Getting Started with DataStar (Early Adopter First Release)

DataStar is Optilogic’s new AI-powered data product designed to help supply chain teams build and update models & scenarios and power apps faster & easier than ever before. It enables users to create flexible, accessible, and repeatable workflows with zero learning curve—combining drag-and-drop simplicity, natural language AI, and deep supply chain context.

Today, up to an estimated 80% of a modeler’s time is spent on data—connecting, cleaning, transforming, validating, and integrating it to build or refresh models. DataStar shrinks that time by up to 50%, enabling teams to:

• Answer more questions faster
• Unlock repeatable value across business review
• Focus on strategic decisions, not data wrangling

The 2 main goals of DataStar are 1) ease of use, and 2) effortless collaboration, these are achieved by:

• Providing AI-powered, no-code automation with deep supply chain context
• Supporting drag-and-drop workflows, natural language commands, and advanced scripting (SQL/Python)
• Full integration into the Optilogic platform: users can prep data, trigger model & scenario runs, and push insights to apps or dashboards
• Enabling scalable, collaborative, cloud-native modeling for repeatable decision-making at speed

DataStar is currently in the Early Adopter (EA) phase and is rapidly evolving while we work towards a General Availability release later this year. Therefore, this documentation will be regularly updated as new functionality becomes available. If you are interested in learning more about DataStar or the Early Adopter program, please contact the Optilogic support team at support@optilogic.com.

In this documentation, we will start with a high-level overview of the DataStar building blocks. Next, creating projects and data connections will be covered before diving into the details of adding tasks and chaining them together into macros, which can then be run to accomplish the data goals of your project.

If you are a part of the Early Adopter program, then you can find many more resources to help you get started with DataStar in the DataStar Early Access group on Optilogic’s Frogger Pond community portal.

DataStar Overview

Before diving into more details in later sections, this section will describe the main building blocks of DataStar, which include Data Connections, Projects, Macros, and Tasks.

As DataStar is currently in the Early Adopter phase, this document will be updated regularly as more features become available. In this section, references to future capabilities which are not yet released are included in order to paint the larger picture of how DataStar will work. In the text it is made clear which parts are and which are not yet available in the first Early Adopter release.

Data Connections

Since DataStar is all about working with data, Data Connections are an important part of DataStar. These enable users to quickly connect to and pull in data from a range of data sources. Data Connections in DataStar:

• Are global to the DataStar application – meaning each project within DataStar can use any of the data sources that have been set up as Data Connections.
• Can also be set up from within a DataStar project – they then become available for use in other DataStar projects too.
• Can be of the following types (the last 5 indicated with an * are not yet available in the Early Adopter program):
  • Postgres – an open-source relational database management system that supports both SQL and JSON querying
  • Excel – spreadsheet editor developed by Microsoft for Windows
  • CSV – files containing data in the comma separated values format, which can be created by and opened in Excel
  • Cosmic Frog – a Cosmic Frog model which is a Postgres database using a specific data schema called Anura. Often the projects in DataStar will populate Cosmic Frog model input tables to build complete models that are ready to be run by one of the Cosmic Frog engines and/or read in Cosmic Frog output tables for output analysis
  • MySQL* – an open-source relational database management system that supports SQL querying
  • SQLite* – an open-source relational database engine used as a library in applications
  • OneDrive* – cloud storage server provided by Microsoft
  • ODBC Connection* – a standard way for applications to connect to various databases. This means that if your data source is not one of the types listed here, you may still be able to connect to it if the target database has a specific ODBC driver available
  • Snowflake* – a cloud-based data platform that provides a data warehouse as a service (DWaaS)

Projects, Macros, and Tasks

Projects are the main container of work within DataStar. Typically, a Project will aim to achieve a certain goal by performing all or a subset of: importing specific data, then cleansing, transforming & blending it, and finally publishing the results to another file/database. The scope of DataStar Projects can vary greatly, think for example of following 2 examples:

• Cleanse and filter a specific set of historical supply chain data.
• Build a Cosmic Frog model from scratch using the raw data from the data sources available in DataStar’s Data Connections, then run the model, analyze its outputs, and finally generate reports at the desired level of aggregation.

Projects consist of one or multiple macros which in turn consist of 1 or multiple macros and/or tasks. Tasks are the individual actions or steps which can be chained together within a macro to accomplish a specific goal. In future, multiple macros can also be chained together in another macro in order to run a larger process. Tasks are split into the following 3 categories in DataStar:

• Transform – using these tasks user can convert the data from their raw state in the Data Connections to the clean and predefined format they desire. These tasks will include those that can import, export, select, group, delete, update, pivot and unpivot data. For the Early Adoptor program, the import transform task is initially available.
• Execute & Automate – these tasks aim to make users as productive as possible by allowing them to run Cosmic Frog models, SQL or Python code, and other Macros as part of a Macro. Notifications can also be sent to alert user that a certain Macro has completed. Just the Run SQL task is available in the first version for the Early Adopter program.
• AI Agents – in future, these tasks will be able to perform common tasks using artificial intelligence in future updates of DataStar. Think of automatically comparing scenario outputs or filling out missing data in input tables.

The next screenshot shows an example Macro called Shipments which consists of 7 individual tasks that are chained together to create transportation policies for a Cosmic Frog model from imported Shipments and Costs data. As a last step, it also runs the model with the updated transportation policies:

Note that not all tasks to build a macro like this are yet available in the first Early Adopter version of DataStar.

Project Sandbox

Every project by default contains a Data Connection named Project Sandbox. This data connection is not global to all DataStar projects; it is specific to the project it is part of. The Project Sandbox is a Postgres database where users generally import the raw data from the other data connections into, perform transformations in, save intermediate states of data in, and then publish the results out to a Cosmic Frog model (which is a data connection different than the Project Sandbox connection). It is also possible that some of the data in the Project Sandbox is the final result/deliverable of the DataStar Project or that the results are published into a different type of file or system that is set up as a data connection rather than into a Cosmic Frog model.

How Data Connections, Projects, and Macros Relate to Each Other

The next diagram shows how Data Connections, Projects, and Macros relate to each other in DataStar:

1. In this example, there are 7 Data Connections configured in DataStar, see the rectangle with green background on the left:
   1. A OneDrive connection called Historical Data (OneDrive connections are not yet available in the first Early Adopter DataStar version)
   2. A Snowflake connection called Enterprise Data (OneDrive connections are not yet available in the first Early Adopter DataStar version)
   3. A Postgres connection called Location Data
   4. A CSV connection called Cost Data
   5. A CSV connection called Capacity Data
   6. A Cosmic Frog connection called Neo NA Model
   7. A Cosmic Frog connection called Global Model
2. Note that the 2 Cosmic Frog connections displayed here on the right-hand side are the same 2 as shown in the list on the left, they are just repeated in the diagram to facilitate explaining the flow of data.
3. There are 2 projects set up in DataStar, see the 2 rectangles with blue background in the middle:
   1. Project 1 creates the Policies tables for the Cosmic Frog model named Neo NA Model, a network optimization model in the Northern Americas geography.
   2. Project 2 builds, runs, and analyzes a complete Cosmic Frog named Global Model from raw data.
4. Looking at Project 1, we see that:
   1. It uses 3 of the 7 Data Connections available (blue arrows):
      1. 2 to pull data in from: the Historical Data, and Cost Data connections
      2. 1 to publish data into: the Neo NA Model
   2. It has its own Project Sandbox as an additional Data Connection which is specific to this project only.
   3. It contains 3 macros: Shipments, Production, and Inventory. The Shipments macro can look similar to the example one seen in the previous screenshot.
   4. The 3 macros pull data from the Historical Data, Cost Data, and Project Sandbox connections.
   5. The 3 macros publish data into the Project Sandbox and the Neo NA model connections. What is published into the Cosmic Frog model are the completed Transportation Policies, Production Policies, and Inventory Policies tables.
5. Similarly, looking at Project 2, we follow the yellow arrows to understand which Data Connections are used to pull data from and publish data into. Note that the Global Model connection is used to publish results into by the “Publish to Model” macro which populates the model’s input tables and it is also used as a connection to pull data from for the “Output Analysis” macro after the model has run to completion.

Early Adopter Development Note

For the remainder of this document, only current Early Adopter DataStar functionality is shown in the screenshots (with a few exceptions, which will be noted in the text). The text mostly just covers current functionality and will at times reference features which will be included in future DataStar versions. Within DataStar, users may notice buttons, options in drop-down and right-click menus that have been disabled (greyed out or cannot be clicked on), since new functionality is being worked on continuously. These will be enabled over time and other new features will also gradually be added.

Creating Projects & Data Connections

On the start page of DataStar user will be shown the existing projects and data connections. They can be opened, or deleted here, and users also have the ability to create new projects and data connections on this start page.

The next screenshot shows the existing projects in card format:

1. When logged into the Optilogic platform, click on the DataStar icon in the list of available applications on the left-hand side to open DataStar. Your DataStar icon may be in a different location in the list, and if it is not visible at all, then click on the icon with 3 horizontal dots to show any applications that are not shown currently.
2. We are on the Projects tab of the start page in the DataStar application.
3. The projects are shown in card format (the left icon); the other option is to show them as a list (the right icon).
4. When hovering over a project, the option to delete the project becomes visible. If choosing to click on this to delete the project, a message asking user to confirm they want to delete the project comes up before actually deleting it.
5. Users can quickly search the list of projects by typing in the Search text box and projects containing the text will be filtered out.

New projects can be created by clicking on the Create Project button in the toolbar at the top of the DataStar application:

1. User clicked on the Create Project button which opened the Create Project form.
2. Here, a Project Name can be entered.
3. Optionally, user can write a Project Description.
4. Under Project Type, user can currently just create a new Empty Project.
5. Click on the Edit button to change the project’s appearance by choosing an icon and color.
6. Click on the Add Project button to create the project.
7. Note that on the right-hand side, Help for the currently open DataStar form is shown.

The next screenshot shows the Data Connections that have already been set up in DataStar in list view:

1. We are on the Data Connections tab of the start page in the DataStar application.
2. The Data Connections are shown in list format (right icon); the other option is to show them in card format (left icon) similar to the screenshot above of the Projects in card format.
3. For each Data Connection we see the following details in the list: Name, Connection Type, Description, Owner, Last Edited, Last Edited By, Uses, and Actions.
   1. Note that when hovering over the Actions field in a data connection row, an icon to delete the connection become visible. If choosing to click on this to delete the connection, a message asking user to confirm they want to delete the data connection comes up before actually deleting it.
4. Users can quickly search the list of data connections by typing in the Search text box and connections containing the text will be filtered out.

New data connections can be created by clicking on the Create Data Connection button in the toolbar at the top of the DataStar application:

1. The Create Data Connection form has been opened by clicking on the Create Data Connection button.
2. First, a Data Connection Name needs to be entered.
3. Optionally, user can write a Connection Description.
4. The type of connection can be chosen from the Connection Type drop-down list. See the “Data Connections” section further above for a full list of connection types and a short description of each.

The remainder of the Create Data Connection form will change depending on the type of connection that was chosen as different types of connections can require different inputs (e.g. host, port, server, schema, etc.). In our example, user chooses CSV Files as the connection type:

1. The Connection Type is now showing CSV Files per the selection user made.
2. There are 2 options to select the CSV source file:
   1. The CSV file to be used for the Data Connection can be dragged and dropped onto this “Drag and drop” area from user’s computer. It will then be uploaded to the user’s /MyFiles/DataStar folder on the Optilogic platform. In case a file of the same name already exists in that location, it will be overwritten.
   2. User can browse the list of CSV files that exist in their Optilogic account already (not limited to files under /MyFiles/DataStar, will show all CSV files in their account) to select one as the source for the data connection. Note that:
      1. In case not all 3 columns shown in the screenshot are visible, user can scroll right to also see the location in user’s Optilogic workspace of the file.
      2. The columns in this grid can be dragged to change the order of the columns, and they can also be resized by clicking on the vertical bar in between the columns (the mouse changes to 2 arrows pointing away from each other), holding the mouse down and moving right or left.
3. After selecting the CSV file to be used for the Data Connection, user can click on the Add Connection button to create the new data connection.

In our walk-through here, user drags and drops a Shipments.csv file from their local computer on top of the Drag and drop area:

1. User dragged and dropped their local Shipments.csv file in the “Drag and drop” area.
2. Once the upload of the file is finished, a message in green font indicating the upload completed successfully is shown.
3. The Shipments.csv file is now listed in the list of CSV files the user has available in their Optilogic account. As expected, the location of this file is /MyFiles/DataStar. Click on the file in the list to select it.
4. User can then click on the Add Data Connection button to create the connection.

Inside a DataStar Project

Now let us look at a project when it is open in DataStar. We will first get a lay of the land with a high-level overview screenshot and then go into more detail for the different parts of the DataStar user interface:

1. At the top of the DataStar application, users will find a toolbar:
   1. Clicking on the icon all the way to the left will take user back to DataStar’s start page where the lists of existing projects and data connections are shown, see also the previous section “Creating Projects & Data Connections”.
   2. The left part of the toolbar contains from left to right:
      1. Create Macro button: click on this button to create a new macro
      2. Data Connections drop-down menu: options in the menu are to create a new data connection and, in future, to upload data.
      3. Manage Variables button: in future, this button will be enabled so users can pass in values that can be used/updated in their macros.
   3. The right part of the toolbar gives users quick options to access Leapfrog AI, export the project as an Excel App (future capability), and to run macros.
2. In the pane on the left-hand side of the application, either the list of Macros that the project contains (left tab) or the list of Data Connections (right tab) the project uses is shown, depending on which tab user has clicked on. Note that the Data Connections tab is currently not accessible, it will become available in a few weeks’ time. In this screenshot, the Macros tab is shown.
   1. Macros can be expanded/collapsed; when expanded you see a list of all the tasks/macros that make up the macro. This will be shown in more detail below.
   2. Likewise, data connections will also be able to be expanded/collapsed. Details on this will be added to this documentation upon release.
3. In the pane on the right-hand side of the application, there are 3 tabs, from left to right:
   1. Tasks – here tasks from the Transform, Execute & Automate, and AI Agents categories can be chosen and dragged and dropped onto the Macro Canvas (the central part of the DataStar application) to add them to the currently active macro.
   2. Configuration – the specific configuration parameters for the currently selected task, macro, or data connection can be set or updated here.
   3. Leapfrog – start or continue a conversation with Leapfrog here. Use natural language prompts, and Leapfrog will configure tasks for you!
4. The central part of DataStar is called the Macro Canvas. Tasks can be dragged and dropped onto here and then connected to each other to build out a macro that will accomplish a specific data process.
5. At the bottom of the Macro Canvas, 2 tabs are showing:
   1. Logs – here it will be tracked which task was run when and if it completed successfully.
   2. Task Results – this will show the resulting table of the currently selected task; not yet included in the Early Adopter first release.
6. Please note that the 3 panes on the left-hand side, right-hand side, and to the bottom of the Macro Canvas can all be collapsed and expanded as desired. This can be done by clicking on the icons with the 2 greater than/less than signs, or 2 arrowheads pointing up/down.

Macros Tab

Next, we will dive a bit deeper into a macro:

1. The macro named “Customers from Shipments” is selected on the Macros tab on the left-hand side panel of DataStar.
2. The macro has been expanded, so we see the list of tasks that are part of this macro. Users will note that:
   1. Each macro has by default a task named Start, which has its own specific icon and blue color. This task cannot be removed or renamed and the first actual task of the macro will be connected to it.
   2. Tasks from the Transform category have light blue icons associated with them, and those from the Automate & Execute category are green. The icon itself also indicates the type of task it is. For example, the “Import Raw Shipments” task is an Import task from the Transform category, and the “Create Unique Customers” task is a Run SQL task from the Automate & Execute category.
   3. Right-clicking on a Macro or a Task will bring up a context menu which can be used to Rename or Delete the Macro or Task.
3. Use the Search text box to quickly find a macro/task whose name contains certain text.
4. This button can be used to expand or collapse all macros with one click.
5. Click on the Create Macro button in the toolbar to add a new Macro to the project.

Macro Canvas

The Macro Canvas for the Customers from Shipments macro is shown in the following screenshot (note that the Export task shown is not yet available in the Early Adopter DataStar release):

1. The tab tells us which macro we are looking at. Note that multiple macros can be open in multiple tabs and user can easily switch between them by clicking on the tab of the desired macro.
2. The canvas currently shows 3 of the tasks that are part of the Customers from Shipments macro. The bottom part of a task contains the name and the top colored part of a task shows what type of task it is. For example:
   1. The task at the top connected to Start is an Import task from the light blue Transform category; its name is “Import Raw Shipments”.
   2. The task at the bottom left is a Run QSL task from the green Execute & Automate category; its name is “Create Unique Customers”.
3. Tasks can be dragged and dropped onto the canvas from the Tasks list in the right-hand side pane. Once on the canvas, user can connect tasks by clicking in the middle of the right edge of the first task, holding the mouse down, and then clicking in the middle of the left edge of the next task. Please note that:
   1. DataStar helps users by showing a bigger circle when hovering over the middle of a left or right edge of a task.
   2. Tasks can be connected to multiple other tasks. If there are for example 2 tasks connected to a third task that succeeds the first 2, then this third task will not execute until both preceding tasks have completed.
   3. To delete a line that connects 2 tasks: click on the line (it will then become a dotted orange line), and then hit the Backspace key.
4. In the left bottom corner of the canvas user has access to following controls, from top to bottom:
   1. Zoom in: clicking on this plus icon will increase the size of the tasks on the canvas, less of the total macro will be visible.
   2. Zoom out: clicking on this minus icon will decrease the size of the tasks on the canvas, more of the total macro will be visible.
   3. Fit view: clicking on the icon with 4 square corners will set the position and zoom-level of the canvas such that all tasks/macros that are part of the macro will be shown on the canvas.
   4. Toggle interactivity: not currently used.
5. The grey rectangle at the right bottom of the canvas shows a small diagram of where all the tasks that are part of the macro are positioned. The smaller white rectangle within this grey rectangle indicates which part of the entire macro the canvas is showing currently. This is helpful when you have a macro with many tasks and you want to pan through it while it is zoomed in at a certain level.

In addition to the above, please note following regarding the Macro Canvas:

1. Clicking on a task in the canvas does several things:
   1. Selects the task (i.e. highlights it) in the macro(s) it is part of in the Macros tab on the left-hand side pane.
   2. Opens the Configuration of the task in the right-hand side pane.
   3. In a future update, it will also show the results of the most recent time the task was run in the Task Results tab in the pane at the bottom of the Macro Canvas.
2. Hovering over a task will make a Delete icon visible: ; this can be clicked on to remove the task. A confirmation message to ensure user wants to delete the task will come up first before the task is actually removed.
3. Users can position the canvas as they desire by clicking on it, holding the mouse down and then moving the mouse to drag the canvas in any direction.
4. Users can also zoom in/out on the canvas by using the mouse or 2 fingers on a trackpad (move closer to each other to zoom out and move further apart to zoom in).
5. Keyboard shortcuts to zoom in/out are available too: Ctrl & +/= to zoom in and Ctrl & -/_ to zoom out.

Tasks Tab

We will move on to covering the 3 tabs on right-hand side pane, starting with the Tasks tab:

1. We are on the Tasks tab on the right-hand side pane in DataStar.
2. As previously discussed, there are initially 2 task categories:
   1. Transform – these tasks can be used to perform often used data actions and currently just includes the Import task (Export coming soon!).
   2. Execute & Automate – these tasks aim to make users more productive by incorporating automation and currently only includes the Run SQL task.

User can click on a task in the tasks list and then drag and drop it onto the macro canvas to incorporate it into a macro.

Configuration Tab of a Task

When adding a new task, it needs to be configured, which can be done on the Configuration tab. When a task is newly dropped onto the Macro Canvas its Configuration tab is automatically opened on the right-hand side pane. To make the configuration tab of an already existing task active, click on the task in the Macros tab on the left-hand side pane or click on the task in the Macro Canvas. The configuration options will differ by type of task, here the Configuration tab of an Import task is shown as an example:

1. We are on the Configuration tab on the right-hand side pane in DataStar.
2. The description of the type of task that was selected is shown here, in this case of the Import task in the Transform category.
3. The name of the task can be entered here.
   1. Once the task name has been saved, it is also listed at the top of the configuration form.
4. The Data Connection section needs to be configured.
   1. For each section within a task configuration, there is an indicator telling user the status of this section of the configuration. Here the green check mark indicates the Data Connection section of the task configuration has been completed. When this icon is orange, it means the configuration is not yet finished.
   2. Sections within a configuration can be expanded/collapsed by clicking on the down/up caret icon.
5. Within the Data Connection configuration section, first the Source is specified:
   1. Select the connection that will function as the source for the import task from the drop-down list containing all data connections set up in the project.
   2. For data connections with multiple tables (such as a Cosmic Frog model), user can select the table to use as the source from the drop-down list. In our case, we are using the Shipments connection, which is a CSV file, so the 1 table in this file is used, and user does not need to select anything from the drop-down list.
   3. If a new data connection that is not yet part of the project is to be used as the source, user can click on the plus icon to add a new Data Connection.
6. Next, the Destination of the import task is configured:
   1. Select the connection that will function as the destination for the import task from the drop-down list. This list will contain the Postgres data connections (including the Project Sandbox and Cosmic Frog models) which are set up in the project. Oftentimes, the Project Sandbox will be the destination connection for Import tasks as the imported data will almost always still need to be cleansed, validated, and blended before reaching its final state.
   2. Enter the name of the new table to be created in the destination data connection.
   3. If a new data connection that is not yet part of the project is to be used as the destination, user can click on the plus icon to add a new Data Connection.

Please note that:

• The table name is set to RawShipments in the configuration, and it will be imported to the Project Sandbox as a table named raw_shipments, so the name is converted to all lowercase and where originally a capital followed a lower-case letter, an underscore is added.
• If there are spaces in the column names in the CSV file, these will be replaced by underscores when importing into the Project Sandbox.

Leapfrog Tab

Leapfrog combines an extensive knowledge of PostgreSQL with the complete knowledge of Optilogic’s Anura data schema (used by all Cosmic Frog models), and all the natural language capabilities of today’s advanced general purpose LLMs. Within the DataStar context, users can give Leapfrog a prompt, and it will configure a task in response. Users can optionally add the task to a macro. Here, the Leapfrog functionality within DataStar will be briefly introduced. For more in-depth documentation on Leapfrog in DataStar, please see the “Leapfrog in DataStar: User Enablement Document” in the DataStar Early Access group on the Frogger Pond community portal.

1. Leapfrog can be accessed by clicking on the “How can I help you” text bubble or the frog icon in the toolbar at the top of DataStar, or by clicking on the Leapfrog tab on the right-hand side pane.
2. User can type a prompt into the “Write a message…” free type text box. Here user is asking to create unique customers from the destination stores that are present in the raw_shipments table, which was imported into the Project Sandbox. Extra instructions to average the latitude and longitude if there are multiple records for the same destination store are given in order to calculate a latitude and longitude for each customer.
3. Hit enter or click on the blue Send icon on the right to submit the prompt.

Leapfrog’s response to this prompt is as follows:

1. The prompt submitted by the user is listed at the top.
2. Leapfrog first describes in natural language what it has done in response to the prompt.
3. It is creating a Run SQL task named “Create customers table” as the response to the prompt.
4. The Data Connection section lists that the target connection is the Project Sandbox.
5. In the SQL Script section, the SQL query that will be executed if adding this task as a Run SQL task to a macro is shown.
   1. User can click on this expand icon to show the SQL Query in a bigger Code Editor window. The complete SQL Query reads: CREATE TABLE customers AS SELECT destination_store AS customername, AVG(destination_latitude) AS latitude, AVG(destination_longitude) AS longitude FROM raw_shipments GROUP BY destination_store
6. Clicking on the “Add to Macro” button will add a Run SQL task named “Create customers table” with this configuration to the Macro Canvas.

Within a Leapfrog conversation, Leapfrog remembers the prompts and responses thus far. User can therefore build upon previous questions, for example by following up with a prompt along the lines of “Like that, but instead of using a cutoff date of August 10, 2025, use September 24, 2025”.

Please note that in the first Early Adopter release of DataStar, running a Run SQL task on a table in the Project Sandbox is not yet possible, this will be added very soon.

Running a Macro

Users can run a Macro by selecting it and then clicking on the green Run button at the right top of the DataStar application:

1. The “Customers from Shipments” macro is open and is also selected in the Macros tab on the left-hand side pane (not shown).
2. The green Run button is enabled and clicking this will immediately kick off the macro run.

Please note that:

• If a task is selected in the Macros tab on the left-hand side pane, the Run button will not be available (greyed out), so in order to run a macro, user needs to select it by clicking on it in the Macros tab first.
• Macros do not need to be complete to be run, it is good practice to run partially completed macros before completely building out a macro without testing it along the way. In future, individual tasks can be run independently too.

Logs Tab

Next, we will cover the Logs tab at the bottom of the Macro Canvas where logs of macros that are running/have been run can be found:

When a macro has not yet been run, the Logs tab will contain a message with a Run button, which can also be used to kick of a macro run. When a macro is running, the log may look similar to the following for the first Early Adopter DataStar release:

1. At the top of the log the name of the macro that is running and how long it has been running for so far are listed.
2. In the table, the status of the individual tasks of the macro are listed. The tasks are currently listed with their Task ID, in future this will include the task names too. If a task succeeds a previous task, its status will be blocked until the preceding task has completed.

Please note that:

• A log is recorded in the Logs tab when a macro is running, user can watch the real-time updates if the Logs tab is open.
• No log is available for a macro that has not yet been run.
• When re-running a macro, its previous log will be overwritten by the one of the current run. Therefore, the log shown for a macro will always be the one of its most recent run.

Data Connections Tab

The Data Connection tab on the left-hand side pane is not yet active in the first Early Adopter release but will very soon be available. It will then look similar to the following screenshot, listing the available connections and their tables when expanded:

Once this tab is available within DataStar, users will also be able to click on the tables contained in the connections which will open them in the central area of DataStar (where otherwise the Macro Canvas of the active macro is shown).

In the very short term, users can view the contents of their connections in other applications on the Optilogic platform, depending on the type of connection:

• Lightning Editor: for CSV files
• SQL Editor: for Postgres DB connections, which includes the Project Sandbox and Cosmic Frog models. See this SQL Editor Overview help center article on how to use the SQL Editor. For the Project Sandbox, please note that:
  • The name of the Project Sandbox database is the same as the project name
  • The tables that are created in the sandbox can be found under the “starburst” schema

For example, in the example project named “Create Customers” used in this documentation, the shipments.csv file was imported into the Project Sandbox (its database name is Create Customers, same as the project name) as a new table named raw_shipments, this table can be viewed in the SQL Editor:

We hope you are as excited about starting to work with DataStar as we are! Please stay tuned for regular updates to both DataStar and all the accompanying documentation. As always, for any questions or feedback, feel free to contact our support team at support@optilogic.com.