Gist cb8a0ff17a4daca84e707ff4148e4632

Gist 47d998760fa7271ac3157ce7d9555612

This example demonstrates how to query and load data from external sources using Power Query's OleDb.Query function, specifically connecting to a local CSV file and an XML catalog. The code shows two approaches: one using Microsoft's ACE OLEDB provider to read a delimited text file, and another using the MSDAOSP provider with MSXML2.DSOControl to access XML data. The visualization highlights the raw query definitions rather than rendering graphical output, focusing on the data-acquisition layer. Both queries reference file paths on the local system, with the CSV query specifying the file directory and the XML query pointing to a catalog file.

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Gist f90e0dcc12099646652c5dcbc8400a24

This example visualizes the live query log of a Power BI tabular model by monitoring the Analysis Services workspace’s in-memory engine. It shows the most recent command and its start time, appending each new entry to a running history. The code uses the Power Query M language, calling `AnalysisServices.Database` to query the `$SYSTEM.DISCOVER_SESSIONS` DMV and `Folder.Files` to locate the `msmdsrv.port.txt` file that defines the dynamic port. The rendered output is a table combining previous and new log records via `Table.Combine`, with no external charting library involved.

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Gist 8bb4970e2700b40a3dce1d785654d907

This visualization uses a force-directed graph to map the relationships between identifiers extracted from a Power Query M codebase. The data originates from a gist by Hugoberry, showing dependencies among functions, modules, and error-handling patterns. The rendering relies on D3.js’s `forceSimulation` with custom link and node positioning, while each node’s label is drawn using SVG text. The code specifically highlights groupings like `TableAction.*`, `ValueAction.*`, and `Pattern.*`, revealing hierarchical clusters of related API calls. The graph’s layout is animated via D3’s tick handler, and the visible node sizes suggest a metric like degree centrality, though the exact encoding is not explicit in the snippet.

58% match

Gist 7fcfddfdb64357791cf2524677924c12

This visualization parses and displays the hierarchical structure of a DAX query plan, showing operators and their parameters as an indented tree. The code uses the Moo lexer to tokenize the plan text, then a custom parser builds a tree from indentation and operator tokens. It renders the tree using D3.js force simulation, with nodes representing operators and edges connecting parent-child relationships, while leaf nodes display property values like types and ranges. The visualization makes the query plan’s execution flow and data lineage immediately apparent.

Exporting data out of Power Query into SQL Server via OleDb route

This example demonstrates a direct method for inserting data from Power Query into a SQL Server database by leveraging an OleDb connection. The code constructs a dynamic INSERT statement using `Text.Format`, embedding a numeric value and a string literal into the SQL query. It connects via the `OleDb.DataSource` function with a provider string for SQLOLEDB, targeting a local server and the `QueryPower` database. The approach shows how to bypass more complex data flow layers and write raw SQL directly through the OleDb interface, though it relies on string concatenation for parameter values.

Gist bae6758b4e3645b55f022e6432f29458

This visualization demonstrates the interaction between R and PowerShell, showing how a system call from an R environment triggers a PowerShell script that manipulates data. The code uses the `system2` function to execute a PowerShell command with a specified file path, passing arguments to run `coolScript.ps1`. The example highlights the practical integration of two scripting environments, where R serves as the orchestrator for external process execution. The rendering relies on the command-line interface rather than graphical libraries, focusing on the workflow of invoking and managing external scripts from within an R session. The data flow is implicit in the script's execution, emphasizing interoperability over visual output.

Gist 4905f6e091305c6a3ff3c1fe2c0a50b2

This example visualizes the structure of a DAX query plan by parsing a text-based log file into a hierarchical tree. It uses the Moo lexer to tokenize lines, identifiers, column references, and operator types, then builds a nested representation of the plan. The visualization likely renders this tree to show the flow of logical and physical operations, with each node displaying its operator type and associated properties such as filters, joins, or spooling details. The code defines custom token patterns for DAX-specific syntax, including column references like `'Table'[Column]` and measure references like `[Measure]`, and parses them into structured objects for display.

Gist 44e95c5ae2e212c1b41485ea7e31e0c4

This example shows a Power Query function that connects to an embedded Power BI Desktop tabular model and retrieves session information from the `$SYSTEM.DISCOVER_SESSIONS` dynamic management view. The function first locates the most recently accessed user profile folder, reads the `msmdsrv.port.txt` file to obtain the local port number, then uses that port to list available databases via the `AnalysisServices.Databases` API. It selects the first database and executes the provided DAX query using `AnalysisServices.Database`. The code demonstrates dynamic file system traversal, port discovery, and parameterized Analysis Services queries within the Power Query M language.