The 2.8 version of DAX Studio introduced a feature to define a “Filter Dump” measure. The purpose of this measure is to analyze the filter context of every column of the model (or of only one table), creating a string with the name of the columns that have an active filter in the filter context followed by the items that are applied as a filter. In order to create that measure, right-click on a table name in the Metadata pane of DAX Studio and select one of the highlighted menu items.

if lines:

But (speculation alert!) I wonder if we might see a ‘deployment era’ for data just like we’re currently seeing in software. Instead of massive horizontal platforms covering entire information categories like people, companies, or events, I wonder if more targeted, bespoke, vertical- or application-focused data businesses will emerge — data for very specific niches. We shall see!

My databases class at UCLA wasn’t very challenging so I never truly understood the internals of databases. This always bothered me so I vowed to build a simple database from scratch to learn the fundamentals. MIT’s Database Systems course has their students implement a simple database from scratch and it provided me the perfect guidance in achieving my goal. The SimpleDB database I built has basic RDBMS features like a SQL query parser, transactions, and a query optimizer. This blog post provides an outline of SimpleDB’s architecture and implementation.

SELECT CUBE_NAME
, DIMENSION_CAPTION AS TableName
, DIMENSION_CARDINALITY AS RowCount
FROM $system.MDSchema_Dimensions
ORDER BY DIMENSION_CARDINALITY DESC

On closer inspection of the Particle class, you may notice that the color and sprite fields consume a lot more memory than other fields. What’s worse is that these two fields store almost identical data across all particles. For example, all bullets have the same color and sprite.

Either using Snowflake external table, or using Delta table in Databricks.

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