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.

Version & Draft Version

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

Without Maker/Checker Enabled

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

With Maker/Checker Enabled

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

Approval State

Legacy column - always blank.

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.

Without Maker/Checker Enabled

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

With Maker/Checker 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.

Modified By & Modified

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

Without Maker/Checker Enabled

The last user to modify the record, and when it happened.

With Maker/Checker 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.

Deleted By & Deleted

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

Without Maker/Checker Enabled

A deleted record will have Deleted By and Deleted filled in.

With Maker/Checker Enabled

Deleted By and Deleted are based on the user/time when the Delete Request was created, not when it was approved.

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.

View All Columns

This permission allows a user to view all columns within the table.

Note that this applies to any new columns that are added to the table after providing this permission as well.

View Specific Columns

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

Edit All Columns

This permission allows a user to edit all columns within the table.

Note that this applies to any new columns that are added to the table after providing this permission as well.

Edit Specific Columns

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

Approve All Columns

This permission allows a user to approve all columns within the table. This also allows users to approve Create and Delete requests.

Note that this applies to any new columns that are added to the table after providing this permission as well.

Approve permissions only apply with Maker/Checker turned on.

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 with Maker/Checker turned on.

Link Columns

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

Autonomous Data Fabric Platform

For ‘Builders’

Cinchy’s Autonomous Data Fabric platform introduces a complete re-think to the ‘integration-based’ paradigm where up to 50% of the resources on IT projects are spent on integration alone. The platform combines a network-based architecture with the unique ability to decouple data from applications (“Autonomous Data”). This powerful combination renders the 40-year old paradigm of “integration” obsolete, which means what used to require half your time and energy now takes just a tiny fraction.

There are two types of individuals that use the platform. The first are end-users who are able to view and manage all data that they have been granted access through Cinchy’s Universal Data Browser. The second type is ‘Builders’ who are really the creators of the Data Fabric. Builders are responsible for building out the organization's Fabric including table schemas, establishing access controls, and building application experiences for end-users. By significantly reducing the heavy reliance on integration, Builders can produce enterprise-grade solutions in what would have taken months in weeks for an unlimited number of end-users.

Builders can leverage Cinchy as one platform to simplify solutions delivery

1. Connect

2. Protect

3. Collaborate

4. Build

5. Reuse

Connect

Create your new network of data

An Autonomous Data Fabric 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 Fabric, its data can be used in conjunction with data from any other source on the Fabric with no further integration efforts. The more sources you connect, the more powerful and efficient your Data Fabric becomes. You can extend data on the Fabric 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 your Autonomous Data Fabric 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

Cinchy’s Autonomous Data Fabric platform features an intuitive Drag and Drop Query Builder that allows Builders to create using the Cinchy Query Language (CQL), a proprietary language specific to Cinchy’s Autonomous Data Fabric technology. All queries can be easily saved and shared, and query results automatically generate a full no-code API.

Consolidate legacy systems, create new solutions

By decoupling the data from the application, our Autonomous Data Fabric 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 Fabric 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 your Data Fabric, the more it’s capable of doing.

Any new data you add to the Fabric 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 via the Fabric 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, the Autonomous Data Fabric delivers solutions faster and faster as more data is added to it.

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>

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

In this section you will find other methods of connecting to Cinchy, for example via ODBC and JDBC.

Columns

Row Condition

This follows the same syntax as a view filter query.

Ordinal

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

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.

Table

Table in which to apply the conditional formatting.

Highlight Columns

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

Example

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

{
  "data": {
    "button.create": {
      "translation": "Create",
      "language": "en",
      "region": "US",
      "defaultText": false
    },
    "button.cancel": {
      "translation": "Cancel",
      "language": null,
      "region": null,
      "defaultText": true
    },
    "button.favorite": {
      "translation": "Favourite",
      "language": "en",
      "region": "CA",
      "defaultText": false
    },
    "button.delete": {
      "translation": "button.delete",
      "language": null,
      "region": null,
      "defaultText": false
    }
  }
}

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.

System Tables

There are 3 tables in Cinchy to provide language support. [Cinchy].[Literal Groups], [Cinchy].[Literals], and [Cinchy].[Literal Translations].

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.

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).

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).

From the home screen, select Create in the top left hand corner to get started.

Standard vs 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.

A standard table will not allow you to create geography and geometry columns. (Any existing tables created before installing Cinchy Platform v4.19.0 are standard tables).

Starting from Scratch

Info Tab

Table Name

Mandatory field. Must be unique within the Domain.

I.e. You can have a [Football].[Teams] table and a [Basketball].[Teams] table, but not two tables named [Teams] under the Football domain.

Icon

You can optionally pick an icon, as well as color for your table. This will be displayed on the home screen.

Domain

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

Description

You can give your table a description. This description will be displayed on the home screen to users browsing the marketplace.

Example

Columns/Links Tab

Your table must have at least one column to start. See Column Types to decide what type of column you should create and how to create it. Depending on the type, you will create it either under the columns or the links tab.

You can now click Save to create your first table!

Import a CSV

Domain

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

File

You must upload a .csv file. The column names must not conflict with System Columns.

Default Settings

When creating a table via Import a CSV, a few settings will be set by default. These can be modified after the table is imported through the Design Table tab.

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.

Default View

When you first create a table, a default view called All Data will be created for you under Manage Data.

You can create additional views or edit the All Data view under Managing Data.

Bookmarks and Marketplace

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 Marketplace if they have permissions to.

You can click the table in the home screen to get back to the Data Management screen for your table.

Common Fields

Name

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

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 created in the [Cinchy].[Data Security Classifications] table by an administrator.

Currently there is no functionality tied directly to Data Security Classification - the tagging is just for internal auditing purposes. Future security settings will be tied to Data Security Classifications, rather than simply done at a column level.

Description

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

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.

Regular Columns

Text

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

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

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).

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

Custom data formatting provides additional flexibility in how dates (and times) can be displayed within a Date column type:

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

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.

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.

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.

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.

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.

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.

Link Columns

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

Hierarchy Columns

Hierarchy columns are simply link columns referencing the current table. Some example uses of hierarchies:

• Related Software Changes

• Manager

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.‌‌

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.‌‌

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.‌‌

To change the structure of an existing table (i.e. Rename columns, add new columns, change data type) go to Design Table in the left navigation.

To create a new table, please see creating your first table.

Data Controls can be selected in the left navigation menu.

Currently anyone in the Cinchy Administrators group has access to perform any action on any objects.

You can use multiple rows to provide entitlements to a user.

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.

Click Table Level Entitlements for a detailed description of the available entitlement options.

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.

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:

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. 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.

  • For more on Included Columns, click here

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. 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.

3.1 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 6). 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 7):

• 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 8).

For more formation on creating, modifying or managing Partitioning, please visit Microsoft's Partitioned table and Indexes documentation.

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.‌

Choosing 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.‌

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.‌

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.‌

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. You can add additional display columns in the advanced options for link columns.

‌

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

Link Filter

‌The link filter filters out records from the drop down list. 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.‌

Relationships

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

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.‌

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.‌

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.

Marketplace

Marketplace allows a user to see the table within the marketplace. They can search for the table on the My Network screen.

Bulk Export

This permission allows a user to export data from the table via the Manage Data screen.

Direct Query

This permission allows a user to query the data from the table directly in the Query Builder.

Design Table

This permission allows a user to alter the structure of the table.

This is a builder/administrative function and should not generally be granted to end users.

Design Controls

This permission allows a user to change the permissions on the table.

This is a builder/administrative function and should not generally be granted to end users.

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+.

Web Data Connector

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.

Pre-requisites

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

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 Pre-requisites 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.

1. Creating a Saved Query

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

2. Fill out the following information:

1.1 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.

1.2 The Query Tab

In the Query screen, you can modify and run your query.

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:

1. Queries in Cinchy are what Excel connects to. If you don't have one that represents your dataset, you'll need to create that first. In this example, we will use a query called API Test:

2. When you open the query you'll notice on the right-hand side a green button that says REST API

3. Click on the REST API button and you'll see the below popup. In the textbox is the URL for the API endpoint. You can click the clipboard icon to copy the URL.

4. In this example the URL is: http://your.cinchy.instance.domain/API/YourQueryDomain/API Test You'll notice that the structure here is <your Cinchy instance URL>/API/<the name of your query> and optionally at the end you may have querystring parameters. For access via Excel we're going to use Basic authentication and a result format of CSV, which uses a slightly different URL endpoint. Instead of /API/ in the URL, it's going to be /BasicAuthAPI/ and we're going to add a querystring parameter - ResultFormat=CSV. For this example your URL for accessing this dataset through PowerBI is going to be: http://your.cinchy.instance.domain/BasicAuthAPI/YourQueryDomain/API Test?ResultFormat=CSV

5. Launch Excel. To access the dataset, click on Data in the menu bar, then Get Data > From Other Sources > Blank Query from the menu:

6. In the window that launches there is a textbox where you can enter an expression. Here you will enter the below text (note your modified URL from Step 4 between quotes): =Csv.Document(Web.Contents("http://your.cinchy.instance.domain/BasicAuthAPI/YourQueryDomain/API Test?ResultFormat=CSV"))

7. 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. The data may return in HTML format initially and not be what you're expecting: To correct this, click the Data Source Settings button, then under Credentials click the Edit button:

8. Select Basic on the left, enter the credentials for a Cinchy User Account that has access to run this query and click OK. Click OK in the Edit Permissions dialog and click Close in the Data Source Settings Dialog. This process of entering your credentials won't occur with each query, it's just the first time and then they're saved locally.

9. Click Refresh Preview and you should see the data. Click Close & Load and your dataset will now be displayed in the Excel worksheet.

Cinchy’s JDBC Driver can be used with the SAS/ACCESS Interface to JDBC Driver capability to connect to your Cinchy instance. This section will discuss configuration and connection to your Cinchy data experience.

Prerequisite

Cinchy's JDBC Driver must be moved to the "/opt/Cinchy/client/JDBC" directory in the SAS server before attempting to configure.

Configuration

To configure SAS, you only need Cinchy’s JDBC driver that is accessible from your SAS session.

The SAS/ACCESS Interface to JDBC LIBNAME statement allows you to assign a library reference to your data source. This feature lets you reference a table directly in a DATA Step or in a PROC Step. This example shows the basic LIBNAME statement to connect to Cinchy.

libname libCinchy jdbc classpath = "/opt/Cinchy/client/JDBC" class = "org.cinchy.jdbc.Driver" URL = "jdbc:cinchy://<<Cinchy SSO URL>>/identity/connect/token?client_secret=<<GUID>>;client_id=<<ClientID>>;username=<<Username>>;password=<<PASSWORD>>;grant_type=password;scope=js_api;";

For more information on the parameters please see here.

1. Displaying a Saved Query

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

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.

1.1 Pivot Reports

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

1.2 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!

For more information on building forms within Cinchy, please

Introduction to Cinchy DXD

CinchyDXD is a utility (downloadable) 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.

Build the Data Experience (DX)

Table Creation

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

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

2. From under “My Network”, click the Create button

3. Select Table

4. Select From Scratch

5. Create the table with the following properties

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

Enter Sample Data

1. In your newly created table, enter the following sample data:

Create Query

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

1. From “My Network”, click the create button

2. Select Query

3. In the query builder locate the Currency Exchange Rate table and drag it to the “FROM” line Hint: you will find the Currency Exchange Rate table in the “Sandbox” domain, to expand the “Sandbox” domain, click on the gray arrow (or double click)

4. In the “SELECT” line drag and drop the “Rate” column and enter in the following: SELECT [Rate] * @Amount AS 'Converted Amount' Hint: you will find the Rate column by expanding the Currency Exchange Rate table, similarly to expanding the “Sandbox” domain

5. Enter in the following for the WHERE clause

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

7. Test the query by entering in the following and clicking the submit button

@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

10. Click the Save button

Package the Data Experience (DX)

Download 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, you must have CinchyCLI installed. For further installation instructions please refer to CLI (https://cli.docs.cinchy.com/) documentation

To access the Data Experience Deployment utility please contact Cinchy support (support@cinchy.com).

To download the Utility:

1. Login to Cinchy

2. Navigate to the Versions Table

3. Select the Experience Deployment Utility View

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

Note: the CinchyDXD utility is only upwards compatible with Cinchy version 4.6+

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)

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

Tip: From within your file explorer window (folder: Cinchy DXD v.X) type “Powershell” into the file path. It will launch a Powershell window already at the folder path.

One Time Setup: Powershell

There is a one (1) time powershell setup that is required when using CinchyDXD.

1. From your Powershell window type cin

2. Hit Tab on your keyboard

3. Hit Enter on your keyboard

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

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

7. Hit Enter on your keyboard

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

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

Cinchy DXD Tables Overview

There are four (4) tables in Cinchy that are used for packing up and deploying a Data Experience.

Note: The Data Experience is defined and packed in what will be referred to moving forward as the “Source Environment”. Where the environment that the Data Experience will be deployed to will be referenced to as the “Target Environment”.

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

2. Data Experience Reference Data Table - this is 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

STEP 1: Define the Data Experience

When setting up a Data Experience definition, you will need one (1) 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

2. Complete the enter/select the following:

Note: If you make changes to the DX in the future, you are NOT required to build a new Data Experience Definition in this table, you will update the existing definition. If you need to review what the definition looked like historically, you can view it via the Collaboration log.

STEP 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.

Note: this table set up will be similar to how you would set up a CLI.

1. Locate and open the Data Experience Reference Data table

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.

STEP 3: 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

Reminder: you can launch Powershell right from your file explorer window in the CinchyDXD folder by entering in the folder path “powershell” and hitting enter on your keyboard. Saving you an extra step of navigating to the CinchyDXD folder manually in Powershell.

2. In the Powershell window type in cin and hit tab on your keyboard

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

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

5. Hit Enter on your keyboard

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

6. Let’s now set up our mandatory export parameters

NOTE: the parameters executed in Powershell can exist on one line in powershell, however for legibility (below) the parameters have been put on separate lines. If you are putting your parameters on separate lines you will be required to have backticks quote ` for the parameters to execute.

NOTE: please ensure that you are using the sample below as a sample. You will be required to provide values that correspond to:

• the URL for the source environment

• the User ID for the user who is performing the export

• the Password for the user who is performing the export

• your folder path for where CLI is stored

• your folder path for where the CLI output files are written to

• the GUID for the Data Experience that is generated in the Data Experience Definition table

• your own version naming convention

• your folder path for where your CinchyDXD output files are written to

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

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.

STEP 4: Validate Export

Now that the export is completed, be sure to validate the following:

1. DXD Export Folder is populated

2. Data Experience Release table is populated in the source environment

3. Data Experience Release Artifacts table is populated in the source environment

Install the Data Experience (DX)

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 i.e. 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>

STEP 1: Install the Data Experience

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

1. Open File Explorer, navigate to the DX exported folder (see Step 4: Validate Export [e.g. Currency Converter folder])

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, the powershell window will appear

4. In the Powershell window type in cin and hit tab on your keyboard

5. In the Powershell command line hit next to .\CinchyDXD.ps1 type install

6. Hit Enter on your keyboard

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

7. Let’s now set up our mandatory install parameters

NOTE: the parameters executed in Powershell can exist on one line in powershell, however for legibility (below) the parameters have been put on separate lines. If you are putting your parameters on separate lines you will be required to have backticks quote ` for the parameters to execute

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" `

Note: be sure that the user(s) and group(s) required to install a DX are existing in your target environment. If they are not, Powershell will generate an error message when you attempt to install the DX.

8. Enter the install parameters into the Powershell window

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.

STEP 2: Validate Install

Now that the install is completed, be sure to validate the following:

1. Models Table in populated in the Target environment with the model that was installed

2. Currency Exchange Rate tale exist in the Target environment

3. Currency Converter query exist in the Target environment

4. Data Experience Definitions table is populated with the DX parameters that were setup in the Source environment

5. Data Experience Releases table in the target environment is populated

Update the Data Experience (DX)

There are a few updates that are required in the Data Experience that has been created in our source environment. We do not want to have to repeat the updates in both the source and target environments. In the upcoming sections we will look at how to update the data experience in the source environment, re-package (re-export) the data experience and reinstall the data experience in the target environment.

Table Updates

1. Log back into the 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:

Be sure to save your change before leaving the table.

Query Updates

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

Reminder: Be sure to update the @Currency_1 and @Currency_2 labels to better reflect the input fields

2. Test the query to validate that it is still functioning

3. Be sure to save your query

Re-Package the Data Experience (DX)

Now that you have made any necessary changes to the DX it is time to re-export the package out of our source environment and re-install it into our target environment.

It is important to note that you should not create a new record in the Data Experience Definition table though the evolution of your DX, the initial record will be used to expand the definition of the DX. For example, if new objects (tables, queries, applets) integrations and UDF’s are added to the DX, the DX definition will need to be updated. To see the historical definitions of the DX, please see the Collaboration Log.

STEP 1: Update Data Experience Table

If you have added or removed any of the following 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

STEP 2: Update Reference Data Table

If you have added or removed any of the following 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

STEP 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

Reminder: you can launch Powershell right from your file explorer window in the CinchyDXD file, saving you an extra step of navigating to the CinchyDXD folder manually in Powershell.

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

Reminder: If you do not remember the mandatory parameters, you can click the enter on your keyboard after typing in .\CinchyDXD.ps1 export, Powershell will provide you with the required and optional components to export the data experience.

5. Let’s now enter the mandatory export parameters

NOTE: the parameters executed in Powershell can exist on one line in powershell, however for legibility (below) the parameters have been put on separate lines. If you are putting your parameters on separate lines you will be required to have backticks quote ` for the parameters to execute

NOTE: you will need to update your version number 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

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.

STEP 4: Validate Export

Now that the export is completed, be sure to validate the following:

1. DXD Export Folder is populated

2. Data Experience Release table is populated in the source environment

3. Data Experience Release Artifacts table is populated in the source environment

Re-Install the Data Experience (DX)

STEP 1: Re-Run CinchyDXD Install

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

1. Open File Explorer, navigate to the exported folder (see Step 4: Validate Export)

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, the powershell window will appear

4. In the Powershell window type in cin and hit tab on your keyboard and type install

5. Enter the install parameters into the Powershell window

NOTE: the parameters executed in Powershell can exist on one line in powershell, however for legibility (below) the parameters have been put on separate lines. If you are putting your parameters on separate lines you will be required to have backticks quote ` for the parameters to execute

Sample: .\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.

STEP 2: Validate Install

Now that the install is completed, be sure to validate the following in the target environment.

1. Models Table in populated in the Target environment with the model that was installed

2. Currency Exchange Rate table exist in the Target environment with the new column names

3. Currency Converter query exist in the Target environment with the new column names and labels

4. Data Experience Definitions table should not change unless you have added or removed column details within this table

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

Insert Row

This permission allows a user to create new rows in the table.

Delete Row

This permission allows users to delete rows in the table.

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}

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

Simple Example

Edit Specific Columns: Age Editable Row Filter: [Age] > 30

Example with Viewable Data

View Specific Columns: First Name, Last Name Viewable Row Filter: [End Date] IS NULL OR [End Date] > GetDate()

Layer On Another Entitlement

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

For the All Users group: 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()

1. Queries in Cinchy are what PowerBI connects to. If you don't have one that represents your dataset, you'll need to create that first. In this example, we will use a query called API Test:

2. When you open the query you'll notice on the right-hand side a green button that says REST API

3. Click on the REST API button and you'll see the below popup. In the textbox is the URL for the API endpoint. You can click the clipboard icon to copy the URL.

4. In this example the URL is: http://your.cinchy.instance.domain/API/YouQueryDomain/API Test You'll notice that the structure here is <your Cinchy instance URL>/API/<the name of your query> and optionally at the end you may have querystring parameters. For access via PowerBI we're going to use Basic authentication and a result format of CSV, which uses a slightly different URL endpoint. Instead of /API/ in the URL, it's going to be /BasicAuthAPI/ and we're going to add a querystring parameter - ResultFormat=CSV. For this example your URL for accessing this dataset through PowerBI is going to be: http://your.cinchy.instance.domain/BasicAuthAPI/YourQueryDomain/API Test?ResultFormat=CSV

5. Launch PowerBI. To access the dataset, click on Get Data and then select Blank Query from the menu:

6. In the window that launches there is a textbox where you can enter an expression. Here you will enter the below text (note your modified URL from Step 4 between quotes): =Csv.Document(Web.Contents("http://your.cinchy.instance.domain/BasicAuthAPI/YourQueryDomain/API Test?ResultFormat=CSV"))

7. Once you've entered that text either click the checkmark to the left of the input box or click away and it will automatically attempt to run the expression. What you should then see is a prompt to edit credentials.

8. Click the Edit Credentials button and you should see the following popup:

9. Select Basic on the left, 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 (only if you want to, 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. Once you click Connect you should see the data

11. You can now apply any transformations to the dataset that you wish. For instance, in this example we need to click the button at the top that says Use First Row as Headers, but you may have additional changes. In this example we also changed the name from Query1 to Product Roadmap

12. Once you're done, click Close & Apply. Now the metadata shows up on the right hand side and you can begin to use it to create your visualizations