The enterprise insights are behind T-SQL and OracleSQL

Tl;DR

Can we move from langchain x pgsql to BAML x DB interaction to extract its insights?

Intro

This is all around:

Enterprise Insights

We are going to simulate these with containers.

Just as demostrated here with pgsql.

What if we could gain more control over whats going on, instead of relying on Langchain?

source datachat_venv/bin/activate
#Then, run the extraction script:
python3 baml-extract-schema.py --db-uri "postgresql://admin:securepassword@localhost:5432/umami_warehouse" --question "What are the most visited pages?"

That part, BAML can take care of.

But first, a look to few popular storage for enterprises.

TSQL

Transact SQL - The one that

Oracle SQL

DuckDB vs ClickHouse vs SQLite

Some people say that duckdb is the opposity of redshift.

The most common shorthand is: DuckDB is to Redshift (both columnar, OLAP) what SQLite is to PostgreSQL.

This is where it all connects in the D&A space:

mindmap
  root((Data Modeling))
    OLTP
      [Process: Transactional]
      Concept: ER Modeling
        )Normalized - 3NF(
        Goal: Data Integrity
        Low Redundancy
      Characteristics
        ACID Compliance
        Row-based Storage
        High Write Speed
        Low Latency - ms
      Tools
        PostgreSQL
        MySQL
        MariaDB
        SQLite
    OLAP
      [Process: Analytical]
      Concept: Dimensional Modeling
        )Star / Snowflake Schema(
        Goal: Query Performance
        High Redundancy
      Characteristics
        Fact & Dimension Tables
        Columnar Storage
        High Read Speed
        Complex Aggregations
      Tools
        ClickHouse
        DuckDB - Local
        Snowflake
        BigQuery
    The Bridge
      ))ETL / ELT((
      )Data Warehouse(

The Data Lakehouse is essentially the “modern evolution” that tries to delete the line between your Postgres (OLTP) and your ClickHouse/DuckDB (OLAP).

If OLTP is for writing and OLAP is for reading, the Lakehouse is for unifying.

mindmap
  root((Data Management))
    OLTP (Postgres/MariaDB)
      ::icon(fa fa-database)
      Focus: Live Transactions
      Format: Row-based tables
    
    OLAP (DuckDB/ClickHouse)
      ::icon(fa fa-chart-line)
      Focus: Fast Analytics
      Format: Memory/Columnar
      
    Data Lakehouse (The Hybrid)
      Concept: Best of both worlds
      Architecture
        Object Storage (S3/MinIO)
        Table Formats (Delta Lake/Iceberg)
      File Formats (The "Big Data" part)
        Avro: Fast Write/Streaming
        Parquet: Fast Read/Analytics
      Key Benefits
        ACID on top of Files
        One single source of truth
        Cheaper than Warehouse

Using BAML

I was testing BAML last year here.

Its not as popular as langchain, but still: https://github.com/BoundaryML/baml

Tt resonated a lot with the way langchain generates the query to the databases.

So could not resist to explore how to do a custom and more controlable solution around BAML.

What I wanted is this: and remove the langchain dependency

1. Extract Schema: Use SQLDatabase to get the metadata.
2. Generate & Separate: Call BAML and extract the three distinct fields (sql, explanation, promptRationale).
3. Execute: Run the query against the DB using the existing connection.
4. Final Output:
   • Print the Rationale (Why we did this).
   • Print the SQL (What we are running).
   • Print the Data Table (The results).

./datachat_venv/bin/baml-cli generate --from z-langchain2baml/baml_src

graph LR
    U[User Question] --> S[Python Script]
    S --> |Conn| DB[(PostgreSQL)]
    DB --> |Full Schema| S
    S --> |Context + Question| B[BAML Client]
    B --> |Prompt| L[LLM GPT-4o]
    L --> |JSON Result| B
    B --> |Typed Object| S
    S --> Out[Final SQL & Explanation]
    Out --> DB
    DB --> Result[Data Result]
    Result --> S

1. Activate Environment:

source datachat_venv/bin/activate
#pip install tabulate

2. ReCreate baml arficats:

baml-cli init #configure your baml_src
baml-cli generate #it generates baml_client, which the main python script uses

Working with BAML follows a very specific and reliable cycle:

1. Define (baml_src/): You define your data models (classes) and functions in .baml files.
   • database.baml: Defines the SqlResult structure and the GenerateSQL function.
   • clients.baml: Configures the LLM providers (e.g., GPT-4o).
2. Generate (baml_client/): Running baml-cli generate turns your BAML definitions into a type-safe Python client. You never edit this folder manually.
3. Call (Python): Your application script (baml-qna.py) imports the generated client and calls the functions just like regular Python methods.

How to Tweak the Logic (baml_src/)

If you want to customize the behavior, here is what each file does:

• database.baml: The Core Logic. This is where you define the class (what data the LLM returns) and the function (the actual prompt). If you want to change how the LLM thinks or what info it provides, edit this.
• clients.baml: The Model Settings. Here you define which LLM providers to use (OpenAI, Anthropic, etc.), which models (GPT-4o, Claude 3.5), and parameters like temperature or api_key environment variables.
• generators.baml: The Integration Settings. This tells BAML to generate a Python client and where to put it. You rarely need to touch this unless you are switching languages or changing the output path.

graph LR
    Start([Start]) --> Init[baml-cli init]
    Init --> Src[baml_src folder]
    Src --> Edit[Edit .baml files]
    Edit --> Gen[baml-cli generate]
    Gen --> Client[baml_client folder]
    Client --> Py[Python Script]
    Py --> Call[Client Calls LLM]
    
    subgraph "Manual Input"
    Edit
    end
    
    subgraph "Auto-Generated"
    Client
    end

3. Run Execution Script: provide your local pgsql connection

# From the project root
python3 z-langchain2baml/baml-qna.py --db-uri "postgresql://admin:securepassword@localhost:5432/umami_warehouse" --question "What are the most visited pages?"

By separating promptRationale, we gain:

• Debugging: Understand if the LLM misinterpreted the schema.
• Auditing: Keep track of why certain joins or filters were applied.
• User Trust: Show the user the “thinking process” before showing the data.

mindmap
  root((baml-qna.py))
    BAML
      Structured Output
      Type-safe Python Client
      Prompts & Schema Context
      Model Clients (GPT-4o)
    PostgreSQL
      Data Warehouse
      Schema Metadata source
      SQL Execution target
    General Python Logic
      Orchestration
      LangChain: Schema Extraction
      Pandas: Data Result Formatting
      SQLAlchemy: DB Connection

UI Wrapper

To make the solution sellable to enterprises: we need a UI.

And the good news is that we already vibe coded that: here.

Even with a related tech talk

Well, Ok.

Not only a UI, but a way to get plots and potentially dashboards done from natural language.

Or as people call this now: a generative BI solution.

Thats coming up next: z-baml-genbi

Imagine having such graphs generated directly from your QnA. No SQL, No dashboarding.

Conclusions

Another example of how we are moving From how to what and why.

Code is cheap now. Software isnt at least for now.

From this great post and video

The danger is now more on not to get distracted with the daily tool or workflow that gets released.

Go with whatever: cursor, antigravity, claude code, lovable, opencode, crush…

But just go and try.

The challenge is now the distribution / orchestration / marketing, which are other OpEx, not the coding thing.

Do you even know the audience? is it even listening?

Are you building sth for an empty room?

Time to go from builder to creator and finding people to care about your thing.

The related tech talk

If there was any doubt, if put together a ppt, taking into consideration these engagement points:

git clone https://github.com/JAlcocerT/selfhosted-landing
cd y2026-tech-talks/4-baml-db-insights

FAQ

A Recap on D&A for Interviews

1. Document Logic (The Planning)

• BRD (Business Requirements): Answers “WHY build this?” (The Vision & Goals).
• PRD (Product Requirements): Answers “WHAT are we building?” (The Features & Roadmap).
• FRD (Functional Requirements): Answers “HOW does it work?” (The Technical Logic & CRUDs).

2. Data Logic (The Analytics)

• Fact Tables: Answer “WHAT happened (and how much)?”
  • Examples: visit_count, revenue, quantity_sold.
• Dimension Tables: Answer “WHO / WHERE / WHICH context?”
  • Examples: customer_name, product_category, country_origin.

If you were to grow your Northwind project into a “Big Data” architecture:

Postgres (OLTP) handles your orders.

An ETL tool takes those orders and saves them as Avro files in a folder (Data Lake).

A “Lakehouse” tool (like Apache Iceberg or Delta Lake) converts those to Parquet.

DuckDB or ClickHouse then queries those Parquet files directly.

The Traditional BI Lifecycle (Hand-Crafted)

To demonstrate the value of Gen-BI, we must first understand the manual effort required to build a high-performance Power BI dashboard aligned with business goals.

The Bridge from Raw Data to Insights

Building a professional Power BI solution involves a rigorous 4-step process:

1. Data Acquisition (DL - Data Layer): Connecting to raw sources via SQL or Power Query. This is where the physical connection lives.
2. Dimensional Modeling (The Foundation): Implementing Kimball Methodology.
   • Creating a Star Schema with clear Fact tables (events) and Dimension tables (attributes).
   • This is the “Structural Truth” of the data.
3. Semantic Modeling (The Meaning): Adding the Power BI Layer.
   • Writing DAX Measures (e.g., Year-to-Date, Moving Averages).
   • Setting up Hierarchies (e.g., Category -> Product).
   • Optimizing performance via Aggregations and Column indexing.
4. Visualization (RL): Aligning with the Business Purpose.
   • Selecting visuals that answer specific BRD questions (e.g., “Which region is underperforming?”).
   • Tuning the UX for speed and clarity.

Document Logic (The Planning):

• BRD (Business Requirements): Answers “WHY build this?” (The Vision & Goals).
• PRD (Product Requirements): Answers “WHAT are we building?” (The Features & Roadmap).
• FRD (Functional Requirements): Answers “HOW does it work?” (The Technical Logic & CRUDs).

Data Logic (The Analytics):

• Fact Tables: Answer “WHAT happened (and how much)?”
  • Examples: visit_count, revenue, quantity_sold.
• Dimension Tables: Answer “WHO / WHERE / WHICH context?”
  • Examples: customer_name, product_category, country_origin.

In the world of data engineering, these concepts form the fundamental “fork in the road” between how we store data for action versus how we store it for analysis.

1. OLTP: The “Action” Layer

OLTP (Online Transaction Processing) systems are built to handle the daily operations of a business (e.g., swiping a credit card, updating a password, or placing an order).

• Mapping: ER Modeling Normalization
• The Goal: Data Integrity. You want to ensure that if a customer changes their address, you only have to update it in one place.
• Why Normalization? By breaking data into many small, related tables (usually 3rd Normal Form), you eliminate redundancy. This makes “writes” (INSERT, UPDATE, DELETE) lightning-fast and prevents data anomalies.

2. OLAP: The “Analysis” Layer

OLAP (Online Analytical Processing) systems are built for complex decision-making (e.g., “What were our total sales in the Northeast region vs. the Southwest over the last three years?”).

• Mapping: Dimensional Modeling Denormalization
• The Goal: Query Performance & Simplicity. Analysts don’t want to join 50 tables to get one report. They want the data pre-organized for speed.
• Why Denormalization? You intentionally bring data back together. While this creates “redundancy” (the same city name might appear 1,000 times), it drastically reduces the number of “joins” the database has to perform, making “reads” much faster.

3. Star vs. Snowflake (The OLAP Variations)

Within Dimensional Modeling, you have two primary ways to structure your “Dimensions”:

The best way to see the difference between Lloyd Tabb’s Looker and Microsoft’s Power BI is to look at how they handle a simple request: “Calculate our Gross Profit Margin.”

1. The Power BI Way (DAX)

In Power BI, you write a Measure. This is a formula that lives inside your report file. It looks very similar to an advanced Excel formula.

The Code (DAX):

Gross Profit Margin = 
DIVIDE(
    SUM(Sales[Revenue]) - SUM(Sales[Cost]), 
    SUM(Sales[Revenue]), 
    0
)

• Where it lives: Inside a specific .pbix file or a specific dataset.
• The User Experience: A user drags this “Measure” onto a chart. If they want to see it by “Year,” Power BI recalculates that formula for every year on the fly.
• The Problem: If another analyst creates a different report and writes (Total_Rev - Total_Cost) / Total_Rev manually, and forgets to handle the “divide by zero” error, your company now has two different versions of the same metric.

2. The Looker Way (LookML)

In Looker, you don’t write a formula for a specific report. You define the Logic in a central code file called LookML.

The Code (LookML):

view: orders {
  sql_table_name: production.orders ;;

  measure: total_revenue {
    type: sum
    sql: ${TABLE}.revenue ;;
  }

  measure: total_cost {
    type: sum
    sql: ${TABLE}.cost ;;
  }

  measure: gross_profit_margin {
    type: number
    sql: (${total_revenue} - ${total_cost}) / NULLIF(${total_revenue}, 0) ;;
    value_format_name: percent_2
  }
}

• Where it lives: In a centralized Git repository (like GitHub).
• The User Experience: The business user never sees this code. They just see a button labeled “Gross Profit Margin” in a browser.
• The Benefit: Because the logic is defined in one central place, it is impossible for two people to have different “Profit Margin” numbers. If you update the code, every dashboard in the entire company updates instantly.

Summary of the Difference

Lloyd Tabb’s big “Aha!” moment was realizing that data analysts should act more like software engineers—writing reusable, version-controlled code rather than building one-off spreadsheets.

To understand semantic modeling in Power BI, it helps to think of it as the “Translation Layer.”

While Dimensional Modeling is about how you organize data in the warehouse, Semantic Modeling is about how you present that data to humans.

1. Semantic vs. Dimensional Modeling

2. Approaches to Semantic Modeling in Power BI

There are three main “architectural” ways to handle this in the Microsoft ecosystem:

A. The “Golden Dataset” Approach (Centralized)

You create one single, highly polished Power BI file (.pbix) that contains the Star Schema, all DAX measures, and row-level security.

• How it works: You publish this to the Power BI Service. Other users then “Connect to Power BI Dataset” to build their own reports.
• Pro: One single source of truth.
• Con: Can become a bottleneck if one person manages the “Golden” model for the whole company.

B. The “Composite Model” Approach (Hybrid)

This allows you to connect to a “Golden Dataset” but also add your own local data (like an Excel file or a specific SQL table) on top of it.

• How it works: It uses “DirectQuery for Power BI datasets.”
• Pro: Flexibility for departments to customize data without breaking the central logic.
• Con: Can become complex to manage and slightly slower in performance.

C. The “External” Semantic Layer (Headless BI)

Instead of defining logic inside Power BI, you define it in a tool that sits between your warehouse (Redshift/Snowflake) and Power BI.

• Examples: dbt Semantic Layer, Cube, or AtScale.
• How it works: You write your metrics in YAML or SQL. Power BI connects to these tools as if they were a database.
• Pro: If you switch from Power BI to Tableau or Looker, your metrics stay exactly the same.

3. How to “Do” Semantic Modeling Right

Regardless of the approach, follow these rules to ensure your Power BI model is “Semantic”:

1. Hide the Plumbing: Hide all Foreign Key columns (the IDs used for joins). Users should only see descriptive names like “Product Name,” not FK_Prod_ID_99.
2. Explicit Measures: Never let users “Auto-Sum” a column. Create an explicit DAX measure: Total Sales = SUM(Sales[Amount]). This ensures the calculation is the same everywhere.
3. Folders and Descriptions: Use “Display Folders” to group measures (e.g., “Time Intelligence,” “Profitability”) and add descriptions to fields so users see a tooltip explaining what the data means.
4. Hierarchies: Create “Drill-down” paths (Country > State > City) so the model feels intuitive to browse.

If you are coming from the Lloyd Tabb/Looker world, Power BI feels different because the “Semantic” part is often bundled inside the report file. However, by using Power BI Datasets (Live Connection), you can replicate that “Looker Vibe” where the logic is centralized and separated from the visuals.

SQL vs Malloy

Malloy is a modern, open-source language for data modeling and querying, created by Lloyd Tabb (the founder of Looker) and a team at Google.

• https://github.com/malloydata/malloy
  • https://docs.malloydata.dev/documentation/user_guides/sql_experts1

MIT | Malloy is a modern open source language for describing data relationships and transformations.

If LookML was Lloyd Tabb’s first attempt to fix SQL, Malloy is his “clean slate” version.

It is designed to be for SQL what TypeScript is for JavaScript: a more powerful, typed, and modular way to write code that ultimately “compiles” down to standard SQL.

What makes Malloy special?

1. “Escaping the Rectangle”

Standard SQL forces data into “rectangles” (rows and columns). If you want to see an Order and all its Items, you have to “flatten” the data, which leads to duplicate rows and complex JOIN logic.

• Malloy’s Fix: It natively handles nested data and hierarchies. You can query a “Table within a Table” naturally, which makes it much easier to build dashboards that show a summary and a breakdown in the same view.

2. Reusable “Measures” (The Semantic Part)

In SQL, if you want to calculate “Gross Margin,” you have to write that math in every single query.

• Malloy’s Fix: You define gross_margin once in the model. From then on, you just reference it. If the definition changes, you update one line of code, and every query using it is fixed.

3. Automatic “Symmetric Aggregates”

One of the most dangerous things in SQL is the “Fan-out” (doubling your totals because of a Join).

• Malloy’s Fix: It has built-in logic that understands the relationships between tables. It automatically prevents “double counting” when you join a one-to-many relationship, so your sums are always correct without you needing to worry about DISTINCT hacks.

How to use it: because Malloy isn’t a database itself.

It is a compiler.

You write Malloy code, and it converts it into optimized SQL for:

• DuckDB (Native support, great for local Parquet files).
• BigQuery.
• PostgreSQL.
• Snowflake.

Is it still “Experimental”?

As of late 2023, the team released Malloy 4.0, which they officially called the “end of the experimental phase.”

It is now considered a stable, open-source language that is ready for production use, primarily through their excellent VS Code Extension.

Comparing Malloy to DuckDB, SQLite, ClickHouse, and Redshift is a bit like comparing a GPS system to different types of Engines.

Malloy is the Language/Interface (the GPS), while the others are the Databases/Engines (the car). You need one of each to actually “drive” your data.

1. The Big Distinction: Language vs. Engine

2. How Malloy interacts with them

Malloy doesn’t store data.

It connects to these engines and tells them what to do.

• Malloy + DuckDB: The most common “modern” combo. You use Malloy to write clean code, and DuckDB executes it instantly on your local files.
• Malloy + Redshift: Malloy replaces the “messy” SQL you would normally send to Redshift, making your enterprise queries shorter and more readable.
• Malloy + ClickHouse: Currently, Malloy has limited/community support for ClickHouse (it primarily focuses on BigQuery, Postgres, and DuckDB), but the logic is the same: Malloy writes the SQL so you don’t have to.

3. Comparing the “Engines” (The Databases)

4. Which one should you use for your project?

If you are following the Lloyd Tabb philosophy:

1. Use DuckDB as your engine if you want to play with your Northwind data locally on your computer. It’s free and requires zero setup.
2. Use Malloy as your language on top of DuckDB. This will allow you to define your “Gross Profit” or “Total Orders” logic once and reuse it across every chart.
3. Avoid SQLite for this specific task—it’s a great database, but it will struggle once you start doing the “Nested” and “Complex” analytical queries that Malloy is designed for.

Summary

• Malloy is the Smart Translator (created by the Looker founder). https://www.youtube.com/watch?v=pLeF4j_Irg4
• DuckDB is the Local Turbo-Engine.
• ClickHouse/Redshift are the Industrial Power-Plants.

A great video about Apache Iceberg:

Apache Kafka:

A Materialized table is a solution to a d&a problem.

A Materialized View is essentially a “cached” table. While a regular view is just a saved SQL query that runs every time you look at it, a materialized view calculates the result once and stores it physically on the disk as a real table.

Think of it like this:

• Regular View: A recipe. Every time you want a cake, you have to follow the recipe and bake it from scratch. (Fresh, but slow).
• Materialized View: A pre-baked cake. When you want a cake, you just grab it from the fridge. (Instant, but might be a day old).

Why is it useful?

1. Speed (Performance): If your query joins 10 huge tables and takes 2 minutes to run, a materialized view runs it once (e.g., at 3 AM) and saves the result. When you query the view at 10 AM, it’s nearly instantaneous.
2. Saves Money/Compute: You aren’t paying your database (like Snowflake or BigQuery) to recalculate the same complex math 100 times a day.
3. Simplicity: It hides complex “Big Data” messiness. Your data analysts just see a clean, fast table.

Do Power BI or Looker have them?

Technically, “Materialized View” is a database feature (Postgres, BigQuery, Snowflake, etc.), but both Power BI and Looker have their own versions to achieve the same result.

1. Looker: Persistent Derived Tables (PDTs)

Looker doesn’t call them materialized views; it calls them PDTs.

• How it works: You write the SQL for a table in LookML. Looker then creates a physical table in your database (e.g., Redshift or BigQuery) and updates it on a schedule.
• Refinement: Looker can even do “Incremental PDTs” where it only adds the newest rows instead of rebuilding the whole thing.

2. Power BI: Calculated Tables / Aggregations

Power BI handles this slightly differently depending on your setup:

• Import Mode: When you “Import” data, the entire dataset is essentially a materialized view. It’s stored in Power BI’s memory.
• Calculated Tables: You can use DAX to create a new table based on others. This table is “materialized” inside the Power BI file during the refresh.
• User-Defined Aggregations: You can tell Power BI to create a hidden, summarized version of a huge “DirectQuery” table. When a user asks for a high-level chart (e.g., “Sales by Year”), Power BI hits the “materialized” summary instead of the billion-row table.

Summary Comparison

When to use one?

• Use a Regular View if your data changes every second (real-time) and the query is simple/fast.
• Use a Materialized View (or PDT) if you have a massive dataset and your dashboards are starting to feel slow or “laggy.”

PGSQL vs The Rest

PGSQl vs RedShift

The short answer is: No, Redshift is not “Serverless PostgreSQL.” While Amazon Redshift was originally based on an older version of PostgreSQL (8.0.2), it has evolved into a fundamentally different animal designed for a completely different purpose.

To help clarify, here is how the two compare and where they fit in your tech stack:

1. Architectural Differences

The most critical difference is how they store and process data.

• PostgreSQL (Row-oriented): Designed for OLTP (Online Transactional Processing). It stores data in rows, which is perfect for small, frequent operations like “Update this user’s email” or “Insert a new order.”
• Redshift (Column-oriented): Designed for OLAP (Online Analytical Processing). It stores data in columns, which is vastly superior for scanning massive datasets to find trends, such as “What was the average revenue per month for the last five years?”

2. Is it Serverless?

Amazon offers a product called Redshift Serverless, which allows you to run analytical queries without managing clusters or nodes. However, this is still a Data Warehouse, not a general-purpose transactional database.

If you are looking for a Serverless PostgreSQL, the actual AWS equivalent is Amazon Aurora Serverless.

3. The “PostgreSQL” Connection

Redshift still feels like Postgres in a few ways:

• Interface: You can use standard Postgres JDBC/ODBC drivers to connect to it.
• Syntax: Many SQL commands are the same, though Redshift has removed many Postgres features (like triggers, indexes, and certain constraints) that don’t make sense for a massive data warehouse.
• Tooling: Most BI tools that support Postgres can also connect to Redshift.

What should you use?

• Use Aurora Serverless (PostgreSQL) if you are building an app or a website and want a database that scales up and down based on user traffic.
• Use Redshift Serverless if you have millions/billions of rows of data and need to run complex analytical reports or “big data” queries.

Neon vs D1

It is helpful to think of Neon and Cloudflare D1 as competitors in the “Serverless SQL” space, but they aren’t both PostgreSQL.

Here is the breakdown of how they compare to each other and to Redshift:

1. Neon: The True “Serverless Postgres”

Neon is a direct competitor to Amazon Aurora Serverless.

It is 100% PostgreSQL-compatible because it literally runs the Postgres engine, but it has a custom-built storage layer that allows it to “scale to zero” (shut down when not in use) and scale up instantly when a request comes in.

• Best for: Application backends, SaaS products, and development environments.
• Key Feature: Branching. You can “branch” your database like a Git repo, creating a perfect copy of your production data for testing in seconds.

2. Cloudflare D1: The SQLite Alternative

Cloudflare D1 is not PostgreSQL—it is built on SQLite.

While it is a “competitor” to Neon in the sense that it provides a serverless database for web developers, the underlying technology is different.

• Best for: Developers already using Cloudflare Workers. It is designed to be geographically close to your users (at the “edge”).
• The Trade-off: SQLite has a different SQL dialect than Postgres. If your app relies on Postgres-specific features (like JSONB or specific extensions), you can’t easily switch to D1.

Comparison Table

Are they Redshift competitors?

Generally, no. * Neon and Cloudflare D1 are built for speedy, small operations (e.g., “Log this user in,” “Show this profile”).

• Redshift is built for massive, heavy lifting (e.g., “Calculate total revenue across 1 billion rows for the last 3 years”).

If you tried to run Redshift-style “Big Data” queries on Neon, it would get very expensive and likely slow down.

Conversely, if you tried to use Redshift as your website’s primary database for user logins, your site would feel sluggish because Redshift isn’t optimized for millisecond-fast individual row lookups.

Which one should you choose?

• Choose Neon if you want a powerful, modern PostgreSQL experience without managing servers.
• Choose Cloudflare D1 if you are already in the Cloudflare ecosystem and need a simple, fast SQL database for a global app.
• Choose Redshift if you are doing Data Science or building a Business Intelligence dashboard.