Table of Contents

1. Creating a Table from Scratch

1. Navigate to the Cinchy homepage. In the upper left-hand corner, click on Create to get started. (Image 1)

2. Select either a Standard or a Spatial Table (Image 2), per the descriptions below.

• Spatial Table: A spatial table allows you to create geography and geometry column types, as well as geospatial indexes. You will not be able to create partitions on a spatial table.

• Standard Table: You cannot create geography or geometry columns in a standard table.

3. Select From Scratch (Image 3).

4. A new page will open with the Table Info tab (Image 4). Input the following information:

• Table Name: This is a mandatory field, and must be unique to the domain you select.

• Icon: You can pick an icon and colour to differentiate your table on thee home screen.

• Domain: This is a mandatory field. Select the domain that this table will reside under. If you are have administrative privileges, you can also create new domains from this screen.

• Description: You can give your table a description, which will be displayed on the homepage.

5. When you are finished with the Info page, select Columns from the left-hand navigation bar (Image 5). See here for additional information about column types.

6. A new page will open with the Columns tab (Image 6). Every table in Cinchy must have at least one column. Input the following information:

• Column Name: Input a unique column name.

• Data Type: You can select from the following:

  • Text: All data in this column must be input as text

  • Number: All data in this column must be input numerically

  • Date: All data in this column must be in date format. The default is yyyy-mm-dd however you can change that.

  • Yes/No: All data in this column must be in Yes/No format

  • Calculated: Data is this column is calculated using a CQL expression

  • Choice: Data entered in this column must be selected from a set of choice answers that you provide

  • Link: Data in a link column is pulled from elsewhere on Cinchy

• Description: Enter a description of your column

• Data Security Classification: You can select from Public, Internal, Confidential, or Restricted. Additional options can be created in the [Cinchy].[Data Security Classifications] table by an Administrator.

  • Restricted: Restricted data is what we can call "high stakes". If compromised or accessed without authorization, it could lead to criminal charges, massive legal fines, or cause irreparable damage to the company. This is the most sensitive data, so you would have to treat it extra carefully. Examples include intellectual property, proprietary information or data protected by state and federal regulations.

  • Confidential: Often, access to confidential data requires additional authorization and explanation of why access to the data is needed. Examples of confidential data include social security numbers, credit card details, phone numbers or medical records. Depending on the industry, confidential data is protected by laws like GDPR, HIPAA, CASL and others.

  • Internal: This type of data is strictly accessible to internal company personnel or employees who are granted access. This might include internal-only memos, business performance, customer surveys or website analytics.

  • Public: This type of data is freely accessible to all employees and company personnel. It can be freely used, reused, and redistributed without repercussions. An example might be job descriptions, press releases or links to articles.

• Advanced Settings: Select any checkboxes that pertain to your column. See here for more information about these parameters.

7. Click on Design Controls > Entitlements in the left navigation pane to set your permissions (Image 7). You may set these as granular as you choose. You may also set permissions on a view by view basis. See here for more about data controls and entitlements.

8. Click Save to finalize your table.

9. You may return to change the structure of the existing table (i.e. Rename columns, add new columns, change data type) by clicking on the Design Table button on the left-hand navigation (Image 8).

2. Creating a Table by Importing a CSV

1. Navigate to the Cinchy homepage. In the upper left-hand corner, click on Create to get started (Image 9).

2. Select either a Standard or a Spatial Table (Image 10), per the descriptions below.

• Spatial Table: A spatial table allows you to create geography and geometry column types, as well as geospatial indexes. You will not be able to create partitions on a spatial table.

• Standard Table: You cannot create geography or geometry columns in a standard table.

3. Select Import a CSV (Image 11).

4. Enter the following information:

• Domain: Select the domain that you table will reside under. If you are have administrative privileges, you can also create new domains from this screen.

• File: In order to create the table, you must upload a .csv file.

5. When creating a table via Import a CSV, a few settings will be set by default:

• Default Table Name: The name of the file will be used as the name of the table (a number will be appended if there is a duplicate - ex. uploading Teams.csv will create a table named Teams 1, then Team 2 if uploaded again). You can always rename the table after it has been created.

• Default Table Icon: The icon defaults to a green paintbrush.

• Default Column Types: Columns by default will be created as a text field, with a maximum length of the longest value in the column. If a column has only numeric values in it, it will be created as a numeric column.

6. To update these settings, navigate to the Design Table tab on the left navigation bar (Image 12).

3. Table Views

1. When you first create a table, a default view called All Data will be created for you, which you can find on the left navigation bar under Manage Data (Image 13).

2. You can create additional views by clicking on "+Create View" (Image 14).

3. You may chose to create a view From Scratch or by Copying an Existing view (Image 15).

From Scratch:

1. Select "From Scratch".

2. The Columns tab will open. Create a Name for your View (Image 16).

3. If you'd like this to become the default view, toggle the default setting to On (Image 16).

4. Select the column(s) that you want to be visible in this view (Image 17). You may rearrange the column order using drag and drop.

5. Click on the Sort tab in the left navigation bar (Image 18).

6. Use this screen to select which columns you'd like to sort your data by, and in which order. You may rearrange the columns using drag and drop (Image 19).

7. Click on the Filter tab in the left navigation bar. Here, you may use query language to focus your view (Image 20).

8. Click on the Permission tab in the left navigation bar. Here, you may set permissions for who can use this view. By default, it is set to All Users (Image 21).

9. Select Save to finalize your view.

From Existing:

1. Select "From Existing".

2. Select which view you would like to copy (Image 22).

Updating a View

1. To update any view, including the Add Data view, click on the pencil icon next to the view's name under Manage Data (Image 23).

4. Bookmarks and the Homepage

Once you create a table, it will be added to your bookmarks by default. Other users (or if you un-star the table from your bookmarks) will see it in the Homepage if they have permissions to.

1. What is a Builder?

“Cinchy Builders” use the Cinchy platform to build an unlimited number of business capabilities and use-cases.

The “Cinchy Builder” has access to perform the following capabilities:

• Change Table Schema (use Cinchy’s “Design Table” functionality)

• Grant access (use Cinchy’s “Design Controls” functionality)

• Edit Cinchy Data in Cinchy System Tables

• Create, Save, and Share Cinchy Queries

• Perform ad hoc Cinchy Queries on the Cinchy data network

• Import/export packaged business capabilities (i.e. deployment packages)

• Build Cinchy Experiences

• Perform integration with Cinchy (e.g. Cinchy Command Line Interface [CLI] operations)

• Create and Deliver an unlimited number of Customer Use Cases within Cinchy

• A builder can be part of the

The “End-Users” of the Cinchy platform are those that use the functionalities created by the “Cinchy Builders” to their business objectives.

of the Cinchy Platform can:

• Use Tables, Saved Queries, and Experiences created by “Builders”

2. What do Builders Do?

Builders can leverage Cinchy as one platform to simplify solutions delivery as they:

1. Connect

2. Protect

3. Collaborate

4. Build

5. Reuse

Connect

Create your new network of data

Dataware eliminates point-to-point integration, reducing cost and complexity (including real-time, batch, on-prem, cloud, legacy, and SaaS services) and allowing custom data-sync configurations. This drives faster time to market, lower costs and improved usability.

Connect data to make silos and integration obsolete

Once a data source is connected to your data network, its data can be used in conjunction with data from any other source on the network with no further integration efforts. The more sources you connect, the more powerful and efficient it becomes. You can extend data on the network with attributes and entirely new entities, including calculated data, derived data, AI models, and user-managed data.

Protect

Manage and protect data down to the individual cell

Data on Cinchy is protected by cellular-level access controls, data-driven entitlements, and superior data governance. This includes meta architecture, versioning, and write-specific business functions that restrict user views, such as a managed hierarchy. Owner-defined permissions are universally enforced, significantly reducing the effort of managing them at the enterprise level. You can use existing Active Directory and SSO access policies to set controls for an individual user, external system, or user-defined functions (such as approving updates row by row or using bulk approvals).

Collaborate

Track version history for full lifecycle of data

All data is automatically version-controlled and can easily be reverted to previous states. You can see changes made by users, systems, or external applications through Data Synchronization or by using a Collaboration Log.

View and manage data through a single UI

Use the universal Data Browser to view, change, analyze, and otherwise interact with ALL data on the Fabric. Non-technical business users can manage and update data, build models, and set controls, all through an easy and intuitive UI.

Build

Create and share queries

Consolidate legacy systems, create new solutions

By decoupling the data from the application, our Autonomous Data Network lets you consolidate legacy applications to increase operational agility and reduce overhead. You can create enterprise-grade solutions using the Application SDK as quickly and easily as you would build Excel-based solutions and without the operational risk. Application SDK (React Native, Angular, and REST SDKs) lets you build custom applications for end users.

Add third-party visualization tools

For even more flexibility, connect your Data Network to third-party Data Visualization tools. You’ll be able to run cross-queries against all data on the Fabric while maintaining universal access policies for users, systems, and external applications.

Re-use

The more you use Dataware the more it’s capable of doing.

Any new data you add to the network will work in conjunction with any previously existing data, instantly and easily. This means you can re-use data in new ways, with no time-consuming integration efforts. Teams can collaborate directly in real-time, allowing it to act as a central data hub while simplifying integration. Unlike traditional data architecture projects, which grow more complicated as they involve more data sources, dataware delivers solutions faster and faster as more data is added to it.

Table of Contents

1. Overview

You are able to attach files and images to any row in a Cinchy table by creating a linked column that is linked to the 'Files' system table of your platform.

If you have access to view the 'Files' table, you can also view every attachment on your system.

2. How to Attach a File

1. Navigate to the table where you want to attach your file.

2. Click Design Table > Columns

3. Add a new column with the following parameters (Image 1):

   1. Column Name: We recommend you name this something easy to recognize, like "Images and Files"

   2. Data Type: Link

   3. Linked Table: You will link this to the "Cinchy\Files" table

   4. Linked Column: File Name

   5. Advanced Settings: Make sure to select the "Multi-select" checkbox if you want the capability to add more than one file to a row.

4. Click Save

5. Navigate back to your table and locate your newly created files column.

6. To attach a file, click on the upload button (located in the top right hand corner of any cell in the column) (Image 2).

7. From the pop-up window, select Choose Files to pick your file from your machine, then click Submit (Image 3).

8. Once uploaded, your cell should look like this (Image 4):

3. Deleting an Attached File

To fully delete your attachment from the platform, you will need to navigate to the Files system table and delete the row associated with your attachment.

Your file will appear crossed out in your original table and cannot be opened or downloaded (Image 5).

1. Naming Convention

‌Cinchy is built as a simple, business user friendly application. This means that you should use business friendly terms to name your tables and columns. For example, you want to name a column “Full Name” rather than full_name, fullName, fName etc.‌‌

2. Domains

‌Domains essentially act as folders to be able to organize your data. Generally you will want to split domains by business lines (ex. Sales, Marketing, Human Resources, Product Development). The key thing is to keep it consistent so users have a general idea where to go to find information.‌‌

3. Descriptions

‌You can add descriptions to your tables and columns. Descriptions allow other users to use data in a more self-serve fashion, and also helps prevent misunderstandings of the meaning of your data.‌‌

Table descriptions are shown in the My Network screen, and will show up in search as well.‌ (Image 1).

Column descriptions show up when you hover on the column in the Manage Data screen (Image 2 and 3).

1. Columns

There are formatting rules for columns:

1.1 Row Condition

This follows the same syntax as a view filter query.

1.2 Ordinal

Order in which the formatting rules will apply on the same table. Ordinal 1 will show up above ordinal 2.

1.3 Highlight Color

Color to highlight the cell. If you want to add your own colors you can do so within System Colours and check off highlight under usage.

1.4 Table

Table in which to apply the conditional formatting.

1.5 Highlight Columns

Columns to apply the conditional formatting rules to. You do not need to include any row condition columns within the highlight columns.

2. Example

You can find the Data Controls menu on the left-hand navigation bar of a table.

From here you may select to:

• Change your Data Entitlements

• Erase your Data

• Compress your Data

Table of Contents

1. System Columns

Cinchy contains system columns used to perform various functionality. These columns cannot be modified directly by a user.

1.1 Cinchy Id

Cinchy Id is a unique identifier assigned automatically to all records within a normal table. The Cinchy Id is associated with the record permanently and is never reassigned even if the record is deleted.

1.2 Version & Draft Version

Version and Draft Version are used to track changes and versions.

Without Change Approvals Enabled

Any changes made to a record increments the Version. Draft Version is always 0.

With Change Approvals Enabled

Any data approval increments Version and resets Draft Version to 0. Any proposed changes increments the Draft Version.

1.3 Approval State

This is a legacy column. It is always blank.

1.4 Created By & Created

Created By is a linked column to the [Cinchy].[Users] table, of the user who created the record.

Created is the time when the record was created, per the logged-in user's timezone.

Without Change Approvals Enabled

Created By and Created will be the same for all records with the same Cinchy Id.

With Change Approvals Enabled

Created By and Created is based on the first user to make changes on an approved record. So the user where Draft Version = 1.

1.5 Modified By & Modified

Modified By is a linked column to the [Cinchy].[Users] table, of the user who last modified the record.

Without Change Approvals Enabled

The last user to modify the record, and when it happened, per the logged-in user's timezone.

With Change Approvals Enabled

The last user to either modify the record (Draft Version != 0) or approve the record (Draft Version = 0). The timestamp for when that version was generated.

1.6 Deleted By & Deleted

If a record is deleted, it will show up in the Recycle Bin.

Without Change Approvals Enabled

A deleted record will have Deleted By and Deleted filled in, with the timezone set to the logged-in user's.

With Change Approvals Enabled

Deleted By and Deleted are based on the user/time when the Delete Request was created, per the logged-in user's timezone., not when it was approved.

1.7 Replaced

• There is always only one latest/up to date record at a time. Anytime changes are made to a record, a new version (normal or draft) is created, and the previous version is updated with a Replaced timestamp.

• Any record where Replaced is empty is the current version of that record.

2. Common Fields

2.1 Name

Each column must have a unique name. They must also not conflict with system columns (even if you are not using Change Approvals on the table).

2.2 Data Security Classification

Each column has a data security classification. This defaults to blank, and can be set to one of 4 pre-configured settings (Public, Internal, Restricted, Confidential) or additional options can be created in the [Cinchy].[Data Security Classifications] table by an administrator.

• Public: This type of data is freely accessible to all employees and company personnel. It can be freely used, reused, and redistributed without repercussions. An example might be job descriptions, press releases or links to articles.

• Internal: This type of data is strictly accessible to internal company personnel or employees who are granted access. This might include internal-only memos, business performance, customer surveys or website analytics.

• Confidential: Often, access to confidential data requires additional authorization and explanation of why access to the data is needed. Examples of confidential data include social security numbers, credit card details, phone numbers or medical records. Depending on the industry, confidential data is protected by laws like GDPR, HIPAA, CASL and others.

• Restricted: Restricted data is what we can call "high stakes". If compromised or accessed without authorization, it could lead to criminal charges, massive legal fines, or cause irreparable damage to the company. This is the most sensitive data, so you would have to treat it extra carefully. Examples include intellectual property, proprietary information or data protected by state and federal regulations.

2.3 Description

Each column can optionally have a description. The description is displayed when you hover on the column header in Data Management.

2.4 GUID

A GUID is a globally unique identifier, formatted as a 128-bit text string, that represents a unique identification. Every column in Cinchy is automatically assigned one. For more information on GUIDs, you can review the documentation here.

2.5 Common Parameters

Add to Default View

Checked by default. After saving your changes this will add the column to be displayed in the default table (All Data by default). Generally it makes sense to be checked since there should be a view where all columns are displayed.

Mandatory

Makes the column a mandatory field. You will not be able to save or alter a record in a state where a mandatory field is blank.

Unique

Requires all values in the column to be unique. Adding a new record or modifying a previous record into a state where it is a duplicate of another record will cause an error and cannot be saved.

Multi-Select

Some fields can also be set to multi-select.

For example, the column Players in [Football].[Teams] can be a multi-select field since each team will have multiple players.

Allow Linking

Checked by default. This allows other tables to use the column as a link/relationship.

See Linking Data to get more context on how they are used.

Allow Display in Linked Views

Checked by default. Some columns may not make sense for linking but can be useful to display when someone is choosing an option.

See Linking Data to get more context and tips.

Encrypt

If Data At Rest Encryption is enabled, you will see the option of Encrypt for columns. If this is checked, the column will be encrypted within the database. This is useful for hiding sensitive information so that people with access to the database directly do not see these fields.

There is no difference in user experience within the Cinchy platform. The data is displayed in plain text on the UI or via the query APIs.

3. Regular Columns

3.1 Text

Text columns have a maximum length, set to 500 by default.

These are equivalent to VARCHAR(n) data type in SQL.

3.2 Number

You can choose from 3 display formats for number - regular, currency, and percentage. You can also decide how many decimal places to display (0 by default). Note that these are both display settings, and will not affect how the number is stored.

These are equivalent to FLOAT(53) data type in SQL (default, 8-byte field).

3.3 Date

There are several Date column type display format options available in Cinchy:

• MMM DD, YYYY (e.g. Oct 31, 2016)

• YY-MM-DD (e.g. 16-10-31)

• DD-MM-YYYY (e.g. 31-10-2016)

• DD-MMM-YY (e.g. 31-Oct-16)

• Custom Format

These are equivalent to DATE() data type in SQL.

3.4 Yes/No

You must select a default value of yes (1) or no (0) for yes/no fields.

These are equivalent to bitdata type in SQL.

3.5 Calculated

A calculated column is evaluated using other fields on the record. It will also have a result type - which is the form in which the calculated results will be stored.

For example, you can have a column [Full Name] that is CONCAT([First Name], ' ', [Last Name]).

These are equivalent to computed columns in SQL.

3.5.1 Cached vs Live Calculated Columns

When creating a calculated column, you will notice the option to have it cached or not, located under Advanced Settings. This was an option introduced in version 4.0 of the platform.

A cached calculated column stores your data for fast retrieval and querying. Calculated columns are defaulted to cached. This is an actual column in your table that is based on a defined CQL formula. This column will recalculate when data is changed in the same row.

In the below example, the Label column is a calculated column that connects various name columns together. You can see that "Connect your data" appears in each label. If you then wanted to change the name in row one from "Connect your data" to "Connect all your data", only that specific row would recalculate automatically to update the label. To update any other row (within this table or another) with a calculated column that references that data, you would need to manually make changes to each in order to prompt a recalculation.

The other option is to uncheck the cached button, and to have a live calculated column instead. A live column runs the query in real time, on the fly: it is essentially a stored formula that is only executed when you read or query the record, and is executed each time you do so.

In the above example, if the Label column was a live calculation, the formula would run every time you refresh the screen or requery the data. If changes have been made (in this example, changing "Connect your data" to "Connect all your data"), the calculated column will automatically reflect them, regardless of if it references another row or even another table.

While live calculated columns can be very powerful this way, they also have their drawbacks, including possibly throwing errors and being very resource intensive. In some cases, live calculated columns can even break a table (for example if you have a live calculated column linking to another live calculated column, you may run into errors).

3.6 Choice

You can create a choice column (single or multi-select) in Cinchy. In this scenario, you specify all your choices (1 per newline) in the table design. A user will only be able to select from the options provided.

4. Geospatial Columns

If you created a spatial table, you will have access to the geography and geometry column types. These columns also have the option to be indexed via Index in the advanced settings on the column itself.

4.1 Geometry

In the UI, this takes a well-known text (WKT) representation of a geometry object. You can modify or paste the WKT representation directly in the editor on the UI. Geometric functions can be performed on this column through CQL and calculated columns.

4.2 Geography

In the UI, this takes a well-known text (WKT) representation of a geography object. You can modify or paste the WKT representation directly in the editor on the UI. Geographic functions can be performed on this column through CQL and calculated columns.

5. Link Columns

Link columns allow you to establish inherent relationships with other records in other tables. See Linking Data for more details.

6. Hierarchy Columns

Hierarchy columns are simply link columns referencing the current table. For example, the below table contains a list of documentation pages, some of which also have sub-level pages (or even sub-sub-level pages). Using a Hierarchy Column allows you to quickly view the relationships between data.

Example 1: API Overview is the parent page. It had four sub-pages: API Authentication, API Saved Queries, ExecuteCQL, and Webhook Ingestion. Clicking on any of the links within the Sub-Pages column would return you to the row for that specific data set.

Example 2: Builder Guides is the parent page. It has five sub-pages: Best Practices, Creating Tables, Saved Queries, Integration Guides, and CInchy DXD Utility. In this example, we also have another level of hierarchy, wherein Best Practices is also a parent page, and Multilingual Support is its sub-page.

Another common use of Hierarchy columns are to show Manager/Employee relationships

1. Overview

Data erasure allows you to permanently delete data in Cinchy. As the data owner, you can set an erasure policy on your table if you need to delete data for compliance reasons (Image 1).

The actual erasing of data happens during the maintenance window. Please check with your system administrators to confirm when maintenance is scheduled.

Once data is erased, any links pointing to erased data will look like this (Image 2):

2. Change Approval Enabled Tables

The time is counted based on the record's modified time stamp, not the deleted time stamp. This means for change approval records it is the time when the pending delete request was approved and moved to the Recycle Bin, not when the delete request was made.

Table of Contents

1. Overview and Example

1. When viewing a table, click on Data Controls > Entitlements from the left navigation menu (Image 1).

2. Currently both the table creator and anyone in the Cinchy Administrators group has access to perform any action on any objects. You can give granular entitlements at a Group or a User level, for both viewing and editing access (Image 2).

3. In the above scenario, John Smith is part of the Developers group. He is able to view all columns via the entitlement to the Developers group, and he is able to edit both the First Name and Last Name column through different entitlements.

2. Table Level Entitlements

There are certain entitlements in the Data Controls menu that apply to the entire table.

2.1 Marketplace

Approving this entitlement enables users to see and serarch for the table in the Marketplace/Homepage.

2.2 Bulk Export

Approving this entitlement enables users to export data from the table via the Manage Data screen (Image 3).

2.3 Direct Query

Approving this entitlement enables users to query the data from the table directly in the Query Builder (Image 4).

2.4 Design Table

Approving this entitlement enables users to alter the structure of the table.

2.5 Design Controls

Approving this entitlement enables users to change the permissions on a table.

3. Column Level Entitlements

There are certain entitlements in the Data Controls menu that apply only to columns.

3.1 View All Columns

Approving this entitlement enables users to view all columns within the table.

3.2 View Specific Columns

This is a drop down where you can select the specific columns you want to grant view access to for users.

3.3 Edit All Columns

Approving this entitlement enables users to edit all columns within the table.

3.4 Edit Specific Columns

This is a drop down where you can select the specific columns you want to grant edit access to for users.

3.5 Approve All Columns

Approving this entitlement enables users to approve all columns within the table. This also allows users to approve Create and Delete requests.

Approve permissions only apply when Change Approvals are enabled.

3.6 Approve Specific Columns

This is a drop down where you can select the specific columns you want to grant approve access to for users.

Approve permissions only apply when Change Approvals are enabled.

3.7 Link Columns

Link columns require both permission to the column within this table, as well as the column in the link column itself.

4. Row Level Entitlements

These are entitlements that apply to specific rows. Used in conjunction with Column Level entitlements this allows for granular cell level entitlements.

4.1 Insert Row

Approving this entitlement enables users to create new rows in the table.

4.2 Delete Row

Approving this entitlement enables users to delete rows in the table.

4.3 Viewable & Editable Row Filter

This is a CQL fragment that applies a filter to which rows will be viewable or editable. Think of the column entitlements and the fragment as a SQL statement applied to the table.SELECT {Edit Selected Columns} WHERE {Editable Row Filter}

4.4 Examples for Row Filter

Most of these examples will be with the editable row filter so it is easy to see the underlying data for comparison. However this can be done for viewable row data as well.

Sample Data

(Image 5)

Simple Example

With the following entitlements (Image 6):

• Edit Specific Columns: Age

• Editable Row Filter: [Age] > 30

Example with Viewable Data

(Image 7)

• View Specific Columns: First Name, Last Name

• Viewable Row Filter: [End Date] IS NULL OR [End Date] > GetDate()

Layer On Another Entitlement

(Image 8)

• View Specific Columns: All

• Edit Specific Columns: First Name, Last Name, Age

• Viewable Row Filter: [First Name] = 'John'

• Editable Row Filter: [First Name] = 'John'

Example for Current User

(Image 9)

For the All Users group:

(Image 10)

• View All Columns: Check

• Edit Selected Columns: First Name, Last Name

• Editable Row Filter: [User Account].[Cinchy Id] = CurrentUserId()

To allow a user to edit certain fields of their own data, you will need an association from a user to the [Cinchy].[Users] table. You can then use the following function to allow edit for that user, where [...] is the chain of link columns to get to the Users table.

[...].[Cinchy Id] = CurrentUserId()

Overview

This page documents the method of resstoring deleted taables, columns, and rows in your Cinchy table.

1. Restoring a Deleted Row

If you wish to restore a row that has been deleted, you can do so through the following steps:

1. In the table where you want to restore the row, navigate to the Recycle Bin.

2. Locate the deleted row.

3. Right click anywhere in the row > Restore Row (Image 1).

4. You should see a "Restore Successful" pop-up.

2. Restoring a Deleted or Changed Column

If you wish to restore a column that has been deleted or changed (such as a name change), you can do so through the following steps:

1. Navigate to the [Cinchy].[Tables] table.

2. Find the row with the table that has the column you want to restore > right click anywhere in the row > Collaboration Log > Revert to a previous version (Image 2).

3. You should see a "Revert Successful" pop-up.

3. Restoring a Deleted Table

If you wish to restore a table that has been deleted, you can do so through the following steps:

1. Navigate to the [Cinchy].[Tables] table.

2. Navigate to the Recycle Bin.

3. Find the row with the table that you want to restore > right click > "Restore Row" (Image 3)

4. You should see a "Restore Successful" pop-up.

If you need to manage space within your database, you can set a data compression policy. Currently we allow you to permanently delete versions in the collaboration log. Be aware that the current version of compression is a LOSSY process (data will be permanently deleted). Take that into consideration when configuring a policy.

The actual compression of data happens during the maintenance window. Please check with your system administrators to confirm when maintenance is scheduled.

Change Approval Enabled Tables

The number of versions is based on the major version and not the minor version. This means for a record on version 35.63 with a keep most recent 10 versions, it will keep all versions 26.0 +, rather than all versions 35.44+.

Table of Contents

1. Indexing

Indexing is used to improve query performance on frequently searched columns within large data sets. Without an index, Cinchy begins a data search with the first row of a table and then follows through the entire table sequentially to find all relevant rows. The larger the table(s), the slower the search.

If the table you are searching for has an index for its column(s), however, Cinchy is able to search much quicker.

In the below example, we will set up a query for a Full Name field. When you create an index for that field, an indexed version of your table is created that is sorted sequentially/alphabetically.

When you run your query on this index, that table will be searched using a binary search.

A binary search will not start from the top record. It will check the middle record with your search criteria for a match. If a match it not found, it will check whether the found value is larger or smaller than the desired value. If smaller, it reruns the data check with the top half of the data, finding the median record. If larger, it reruns the data check with the bottom half of the data, finding the median record. It will repeat until your data is found.

1.1 Setting up an Index

In this example, we have a table with employee names (Image 1). We want to search for "John Smith", using the Full Name column.

1. To set up your index, select Design Table from the left navigation tab.

2. Click Indexes (Image 2).

3. Select "Click Here to Add" and fill out the following information for a new index. Click save when done (Image 3):

• Index Name.

• Select the column(s) to add to your index. For our example we have selected the Full Name column to be indexed.

  • You can select more than one column per index.

• Select the Included column(s) to add to your index, if applicable.

  • The difference between regular columns and Included columns is that indexes with included columns provide the greatest benefit when covering your query because you can include all columns your query may reference, such as columns with data types, numbers, or sizes not allowed as index key columns.

4. We can now query our full name column for John Smith and receive our results quicker than if we hadn't set up our index (Image 4).

2. Full Text Indexing

A full-text index is a special type of index that provides index access for full-text queries against character or binary column data. A full-text index breaks the column into tokens and these tokens make up the index data.

2.1 Setting up a Full Text Index

1. Click on Design Table > Full-text Index

2. Add in the desired column(s) and click save when done (Image 5).

3. Columnar Indexing

You may want to use a columnar index when:

• Your table has over 1 million records.

• Your data is rarely modified. Having large numbers of deletes can cause fragmentation, which adversely affect compression rates. Updates are also processed as deletes followed by inserts, which will adversely affect the performance of your loading process.

3.1 Setting up Columnar Indexing

1. Click on Design Table > Columnar Index

2. Add in the desired column(s) and click save when done (Image 6).

4. Partitioning

Partitioning data in a table is essentially organizing and dividing it into units that can then be spread across more than one file in a database. The benefits of this are:

• Improved efficiency of accessing and transferring data while maintaining its integrity.

• Maintenance operations can be performed on one or more partitions more efficiently.

• Query performance is improved based on the types of queries most frequently run.

When creating a partition in Cinchy, you use the values of a specified column to map the rows of a table into partitions.

4.1 Setting up a Partition

In this example we want to set up a partition that divides our employees based on a Years Active column (Image 7). We want to divide the data into two groups: those who have been active for two years or more, and those who have only been active for one year.

1. Click on Design Table > Partition

1. Fill in the following information and click save when done (Image 8):

• Partitioning Column: this is the column value that will be used to map your rows. In this example we are using the Years Active column.

• Type: Select either Range Left (which means that your boundary will be <=) or Range Right (where you boundary is only <). In this example we want our boundary to be Range Left.

• Add Boundary: Add in your boundary value(s). Click the + key to add it to your boundary list. In this example we want to set our boundary to 2.

Once set up, this partition will organize our data into two groups, based on our boundary of those who have a Years Active value of two or above.

2. You can now run a query on your partitioned table (Image 9).

1. Introduction to Cinchy DXD

CinchyDXD is a downloadable utility used to move Data Experiences (DX) from one environment to another. This includes any and all objects and components that have been built for, or are required in, support of the Data Experience.

The following sections in this document will outline the basics of how to build, export, and install a DX’s.

Items of note moving forward in this document:

• Source Environment is the environment in which the DX is built.

• Target Environment is the environment in which the DX will be installed

• The example DX is a simple Currency Converter DX that consists of

  • One (1) table

  • One (1) query

• This example does not include the following:

  • NO applets

  • NO integrated clients

  • NO Data Sync Configurations

  • NO Reference Data

  • NO Models

  • NO Groups

  • NO System Colours

  • NO Formatting Groups

  • NO Literal Groups

Future iterations of this document will add to this example's complexity level.

2. Overview of Steps

The general steps to deploying the CinchyDXD Utility are as follows:

• Building the Data Experience (CinchyDXD)

• Packaging the Data Experience (CinchyDXD)

• Installing the Data Experience (CinchyDXD)

• Updating the Data Experience (CinchyDXD)

• Repackaging the Data Experience (CinchyDXD)

• Reinstalling the Data Experience (CinchyDXD)

1. Overview

A GUID is a globally unique identifier, formatted as a 128-bit text string, that represents a unique identification. Both Cinchy Tables and Columns have a GUID.

This feature is particularly useful when deploying between Cinchy instances.

For example, in a model deployment, you must have matching GUIDs on your columns in order for them to properly load between environment A and environment B. There might be times when these GUIDs don’t automatically match, however, such as if you manually added a new column to environment B and also manually added it to environment A.

In this case, the two columns would have different GUIDs, and the model deployment would fail. With this new feature, however, you can match up conflicting GUIDs to properly load your model.

1. Viewing and Editing GUIDs

You have the ability to display and edit these table and column GUIDs within the Design Table screen.

Table GUIDs musts be unique to your specified environment.

Column GUIDs must be unique to the table.

1. Table GUIDs can be found under Design Table > Info > GUID (Image 1).

2. Click on the pencil icon to edit the GUID.

3. Column GUIDs can be found under Design Table > Columns > GUID (Image 2).

4. Click on the pencil icon to edit the GUID.

Introduction

After you have made any applicable changes to your DX it is time to re-export the package out of your source environment.

1. Update the Data Experience Table

If you have added or removed any of the following while updating your DX, you will need to update the Data Experience Definition table:

• Name

• Tables

• Integrated Clients

• Data Sync Configurations

• Reference Data

• User Defined Functions

• Models

• Groups

• System Colours

• Saved Queries

• Applets

• Literal Groups

• Builders

• Builder Groups

• Sync GUID

2. Update Reference Data Table

If you have added or removed any of the following while updating your DX, you will need to update the Data Experience Reference Data table:

• Name

• Ordinal

• Filter

• New Records

• Changed Records

• Dropped Records

• Table

• Sync Key

• Expiration Timestamp Field

• Sync GUID

3. Re-Run CinchyDXD Export

Using Powershell you will now export the Data Experience you have defined within Cinchy.

1. Launch Powershell and navigate to your CinchyDXD folder

2. In the Powershell window type in cin and hit tab on your keyboard 3. In the Powershell command line next to .\CinchyDXD.ps1 type in export 4. Hit Enter on your keyboard

5. You must now enter your mandatory export parameters.

Sample: .\CinchyDXD.ps1 export ` -s "<source Cinchy url>" ` -u "<source user id>" ` -p "<source password>" ` -c "C:\Cinchy CLI v4.0.2" ` -d "C:\CLI Output Logs" ` -g "8C4D08A1-C0ED-4FFC-A695-BBED068507E9" ` -v "2.0.0" ` -o "C:\CinchyDXD_Output" `

6. Enter the export parameters into the Powershell window (Image 1).

7. Hit Enter on your keyboard to run the export command

Powershell will begin to process the export. Once the export is complete, Powershell will provide you with an export complete message (Image 2).

4. Validate the Export

1. Ensure that the DXD Export Folder is populated (Image 3).

2. Ensure that the Data Experience Release table is populated in the source environment (Image 4).

3. Ensure that the Data Experience Release Artifacts table is populated in the source environment (Image 5).

1. System Tables

System tables are included out-of-the-box with your Cinchy platform, and can be used to track and manage a variety of data.

You can easily query for a list of your system tables using the below:

SELECT [Name]
FROM [Cinchy].[Tables]
WHERE [Deleted] IS NULL
AND [Domain] = 'Cinchy'

The system tables included are:

• Applets: This system table manages a list of all your integrated applications

• Data Experience Definitions: This is a system table for managing data experience definitions

• Data Experience References: This is a system table for managing reference data for data experiences

• Data Experience Release Artifacts: This is a system table for maintaining data experience release artifacts

• Data Experience Releases: This is a system table for maintaining data experience releases

• Data Security Classifications: This is a system table for maintaining data security classifications

• Data Sync Configurations: This system table manages a list of all your data sync configurations.

• Domains: This system table manages a list of all the domains in your instance.

• Execution Log: This system table tracks the execution logs of data syncs

• Formatting Rules: This system table manages your formatting rules

• Groups: System table for managing all groups

• Literal Groups: This system table maintains a list of groups

• Literal Translations: This system table maintains a list of literal translations

• Literals: This system table maintains a list of literals

• Regions: This system table maintains a list of regions

• Saved Queries: This system table manages a list of all user saved queries that can be exposed via the REST API

• System Colours: System table for maintaining colours

• Table Access Control: This system table maintains a list of all table access controls in your instance

• Table Columns: This system table manages a list of all the system column definitions

• Tables: This system table manages a list of all the tables in your instance.

• User Defined Functions: This system table manages a list of all your user defined functions

• Users: System table for managing all user information including enabling/disabling the ability to create tables, queries, etc.

• Views: This system table manages a list of all the views in your instance.

Table of Contents

1. About Linking

Linking is done by the Cinchy ID, which is unique. When you create a link column, you select a column to link to. This is simply a decision on which field to show from the linked record. You should pick a unique field to link on to avoid confusion if possible.

Once a record is created, its Cinchy ID never changes. This means that modifying the row of data in the linked table will not change the relationship in your table to that row. This also means that if you did not use a unique column, even though the UI looks the same, you are actually linking to different rows.‌

2. Choosing a Linked Column

‌In general, you should only use unique columns as the linked column. This needs to be balanced with readability in other tables.

For example, Full Name might not be unique to every employee, but it is a lot more readable and understandable than Employee ID. In other cases, it makes sense to link via an ID and simply add a display column to show relevant information.‌

2.1 Allow Linking

‌To help other builders follow best practices of only linking to unique (or close to unique, such as Full Name) columns, you should un-check the Allow Linking checkbox for non-unique columns so they will not be able to use it for linking.‌

2.2 Allow Display in Linked View

‌If this option is unchecked, it prevents users from showing this column in another table.

For example, if you have an ID card # within an employees table, you may not want to display it to the rest of the company because it simply would not be relevant when they are linking to employees and want to see additional information (such as department, title, location). Arguably, a lot of these columns are also taken care of by access controls (since most people will not have access to view that column).‌

Generally unchecking this box should be done sparingly, as it does not impact the security of your data, only how convenient it is to see it.‌

2.3 Display Columns

‌When you select a record to link to on the Manage Data screen, it can be useful to see additional information about the records to ensure that it is the record you want to link to (Image 1). You can add additional display columns in the advanced options for link columns (Image 2).

When you type in the cell, all displayed columns will be searched through, not just the Linked Column (Image 3). (Green does not have a B in it, but #00B050 does so the Green record shows up)

‌

2.4 Link Filter

‌‌The link filter can be used to filter records from the table being linked. This is useful for reducing the options to only the typical use case. Commonly used for filtering the drop down to a list of active users or other resources, while not preventing someone from entering missing records with inactive resources.‌

Note that this is simply a display filter, it does not prevent other values from being entered as long as they are valid records in the linked table.‌

3. Relationships

‌You can define 1 to 1, 1 to many, and many to many relationships.‌

3.1 1:1 Relationship

‌Generally it is rare to link 1:1 relationships since they should usually be in the same table. For example, you would not have a separate table for Employee Phone Number and Employee Address, they would simply be two columns within the Employees table. However there are cases nonetheless where it makes sense, for example, a Keycard tracking table where each keycard has 1 assigned employee.‌

To enforce a 1:1 relationship within Cinchy, you set the unique constraint and leave it as single-select when creating a link column.‌

3.2 1:Many Relationship

‌A common relationship to have is a one to many relationship. For example, one customer can have multiple invoices.‌

To enforce a 1:many relationship within Cinchy, you want to create a link column in the table on the “many” side of the relationship (in the above example, in the invoices table) and leave the link column as single select.‌

3.3 Many:Many Relationship

‌You can also have a many to many relationship. For example, you can have multiple customers, and multiple products. Each customer can order multiple products, and each product can be ordered by multiple customers. Another example is books and authors. An author can write multiple books, but a book can also have multiple authors. There are two ways to express many to many relationships.‌

In the case of multiple customers and multiple products, we want to use orders as an intermediary table to create indirect relationships between customers and products. Each order has 1 customer, and each order has multiple products in it. We can derive the relationship between customers and products through the orders table.‌

To create a many:many relationship through a different entity, you want to create a table for orders. Within orders, you want to create a single-select link to customers and a multi-select link to products.‌

In the case of books and authors, it makes sense to create a multi-select link column in the Books table where multiple authors can be selected.‌

To create a multi-select link column in Cinchy, you simply check off the Multi-Select option when you create a new link column.

Table of Contents

1. Accessing a Saved Query

Saved queries allows you to query any data within Cinchy (respecting entitlements) and save them to be used as APIs by external systems.

You can access your Saved Query directly by either CinchyID or the domain and name of the Saved Query.

<baseurl>/Query/Execute?queryId=<cinchyid>

<baseurl>/Query/Execute/<domain>/<saved query name>

You can find this information in the Saved Queries table.

You can also search your Cinchy Homepage to find your Saved Query.

2. Creating a Saved Query

1. From the homepage, select Create > Query (Image 1)

2. Fill out the following information:

2.1 The Info Tab

Under the Info tab, you can fill out information on the query if you wish to save it (Image 2):

• Query Name: ‌Mandatory field. Must be unique within the Domain.‌

• Icon: ‌You can optionally pick a non-default icon, as well as color for your table. This will be displayed in My Network.

• Domain: ‌You need to select a Domain your query will reside in. As an admin, you can also create new domains in this screen.

• Description: ‌You can give your query a description. This description will be displayed on the home screen to users browsing the marketplace. It will also be searchable.

• Return Type: There are six different return types:

  • Query Results (Approved Data Only)

    This is the default return type, it returns a table from a select query with only approved data for Maker/Checker-enabled tables, or all data for tables without Maker/Checker-enabled. This is generally used for external APIs as you will want to query approved data, rather than drafts.

  • Query Results (Including Draft Data)

    This return type returns a table from a select query with only draft data for Maker/Checker-enabled tables. Use this return type when looking to display results of records that are pending approval.

  • Query Results (Including Version History)

    This return type returns a table from a select query with historical data for all tables, as seen in the Collaboration Log of any record. This data includes all changes that happened to all records within the scope of the select query.

  • Number of Rows Affected

    This return type returns a single string response with the number of rows affected if the last statement in the query is an INSERT, UPDATE, or DELETE statement.

  • Execute DDL Script

    Use this return type when your query contains DDL commands that implement schema changes such as CREATE|ALTER|DROP TABLE, CREATE|ALTER|DROP VIEW, or CREATE|DROP INDEX.

  • Single Value (First Column of First Row)

    This return type returns a result of 1 row x 1 column, irrespective of the underlying result set.

2.2 The Query Tab

In the Query screen, you can modify and run your query (Image 3).

On the left hand side you have the Object tree, which shows you all the domains, tables, and columns you have access to query within Cinchy. You can search or simply navigate by expanding the domains and tables.

You can drag and drop the columns or table you're looking for into the Query Builder.

Once you are satisfied with your query, you can click save to keep a copy. You can then find your query in the "Saved Queries" table (Image 4):

3. Displaying a Saved Query

1. Once you've set up your saved query, you can find it on your homepage (Image 5).

2. Clicking the query will allow you to "Execute Query" and show you the result set (if there is a SELECT at the end). Sometimes the query will have parameters you need to fill out first before executing (Image 6).

3.1 Pivot Reports

1. Once you execute a query, you can switch the Display to Pivot Mode to see different visualizations of the data (Image 7).

3.2 Pivot URLs

If you want to share the report, you can click the Pivot URL button on the top right to copy the URL to that pivoted report. Simply add it as an applet and bookmark it to return to the pivoted view!

4. Additional Information

For more information and documentation on Cinchy Query Language (CQL), please click here.

Table of Contents

1. Table Creation

Create your data experience (DX) in a virtual data network.

1. Log in to Cinchy: URL: <cinchy source URL> User ID: <source user id> Password: <source password>

2. From the Homepage, select Create

3. Select Table > From Scratch

4. Create the table with the following properties (Image 1).

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

7. Click the “Click Here to Add” a column tab to add a column

8. Click the Save button to save your table

2. Enter Sample Data

1. In your newly created table, enter the following sample data (Image 2).

3. Create the Query

Create a simple query that pulls information from the Currency Exchange Rate table that will allow a simple currency exchange calculation.

1. From the Homepage, select Create.

2. Select Query.

3. In the query builder locate the Currency Exchange Rate table and drag it to the “FROM” line (Image 3).

4. In the “SELECT” line drag and drop the “Rate” column and enter in the following (Image 4): SELECT [Rate] * @Amount AS 'Converted Amount'

5. Enter in the following for the WHERE clause (Image 5):

WHERE [Deleted] IS NULL AND [Currency 1] = @Currency_1 AND [Currency 2] = @Currency_2

6. Click the Execute (or play) icon to run the query (Image 6):

7. Test the query by entering in the following and clicking the submit button (Image 7):

@Amount: 100 @Currency_1: CAD @Currency_2: USD

8. Save the Query by clicking on the Info tab (Left Navigation) 9. Enter in the following details for the query (Image 8):

10. Click the Save button (Image 9).

1. Download the CinchyDXD Utility

The CinchyDXD utility is used to take all of the components (e.g. tables, queries, views, formatting rules, UDF’s etc…) of a DX and package them up so they can be moved from one environment to another.

The CinchyDXD utility is only required (made accessible) for the environment that is packing up the data experience. It is not required for the destination (or target) environment.

For CinchyDXD to work,

To download the Utility:

1. Login to Cinchy

2. Navigate to the Releases Table

3. Select the Experience Deployment Utility View

4. Locate and download the utility (e.g. Cinchy DXD v1.3.1.zip)

5. Unzip the utility and place the folder at any location on a computer that also has CinchyCLI installed

6. Create a new folder in the same directory that will hold all of the DX exports generated (e.g. CinchyDXD_Output) (Image 1).

This folder will then hold all of your deployment packages.

7. Launch a Powershell console window

8. From the console, navigate to the CinchyDXD directory (Image 2 and 3).

2. One Time Setup: Powershell

There is a one-time powershell setup that is required when using CinchyDXD.

1. From your Powershell window type cin

2. Hit Tab on your keyboard (Image 4).

3. Hit Enter on your keyboard (Image 5).

You will get an error message (above) that CinchyDXD.ps1 cannot be loaded because the running script is disabled.

To resolve this error:

4. From your start menu, search for Powershell and select Run as Administrator (Image 6).

5. When prompted “if you want to allow this app to make changes on your device”, select Yes.

6. In your Powershell Administrator window enter Set-ExecutionPolicy RemoteSigned (Image 7).

7. Hit Enter on your keyboard (Image 8).

8. When prompted with the Execution Policy Changes, enter A for “Yes to All”

9. Close the Powershell Administrator window

10. Navigate back to your Powershell window for the CinchDXD v.X window

11. From your Powershell window type cin

12. Hit Tab and then Enter on your keyboard (Image 9).

The basic CinchyDXD instructions will be displayed. You will be able to execute commands such as exporting and installing a Data Experience.

3. Cinchy DXD Tables Overview

There are four tables in Cinchy that are used for packing up and deploying a Data Experience (Image 10).

1. Data Experience Definition Table: Where the data experience is defined (e.g. tables, queries, views, formatting rules, UDF’s etc.)

2. Data Experience Reference Data Table: Where we define any data that needs to move with the Data Experience for the experience to work (e.g. lookup values, static values that may need to exist in tables - it typically would not be the physical data itself)

3. Data Experience Releases Table: Once a Data Experience is exported, an entry is created in this table for the export containing:

   • Version Number

   • Release Binary is the location where you can archive/backup your release history in Cinchy Please Note: if you have your own release management system, you do have the option to opt out of archiving the releases in Cinchy and check the release into your own source control

   • Release Name

   • Data Experience

4. Data Experience Release Artifact Table: Stores all of the files that are part of the Data Experience package as individual records along with all of the binary for each record

3.1 Define the Data Experience

When setting up a Data Experience definition, you will need one definition for each Data Experience you wish to package and deploy to a given number of Target Environments.

1. Locate and open the Data Experience Definitions table (Image 11).

2. Complete the following (Image 12):

3.2 Define the Reference Data

When setting up a Data Experience Reference Data definition, you will need one (1) definition for each Reference Data table you wish to package and deploy with your Data Experience to the Target Environment.

1. Locate and open the Data Experience Reference Data table (Image 13).

Based on the configuration set up in this table, Cinchy will export the data and create CSV and CLI files.

Please note in this example we do not have Reference Data as part of our Data Experience.

4. Export the Data Experience

Using Powershell you will now export the Data Experience you have defined within Cinchy.

1. Launch Powershell and navigate to your CinchyDXD folder (Image 14).

2. In the Powershell window type in cin and hit Tab on your keyboard (Image 16).

3. Hit Enter on your keyboard, you will see a list of commands that are available to execute (Image 17).

4. In the Powershell command line hit your “up” arrow key to bring back the last command and type export next to it (Image 18).

5. Hit Enter on your keyboard (Image 19).

The Powershell window will provide you with the required and optional components to export the data experience.

6. You must now set up any mandatory export parameters

Sample: .\CinchyDXD.ps1 export ` -s "<cinchy source url>" ` -u "<source user id>" ` -p "<source passsword>" ` -c "C:\Cinchy CLI v4.0.2" ` -d "C:\CLI Output Logs" ` -g "8C4D08A1-C0ED-4FFC-A695-BBED068507E9" ` -v "1.0.0" ` -o "C:\CinchyDXD_Output" `

7. Enter the export parameters into the Powershell window (Image 20).

8. Hit Enter on your keyboard to run the export command.

Powershell will begin to process the export. Once the export is complete, Powershell will provide you with an export complete message (Image 21).

4.1 Validate Export

1. Ensure that the DXD Export Folder is populated (Image 22).

2. Ensure that the Data Experience Release table is populated in the source environment (Image 23).

3. Ensure that the Data Experience Release Artifacts table is populated in the source environment (Image 24).

Table of Contents

Introduction

There are a few updates that are required in the Data Experience that has been created in your source environment. You do not want to have to repeat the updates in both the source and target environments. The upcoming section will show how to update the data experience in the source environment so that you can then re-package and reinstall in the target environment.

1. Table Updates

1. Log back into your source environment using the following: URL: <cinchy source url> User ID: <source user id> Password: <source password>

2. Make the following changes to the Currency Exchange Rate table:

3. Save your changes before leaving the table.

2. Query Updates

1. Update the Currency Converter query to reflect column name changes that were made in the Table Updates section above (Image 1).

2. Test the query to validate that it is still functioning (Image 2 and 3).

3. Save your query.

Table of Contents

1. Introduction

The install of a Data Experience is executed in a different environment than that of the export. Please ensure that before moving forward with the following instructions you have an environment to install the data experience into.The install of a data experience MUST be done in the same version. Your source and target environment version MUST be the same (e.g. Source Version = 4.11 | Target Version = 4.11)

Below are the details that will be required for the installation environment:

• Source: <cinchy target url>

• UserID: <target user id>

• Password: <target password>

2. Install the Data Experience

Using Powershell you will now install the Data Experience you have exported out of Cinchy.

1. Open the File Explorer and navigate to your DX exported folder (Image 1).

2. In the folder path URL for the exported data experience type in powershell to launch Powershell for that path (Image 2).

3. Hit Enter on your keyboard, the powershell window will appear (Image 3).

4. In the Powershell window, type in cin and hit tab on your keyboard (Image 4).

5. In the Powershell command line, type install (Image 5).

6. Hit Enter on your keyboard (Image 6).

The Powershell window will provide you with the required and optional components to install the DX.

7. You must now set up your mandatory install parameters

Sample: .\CinchyDXD.ps1 install` -s "<target Cinchy url>" ` -u "<target user id>" ` -p "<target password>" ` -c "C:\Cinchy CLI v4.0.2" ` -d "C:\CLI Output Logs" `

8. Enter the install parameters into the Powershell window (Image 7).

9. Hit Enter on your keyboard to run the install command. Once the Data Experience has been installed you will get a message in Powershell that the install was completed (Image 8).

3. Validate the Install

1. Ensure that the Models Table is populated in the target environment with the model that was installed (Image 9).

2. Ensure that the Currency Exchange Rate table exist in the target environment (Image 10).

3. Ensure that the Currency Converter query exist in the target environment (Image 11).

4. Ensure that the Data Experience Definitions table is populated with the DX parameters that were set up in the source environment (Image 12).

5. Ensure that the Data Experience Releases table in the target environment is populated (Image 13).

Table of Contents

1. Re-Run CinchyDXD Install

Using Powershell, you must now install the Data Experience you have exported out of Cinchy.

1. Open File Explorer and navigate to your exported folder (Image 1).

2. In the folder path URL for the exported data experience type in powershell to launch Powershell for that path.

3. Hit Enter on your keyboard (Image 2).

4. In the Powershell window, type cin and hit Tab on your keyboard. Type install (Image 3).

5. Enter the install parameters into the Powershell window:

Sample (Image 4): .\CinchyDXD.ps1 install -s "<taget Cinchy url>" ` -u "<target user id>" ` -p "<target password>" ` -c "C:\Cinchy CLI v4.0.2" ` -d "C:\CLI Output Logs" `

6. Hit Enter on your keyboard to run the install command. Once the Data Experience has been installed you will get a message in Powershell that the install was completed (Image 5).

2. Validate Install

1. Ensure that the Models Table is populated in the target environment with the model that was installed (Image 6).

2. Ensure that the Currency Exchange Rate table exists in the target environment with the new column names (Image 7).

3. Ensure that the Currency Converter query exists in the target environment with the new column names and labels (Image 8).

4. Ensure that the Data Experience Definitions table has not changed, unless you have added or removed column details within this table (Image 9).

5. Ensure that the Data Experience Releases table in the target environment is populated with the new release version number from the install (e.g. 2.0.0) (Image 10).

Table of Contents

1. Translate API

Translation API

POST <Cinchy-URL>/API/Translate

Pass in a list of literal GUIDs, along with a language and region. If translations are found in that language, they will be returned.

Request Body

Logic

• If the translation exists in the language and region specified, it will be returned.

• If the translation exists in the language but not the specified region, it will still be translated and returned.

• If the GUID exists but it is not available in the specified language, the default text in the Literals table will return.

• If the GUID does not exist or you do not have permission to it, it will return the GUID back as the translation.

2. System Tables

There are 3 tables in Cinchy to provide language support.

1. [Cinchy].[Literal Groups]

2. [Cinchy].[Literals]

3. [Cinchy].[Literal Translations].

2.1 Literal Groups

This table can optionally be used to group the translations. The default Cinchy strings belong to the Cinchy literal group. We recommend you create one literal group per applet or UI so you can retrieve the full list of GUIDs required for that page/applet easily.

2.2 Literals

This table defines all the strings that you want to translate.

Default Text

String that displays if no translation is found for the language specified.

GUID

GUID used to refer to the literal. A UUID will be generated by default, but can be overrode using the Guid Override field to something more human-readable.

Literal Group

As mentioned above, this can be used to group your strings so they can be easily retrieved. Note that this is a multi-select so you can use a single literal for multiple applets (including using the default Cinchy literals and translations for custom applets).

2.3 Literal Translations

This is the table where the translations are stored.

Translated Text

This is the translated string that is returned.

Literal

This is the literal the translation is for.

Language and Region

A language must be specified for a translation. Region can also be optionally specified for region specific words (ex. color vs colour).​

1. Excel

There are various methods you can use to establish a connection between Cinchy and Microsoft Excel, such as using Basic Auth, Personal Access Tokens, or Bearer Tokens.

Review each section below for further details.

1.1 Prerequisites

Excel connects to queries within Cinchy, therefore prior to using any of the connection methods below you will need to create one that represents your dataset. Once created, you will need to copy down the REST API URL endpoint, located as a green button on the right-hand side of the Execute Query screen.

The structure of the URL endpoint is <your Cinchy instance URL>/API/<the name of your query>. You might optionally have querystring parameters at the end as well.

For example: http://your.cinchy.instance.domain/API/YourQueryDomain/API Test

1.2 Using Basic Auth

1. Launch Excel and navigate to Data > Get Data > From Other Sources > Blank Query (Image 1).

1. In the expression box that appears, enter the below text to add in your query as your data source (Image 2):

=Csv.Document(Web.Contents("API ENDPOINT URL"))

Example:

=Csv.Document(Web.Contents("http://your.cinchy.instance.domain/BasicAuthAPI/YourQueryDomain/API Test?ResultFormat=CSV"))

1. Once you've entered that text either click the check mark to the left of the input box or click away and it will automatically attempt to run the expression.

2. The data may return in HTML format initially and not be what you're expecting. To correct this:

   1. Click the Data Source Settings.

   2. Select Basic and enter the credentials for a Cinchy User Account that has access to run this query.

   3. Click OK.

   4. Within the Edit Permissions dialogue, click OK.

   5. Within the Data Source Settings dialogue, click Close.

   6. Click Refresh Preview.

   7. Click Close & Load and your dataset will be displayed in the Excel worksheet.

1.3 Using a Personal Access Token

1. If needed, follow the documentation here to generate a new Personal Access Token (PAT).

2. Launch Excel and navigate to Data > From Web.

3. Click Advanced and input the following values (Image 3):

   1. URL Parts: This is the Query API URL that you created in the Prerequisites section.

   2. HTTP Request Header Parameters:

      1. In the first text box input Authorization

      2. In the second text box type Bearer + your PAT. For example: "Bearer BGFHFHOJDF76DFDFD777"

1. Click OK.

2. Click Load to use the query data in Excel (Image 4).

1.4 Using a Bearer Token

1. If needed, follow the documentation here to generate a Bearer Token.

2. Launch Excel and navigate to Data > From Web.

3. Click Advanced and input the following values (Image 5):

   1. URL Parts: This is the Query API URL that you created in the Prerequisites section.

   2. HTTP Request Header Parameters:

      1. In the first text box input Authorization

      2. In the second text box type Bearer + your token. For example: "Bearer eyUzI1NiIsImtpZCI6IkE4M0UwQTFEQTY1MzE0NkZENUQxOTFDMzRDNTQ0RDJDODYyMzMzMzkiLCJ0eXAiO"

1. Click OK.

2. Click Load to use the query data in Excel (Image 6).

2. Power BI

There are various methods you can use to establish a connection between Cinchy and Power BI, such as using Basic Auth, Personal Access Tokens, or Bearer Tokens.

Review each section below for further details.

2.1 Prerequisites

Power BI connects to queries within Cinchy, therefore prior to using any of the connection methods below you will need to create one that represents your dataset. Once created, you will need to copy down the REST API URL endpoint, located as a green button on the right-hand side of the Execute Query screen.

The structure of the URL endpoint is <your Cinchy instance URL>/API/<the name of your query>. You might optionally have querystring parameters at the end as well.

For example: http://your.cinchy.instance.domain/API/YourQueryDomain/API Test

2.2 Using Basic Auth

1. Launch Power BI and navigate Get Data > Web (Image 7)

6. In the window that launches, you will enter the below text, using your own URL endpoint where the highlighted text indicates (Image 8): =Csv.Document(Web.Contents("http://your.cinchy.instance.domain/BasicAuthAPI/YourQueryDomain/API Test?ResultFormat=CSV"))

7. Click on the checkmark icon and Power BI will automatically attempt to run the expression (Image 9).

8. Click Edit Credentials > Basic (Image 10). Enter the credentials for a Cinchy User Account that has access to run this query and select the level at which to apply these settings. By default it's the root URL.

This process of entering your credentials won't occur with each query, it's just the first time and then they're saved locally.

10. Click Connect to see your data (Image 11).

11. You can now apply any transformations to the dataset.

In this example we also changed the name from Query1 to Product Roadmap and have edited to use the first row as a header (Image 12).

12. Click Close & Apply. The metadata now shows up on the right hand side and you can begin to use it to create your visualizations (Image 13).

2.3 Using a Personal Access Token

1. If needed, follow the documentation here to generate a new Personal Access Token (PAT).

2. Launch Power BI and navigate to Data > Web.

3. Click Advanced and input the following values (Image 14):

   1. URL Parts: This is the Query API URL that you created in the Prerequisites section.

   2. HTTP Request Header Parameters:

      1. In the first text box input Authorization

      2. In the second text box type Bearer + your PAT. For example: "Bearer BGFHFHOJDF76DFDFD777"

1. Click OK.

2. Click Load to use the query data in Power BI.

3. You can now apply any transformations to the dataset.

In this example we also changed the name from Query1 to Product Roadmap and have edited to use the first row as a header (Image 15).

1. Click Close & Apply. The metadata now shows up on the right hand side and you can begin to use it to create your visualizations (Image 16).

2.4 Using a Bearer Token

1. If needed, follow the documentation here to generate a Bearer Token.

2. Launch Power BI and navigate to Get Data > Web.

3. Click Advanced and input the following values (Image 17):

   1. URL Parts: This is the Query API URL that you created in the Prerequisites section.

   2. HTTP Request Header Parameters:

      1. In the first text box input Authorization

      2. In the second text box type Bearer + your token. For example: "Bearer eyUzI1NiIsImtpZCI6IkE4M0UwQTFEQTY1MzE0NkZENUQxOTFDMzRDNTQ0RDJDODYyMzMzMzkiLCJ0eXAiO"

1. Click OK.

2. Click Load to use the query data in Power BI.

3. Tableau

Cinchy exposes a Tableau Web Data Connector that provides access to Cinchy Saved Queries as data sources in Tableau. Tableau versions 2019.2+ are supported.

3.1 Prerequisites

To get started, you must add a record into the Integrated Clients table in the Cinchy domain with the below values.

3.2 Connecting From Tableau

1. Launch Tableau

2. Under Connect -> To a Server select the Web Data Connector option

3. Enter the URL from the Permitted Login Redirect URLs field on the Integrated Clients record created under the Prerequisites section above

4. The Cinchy login screen will appear, enter your credentials

5. Select one or more queries to add to your data set. The result of each query will be available as a Table in Tableau. If a query has parameters, you will be prompted to provide the parameter values before you can add it to your collection.

6. Click the Load button

The Cinchy query results will now be accessible for you to create your visualization.