Exploring AI-Enhanced Development: My Experience with Codeium’s Windsurf IDE

AI-powered tools are transforming the way we code, and I recently got a chance to dive into this revolution with Codeium’s Windsurf IDE. My journey spanned two exciting projects: updating the theme of my mdsinabox.com project and building a Terraform provider for MotherDuck. Each project offered unique insights into the capabilities and limitations of AI-enhanced development. It should be noted that I did pay for the “Pro Plan” as you get rate limited really quickly on the free tier.

Project 1: Updating the Theme on mdsinabox.com

My first project involved updating the theme of my evidence.dev project. Evidence.dev is a Svelte-based app that integrates DuckDB and charting (via ECharts). Styling it involves navigating between CSS, Svelte, TypeScript, and SQL—a perfect storm of complexity that seemed tailor-made for Windsurf’s AI workflows.

I aimed to update the theme fonts to use serif fonts for certain elements and sans-serif fonts for others. Initially, I asked the editor to update these fonts, but it failed to detect that the font settings were managed through Tailwind CSS—a fact I didn’t know either at the time. We wasted considerable time searching for where to set the fonts.

the windsurf editor using cascade (right pane) to update the code

After a frustrating period of trial and error of pouring over project internals, and later reading documentation, I realized that Tailwind CSS controlled the fonts. Once I instructed the editor about Tailwind, it identified the necessary changes immediately, and we were back on track.

updated theme on the nba team pages

However, one gripe remained: Windsurf’s model didn’t include the build files for the Evidence static site, so I had to manually copy files to another directory for it to work. Additionally, debugging errors using the browser’s source view proved more efficient than relying on the editor. These limitations were a bit frustrating, but the experience highlighted the importance of understanding your project’s architecture and guiding AI tools appropriately. Access to a browser emulator would massively improve the debugging experience.

Project 2: Building a Terraform Provider for MotherDuck

The second project was sparked by a potential customer’s request for a Terraform provider for MotherDuck. While I was familiar with Terraform conceptually, I’d never used it before. With the recent launch of our REST API at MotherDuck, this felt like the perfect opportunity to explore its capabilities.

I instructed Windsurf, “I want to make a Terraform provider. Use the API docs at this URL to create it.” The editor sprang into action, setting up the environment and framing the provider. While its initial implementation of the REST API was overly generic and didn’t work, the tool’s ability to see the entire codebase end-to-end made it relatively straightforward to refine. I did have to interject and say “here is an example curl request that I know works, make it work like this” which was enough to get it unstuck.

intervening with cascade to tell it to change directory instead of run go init (again)

As an aside, observing it at times was quite comical as it seemed to take approaches that were obvious incorrect, especially when I was dealing with some invalid authorization tokens. It would almost say “well I trust that my handler has given me a valid token, so it must be something else” and just start doing things that were obviously not going to work.

Anyway, once the main Terraform file was built, I tasked the editor with writing tests to validate its functionality. It recommended Go, a language I had no prior experience with, and even set up the environment for it. Through a mix of trial and error and manual intervention (particularly to address SQL syntax issues like the invalid ‘attach if not exists’ statement in MotherDuck), I managed to get everything working. From start to finish, including testing, the entire process took around four hours—which seemed pretty decent given my experience level.

Conclusion

My experience with Codeium’s Windsurf IDE revealed both the promise and the current limitations of AI-enhanced development. The ability to seamlessly navigate between languages and frameworks, quickly scaffold projects, and even tackle unfamiliar domains like Go was incredibly empowering. However, there were moments of friction—misunderstandings about project architecture, limitations in accessing build files, and occasional struggles with syntax. Getting these models into the right context quickly is pretty difficult with projects that have lots of dependencies and overall my projects are fairly low complexity.

Still, it’s remarkable how far we’ve come. AI-enabled editors like Windsurf are not just tools but collaborative partners, accelerating development and enabling us to take on challenges that might have otherwise seemed impossible. As these technologies continue to mature, I can’t wait to see how I can use them to build even more fun projects.

LLMs in SQL? A real-world application to clean up your CRM data

Executive Summary

Firmographic data, like industry classification, employee count, and location, is notoriously messy and challenging to analyze. Modern databases offer a unique opportunity to clean and maintain this data: by leveraging an LLM, you can synthesize industry information for your accounts directly within a SQL query.

I recently took on exactly this challenge, so this isn’t an abstract solution. I’m using generic data below for data privacy reasons, but this is a real-world solution for a real-world problem I solved for a RevOps team.

h/t to Jacob Matson who demonstrated the underlying concept in MotherDuck:

Problem 1: Maintaining Firmographic Data is a Challenge

One dreaded question for analysts on the sales or revenue team: “quick qq: Can I get our sales broken out by industry?” This question often signals a few late nights spent cleaning up industry data to provide a rough answer to stakeholders who, meanwhile, are wondering, “Why is this taking so long? Don’t we already pay for firmographic data?”

How often does industry data look like this? Way too often.

The never-ending hamster wheel of maintaining firmographic data is a universal challenge. There’s a reason why Dun & Bradstreet took in $2.3B in revenue in 2023 providing this data: it’s not easy to aggregate or keep up to date.

Problem 2: Overwhelming Industry Categories

There is another visible failure point in the chart above: an excessive number of industry categories.

A hidden aspect of any industry cleanup project is narrowing down to a manageable list of 10–15 industry categories. Leave the intricate sub-industries for later—they’re harder to get right, demand 10 times the maintenance effort, and are rarely used. Only address them when there’s a strong business case to do so.

Solving Problem #2, First

Before we can get to the neat LLM-in-SQL work, we need a clear list of industries to ask the LLM to leverage. Usually you want to make this list in collaboration with business, but GPT-4o can generate a solid starting point as well. Here’s the final list I landed on after refining GPT-4o’s starting point to match my preferences::

  1. Technology
  2. Healthcare & Life Sciences
  3. Financial Services
  4. Retail & Consumer Goods
  5. Manufacturing & Industrial
  6. Education
  7. Public Sector & Government
  8. Energy & Utilities
  9. Real Estate & Construction
  10. Transportation & Logistics
  11. Hospitality & Recreation
  12. Professional Services
  13. Nonprofit & Social Impact
  14. Media & Entertainment

Keep in mind, this isn’t a trivial task if your company hasn’t tackled it seriously before. There are a near-infinite amount of industry breakdowns out there, so you need to give purposeful thought to how to group industries based on your own customer base. Your list will likely differ from` others.

Solving Problem #1 Using an LLM in SQL

Now that we have an industry list, it’s time to ask an LLM to help. You could always upload that CSV to ChatGPT and ask it there, but that requires manual steps (download csv / upload to GPT / download csv / upload to DWH) which could get in the way of automating this in the future.

But Snowflake gives us the option to ask an LLM these questions directly in SQL, directly using context from our CRM and other data. Let’s take advantage!

For the test, I found 25 companies in my local area and wrote down only their names. Some are national brands or chains, while others are small mom-and-pop shops and everything in between. This gives me a list like the one below:

OK great! Now we need to get the syntax for querying an LLM in Snowflake. There’s plenty of documentation covering the available LLMs and functions, and the specific function we want to use is Complete. I tested all of the LLMs in Snowflake for this task, and the most consistently accurate one with reasonable processing times was llama3.1-70b, so we’ll use that LLM for now.

Initial LLM code

And here were the results. They are … uh … about what one expects on a first try. Overly verbose.

Pls send help

OK, time to do some “Prompt Engineering” to get this correct, because getting a paragraph for a response is incredibly not helpful.

Specifically, I added a line saying: Only return the industry in each response, I want no other text or explanations.

This works well! Very limited extra text, but it isn’t really categorizing like I want it to. For instance, I consider the After Image Styling Salon to be a Professional Service, not a Retail/Consumer Good.

The LLM would benefit from additional context for each industry for decision making. This is exactly what I would do if giving this task to someone at work. Given ambiguity, both a person and an LLM make arbitrary decisions.

Here’s the adjusted query:

Adjusted query with details for each industry included

The context helped! Notably, I found additional improvement when I described specific decision points in the text. Once I told the prompt that places like Auto Repair and Beauty were Professional Services, it correctly categorized them. Lesson being, if you are sure a specific company type should be in a specific industry, be explicit.

Here’s the new output:

Is this perfect? No. but it’s a great start given that it’s working solely off company name and several of these are small or family-owned.

Green = Match, Red = Not a Match, Yellow = Close

Overall:

  • 16 of the 25 I consider correct or nearly correct (72%)
  • 7 of the 25 I consider incorrect (28%)

Not a bad hit rate for a few minutes of an LLM prompt, with room to improve!

I get why names like “Taco Book” threw the LLM for a loop. That’s a local taco shop, but with the name “Book” in the title and very little online about the company, I get why the coin flip ended up on the side of Retail & Consumer Goods.

What next?

I foresee potential to use additional CRM data to make this approach even more accurate. Company descriptions, headquarters data, sales emails, etc. If it can get to ~7/10 off of name alone imagine the possible improvement with more refined prompt engineering combined with extra data context would .

For example, if you had information in your CRM about Taco Book as a restaurant, the LLM would pick up the industry rather quickly.

This feels like an application of LLMs in SQL that’s only scratching the surface of what is possible. RevOps teams everywhere may soon have a tool to update that pesky CRM data, and surely there are other use cases for this across other domains.

I’d love to hear from you if you are working on something similar!

Defining Analytics Engineering

No one knows what it means, but it’s provocative. It gets the people going!

Chazz Michael Michaels

Context: This is the first of a series of posts looking at Analytics Engineering as I’ve lived and breathed it since 2014, largely in the SaaS industry. This is not meant to provide universal truths, but rather give insight into one way to make sense of the data roles around us and how analytics engineering fits into them.

Definition and why the role matters

Analytics Engineering is the process of transforming raw, unaltered data into well-structured, governed datasets that enable meaningful analysis and measurement of operations.

This is a fairly recent title, popularized by the data transformation tool dbt in this writeup by Claire Carroll. It didn’t really take off until the advent of cloud data warehouses

At first, this role doesn’t seem necessary. Can’t people just … export data into Excel and do analysis? Haven’t data analysts been working for decades without “Analytics Engineers” existing?

Two replies:

  1. Yeah, you can. Which works … until it doesn’t. But eventually scale becomes a problem as people waste time as Excel jockeying instead of whatever else they should be doing
  2. Data analysts and others were already doing this work, it just didn’t have a name yet or was tool-specific (e.g., data munging in SQL Server Integration Studio)

I found myself doing analytics engineering in my very first data job 10 years ago. My title was “data analyst” and I described myself that way, but my work was curating datasets for executives and other business teams to leverage for decision making. The prior Excel-export model was running into scaling issues and I was able to save everyone time by automating data availability.

Analytics Engineering is both old and new. People have been doing the work for a long time, even if the title is relatively new.

How Analytics Engineering fits within the data ecosystem

A simplified view of the three main pillars of the data world

Analytics Engineering fits roughly between two types of data roles many are familiar with: Data Engineering and Data Analytics/Science. The far-too-simple dividing line between the roles is:

Data Engineering: Brings raw data from disparate systems into a single data warehouse

Analytics Engineering: Transforms the raw data within the warehouse into something useful by cleaning, adding business logic, etc,

Data Analytics/Science: Leverages datasets created by analytics engineers to produce analyses, dashboards, models and more for stakeholders across the organization

Reality: Things aren’t that clear cut

Life is never as nice as a graphic of an over-simplified view of the world. Analytics Engineering is no different, it’s a messy role in the middle of messy data. Here’s a closer representation to how these three categories fit together:

I could make this a lot messier. The lines between data teams are incredibly blurry.

Data engineers typically do some analytics engineering. Data analysts typically do analytics engineering. Analytics engineers do some of both.

Which makes sense! With the roles ill defined at most companies (including Analytics Engineering missing entirely!), you’ll get a mishmash of people doing a variety of these tasks. Often times, people are indirectly incentivized to blur the lines in their role to get a task or two done.

What’s next?

We’ll dive deeper into the core value prop of an analytics engineer: transforming raw, unaltered data into well-structured, governed datasets that enable meaningful analysis and measurement of operations.

Running uv in dev containers & github actions

Over the weekend I spent some time getting uv running on mdsinabox.com to see what the hubbub was about. As it turns out, it was harder than expected because of permission issues inside of dev containers & github actions.

The existing documentation on the uv github repo as well as docker instructions from ryxcommar’s blog are not pointed at my scenario, which is running it in a docker image and in CI. This blog post is up so if others run into this issue, they can find it and add it to their set up as well.

How to run uv in a dev container

Since we are using system python with uv, we need to tweak some settings in our dev container. There are two changes to make: (1) run as root user, and (2) add ““chmod 777 /tmp to your postCreateCommand. In your devcontainer.json, add or modify the following lines:

"postCreateCommand": "chmod 777 /tmp",
"remoteUser": "root"

Then you can run `uv pip install --system -r requirements.txt in your devcontainer to add libraries as needed.

How to run uv in Github Actions

Now that we are using system python in our dev container, we also need to add one step to get the perms setup in CI. And that step is to add a python setup step in the Github action before running uv.

...    
    steps:
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.11'
...

Using the “actions/setup-python@v2” github action step will set up your runtime environment to properly interact with `uv pip install --system. Shout out to Charlie, of course, who very helpfully PR’d this into the mdsinabox repo.

Hope you find this useful! Please feel free to drop me a line on twitter @matsonj if you have any comments or feedback.

Analysis of Super Bowl Squares

Analysis & Narrative Written by Jacob Matson & Matt Levine, February 2024. [pdf]

Executive Summary

  • The variant of “Super Bowl Squares” that we analyzed is one in which the entrant is assigned a digit (0-9) for Team A’s final score to end with and a digit for Team B’s final score to end with 1
  • We compiled the final game scores from the 30 most recent NFL seasons to determine the frequency that each of the 100 potential “Squares” has been scored a winner
  • We then compared these frequencies with the publicly available betting odds offered on the ‘Super Bowl Squares – Final Result’ market by DraftKings Sportsbook to ascertain the expected value (EV) of each square
  • The analysis determined that all 100 of the available squares carried a negative expected value ranging from [-4.0% to -95.2%], and that buying all 100 squares would carry a negative expected value of approximately [-39.7%]

Our Methodology

  • We collected final game scores data from Pro Football Reference for the last 30 full NFL seasons, as well as the current NFL season through the completion of Week 17.  We also included all Super Bowl games that took place prior to 30 seasons ago
  • Games that ended in a tie were excluded since that is not a potential outcome for the Super Bowl
  • We calculated raw frequencies for each of the 100 available squares, and then weighted the Niners’ digit 55% to the digit represented by the winner of the historical games, and 45% to the digit represented by the loser of the historical games.  The [55% / 45%] weighting is reflective of the estimated win probability implied by the de-vigged Pinnacle Super Bowl Winner odds of ‘-129 / +117’ 2 3
  • The weighted frequencies were then multiplied by the gross payouts implied by DraftKings Sportsbook Super Bowl Squares – Final Result odds 2

Findings & Results

Raw Frequencies

Sample Size:  n = 8,162 games

  • Most frequent digit for losing team is ‘0’, occurring ~20.5% of the time
  • Most frequent digit for winning team is ‘7’, occurring ~15.5% of the time
Losing DigitWinning DigitFrequency
703.99%
033.97%
473.47%
073.32%
043.11%
Top 5 most frequent winning squares

Weighted Frequencies

Sample Size:  n = 8,162 games

  • Most frequent digit for Niners is ‘7’, occurring ~16.9% of the time
  • Most frequent digit for Chiefs is ‘0’, occurring ~17.4% of the time
Niners DigitChiefs DigitFrequency
073.69%
703.62%
743.30%
303.29%
473.27%
Top 5 most frequent winning squares

Expected Value by Square5

Niners DigitChiefs DigitExpected Value6
07(4.04%)
30(4.55%)
70(5.79%)
Top 3 Best Expected Value Squares
Niners DigitChiefs DigitExpected Value
22(95.19%)
55(77.80%)
25(74.08%)
Top 3 Worst Expected Value Squares

Raw Frequencies for Total Points u47.5

Sample Size:  n = 5,127 games

  • Most frequent digit for losing team is ‘0’, occurring ~25.2% of the time
  • Most frequent digit for winning team is ‘4’, occurring ~16.3% of the time
Loser DigitWinner DigitFrequency
035.68%
705.09%
074.76%
043.98%
743.39%
Top 5 most frequent winning squares

Raw Frequencies for Total Points o47.5

Sample Size:  n = 3,035 games

  • Most frequent digit for losing team is ‘4’, occurring ~20.5% of the time
  • Most frequent digit for winning team is ‘1’, occurring ~17.9% of the time
Loser DigitWinner DigitFrequency
476.10%
714.09%
413.39%
142.97%
082.93%
Top 5 most frequent winning squares

Selected Conclusions

  • Participating in the “Super Bowl Squares – Final Result” market on DraftKings Sportsbook has a substantially negative overall expected value, and likely has a negative expected value for every single one of the 100 available squares
    • This conclusion is logically continuous with the fact that the probabilities implied by DraftKings’ available odds sum to a total of ~165.9%; the market has substantial “juice” or “vig” overall
    • The available odds on relatively common squares (e.g., [0:7], [3:0], [7:0]) are much closer to “fair” vs. the rarest square outcomes (e.g., [2:2], [5:5], [2:5])
      • This strategy by DraftKings entices bettors to place a substantial dollar volume of wagers on the “almost fair” squares that have a reasonable chance of winning
      • Secondarily, it mitigates the negative financial impact to DraftKings that could arise in the event of a “black swan” final game score, such as [15 – 5] or [22 – 12]
    • A participant who has a bias towards a “high-scoring” vs. “low-scoring” game would place materially different value on certain square outcomes. Amongst the most pronouncedly:
    • If one believes the game will be “low-scoring”, he should greatly value the losing team’s digit ‘0’, which occurs in 25.2% of low-scoring games in the dataset, but only in 12.7% of high-scoring games in the dataset
    • If one believes the game will be “high-scoring”, he should greatly value the winning team’s digit ‘1’, which occurs in 17.9% of high-scoring games in the dataset, but only in 9.8% of low-scoring games in the dataset

Areas for Research Expansion

  • The most substantial limitation in our analysis is that the square frequencies are derived solely from historical game logs, as opposed to a Monte Carlo simulation model of this year’s Super Bowl matchup
    • As such, an analyst of this data is forced to balance (i) choosing the subset of games that are most comparable to the game being predicted, and (ii) leaving a sufficiently large number of games in the dataset to mitigate the impact of outlier game results
  • The variant of Super Bowl Squares that we analyzed (“Final Result”) is one of several commonly played variants, each of which has its quirks that would impact the analysis.  Perhaps the most common is the variant in which winning squares are determined by the digits in the score at the end of ANY quarter (as opposed to only at the end of the game)
  • Further analysis could yield interesting insights regarding how the value of a given square changes as the game progresses.  As an example, say that a team scores a safety (worth two points) in the 1st quarter of the game.  Which final square results would see the greatest increase in estimated probability?  Which would see the greatest decrease?  Are there any squares that would only be minimally impacted? 
  1. See ‘Appendix A’ for elaboration on the winning criteria for this variant. ↩︎
  2. Pinnacle Super Bowl Winner odds and DraftKings Sportsbook Super Bowl Squares – Final Result odds were both updated as of approximately 9 PM EST on February 9, 2024. ↩︎
  3. See ‘Appendix B’ for elaboration on the benefit and detailed methodology of weighting the raw square values relative to win probability. ↩︎
  4. Pinnacle Super Bowl Winner odds and DraftKings Sportsbook Super Bowl Squares – Final Result odds were both updated as of approximately 9 PM EST on February 9, 2024. ↩︎
  5. See ‘Appendix C’ for the DraftKings Sportsbook odds that were applied to each square in order to calculate expected value.  Odds were updated as of approximately 9 PM EST on February 9, 2024. ↩︎
  6. Parentheses reflect negative values.  For example, “(5.42%)” would reflect a negative expected value of 5.42%. ↩︎

Appendix A:  Winning Criteria

  • The variant of “Super Bowl Squares” that we analyzed is settled based on the final digit of each team’s score once the game has been completed
  • Both teams’ digits must match for a square to be deemed a winner.  As such, there are 100 potential outcomes, and there will always be exactly 1 victorious square out of these 100 potential outcomes.
  • A partial set of the final scores that would result in victory for an entrant with the square “Chiefs 7 – Niners 3” are as follows:
Chiefs 7 / Niners 3 Chiefs 7 / Niners 13Chiefs 7 / Niners 23Chiefs 7 / Niners 33
Chiefs 17 / Niners 3Chiefs 17 / Niners 13Chiefs 17 / Niners 23Chiefs 17 / Niners 33
Chiefs 27 / Niners 3Chiefs 27 / Niners 13Chiefs 27 / Niners 23Chiefs 27 / Niners 33

Appendix B:  Weighted Square Value

Weighting is reflective of the estimated win probability implied by the de-vigged Pinnacle Super Bowl Winner odds of ‘-129 / +117’ [55% / 45% ]

Key Insight: If the winner is known, the square “Winner 1:0 Loser” increases from 1.2% to 2.2% probability, roughly doubling.

Appendix C:  DraftKings Sportsbook Available Odds

Simple dbt runner

This post was co-written by Alex Noonan, Dani Mermelstein & Jacob Matson.

Introduction

In light of the recent price increases observed in products like dbt Cloud, coupled with the general pressure to maintain cost efficiency, we wanted to build a project template for running dbt in a production environment using GitHub Actions & AWS S3. Behold: the simple dbt runner!

The key features of this project are:

Scheduled Runs: You can set up automated dbt commands to run on a schedule, ensuring that your data modeling and transformation tasks are executed reliably and consistently.

Post-PR Merges: After merging a pull request into your project’s main branch, you have the option to trigger dbt runs. We recommend choosing either a full run or a state-aware run (which focuses only on modified models) to keep your project organized and efficient.

PR Commits Testing: To enhance your development process, dbt CI runs automatically on pull request commits. This helps you ensure that any changes you make are compatible and do not introduce unexpected issues into your data pipelines.

State Awareness: To utilize the state-aware workflow, it’s important to set up an S3 bucket to persist the manifest.json file. Additionally, Leveraging an S3 bucket to host the project documentation website, streamlines the documentation creation and adjustments within the development process.

Project and Environment Setup

1. Fork this repo and copy your whole dbt project into the project_goes_here folder.
2. Update your repository settings to allow GitHub Actions to create PRs. This setting can be found in a repository’s settings under Actions > General > Workflow permissions. It should look like this:

3. Go to the Actions tab and run the Project Setup workflow, making sure to select the type of database you want to set up – This opens a PR with our suggested changes to your profiles.yml and requirements.txt files. We assume if you’re migrating to self-hosting you need to add a prod target to your profiles.yml file, so this action will do that for you and also add the database driver indicated.
4. Add some environment variables to your GitHub Actions secrets in the Settings tab. You can see which vars are needed based on anything appended with ${{ secrets. in the open PR. Additionally, you need to define your AWS secrets to take advantage of state-aware builds – AWS_S3_BUCKET, AWS_ACCESS_KEY, & AWS_SECRET_KEY.
5. Run the Manual dbt Run to test that you’re good to go.
6. Edit the Actions you want to keep and delete the ones you don’t.

GitHub Actions Overview

Initially, we wanted to build out the project to a boilerplate CloudFormation stack that would create AWS resources to run a simple dbt core runner on EC2. We pivoted to using GitHub actions for cost and simplicity. GitHub gives you 2,000 free minutes of runner time. This works well for personal projects or organizations with sub-scale data, and if you need to scale beyond the free minutes, the cost is reasonable. Building with Github actions easily facilitates continuous integration, allowing you to automatically build and test data transformations whenever changes are pushed to the repository. 

To cover most simple use cases we built some simple actions that run dbt in production to automate key aspects of your data pipeline. 

Scheduled dbt Commands: You can set up scheduled dbt commands to run at specified intervals. This automation ensures that your data transformations are consistently executed, helping you keep your data up-to-date without manual intervention.

Pull Request Integration: After merging a pull request into the main branch of your repository, you can trigger dbt runs. This is a valuable feature for ensuring that your data transformations are validated and remain in a working state whenever changes are introduced. You have the flexibility to choose between a full run or a state-aware run, where only modified models are processed. This granularity allows you to balance efficiency with thorough testing.

dbt CI Runs: Pull requests often involve changes to your dbt models. To maintain data integrity, dbt CI checks are performed on pull request commits. This ensures that proposed changes won’t break existing functionality or introduce errors into your data transformations. It’s a critical step in the development process that promotes data quality.

State-Aware Workflow: The state-aware workflow requires an S3 bucket to store the manifest.json file. This file is essential for tracking the state of your dbt models, and by persisting it in an S3 bucket, you ensure that it remains available for reference and consistency across runs. Additionally, this S3 bucket serves a dual purpose by hosting your project’s documentation website, providing easy access to documentation related to your data transformations.

S3 Bucket and docs update

Hosting your dbt docs on S3 is a relatively simple and cost-effective way to make your documentation available. The process to generate the docs and push them to s3 happens during the “incremental dbt on merge”, “dbt on cron” jobs. The docs get generated by the “dbt docs generate” command and then are pushed to S3 by the upload_to_s3.py file. Adding this step to the workflow ensures the documentation is always current without much administrative complexity. 

We added a CloudFormation template that creates an S3 bucket that is public facing as well as an IAM user that can get and push objects to the bucket. You will need to generate AWS keys for this user and add them to your project environment variables for it to work. If you are unfamiliar with CloudFormation we added some notes to the README.

The ‘e’ in Hex is for ELT

quick note: the justification for doing this is worth like a 17 page manifesto. I’m focusing on the how, and maybe I’ll eventually write the manifesto.

General Approach

This specific problem is loading Point-of-Sale data for a vertical specific system into a database for analysis on a daily basis, but could be generalized to most small/medium data use cases where ~24 hour latency is totally fine.

The ELT pipeline uses Hex Notebooks and dbt jobs, both orchestrated independently with crons. dbt is responsible for creating all tables and handling grants as well as data transformation, while Hex handles extract and load from a set of REST APIs into the database. Hex loads into a “queue” of sorts – simply a table in Snowflake that can take JSON pages and some metadata. Conceptually, it looks like this.

Loading data with Hex

Since Hex is a python notebook running inside of managed infrastructure, we can skip the nonsense of environment management, VMs, orchestration, and so on and just get to loading data. First things first, lets add the snowflake connector to our environment.

Bash
!pip3 install snowflake-connector-python

Now that we have added that package our environment, we can build our python functions. I’ve added some simple documentation below.

Python
import requests
import os
import json
import snowflake.connector
from snowflake.connector.errors import ProgrammingError
from datetime import datetime

# login to snowflake
def snowflake_login():
    connection = snowflake.connector.connect(
        user=SNOWFLAKE_USER,
        password=SNOWFLAKE_PASSWORD,
        account=SNOWFLAKE_ACCOUNT,
        database=os.getenv('SNOWFLAKE_DATABASE'),
        schema=os.getenv('SNOWFLAKE_SCHEMA'),
        warehouse=os.getenv('SNOWFLAKE_WAREHOUSE'),
    )

    # print the database and schema
    print(f"Connected to database '{os.getenv('SNOWFLAKE_DATABASE')}' and schema '{os.getenv('SNOWFLAKE_SCHEMA')}'")

    return connection

# get the last run date for a specific endpoint and store from snowflake
def last_run_date(conn, table_name, store_name):
    cur = conn.cursor()
    try:
        # Endpoints take UTC time zone
        print(f"SELECT MAX(UPDATED_AT) FROM PROD_PREP.{table_name} WHERE store_name = '{store_name}';")
        query = f"SELECT MAX(UPDATED_AT) FROM PROD_PREP.{table_name} WHERE store_name = '{store_name}'"
        cur.execute(query)
        result = cur.fetchone()[0]
        try:
            result_date = datetime.strptime(str(result).strip("(),'"), '%Y-%m-%d %H:%M:%S').date()
        except ValueError:
            # handle the case when result is None or not in the expected format
            try:
                result_date = datetime.strptime(str(result).strip("(),'"), '%Y-%m-%d %H:%M:%S.%f').date()
            except ValueError:
                print(f"error: Cannot handle datetime format. Triggering full refresh.")
                result_date = '1900-01-01'
    except ProgrammingError as e:
        if e.errno == 2003:
            print(f'error: Table {table_name} does not exist in Snowflake. Triggering full refresh.')
            # this will trigger a full refresh if there is an error, so be careful here
            result_date = '1900-01-01'
        else:
            raise e
    cur.close()
    conn.close()
    return result_date

# Request pages, only return total page number
def get_num_pages(api_endpoint,auth_token,as_of_date):
    header = {'Authorization': auth_token}
    total_pages = requests.get(api_endpoint+'?page=1&q[updated_at_gt]='+str(as_of_date),headers=header).json()['total_pages']
    return total_pages

# Returns a specific page given a specific "as of" date and page number
def get_page(api_endpoint,auth_token,as_of_date,page_num):
    header = {'Authorization': auth_token}
    print(f"loading data from endpoint: {api_endpoint}" )
    page = requests.get(api_endpoint+'?page='+str(page_num)+'&q[updated_at_gt]='+str(as_of_date),headers=header).json()
    return page

# Loads data into snowflake
def load_to_snowflake(store_name, source_api, api_key, updated_date, total_pages, conn, stage_table, json_element):
    cur = conn.cursor()
    create_query = f"CREATE TABLE IF NOT EXISTS {stage_table} ( store_name VARCHAR , elt_date TIMESTAMPTZ, data VARIANT)"
    cur.execute(create_query)
    
    # loop through the pages
    for page_number in range(1,total_pages+1,1):
        response_json = get_page(source_api,api_key,updated_date,page_number)
        raw_json = response_json[json_element]
        raw_data = json.dumps(raw_json)
        # some fields need to be escaped for single quotes
        clean_data = raw_data.replace('\\', '\\\\').replace("'", "\\'")
        cur.execute(f"INSERT INTO {stage_table} (store_name, elt_date, data) SELECT '{store_name}', CURRENT_TIMESTAMP , PARSE_JSON('{clean_data}')")
        print(f"loaded {page_number} of {total_pages}")
    
    cur.close()
    conn.close()

# create a wrapper for previous functions so we can invoke a single statement for a given API
def job_wrapper(store_name, api_path, api_key, target_table, target_table_key):
    # get the updated date for a specific table
    updated_date = last_run_date(snowflake_login(), target_table, store_name)
    print(f"The maximum value in the 'updated_at' column of the {target_table} table is: {updated_date}")

    # get the number of pages based on the updated date
    pages = get_num_pages(api_path,api_key,updated_date)
    print(f"There are {pages} pages to load in the sales API")

    # load to snowflake
    load_to_snowflake(store_name, api_path, api_key,updated_date,pages,snowflake_login(),target_table, target_table_key)

Now that we have our python in place, we can invoke a specific API. It should be noted that Hex also has built-in environmental variable management, so we can keep our keys safe while still having a nice development & production flow.

Python
job_wrapper('store_name','api_url',AUBURN_API_KEY,'end_point_name','endpoint_unique_key')

To deploy this for more endpoints, simply update the api_url, end_point_name, and endpoint_unique_id. You can also hold it in a python dict and reference it as a variable, but I found that to be annoying when troubleshooting.

The last step in Hex is to publish the notebook so that you can set a cron job on it – I set mine to run at midnight PST.

Transforming in dbt

I am using on-run-start & on-run-end scripts in my dbt project to frame out the database, in my case, Snowflake.

SQL
on-run-start:
  - CREATE TABLE IF NOT EXISTS STAGING.sales_histories ( store_name VARCHAR , elt_date TIMESTAMPTZ, data VARIANT, id INT) ;

Now that data is in snowflake (in the RAW schema), we can use a macro in dbt to handle our transformation from pages coming from the API to rows in a database. But first we need to define our sources (the tables built in the on-run-start step) in YAML.

YAML
version: 2

sources:
  - name: SOURCE_NAME
    database: DWH_V2
    schema: STAGING
    tables:
      - name: sales_histories

Repeat for each API end point that you want to stage in your database.

Now consider the following model which transforms the JSON pages to rows:

SQL
{{ config(pre_hook="{{ merge_queues( 'sales_histories' , 'STAGING','ticketId' ) }}") }}

select 
    *,
    data:updated_at::datetime as updated_at
from {{ source( 'POSABIT', 'sales_histories' ) }}

Of course, the real magic here is in the “merge_queues” macro, which is below:

SQL
{% macro merge_queues( table_name, schema, unique_id )%}
    MERGE INTO {{schema}}.{{table_name}} t
        USING (
            with cte_top_level as (
            -- we can get some duplicate records when transaction happen as the API runs
            -- as a result, we want to take the latest date in the elt_date column
            -- this used to be a group by, and now is qualify
                select
                    store_name,
                    elt_date,
                    value as val,
                    val:{{unique_id}} as id
                from RAW.{{table_name}},
                lateral flatten( input => data )
                QUALIFY ROW_NUMBER() OVER (PARTITION BY store_name, id ORDER BY elt_date desc) = 1
            )
            select 
                *
            from cte_top_level
        ) s
        ON t.id = s.id AND t.store_name = s.store_name
        -- need to handle updates if they come in
        WHEN MATCHED THEN
            UPDATE SET t.store_name = s.store_name,
                t.elt_date = s.elt_date,
                t.data = s.val,
                t.id = s.id
        WHEN NOT MATCHED THEN
            INSERT ( store_name, elt_date, data, id)
            VALUES ( s.store_name, s.elt_date, s.val, s.id);

    -- truncate the queue
    TRUNCATE RAW.{{table_name}};
{% endmacro %}

A key note here is that snowflake does not handle MERGE like an OLTP database, so we need to de-duplicate it before we INSERT or UPDATE. I learned this the hard way by trying to de-dupe once the data was into my staging table, but annoyingly this is not easy in snowflake! So I had to truncate and try again a few times.

Now that the data is in a nice tabular format, we can run it like a typical dbt project.

Let me know if you have any questions or comments – you can find me on twitter @matsonj

Other notes

There are lots of neat features that I didn’t end up implementing. A noncomprehensive list is below:

  • Source control + CI/CD for the Hex notebooks – the Hex flow is so simple that I didn’t feel this was necessary.
  • Hex components to reduce repetition of code – today, every store gets its own notebook.
  • Using mdsinabox patterns with DuckDB instead of Snowflake – although part of the reason to do this was to defer infrastructure to bundled vendors.

Running MDS-in-a-box in Docker

I didn’t really set out to learn Docker when I started the MDS-in-a-box project, but as it turns out, Docker is quite a good fit. Part of this is because I desired to run the project in a Github Action, which is a very similar paradigm, and also because I have the notion (TBD) of running a bunch of simulations in AWS Batch. The goal of this post is to show a quick demo and then summarize what I learned – which frankly will also serve as a quick reference for me when I use Docker again.

Running the project in Docker

Once Docker Desktop is installed, building the project is trivial with two ‘make’ scripts.

make docker-build
make docker-run-superset

This takes a few minutes, but once its complete you have a full operational analytics stack running inside your machine.

The first rule of Docker

I learned this one the hard way, as I attempted to add evidence.dev to my existing container. The environment was only based on Python, and I needed to add Node support to it. I tried and tried to modify the dockerfile to get Node working – which leads to the first rule of Docker:

Thou Shalt Use An Existing Base Image

As it turns out, a quick googling revealed that there was already an awesome set of python+node base images. Shout out to this repo which is what I ended up using: Python with Node.js.

Now that I had the Docker container “working” – I needed to actually figure out which docker commands to use.

Docker Quick Reference

These are the commands that I learned and used over and over again as I triaged my way through adding another component to my environment. It is not exhaustive but designed to be a practical list of key commands to help you get started with Docker, too.

  1. docker build – use this to build the image defined in your working directory. In my project, I’m also giving it a name (-t mdsbox) and defining where to save it, so the full command is ‘docker build -t mdsbox .‘
  2. docker run – use this to run your image as a container once its built. You also pass in your environmental variables as part of docker run, so this command gets a bit long. Unfortunately, this is the first command that you see when learning Docker, which makes it look more imposing and scary than it actually is. The general syntax is ‘docker run <docker config> <CLI command>‘.
  3. docker ps – use this command to see which containers are running. This is so you know which containers to stop or to access (via docker exec) within the CLI.
  4. docker stop – this command stops a container. If you run a container from the terminal, you can’t stop it or exit like a process running in the terminal (i.e. with Ctrl+D), so you need to use ‘docker stop <container name>‘ instead!
  5. docker exec – this command lets you run a command on a running container. I found this be absolutely huge for debugging as you can get right into the terminal on your container and futz around with it. The command I used to access it is ‘docker exec -it <container name> /bin/bash‘ which drops you into the terminal.
  6. –publish – I’m including this Docker flag, since this is the flag you invoke to make your application visible on the network. Used in context, it looks something like this: ‘docker run –publish 3000:3000 <container name>‘. It is simply mapping port 3000 on the host to port 3000 on the container.

There are some notable exclusions, like ‘docker pull‘ but this reference is merely to help get started with MDS-in-a-box. By the way, you can check out the latest deployed version at www.mdsinabox.com!

As a note, I want to thank Pedram Navid & Greg Wilson for being my Docker shepherds – I definitely was stuck a few times and your guidance was incredibly helpful in getting things unstuck!

What Good Data Self-Serve Looks Like

I once was tasked with figuring out how to ‘democratize data’ for internal employees. No other instructions, solely a general pain point of ‘the data team is stuck doing ad-hoc tickets’ and ‘stakeholders want to get data on their own.’ After floundering for a while, I set out to figure out what data self-serve looked like at other companies. Seemed simple enough. But I quickly learned things aren’t that simple, and when are they for cross-functional data projects, anyway?

I want to share what I learned during an earnest effort to stand up data self-serve. I know others are struggling with this same project and its ambiguities and humongous scope. I spent time reading, thinking, attempting, failing, trying again, failing again, trying again and seeing success. Let’s dive in.

Executive Summary

Data rarely moves fast enough across companies to enable data-informed decisions. The data team is a bottleneck behind which many requests stack up in a queue. The data team drowns in questions and stakeholders become frustrated.  Long wait times ensue, forcing the business one of three decisions:

(1) Wait to make a decision
(2) Make a decision without data
(3) Departments hire their own data workers.

The data velocity problem is not reasonably solved through sheer volume of hiring. Data workers are expensive and hard to find. Instead, data teams typically pivot to enabling the business via self-serve and data democratization. Ideally, this unlocks the data team to focus on strategic analyses and initiatives and the business is freed to find the data they need without submitting a ticket.

Effective data teams must pivot away from (or avoid entirely!) taking tickets and into partnership with the organization, focusing on building scalable data solutions from which others can self-serve.

Data Self-Serve Definition

Data self-serve is notoriously hard to define. Any definition is inevitably specific to a company and point in time. The definition below is therefore intentionally generic:

Ability for any employee to quickly find and leverage the data and insights they need for their role without funneling through the Data Team

Good Self-Service Always Looks Different

(and bad self-service always looks the same)

Data self-serve done well, by its very nature, looks very different from company to company. The tools, processes, and organization structure supporting self-serve requires tailoring to the organization, rather than following a blueprint.

In contrast, data self-serve nearly always looks the same when done poorly. The data team is overwhelmed with tickets, business users can’t find data and strategic analyses do not happen. 

Put another way, bad data organizations tend to look the same, but working data organizations look very different from each other. 

This puts data teams in a liberating but uncomfortable position.  They’re able to freely define and create the self-service experience that their organization needs at this very moment, but at the same time there’s no blueprint for success.  This requires an interactive approach to find the best solution for their company.

The focus must be on the stakeholders’ needs, not the data team’s needs. Data teams have a habit of making data-self serve in their image instead of thinking about who they’re serving. The focus must be and will be squarely on the data self-serve experience of coworkers.

Typical Hurdles to Self-Serve

It’s easy to conjure up a world where someone types or speaks a question into a machine and gets the data they need. This is the ultimate data self-serve utopia and one on full display in the excellent sci-fi show The Expanse. The main characters routinely verbally ask computers questions like “Pull up every ship within X distance which was made by Y company and left Z destination in the last week.”

There are many hurdles before that sort of world is possible. Let’s cover a few of them:

Data operated as a service

Many data teams operate with a “receive a ticket, answer a ticket” mindset. This limits the team’s output into reactive short-term and smaller-scale asks and puts them as a bottleneck between the business and data. This is not an uncommon problem within the data industry. Data service teams are typically overstretched and struggle to answer all the questions coming their way. The business inevitably assumes the data team isn’t a strategic partner as they don’t seem to operate like one. If you operate like a service desk, you’ll be treated like one.

This team structure rarely scales. I recall a discussion with a C-Suite member who criticized the data team as “Getting me answers so late that I’ve forgotten my question by the time they reply.” Ouch.

In contrast, effective BI teams operate with a product-like mindset that focuses on scale and solutions. They partner closely with stakeholders to solve problems and prioritize ruthlessly based on business impact.

Data foundations not yet ready for self-serve

There is an immense amount of work required to get data ready for self-serve.  Just cleaning up a few raw data tables isn’t enough. Each part of the business (Sales, Marketing, Product, etc.) need different sets of data to answer their unique use cases.

Prepping data into the right shape requires close partnership and collaboration between the data team and its internal stakeholders.  This requires steps like data ingestion and transformation, implementing tooling like Git and dbt and having a team that can support the data lifecycle of a company.

Lack of data literacy

Data literacy, much like self-serve, is a tough term to nail down. This deserves its own discussion entirely, but for now let’s go with a typically squishy definition along the lines of “How well stakeholders can interact with and understand data.”

Training for data literacy is immensely difficult. Even if you have the world’s best data models and data marts and the Modern Data Stack™️ stakeholders will struggle to find value if they can’t grok the internal business data model or fall into common data pitfalls. This hurdle must be overcome no matter how well you do everything else.

Lack of data tools that enable self-serve

Typically there are two primary ways that an individual can self-serve data: SQL on a database or look at a data-team-created dashboard.  SQL is great for technical individuals but is not an option for the majority of employees.  Dashboards are usually widely available but lack customization.  Generally dashboards are a “you get what you get” type of experience, with little to no drill down capability and slow turnaround times from data teams for enhancement requests.

Data teams must provide other options for non-SQL savvy users to explore data in a more ad-hoc sense, leveraging curated/enriched tables created for their department. This can look as simple as providing access to enriched data in Excel or “Reverse ETL” where you send data back to source systems like Salesforce for direct consumption in those contexts.

Data privacy

This varies company to company, but data privacy comes into play depending on industry and company size. And whenever privacy is a factor, data access becomes more difficult. Typical lines in the sand are material non-public information (MNPI) when a company is publicly traded or personally identifiable information (PII) that only specific people should have access to.

Data self-serve almost always runs into data privacy concerns and the height of this hurdle (or wall…) will depend on the company.

But…What Does Good Self-Serve Look Like?!

Even though I described at length that good self-serve usually looks different, there are still some guiding principles to shoot for. These may not be universal and may change depending on your company’s data maturity, but they should be helpful.

(1) Focus initial efforts on specific departments/teams

One common bugaboo is an attempt to boil the ocean. The data team is already spread thin and pivoting everyone to self-serve for all departments at the same time will be too much. Instead, focus on a couple teams or departments with clear self-serve needs. Assign a specific analyst or two for the project who already understands a particular business domain and want to take on the challenge.

This both narrows the scope and increases the likelihood of close partnership with those teams. With partnership will come alignment on business value and understanding of pain points. Everyone wins.

(2) Create roadmap in partnership with stakeholders

Self-serve must necessarily look different from department to department. The needs for Finance are wholly different from Product, Engineering, Field, Legal or Marketing.  This is why a focus on self-serve and a dedicated BI partner is so crucial. Requirement gathering and roadmap creation must be done in close collaboration between BI and each department. Examples of requirements to gather include:

Examples of requirements to gather:

* Use cases
* Defining personas (technical/non-technical/etc)
* Tools needed
* Datasets
* Training/Enablement

(3) Build source of truth data marts

A data mart is a set of tables designed for ease of use by a department for their self-serve needs. These tables are specifically curated by the data team to make data easy to consume and understand for a particular department. 

Just providing individuals with access to the entire database is inevitably overwhelming. There could be hundreds of billions of data points across thousands of columns and hundreds to thousands of tables. Many analysts need a year to become comfortable with data at its most granular state. Expecting non-analysts to just hop in and find value isn’t reasonable.

To avoid this steep learning curve, a curated data mart enables self-service without overwhelming stakeholders.  This curated data mart must be built in close collaboration between the data partner and their stakeholders to find the sweet spot of “plenty of data” and “not confusing”.

Example: The sales team needs a few good tables such as Account, Opportunity and Task from which they can build most any report they need.

(4) Create an adoption and discoverability program

Data discoverability is an enormous challenge that must be tackled on several angles.  The existence of data marts alone is not enough to drive adoption if individuals do not know how they exist or do not know how to leverage them.

To drive adoption, efforts must include:

* Training / Onboarding sessions for all stakeholders
* Clear documentation for all data marts, tools available, key reports
* Weekly office hours
* Monthly & quarterly prioritization meetings
* Deprecation process to clean out old/unused data products

Parting Notes

There’s much more to write and I’ll follow up around defining internal stakeholder personas and choosing technologies that solve different aspects of data self-serve. For now, I hope the key message you took away from this is:

“I’m empowered to figure out how to best do self-serve at my organization.”

There’s an art to this task, and that’s why it’s so difficult to find anyone giving a blueprint. There really isn’t one. And you’ll never “arrive” at the conclusion of this project. You’ll just continually improve it, much like you do all your other data efforts. The fun is in the journey.

Modern Data Stack in a Box with DuckDB

TLDR: A fast, free, and open-source Modern Data Stack (MDS) can now be fully deployed on your laptop or to a single machine using the combination of DuckDB, Meltano, dbt, and Apache Superset.

This post is a collaboration with Jacob Matson and cross-posted on DuckDB.org.

Summary

There is a large volume of literature (1, 2, 3) about scaling data pipelines. “Use Kafka! Build a lake house! Don’t build a lake house, use Snowflake! Don’t use Snowflake, use XYZ!” However, with advances in hardware and the rapid maturation of data software, there is a simpler approach. This article will light up the path to highly performant single node analytics with an MDS-in-a-box open source stack: Meltano, DuckDB, dbt, & Apache Superset on Windows using Windows Subsystem for Linux (WSL). There are many options within the MDS, so if you are using another stack to build an MDS-in-a-box, please share it with the community on the DuckDB Twitter, GitHub, or Discord, or the dbt slack! Or just stop by for a friendly debate about our choice of tools!

Motivation

What is the Modern Data Stack, and why use it? The MDS can mean many things (see examples here and a historical perspective here), but fundamentally it is a return to using SQL for data transformations by combining multiple best-in-class software tools to form a stack. A typical stack would include (at least!) a tool to extract data from sources and load it into a data warehouse, dbt to transform and analyze that data in the warehouse, and a business intelligence tool. The MDS leverages the accessibility of SQL in combination with software development best practices like git to enable analysts to scale their impact across their companies.

Why build a bundled Modern Data Stack on a single machine, rather than on multiple machines and on a data warehouse? There are many advantages!

  • Simplify for higher developer productivity
  • Reduce costs by removing the data warehouse
  • Deploy with ease either locally, on-premise, in the cloud, or all 3
  • Eliminate software expenses with a fully free and open-source stack
  • Maintain high performance with modern software like DuckDB and increasingly powerful single-node compute instances
  • Achieve self-sufficiency by completing an end-to-end proof of concept on your laptop
  • Enable development best practices by integrating with GitHub
  • Enhance security by (optionally) running entirely locally or on-premise

If you contribute to an open-source community or provide a product within the Modern Data Stack, there is an additional benefit!

Trade-offs

One key component of the MDS is the unlimited scalability of compute. How does that align with the MDS-in-a-box approach? Today, cloud computing instances can vertically scale significantly more than in the past (for example, 224 cores and 24 TB of RAM on AWS!). Laptops are more powerful than ever. Now that new OLAP tools like DuckDB can take better advantage of that compute, horizontal scaling is no longer necessary for many analyses! Also, this MDS-in-a-box can be duplicated with ease to as many boxes as needed if partitioned by data subject area. So, while infinite compute is sacrificed, significant scale is still easily achievable.

Due to this tradeoff, this approach is more of an “Open Source Analytics Stack in a box” than a traditional MDS. It sacrifices infinite scale for significant simplification and the other benefits above.

Choosing a problem

Given that the NBA season is starting soon, a monte carlo type simulation of the season is both topical and well-suited for analytical SQL. This is a particularly great scenario to test the limits of DuckDB because it only requires simple inputs and easily scales out to massive numbers of records. This entire project is held in a GitHub repo, which you can find here: https://www.github.com/matsonj/nba-monte-carlo.

Building the environment

The detailed steps to build the project can be found in the repo, but the high-level steps will be repeated here. As a note, Windows Subsystem for Linux (WSL) was chosen to support Apache Superset, but the other components of this stack can run directly on any operating system. Thankfully, using Linux on Windows has become very straightforward.

  1. Install Ubuntu 20.04 on WSL.
  2. Upgrade your packages (sudo apt update).
  3. Install python.
  4. Clone the git repo.
  5. Run make build and then make run in the terminal.
  6. Create super admin user for Superset in the terminal, then login and configure the database.
  7. Run test queries in superset to check your work.

Meltano as a wrapper for pipeline plugins

In this example, Meltano pulls together multiple bits and pieces to allow the pipeline to be run with a single statement. The first part is the tap (extractor) which is ‘tap-spreadsheets-anywhere‘. This tap allows us to get flat data files from various sources. It should be noted that DuckDB can consume directly from flat files (locally and over the network), or SQLite and PostgreSQL databases. However, this tap was chosen to provide a clear example of getting static data into your database that can easily be configured in the meltano.yml file. Meltano also becomes more beneficial as the complexity of your data sources increases.

plugins:
  extractors:
  - name: tap-spreadsheets-anywhere
    variant: ets
    pip_url: git+https://github.com/ets/tap-spreadsheets-anywhere.git
# data sources are configured inside of this extractor

The next bit is the target (loader), ‘target-duckdb‘. This target can take data from any Meltano tap and load it into DuckDB. Part of the beauty of this approach is that you don’t have to mess with all the extra complexity that comes with a typical database. DuckDB can be dropped in and is ready to go with zero configuration or ongoing maintenance. Furthermore, because the components and the data are co-located, networking is not a consideration and further reduces complexity.

  loaders:
  - name: target-duckdb
    variant: jwills
    pip_url: target-duckdb~=0.4
    config:
      filepath: /tmp/mdsbox.db
      default_target_schema: main

Next is the transformer: ‘dbt-duckdb‘. dbt enables transformations using a combination of SQL and Jinja templating for approachable SQL-based analytics engineering. The dbt adapter for DuckDB now supports parallel execution across threads, which makes the MDS-in-a-box run even faster. Since the bulk of the work is happening inside of dbt, this portion will be described in detail later in the post.

  transformers:
  - name: dbt-duckdb
    variant: jwills
    pip_url: dbt-core~=1.2.0 dbt-duckdb~=1.2.0
    config:
      path: /tmp/mdsbox.db

Lastly, Apache Superset is included as a Meltano utility to enable some data querying and visualization. Superset leverages DuckDB’s SQLAlchemy driver, duckdb_engine, so it can query DuckDB directly as well.

  utilities:
  - name: superset
    variant: apache
    pip_url: apache-superset==1.5.0 markupsafe==2.0.1 duckdb-engine==0.6.4

With Superset, the engine needs to be configured to open DuckDB in “read-only” mode. Otherwise, only one query can run at a time (simultaneous queries will cause locks). This also prevents refreshing the Superset dashboard while the pipeline is running. In this case, the pipeline runs in under 8 seconds!

Wrangling the data

The NBA schedule was downloaded from basketball-reference.com, and the Draft Kings win totals from Sept 27th were used for win totals. The schedule and win totals make up the entirety of the data required as inputs for this project. Once converted into CSV format, they were uploaded to the GitHub project, and the meltano.yml file was updated to reference the file locations.

Loading sources

Once the data is on the web inside of GitHub, Meltano can pull a copy down into DuckDB. With the command meltano run tap-spreadsheets-anywhere target-duckdb, the data is loaded into DuckDB, and ready for transformation inside of dbt.

Building dbt models

After the sources are loaded, the data is transformed with dbt. First, the source models are created as well as the scenario generator. Then the random numbers for that simulation run are generated – it should be noted that the random numbers are recorded as a table, not a view, in order to allow subsequent re-runs of the downstream models with the graph operators for troubleshooting purposes (i.e. dbt run -s random_num_gen+). Once the underlying data is laid out, the simulation begins, first by simulating the regular season, then the play-in games, and lastly the playoffs. Since each round of games has a dependency on the previous round, parallelization is limited in this model, which is reflected in the dbt DAG, in this case conveniently hosted on GitHub Pages.

There are a few more design choices worth calling out:

  1. Simulation tables and summary tables were split into separate models for ease of use / transparency. So each round of the simulation has a sim model and an end model – this allows visibility into the correct parameters (conference, team, elo rating) to be passed into each subsequent round.
  2. To prevent overly deep queries, ‘reg_season_end’ and ‘playoff_sim_r1’ have been materialized as tables. While it is slightly slower on build, the performance gains when querying summary tables (i.e. ‘season_summary’) are more than worth the slowdown. However, it should be noted that even for only 10k sims, the database takes up about 150MB in disk space. Running at 100k simulations easily expands it to a few GB.

Connecting Superset

Once the dbt models are built, the data visualization can begin. An admin user must be created in superset in order to log in. The instructions for connecting the database can be found in the GitHub project, as well as a note on how to connect it in ‘read only mode’.

There are 2 models designed for analysis, although any number of them can be used. ‘season_summary’ contains various summary statistics for the season, and ‘reg_season_sim’ contains all simulated game results. This second data set produces an interesting histogram chart. In order to build data visualizations in superset, the dataset must be defined first, the chart built, and lastly, the chart assigned to a dashboard.

Below is an example Superset dashboard containing several charts based on this data. Superset is able to clearly summarize the data as well as display the level of variability within the monte carlo simulation. The duckdb_engine queries can be refreshed quickly when new simulations are run.

season summary & expected wins
playoff results

Conclusions

The ecosystem around DuckDB has grown such that it integrates well with the Modern Data Stack. The MDS-in-a-box is a viable approach for smaller data projects, and would work especially well for read-heavy analytics. There were a few other learnings from this experiment. Superset dashboards are easy to construct, but they are not scriptable and must be built in the GUI (the paid hosted version, Preset, does support exporting as YAML). Also, while you can do monte carlo analysis in SQL, it may be easier to do in another language. However, this shows how far you can stretch the capabilities of SQL!

Next steps

There are additional directions to take this project. One next step could be to Dockerize this workflow for even easier deployments. If you want to put together a Docker example, please reach out! Another adjustment to the approach could be to land the final outputs in parquet files, and to read them with in-memory DuckDB connections. Those files could even be landed in an S3-compatible object store (and still read by DuckDB), although that adds complexity compared with the in-a-box approach! Additional MDS components could also be integrated for data quality monitoring, lineage tracking, etc.

Josh Wills is also in the process of making an interesting enhancement to dbt-duckdb! Using the sqlglot library, dbt-duckdb would be able to automatically transpile dbt models written using the SQL dialect of other databases (including Snowflake and BigQuery) to DuckDB. Imagine if you could test out your queries locally before pushing to production… Join the DuckDB channel of the dbt slack to discuss the possibilities!

Please reach out if you use this or another approach to build an MDS-in-a-box! Also, if you are interested in writing a guest post for the DuckDB blog, please reach out on Discord!