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Databricks is a robust platform for processing and analyzing large volumes of data, supporting workloads that range from data engineering to data science and business analytics. One of the…
How about comparing the mistakes that sabotage your data pipelines to the villains who keep getting in the way of the superheroes’ mission? Picture your pipeline as the hero of the story, tasked with carrying the data to its final destination…
In the data world, building a bronze layer, which organizes raw data into an accessible structure for later transformations, has traditionally involved a lot of coding. Recently, however, I explored a…
In recent years, Databricks has established itself as a powerful platform, much like the Force that guides the Jedi on their missions, helping companies accelerate their data and analytics projects. Just as the union between the…
Databricks and Snowflake are two big names in cloud data solutions. Both platforms have been key to helping companies generate value from their internal and external data assets. Each platform…
There are several ways to pause, resume, or scale a Microsoft Fabric Capacity. Note that this applies only to F-SKUs in Azure, since Premium Capacity (P-SKU) can only be scaled.
In the dynamic landscape of information storage, the methodologies used to structure data play a crucial role in uncovering valuable insights for informed decision-making. Since the…
Effective management of data pipelines is key to ensuring continuous development, testing, and deployment processes. Integrating Git with Microsoft Fabric workspaces offers a…
Following up on the previous article, “Databricks Lakehouse Federation: An Alternative to Zero ETL?”, let’s explore some practical examples of combining data from different sources.
Released in December 2023, the Lakehouse Federation feature is currently in Public Preview, but it has enormous potential. In today’s article, I’ll explore some interesting perspectives, such as the ability to…
CI is the practice of automatically testing every code change, while CD deploys those changes to staging or production systems. In Azure Data Factory, CI/CD involves moving pipelines between…
Hello everyone, this week we’ll go over some best practices for a data ingestion pipeline:
