Educating ChatGPT on Knowledge Lakehouse

Educating ChatGPT on Knowledge Lakehouse

As the usage of ChatGPT turns into extra prevalent, I often encounter prospects and knowledge customers citing ChatGPT’s responses of their discussions. I like the keenness surrounding ChatGPT and the eagerness to find out about fashionable knowledge architectures resembling knowledge lakehouses, knowledge meshes, and knowledge materials. ChatGPT is a superb useful resource for gaining high-level insights and constructing consciousness of any know-how. Nevertheless, warning is important when delving deeper into a selected know-how. ChatGPT is educated on historic knowledge and relying on how one phrases their query, it might supply inaccurate or deceptive info. 

I took the free model of ChatGPT on a take a look at drive (in March 2023) and requested some easy questions on knowledge lakehouse and its parts. Listed here are some responses that weren’t precisely proper, and our clarification on the place and why it went incorrect. Hopefully this weblog will give ChatGPT a chance to study and proper itself whereas counting in the direction of my 2023 contribution to social good. 

I assumed this was a reasonably complete listing. The one key part that’s lacking is a typical, shared desk format, that can be utilized by all analytic companies accessing the lakehouse knowledge. When implementing a knowledge lakehouse, the desk format is a essential piece as a result of it acts as an abstraction layer, making it simple to entry all of the structured, unstructured knowledge within the lakehouse by any engine or software, concurrently. The desk format gives the required construction for the unstructured knowledge that’s lacking in a knowledge lake, utilizing a schema or metadata definition, to deliver it nearer to an information warehouse. A number of the standard desk codecs are Apache Iceberg, Delta Lake, Hudi, and Hive ACID.

Additionally, the information lake layer isn’t restricted to cloud object shops.  Many corporations nonetheless have large quantities of knowledge on premises and knowledge lakehouses are usually not restricted to public clouds. They are often constructed on premises or as hybrid deployments leveraging personal clouds, HDFS shops, or Apache Ozone. 

At Cloudera, we additionally present machine studying as a part of our lakehouse, so knowledge scientists get easy accessibility to dependable knowledge within the knowledge lakehouse to shortly launch new machine studying tasks and construct and deploy new fashions for superior analytics. 

I like how ChatGPT began this reply, however it shortly jumps into options and even offers an incorrect response on the characteristic comparability. Options are usually not the one manner of deciding which is a greater desk format. It depends upon compatibility, openness, versatility, and different elements that may assure broader utilization for various knowledge customers, assure safety and governance, and future-proof your structure. 

Here’s a high-level characteristic comparability chart if you wish to go into the main points of what’s obtainable on Delta Lake versus Apache Iceberg.


This response is a little bit harmful due to its incorrectness and demonstrates why I really feel these instruments are usually not prepared for deeper evaluation. At first look it might seem like an inexpensive response, however its premise is incorrect, which makes you doubt the whole response and different responses as effectively. Saying “Delta Lake is constructed on high of Apache Iceberg” is wrong as the 2 are fully completely different, unrelated desk codecs and one has nothing to do with the conception of the opposite. They have been created by completely different organizations to resolve widespread knowledge issues. 


I’m impressed that ChatGPT received this one proper, though it made a couple of errors with our product names, and missed a couple of which are essential for a lakehouse implementation.

CDP’s parts that assist a knowledge lakehouse structure embrace:

  1. Apache Iceberg desk format that’s built-in into CDP to supply construction to the large quantities of structured, unstructured knowledge in your knowledge lake.
  2. Knowledge companies, together with cloud native knowledge warehouse referred to as CDW, knowledge engineering service referred to as CDE, knowledge streaming service referred to as knowledge in movement, and machine studying service referred to as CML.
  3. Cloudera Shared Knowledge Expertise (SDX), which gives a unified knowledge catalog with computerized knowledge profilers, unified safety, and unified governance over all of your knowledge each in the private and non-private cloud.

ChatGPT is a superb software to get a high-level view of recent applied sciences, however I’d say use it fastidiously, validate its responses, and use it just for the attention stage of the shopping for cycle. As you go into the consideration or comparability stage, it’s not dependable but.

Additionally, solutions on ChatGPT preserve updating so hopefully it corrects itself earlier than you learn this weblog. 

To study extra about Cloudera’s lakehouse go to the webpage and if you’re able to get began watch the Cloudera Now demo.

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