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Why Choose Apache Iceberg for Data Interoperability?
Modern data platforms increasingly separate compute from storage, using object stores as durable data lakes while scaling processing engines. Traditional “data lakes” built on Parquet files and Hive-style partitioning have limitations around atomicity, schema evolution, metadata scalability, and multi-engine interoperability. Apache Iceberg addresses these challenges by defining...

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Using NotebookLM to help with understanding Oracle Analytics Cloud or any other product
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Handling Multi-Column Indexes in Pandas Dataframes
It’s a little annoying when an API changes the structure of the data it returns and you end up with your code breaking. In my case, I experienced it when a dataframe having a single column index went to having a multi-column index. This was a new experience for me, at this time, as I […]

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Creating Test Data in your Database using Faker
A some point everyone needs some test data for their database. There area a number of ways of doing this, and in this post I’ll walk through using the Python library Faker to create some dummy test data (that kind of looks real) in my Oracle Database. I’ll have another post using the GenAI in-database […]

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What a difference a Bind Variable makes
To bind or not to bind, that is the question? Over the years, I heard and read about using Bind variables and how important they are, particularly when it comes to the efficient execution of queries. By using bind variables, the optimizer will reuse the execution plan from the cache rather than generating it each […]

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