Flexible/changing csvs

Hi Vandana,

Thanks for the forum question and good luck with your Power BI development.

This is a great question—and you’re right to pause before accepting “fully flexible” extracts. What sounds convenient upstream can create fragility downstream in Power BI if not controlled.
Below is a practical, experience-based view of the trade-offs and how to handle this safely.
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1) What to be mindful of in Power BI
Schema changes can silently break transformations
Power Query (M) is schema-sensitive. Most steps reference column names explicitly.
Typical issues:
• If a column is renamed → transformations fail
• If a column is removed → refresh errors
• If column order changes → usually fine, but can break index-based logic
• If new columns appear → usually ignored unless you use dynamic logic
Example:
= Table.SelectColumns(Source, {"CaseID", "Status", "CreatedDate"})
If “Status” disappears, refresh fails.
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Data types and model layer impact
Power BI doesn’t just load data—it builds a semantic model.
If schema changes:
• New columns won’t automatically appear in visuals
• Measures depending on renamed fields will break
• Relationships may fail if key columns change
• Data types might be inferred differently (dangerous with CSV)
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CSV-specific risks
CSV is already a weak format compared to structured sources:
• No enforced schema
• No metadata (types, keys, constraints)
• Easy to corrupt with formatting inconsistencies
Adding schema drift on top increases risk significantly.
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Refresh reliability becomes harder to guarantee
Enterprise BI best practice values predictable refreshes.
Flexible schema introduces:
• Uncontrolled changes
• Higher chance of refresh failures
• More time spent troubleshooting data issues
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2) Is fully flexible / schema-changing good practice?
Short answer: No, not for production BI models.
It’s generally considered:
• Acceptable for exploratory or data lake ingestion layers
• Risky for curated BI datasets
• Poor practice for governed reporting environments
In mature BI architectures, you typically separate:
• Raw ingestion (flexible)
• Curated model (stable schema)
Your provider is essentially proposing to merge these layers, which creates instability.
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3) A better balance: flexibility with control
You’re already thinking in the right direction. The goal is:
Allow evolution without breaking stability
Here are proven approaches:
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A) Contract-based schema (strongly recommended)
Define a data contract with the provider:
Allowed:
• Adding new columns
Not allowed:
• Renaming existing columns
• Removing columns
• Changing data types
This gives you:
• Backward compatibility
• Safe evolution
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B) Split datasets: raw vs curated
Pattern:
1. Raw layer (flexible)
o Load CSV as-is
o Minimal transformations
o Schema can drift
2. Curated layer (stable)
o Reference only known columns
o Apply business logic
o Feed reports
This way:
• New columns don’t break anything
• You opt-in to new fields deliberately
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C) Defensive Power Query design
You can make your queries more resilient.
Use column existence checks
= Table.SelectColumns(Source, {"CaseID", "Status"}, MissingField.Ignore)
Avoid hard failures
• Use try … otherwise
• Use Record.HasFields for dynamic logic
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D) Explicit schema enforcement step
At some point, “freeze” the structure:
ExpectedColumns = {"CaseID", "Status", "CreatedDate"},
CleanedTable = Table.SelectColumns(Source, ExpectedColumns, MissingField.Ignore)
This:
• Protects your model
• Ignores unexpected columns
• Prevents schema creep
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E) Change notification process
Even with flexibility, you need governance:
Ask the provider for:
• Notification when fields are added
• Schema change log
• Versioning if possible
Without this, changes become invisible until something breaks.
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F) Consider Parquet or structured alternatives (if possible)
If you can influence it:
• Parquet or database export is far better than CSV
• Supports schema more reliably
• Better performance and typing
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4) Practical recommendation for your situation
Given everything you’ve described, the best position to take is:
1. Accept adding new columns automatically
2. Reject uncontrolled schema changes (rename/remove/type change)
3. Implement a stable curated model in Power BI
4. Treat the CSV as a semi-raw input, not your reporting layer
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5) Simple rule of thumb
Think of it like this:
• Flexibility is good at the edges (data ingestion)
• Stability is essential at the core (reporting model)
Your provider is optimizing for convenience. You need to optimize for reliability and trust in reporting.
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6) One-line answer you can take back to the vendor
You could say:
“We’re happy for new fields to be added automatically, but we require backward-compatible schema—no renaming, deletion, or data type changes to existing fields, and advance notice of changes.”
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Below is a robust, production-style Power Query (M) template for safely ingesting flexible / changing CSV files from a SharePoint folder.
This pattern is designed to:
• Tolerate new columns appearing
• Avoid breaking when columns are missing
• Enforce a stable schema for your model
• Handle multiple files (latest file pattern)
• Keep transformation logic resilient
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Here is an example of a robust Power Query template that safely handles evolving CSVs without breaking your dataset.

Power Query Template: Safe CSV Ingestion
1) Parameters (optional but recommended)
Create a parameter for your folder path:
FolderPath = "https://yourtenant.sharepoint.com/sites/yoursite/Shared Documents/Data/"
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2) Get latest CSV from SharePoint folder
let
Source = SharePoint.Files(FolderPath, [ApiVersion = 15]),

FilterCsv = Table.SelectRows(Source, each Text.EndsWith([Name], ".csv")),

SortByDate = Table.Sort(FilterCsv, {{"Date modified", Order.Descending}}),

LatestFile = SortByDate{0},

FileContent = LatestFile[Content]
in
FileContent
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3) Read CSV safely
let
Csv = Csv.Document(
FileContent,
[
Delimiter = ",",
Encoding = 65001,
QuoteStyle = QuoteStyle.Csv
]
),

PromoteHeaders = Table.PromoteHeaders(Csv, [PromoteAllScalars = true])
in
PromoteHeaders
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4) Standardise column names (optional but helpful)
let
CleanNames = Table.TransformColumnNames(
PromoteHeaders,
each Text.Trim(Text.Clean(_))
)
in
CleanNames
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5) Define your expected schema (core stability layer)
This is the key control step.
ExpectedColumns = {
"CaseID",
"Status",
"CreatedDate",
"ClosedDate",
"Owner"
}
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6) Safely select only known columns
This avoids breaking if columns are missing and ignores unexpected new ones.
let
SelectedColumns = Table.SelectColumns(
CleanNames,
ExpectedColumns,
MissingField.Ignore
)
in
SelectedColumns
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7) Apply data types defensively
Use try ... otherwise so refresh won’t fail.
let
Typed =
Table.TransformColumns(
SelectedColumns,
{
{"CaseID", each try Text.From(_) otherwise null, type text},
{"Status", each try Text.From(_) otherwise null, type text},
{"CreatedDate", each try Date.From(_) otherwise null, type date},
{"ClosedDate", each try Date.From(_) otherwise null, type date},
{"Owner", each try Text.From(_) otherwise null, type text}
}
)
in
Typed
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8) Optional: Capture new/unexpected columns (for monitoring)
This is a best practice for governance.
let
AllColumns = Table.ColumnNames(CleanNames),

NewColumns = List.Difference(AllColumns, ExpectedColumns),

NewColumnsTable = Table.FromList(NewColumns, Splitter.SplitByNothing(), {"NewColumn"})
in
NewColumnsTable
You can:
• Load this as a separate table
• Use it for alerting or QA
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9) Final output (curated table)
let
Final = Typed
in
Final
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What this template protects you from
Safe from:
• New columns appearing in CSV
• Missing columns (no refresh failure)
• Dirty column names
• Type conversion errors
• File replacement in SharePoint
Still requires control:
• Column renames (will behave like missing columns)
• Business logic changes
• Key field integrity
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Recommended architecture with this template
Use two layers:
Raw query (keep everything)
• Loads full CSV
• No schema enforcement
• Hidden from report users
Curated query (the template above)
• References raw query
• Applies ExpectedColumns logic
• Feeds model
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Optional enhancement: Add a schema check flag
You can add a simple warning flag:
let
MissingCols = List.Difference(ExpectedColumns, Table.ColumnNames(CleanNames)),
SchemaStatus =
if List.Count(MissingCols) > 0
then "Missing columns: " & Text.Combine(MissingCols, ", ")
else "OK"
in
SchemaStatus
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Key takeaway
This pattern gives you:
• Flexibility at ingestion (provider can add fields)
• Stability in your model (you control what is used)
• Visibility of changes (optional monitoring step)
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Kind regards

Jens Bonde
Microsoft Office Specialist Trainer

Tel: 0207 987 3777
STL - https://www.stl-training.co.uk
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