At the PASS Summit this year, I attended a session by Michael Rys. In this session he introduced the concept of LETS as an approach to process data in the data lake. If you are familiar with data lake, then you will be familiar of having to apply a schema to the data held within. The LETS approach is purpose design for schematization.
Where ETL stands for Extract, Transform, Load or ELT stands for Extract, Load, Transform – LETS stands for Load, Extract, Transform, Store.
Data are Loaded into the data lake
Data are Extracted and schematized
Data are Transformed in rowsets
Data are Stored in a location, such as the Catalog in Azure Data Lake Analytics, Azure Data Warehouse, Azure Analysis Services, for analysis purposes.
I really like this approach as it makes sense for how data are handled in the data lake. It’s something that I will be advocating and using, and I hope you do too!
Pareto Charts in Power BI and the DAX behind them
The Pareto principle, commonly referred to as the 80/20 rule, is a concept of prioritisation.
Apr
Databricks: Cluster Configuration
Databricks, a cloud-based platform for data engineering, offers several tools that can be used to
Apr
AI Assistance in Microsoft Fabric
The exponential growth of Large Language Models (LLMs) couples with Microsoft’s close partnership with OpenAI
Apr
10 reasons why it’s worth the effort to understand the value of your data
“If leaders really want to create a data driven culture, the journey starts with them!
Apr
Content Safety in Azure AI Studio
Azure AI Content Safety is a solution designed to identify harmful content, whether generated by
Apr
Model Benchmarks in Azure AI Studio
In the constantly changing field of artificial intelligence (AI) and machine learning (ML), choosing the
Apr
Celebrating International Women’s Day: from Classroom to Code
As we celebrate International Women’s Day, I want to share my journey of breaking stereotypes
Mar
Pretty Power BI – Adding Pagination to Bar Charts
Good User Experience (UX) design is crucial in enabling stakeholders to maximise the insights that
Feb