record Behaviour

This defines what action is taken when a new record is found in the sync source. This can be either Insert or Ignore.

Insert

Dropped Record Behaviour

This defines what action is taken when a dropped record is found in the sync source.

This can be either Delete, Ignore, or Expire.

Delete

Changed Record Behaviour

This defines what action is taken when a changed record is found in the sync source.

This can be either Update, Ignore, or Conditional.

Update

Navigate to the Permissions tab. Here you will define your group access controls for your data sync (Image 15). You can set this how you like. For this example, we are giving all user access to Execute, Write, and Read our sync.

Any groups given Admin Access will have the ability to Execute, Write, and Read the data sync.

Navigate to the Jobs tab. Here you will see a record of all successful or failed jobs for this data sync.
Select "Start a Job" (Image 16).

Load your sample .CSV file in the pop-up window (Image 17).

The job will commence. The Execution window that pops up will help you to verify that your data sync is progressing (Image 18).

Navigate to your destination table to ensure that your data populated correctly (Image 19).

3.2 Using a Data Sync XML

In lieu of using the Connections UI, you can also set up a data sync by uploading a correctly formatted XML into the Data Sync Configs table within Cinchy.

We recommend only doing so once you have a good grasp on how data sync work. Note that not all sources/targets follow the same XML pattern.

Below is the completed batch data sync configuration for this example. Review the XMLs and then proceed to section 3.2.3 for further instructions.

3.2.1 Blank XML Example

The below XML shows what a blank data sync could look like for a Delimited File source to a Cinchy Table target.

<?xml version="1.0" encoding="utf-16"?>
<BatchDataSyncConfig name="" version="1.0.0" xmlns="http://www.cinchy.co">
    <Parameters>
        <Parameter name=""/>
    </Parameters>
    <DelimitedDataSource source="" path="" delimiter="" textQualifier="" headerRowsToIgnore="" encoding="" useHeaderRecord="">
        <Schema>
            <Column name="" dataType="" trimWhitespace="true" isMandatory="false" validateData="false"/>
        </Schema>
    </DelimitedDataSource>
    <CinchyTableTarget reconcileData="true" domain="" table="" suppressDuplicateErrors="false" degreeOfParallelism="1">
        <ColumnMappings>
            <ColumnMapping sourceColumn="" targetColumn=""/>
        </ColumnMappings>
        <SyncKey readonly="false">
            <SyncKeyColumnReference name=""/>
        </SyncKey>
        <NewRecordBehaviour type=""/>
        <DroppedRecordBehaviour type=""/>
        <ChangedRecordBehaviour type=""/>
        <PostSyncScripts/>
    </CinchyTableTarget>
</BatchDataSyncConfig>

3.2.2 Filled XML Example

The below filled XML example matches the Connections UI configuration we made in step 3.1. You can review the parameters used in the table below.

Parameter

Description

Example

Name

The name of your data sync.

Contact Import

Parameter

Since this is a local file upload, we also need to set a Parameter. This value will be referenced in the "path" value of the Load Metadata box

Parameter

Source

Defines whether your source is Local (PATH), S3, or Azure.

PATH

Path

Since this is a local upload, this is the path to your source file. In this case, it is the value that was set for the "Parameter" value, preceded by the '@' sign.

@Parameter

Delimiter

The type of delimiter on your source file.

Since our file is a CSV, the delimiter is a comma, and we uses the ',' value.

Text Qualifier

A text qualifier is a character used to distinguish the point at which the contents of a text field should begin and end.

"&quote;

Header Rows to Ignore

The number of records from the top of the file to ignore before the data starts (includes column header).

Column Name

The name(s) of the source columns that you wish to sync. Note that in this example we have selected more columns than we will map, in order to show how Connections ignores unmapped data.

"First Name" "Last Name" "Email Address: "Title" "Company"

Column Data Type

The data type that corresponds to our selected source columns.

"Text"

Domain

The domain of your Cinchy Target table.

Sandbox

Table

The name of your Cinchy Target table.

People

Column Mapping Source Column

The name(s) of the source columns that you are syncing.

"Company" "Title" "First Name"

Column Mapping Target Column

The name(s) of the target column as it maps to the specified source column.

"Company" "Title" "Name"

Sync Key Column Reference Name

"Name"

New Record Behaviour Type

This defines what will happen when new records are found in the source.

INSERT

Dropped Record Behaviour Type

This defines what will happen when dropped records are found in the source.

DELETE

Changed Record Behaviour Type

This defines what will happen when changed records are found in the source.

UPDATE

<?xml version="1.0" encoding="utf-16"?>
<BatchDataSyncConfig name="Contact Import" version="1.0.0" xmlns="http://www.cinchy.co">
    <Parameters>
        <Parameter name="parameter"/>
    </Parameters>
    <DelimitedDataSource source="PATH" path="@parameter" delimiter="," textQualifier="&quot;" headerRowsToIgnore="1" encoding="UTF8" useHeaderRecord="false">
        <Schema>
            <Column name="First Name" dataType="Text" trimWhitespace="true" isMandatory="false" validateData="false"/>
            <Column name="Last Name" dataType="Text" trimWhitespace="true" isMandatory="false" validateData="false"/>
            <Column name="Email Address" dataType="Text" trimWhitespace="true" isMandatory="false" validateData="false"/>
            <Column name="Title" dataType="Text" trimWhitespace="true" isMandatory="false" validateData="false"/>
            <Column name="Company" dataType="Text" trimWhitespace="true" isMandatory="false" validateData="false"/>
        </Schema>
    </DelimitedDataSource>
    <CinchyTableTarget reconcileData="true" domain="sandbox" table="People" suppressDuplicateErrors="false" degreeOfParallelism="1">
        <ColumnMappings>
            <ColumnMapping sourceColumn="Company" targetColumn="Company"/>
            <ColumnMapping sourceColumn="Title" targetColumn="Title"/>
            <ColumnMapping sourceColumn="First Name" targetColumn="Name"/>
        </ColumnMappings>
        <SyncKey readonly="false">
            <SyncKeyColumnReference name="Name"/>
        </SyncKey>
        <NewRecordBehaviour type="INSERT"/>
        <DroppedRecordBehaviour type="DELETE"/>
        <ChangedRecordBehaviour type="UPDATE"/>
        <PostSyncScripts/>
    </CinchyTableTarget>
</BatchDataSyncConfig>

3.2.3 Using the Data Sync XML

Once you have completed your Data Sync XML, navigate to the Data Sync Configurations table in Cinchy (Image 20).

In a new row, paste the Data Sync XML into the Config XML column (Image 21).
Define your group permissions in the applicable columns. In our example, we have given All Users the Admin Access.

The Name and Config Version columns will be auto populated as they values are coming from the Config XML.

Tip: Click on the below image to enlarge it.

Be sure when you are pasting into the Config XML column that you double click into the column before pasting, otherwise each line of the XML will appear as an individual record in the Data Sync Configurations table.

To execute your Data Sync you will use the CLI. If you do not have this downloaded, refer to the documentation here.
In this example we will be using the following Data Sync Commands, however, for the full list of commands click here.

Parameter

Description

Example

-s (server)

Required. The full path to the Cinchy server without the protocol (e.g. cinchy.co/Cinchy).

"pilot.cinchy.co/Training/Cinchy/"

-u (userid)

Required. The user id to login to Cinchy that has execution access to the data sync.

"admin"

-p (password)

Required. The password of the above User ID parameter. This can optionally be encrypted. For a walkthrough on how to use the CLI to encrypt the password, refer to the Appendix section.

"DESuEGqfffsamx55yl256hjuPYxa4ncc+5+bLkoVIFpgs0Lq6hkcU="

-f (feed)

Required. The name of the Data Sync Configuration as defined in Cinchy

"Contact Import"

Launch Powershell and navigate to the Cinchy CLI directory.
Enter and execute the following into Powershell:

.\Cinchy.CLI.exe syncdata -s "pilot.cinchy.co/Training/Cinchy/" -u "admin" -p "DESuEGqmx55yl2PYxa4ncc+5+bLkoVIFpgs0Lq6hkcU=" -f "Contact Import"

Once executed, navigate to your destination table to validate that your data synced correctly (Image 22).

4. Appendix

4.1 Password Encryption

To encrypt a password using Powershell, complete the following:

Launch Powershell and navigate to the Cinchy CLI directory (note, you can always type powershell in the windows explore path for the Cinchy CLI directory)
Enter the following into Powershell .\Cinchy.CLI.exe encrypt -t "password"
Hit enter to execute the command
Copy the password (e.g. notepad, Visual Studio Code, word etc.) so you have it accessible at batch execution time

Please note, you will need to replace "password" with your specific password.

4.2 Execution Logs

The Execution Log table is a system table in Cinchy that logs the outputs of all data syncs (Image 23). You can always review the entries in this table for information on the progression of your syncs.

4.3 Execution Errors

The Execution Errors table is a system table in Cinchy that logs any errors that may occur in a data sync (Image 24). Any data sync errors will also be logged in the temp directory outlined in the data sync execution command (e.g. -d "C:\Cinchy\temp")

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