Page Comparison

1. Open the Translator Project:

   • Start by opening the Translator project in Iguana.

2. Select the “Pull” Sample Data:

   • Choose the Pull example to see how data retrieval is implemented.

3. Understand the Pull Process:

   • Navigate to the APP/APPpull.lua source file.

   • This file shows how:

     • CSV data is generated and stored as a Lua table.

     • The data is formatted and sent in response to the /pull web request.

   • The annotations in the code should make it easy to understand how the data flows from the source system to the web request response.

4. Explore the Push Process:

   • Switch to the Push example to see how Iguana handles incoming data.

   • Navigate to the APP/APPpush.lua source file.

   • This file demonstrates how Iguana receives and processes data sent from the spreadsheet.

   • The annotations in this file clearly explain how data flows from the incoming request into Iguana's processing logic.

By reviewing the annotated code in APPpull.lua and APPpush.lua, you can easily understand how data flows through Iguana for both pulling data into Lua tables and pushing data from external sources.

1. Code Language:

   • The code that handles communication with Excel is written in Visual Basic for Applications (VBA).

2. Communication Process:

   • Data is sent and received using HTTP requests.

   • The adapter parses incoming data and formats outgoing data to work with Excel.

3. Configuration Table:

   • A configuration table in Excel determines which web services to query and where to store the data. It contains the following key columns:

     • Enabled - true if the call is executed when pressing the Get Data from Iguana button

     • Table: Specifies where the data will be stored.

       • The adapter will create or update an Excel table object with this name in a tab of the same name.

     • URL: Provides the URL of the Iguana-powered web service to fetch the data.

     • ParamName and ParamValue: Optional parameters that can be used for more advanced customization when querying the web service.

By leveraging Excel table objects, the adapter ensures that data is dynamically updated and formatted properly. This setup provides flexibility for storing and customizing data retrieval and storage processes.

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Similarly for sending data we have the following configuration table:

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1. Minimal Library:

   • The webservice example uses a lightweight library to serve HTML and CSS assets to the browser.

2. Library Location:

   • This library is located in the WFIL directory.

3. Source Code Directory:

   • The APPW directory contains the source code for the example's:

     • HTML files

     • CSS stylesheets

     • Icons

4. Editable Files:

   • You can customize the example by editing the files in the APPW directory as needed.

Look at APP/APPpull.lua to see how the data needs to be formatted.

In a real application you would most likely be querying the data from a SQL database and then formatting it in the same way as the application.

If you want to add additional web services then edit this code from main:

local APPaction={}
APPaction["/"] = APProot
APPaction["/ExcelExport.xlsm"] = APPspreadSheet
APPaction["/push"]             = APPpush
APPaction["/pull"]             = APPpull     

And define additional functions.

Writing code in Visual Basic for Applications is challenging, so I chose a format that’s simple to parse and generate in VB, avoiding the need for a complex parser.

Since we control both ends of the ‘pipe’ in this case it makes sense to do it this way.

Built-in reporting tools because, while they’re quick to set up, they often hit limits and can’t combine data from different systems, like customer management and accounting.

By extracting data from the source systems and store it in separate 'shadow' tables for easy querying gives more power and flexibility.

It’s a first-principles method to give more flexibility and power when building reports.

Good reporting helps organizations communicate better, but it often falls short because teams don’t focus on the right things.

The Key Steps:

1. Decide What Matters: Work with your team to agree on the most important data to track.

2. Keep Improving: Give the team ways to see the results, learn what’s working, and adjust what they focus on.

When everyone is clear on what’s important and keeps learning from the data, reporting becomes a valuable tool for success.

Pivot Tables are a feature in spreadsheet tools like Excel that allow you to quickly summarize, analyze, and organize large amounts of data in a clear and dynamic way.

Why Are Pivot Tables Excellent for Presenting Data?

1. Flexibility: They let you rearrange, filter, and sort data dynamically to focus on specific insights.

2. Summarization: You can easily calculate totals, averages, counts, and other statistics without needing complex formulas.

3. Visualization: Pivot Tables make it simple to turn raw data into a readable, organized format, highlighting trends and patterns.

4. Interactivity: They allow users to drill down into data for deeper insights or adjust the presentation to answer specific questions.

In short, Pivot Tables transform complex data into easy-to-understand summaries, making them a powerful tool for data presentation and decision-making.

Getting data is all about making stories map to codes. Coding stories is difficult because it essentially means getting a team to negotiate and create a shared language to see common trends in the conversations they are having.

The same idea applies whether you are a medical team dealing with patients or company dealing with customers.

Over formalized traditional IT systems often make it difficult for a team to evolve a language to talk about the problems they are seeing.

One clever ‘hack’ is to use the practice of putting #hash_tags into notes. Almost all applications support free form text and thus you put hash tags into this text as a simple fast way to ‘code’ data.