The hottest Querying Substack posts right now

Comp titles in pitching are about giving an idea of the flavor of your book, not the recipe.
Non-fiction comp titles should be carefully chosen to avoid market saturation or irrelevance.
Avoid using overly complicated or grandiose comp titles, keep them simple for more success.

Consider how to use a semantic layer with streaming data to enhance efficiency and data processing.
Streaming data warehouses handle storage differently than batch data warehouses, keeping fresh data in-memory and reducing compute cost.
The semantic layer abstracts entities, attributes, and metrics, aiding in managing and optimizing queries on streaming data.

In a B-Tree based storage system like BoltDB, you need to be explicit about indexing and querying data, unlike relational databases that automatically handle indexing.
By maintaining bidirectional mappings between terms and targets, and incorporating priorities for sorting, you can efficiently retrieve and sort data without unnecessary overhead.
BoltDB enables indexing data by any criteria, offering flexibility in ranking objects and improving relevance in search results, while eliminating the overhead associated with SQL databases.

You can easily build KQL query templates for Azure services by using the Open Query option in the Azure portal.
The Open Query option in Azure Resource Graph Explorer auto-builds a KQL query for the specific service you're accessing.
Not all Azure services have the Open Query option, only those that utilize Azure Graph for information.

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A semantic data model includes pre-built calculations and relationships.
There are two main types of queries: lookup and aggregating.
In a semantic data model, querying involves selecting dimensions and measures, simplifying the process.

You can easily build KQL query templates for Azure services by utilizing the Open Query option in the Azure portal.
The Open Query option takes you to Azure Resource Graph Explorer and generates a KQL query specific to the service you're accessing.
Not all Azure services have this option, so be sure to check the All Services pane in Azure to see which services support KQL queries.

Stateful stream processors and streaming databases have different approaches in handling data ingestion and state persistence.
Stream processors require knowing and embedding state manipulation logic in advance, while streaming databases offer ad-hoc manipulation by consumers.
Stream processors are ideal for automated, machine-driven decision-making, while streaming databases cater to human decision-makers needing fast, ad-hoc data access.