#apache drill

A lot of people have tried out ChatGPT and other LLMs for code their code writing abilities. My theory was that the LLMs would be really good at writing code to do things they’ve seen before, but not so good at things that were completely new. I started my experiments by asking ChatGPT to write me a function in python to geolocate a phone number. ChatGPT 3.5 did a relatively poor job of this, so…

So You Want to Write a Book…

Well, we did it.  I finally finished the book that I had been working on with my co-author for the last two years.  I thought I’d write a short post on my experiences writing a technical book and getting it published.  I know many people think about writing books, and I’d like to share my experiences so that others might learn from lessons that I learned the hard way.  Overall, it was an…

El nuevo paquete de #actualización de #MapR pone su énfasis en las mejoras para la ejecución del código de #Python y el incremento de velocidad de Apache #Drill.

MapR, uno de los grandes desarrolladores de soluciones Big Data, ha anunciado el lanzamiento de nuevas versiones de MapR-DB, MapR Data Science Refinery y Apache Drill, todas ellas encaminadas a incrementar la velocidad de ejecución del código distribuido y simplificar el desarrollo de aplicaciones sobre Hadoop. Estas novedades están agrupadas en el MapR Expansion Pack (MEP) 4.1, una recopilación…

I can’t keep up with the amount of interesting software appearing. There’s a comparison of Apache Drill and Apache Spark here, and more about Spark and Hadoop here.

Drill supports a variety of NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. A single query can join data from multiple datastores. For example, you can join a user profile collection in MongoDB with a directory of event logs in Hadoop.