Talking to investors in the space, it seems like most people in the data field understand the tool differences pretty clearly, but it's really hard to understan

Talking to investors in the space, it seems like most people in the data field understand the tool differences pretty clearly, but it's really hard to understand as an outsider. What angle are you coming at it from? While it's not in-depth, I thought these articles did a good job of clarifying what each tool is not. https://towardsdatascience.com/25-hot-new-data-tools-and-what-they-dont-do-31bf23bd8e56 https://towardsdatascience.com/25-hot-new-data-tools-and-wha... https://towardsdatascience.com/20-more-hot-data-tools-and-what-they-dont-do-46bc365bea74 https://towardsdatascience.com/20-more-hot-data-tools-and-wh... Most tools boil down to the following categories, with some overlapping into 2 or 3 of them. - Sourcing & Extraction (Fivetran, Stitch, Xplenty) - Testing & Alerting (Montecarlo, Toro Data, Anamalo, Great Expectations) - Cleaning & Transformation (dbt, Talend, Alteryx) - Storage (Bigquery, Redshift, Snowflake) - Delivery & Syncing (Census, Hightouch) - Analyzing & Querying (PopSQL, Jupyter, Count, Hex Tech), - Visualizing & Reporting (Looker, Tableau, Domo) - Model Development (Sagemaker, DataRobot, Algorithmia) - Orchestration & Automation (Shipyard, Airflow, Prefect)