Given that dbt is a data transformation tool, and given the following list of strings:
Business logic in DBT
Exploring Data Modelling with ChatGPT
OpenMetadata 1.0 Release
Parquet File Format: Everything You Need to Know
The Most Efficient Way to Organize Dbt Models
Designing Data Products
What Does dbt Actually Do?
Debug your dbt models like a superhero
Knowledge Graphs for Retail — Connecting People, Products and Platforms
How Unstructured and LlamaIndex can help bring the power of LLM’s to your own data
[Notes] A Crash Course in Causality, Week 5
10 Best Practices for writing SQL in Databricks
Graph Analytics: Determining important nodes in a graph using Neo4j
LlamaIndex and Deep Lake for Financial Statement Analysis
SHAP Happens: Unlocking the Secrets of Model Interpretability
Getting started with LangChain  — A powerful tool for working with Large Language Models
DBT Incremental Models: Advanced Examples — Part 1
Data Modelling Concepts everyone should know
Forecasting with Granger Causality: Checking for Time Series Spurious Correlations
Graphs with Python: Overview and Best Libraries
Getting Started With RDF model using GraphDB
Understanding Causal Effects
Benchmarking PySpark Pandas, Pandas UDFs, and Fugue Polars
Spark — debugging a slow Application
Comparing Grakn to Semantic Web Technologies — Part 1/3
Knowledge Graphs + Large Language Models = The ability for users to ask their own questions

For each string, classify the topic it is most strongly associated with.