One of the complaints I hear consistently from teams that evaluated Great Expectations a few years ago and haven't revisited it: "the API keeps changing." It's a fair criticism. GE has gone through significant interface changes since the early pandas-wrapper days — the DataContext
GPT-4 dropped last month and I've been running it alongside GPT-3.5 on the same data engineering tasks. The performance difference on syntax is negligible — both models generate correct PySpark syntax, both handle standard DataFrame operations, both know the Airflow operator API. On that dimension, the
Five Years of "They're Different Tools" Azure Synapse Analytics launched in 2019 with Synapse Pipelines embedded in it. From the beginning, the Microsoft messaging was: "ADF for enterprise data integration teams, Synapse Pipelines for analytics co-location." Two tools for different use cases. Different
Recently a client mentioned he wanted to run spark in a container for testing Databricks code locally. He Was working from the basic docker-compose yaml file provided by bitnami. That way, the containers would run on his local machine. The problem was accessing the cluster from the host machine. I..
If you’ve tried accessing the site over the past couple of weeks, you might have noticed it wasn’t up 100% of the time. That’s because I was upgrading my network infrastructure. Before the migration, I ran the Ubiquiti EdgeRouter 6p. This is a tremendous little quad-core router that ran completely..
Six months of heavy Copilot and ChatGPT use has produced a clear pattern: the model generates code that passes visual inspection and fails automated checks. Not because the model is careless — because the model doesn't know your standards. It knows Python. It knows PySpark. It does not know
When I began work with SQL Server, I used to run a single Windows server with a single instance of SQL Server 2000. I used this instance to test code and configurations separate from my employer’s servers. It helped me verify if a change could help and by how much...
Setting up Delta Sharing in Databricks is straightforward once you understand the diagram provided in the Azure Databricks documentation. Delta sharing is implemented as a part of Databrick’s Unity Catalog. Unity catalog is the official data governance solution for Databricks. You can consider it an
Recently I had a client that expressed interest in migrating their data warehouse from Azure SQL DB to Databricks. They weren’t looking to move due to any performance issues in Azure SQL DB. They were running on the Hyperscale offering. They were looking to share a common data architecture between..
Recently I was asked to look into Delta Sharing to learn what it’s all about and how it could be used. fter digging in a little bit, It appears that it’s a way to share data in parquet or Delta format. ou can build these shares on top of any...
Anti-pattern You will manually build your ingestion code when you first learn to ingest data into any new engine. This makes sense; you’re just getting started. You want to learn how the engine will read the data and then write it back again. You want to learn how to log...
Anti-pattern When we start developing in a new language or on a new platform, it’s easy to fall into the trap of monolithic design. The free flow from idea to code leads to a single blob of functional code. This leads to quick prototype code that meets the functional requirements....