Bridging Language Divides

2.0 Bridging language divides

Everyone is weary of fighting over programming languages. Who among us hasn’t hopped online and seen clickbait-y headlines disguised as healthy debates like:

• “Is [ insert programming language ] dead?”
• “Why does anyone use [ insert programming language ]?”
• “Which programming language is better: [ x ] or [ y ]?”

Good times.

But the “which language wore it best” debates are not confined to social media. They also play out in enterprises over and over again. For modern data teams, fighting over languages feels like it comes with the territory.

In these battle royales, teams of people rally around their language of choice. Depending on whether or not a new tool supports your team’s language of choice is a big reason for whether or not you adamantly support the adoption of a new tool, or become a zealot fighting tooth and nail to block a new tool from making its way into the system.

Generally, there are a lot of people who talk about R versus Python like it's a war that either R or Python is going to win. I think that is not helpful because it is not actually a battle. These things exist independently and are both awesome in different ways.

- Hadley Wickham, Chief Scientist @ Posit (née RStudio)

When solving real-world problems, I rely on Python's integration with other languages, like R or Julia. Each language provides helpful strengths. I view Python's flexibility to combine with these languages as a "win-win".

- Carol Willing, Python Core Developer

For data people like Data Engineers, Data Analysts, Data Scientists, and Analytics Engineers, their programming language of choice is a very conscious decision for very good reasons:

1. It helps them do what they need to do. Many people are fluent in more than one programming language, but have preferences (some context-dependent), when given the choice.
2. It gives them access to tools that “fit their hand” (h/t JD Long). Increasingly, dataframe APIs meet that need because they provide the right verbs for expressing operations with tabular data structured as rows and columns. In short, they encourage “thinking while coding.”

But the choice of a user interface (UI), whether it is an open source programming language or a specialized dataframe API, usually involves more than pure functionality.

Typically when you ask a programmer to explain to a lay person what a programming language is, they will say that it is how you tell a computer what to do. But if that was all, why would they be so passionate about programming languages when they talk among themselves?

In reality, programming languages are how programmers express and communicate ideas - and the audience for those ideas is other programmers, not computers. The reason: the computer can take care of itself, but programmers are always working with other programmers, and poorly communicated ideas can cause expensive flops.

- Guido van Rossum, Python's BDFL (benevolent dictator for life)

So programming languages are a bridge between programmers. But they are also a bridge for each programmer to a broader community, with many considering themselves community taught (h/t to Caitlin Hudon.)

Python is a community that happens to have a programming language attached to it.

- Dan Callanan, PyCon 2018 keynote

Using a language or other UI that you like, and that gives you a strong sense of identity within a community, can feel pretty magical. For teams, whichever type of UI they prefer, the upside of using the same one is that they can stay in their swimlanes. Using the same UI helps teams swim faster and go further by limiting context switching between tools and systems.

The crux of the problem is when every team chooses different UIs - different teams rely on different tools and infrastructure to access different data to run different workloads. Swimlanes turn into silos, and people start duplicating a lot of work just to keep each team’s silo humming. This means that language choices can cause big rifts, particularly between the “data people” who use the system and the “system people” who design and maintain it.

The data science team…makes ridiculous requests of the data engineering team, like they want to do everything in, I don't know, probably Python, using GPUs and all kinds of crazy s*** like that.

And the data scientists go off and they build their own de facto sketchy…data engineering infrastructure so that they can do things the way they want to do them.

- Josh Wills, speaking as the Director of Data Engineering at Slack in 2016 about the "Infinite Loop of Sadness"™

The collateral damage builds across an organization over time: expensive tooling, employee churn, slow system performance, lost productivity, underwhelming results, stacks of denied requests between teams, and systems that feel both fragile and rigid.

So, how can a data system bridge the divides between disparate programming languages, communities of fervent fans, and teams who need their own lanes to swim in? We think the answer is a composable data system that changes the way we look at the user experience. Because the UI is the first and often only touchpoint for the people using a data system, it is the perfect place to find common ground.

2.1 Finding common ground

All this feuding might make you think that data people are really picky and prickly about their programming languages. You might also think that this all sounds like much ado about nothing. While standards and software can help, the debates can teach us something important about data systems: real people need to use them.

Fighting over programming languages is a symptom of frustrated human beings. While everyone does have their favorite things, most data people are practical: they want to write code to do something useful like analyze data, build and deploy models, make predictions, visualize trends, or run a report.

It is not satisfying to spend hours "putting together jigsaw puzzles with hammers, scissors, and tape, instead of having finely-crafted pieces that are designed to fit together" (rachelbythebay.com).

We spend the majority of our days trying to configure OpenSprocket 2.3.1 to work with NeoGidgetPro5, both of which were developed by two different third-party vendors and available only as proprietary services in FoogleServiceCloud.

- Vicki Boykis, The cloudy layers of modern-day programming

It is no wonder that tensions mount when the talk turns to taking away people’s favorite tools. On the road from “think it” to “do it,” a lot of daily data drudgery happens at the “describe it” stop. The first step in finding common ground is to acknowledge what parts of the system have let us down:

1. The friction of trying to work with multiple suboptimal tools, because there is no single, perfect tool
2. The frustration of the illusion of tool choice when there is none, because powers that be make them for you
3. The struggle of working without interoperable pieces, because things do not work together as well as they could

2.1.1 No magic wands

While data people do indeed have lots of UI choices, most choices fall into one of three categories:

1. SQL (Structured Query Language)
2. A general purpose programming language
3. A dataframe API

But this list is misleadingly simple because it hides a lot of complexity and papercuts that data people absorb daily. In fact, even the picture we painted at the top of this chapter is misleading, because it reinforces the idea that a single tool is sufficient for modern data work.

Few data people are able to live a life of monolingual luxury. You often hear “We are a Python shop,” or “We are an R shop,” but increasingly it is “We are a polyglot shop” (i.e., proficient in more than one language). Or more honestly: we use the tools we need for the task and data at hand.

Unfortunately, to do anything useful with data, any single UI choice will not suffice - there is no magic wand. Most tasks require more than one, and every choice has a tradeoff:

The point of this table is not to point out how all the tools are flawed. The point is that any changes to the system should start by asking “How might we design our data system to support people to do good work using imperfect tools?”

2.1.2 Silos & illusions of choice

Because there is no magic UI wand, it is nice to give people choices. But people are often flummoxed by the illusion of choice when it comes to UIs. Sometimes the choice is between two half-baked options. Or maybe one option looks good on paper, but has a fatal design flaw or missing feature that is a dealbreaker. The choice is usually not “Which magical UI do I get to use today?” but rather “What UI do I need to use for reasons beyond my control?” Every choice requires unlearning and relearning things you already know how to do in other systems.

What's worse than data silos? Data silos that invent their own query language.

To be fair, some of these are SQL flavors, or at least pretend to be, but all with their own quirks that forces me to unlearn everything I knew about SQL to the point that it might as well be something completely different.

- Erik Bernhardsson, Founder and CEO @ Modal

This is common when working in a silo, where all the components are integrated from top to bottom: from the UI to the execution engine and the data storage. Users have to live with the way system developers thought the system should be used. By choosing any part of the system, you commit to the whole system: all layers and all of the silos’ private standards.

Working in a data silo means a user cannot reasonably query data in vendor X with a job running in vendor Y. Even if it is somehow possible, they will still pay many penalties (not to mention higher bills) along the way for trying to bridge across silos.

We do not know why we are here. We do not know who built the Silo. We do not know why everything outside the Silo is as it is. We do not know… when it will be safe to go outside.

- Mayor Jahns, Silo

2.1.3 Trapdoor data pipelines

As if fighting uphill battles against tools and silos is not hard enough, next comes the challenge of having to paste all these disparate pieces together into a working data pipeline. Building data pipelines without standards turns out to be hard. So the pipelines become trapdoors in the system: easy to fall into but hard to climb back out of. Crawling back out often requires more people, more code, more effort, and more tears.

...in today's world, you almost need a data engineer for every consumer of data in [your] organization. So for every data scientist or business analyst, you really need a full time data engineer.

- Tomer Shiran, Founder and CPO @ Dremio

We affectionately call this the “endless implementation loop.” It is what happens when domain expertise and implementation expertise diverge. It goes something like this:

1. The data science team writes a specialized workload and hands it off to the data engineering team…
2. The data engineering team builds and passes it back to the data science team to review it…
3. The data science team flags any number of errors, fixes those but creates new ones, then hands it back to the data engineering team…
4. The endless loop begins!

In our experience, optimistically, about 20% of these kinds of projects claw their way back out from the trapdoor and make it into production. It is not for the faint of heart.

Trapdoor data pipelines happen for a few reasons:

1. Prod is special: Code that works in local/development environments is not the same code that works in a production environment
2. Data is bigger than memory: Code that works with laptop-sized data will not work with “big data”
3. Scaling is hard: Code that works on a single-node engine is not the same code that works in a distributed environment

No one wants to enter the endless loop of implementation, but this is a natural consequence of the way our systems are designed. For example, here is a cautionary tale of data scientists at Microsoft:

Data scientists implemented a data cleaning module using pandas. They tested locally using a sample dataset, but later they had to scale it to a large dataset on Cosmos. They ended up rewriting their module using SCOPE, the query engine on Cosmos. The data scientists could have tried alternate engines like Dask to scale their programs, however, operationalizing new engines in a cloud environment is non-trivial and something which data scientists are not expert at.

- Jindal et al 2021, Magpie: Python at Speed and Scale using Cloud Backends.

Trapdoor data pipelines create a lot of system debris:

• Duplicated code because of the need to rewrite pipelines for different systems
• Stale data because it is hard to keep current data flowing into the pipeline
• Multiple (sometimes large) copies of data spread out across different systems because this is sometimes the easiest way to exchange data between systems that do not interoperate

2.2 A standard playing field

We can all agree on one thing: we are not yet to the point where users feel like data systems were built for the people that need to use them. But faced with this uneven playing field, many organizations try to level the field by embarking on grand unification initiatives. These typically focus on either trying to:

1. Get everyone in the organization (yes, everyone) to unify around a single UI


2. Get everyone in the organization (again, everyone) to unify around a single SQL dialect

People look for ways to make complex problems easy rather than make it easy to work on complex problems...

Asking how we can support our teams who NEED the time and space to work through that, it's often a much more tractable lever than trying to change the inherent nature of the work.

- Dr. Cat Hicks, VP of Research Insights @Pluralsight Flow

These are lofty initiatives, but they share the same fundamental design flaw: they both ask the system’s users to make the inputs simpler for the system to handle, rather than tasking the system with making it simpler for users. There is a third way.

The third way sidesteps programming language preferences, UI favorites, and SQL dialect restrictions altogether by adding a new intermediate language - one that is a special “machines only” language (meaning users do not learn it, use it, or touch it) sandwiched between the UI layer and the execution engine layer.

The intermediate language is a standard that acts as a translator between the UIs and the engines. This kind of standard is called an intermediate representation, or IR. To make it a true language, one layer of the system needs to speak or produce it, and another layer needs to hear or receive it. Here is how that works in a composable data system:

• A UI turns a user’s query into IR.
• An execution engine turns this IR into the specific kind of code that it can execute.

In this way, the third way obviates the need to pick language winners (and losers), and opens up the possibility of doing data engineering differently.

This is a brave new world…a different way of doing data engineering.

A way of doing data engineering where analysts can define pipelines, and make modifications to them...

A world in which everyone can interactively load data up into memory, try out scripts, try out new ideas, play with the results, see if they are a fit, and if they are, quickly move them into production pipelines.

And a world where data engineers…are allowed to work on maybe one of the most truly interesting resource management data engineering problems of our time, which is basically taking this arbitrary DAG of queries and jobs…and finding a way to optimize it and finding a way to process it all very very efficiently.

- Josh Wills, speaking as the Director of Data Engineering at Slack in 2016

The third way uses standards to build a bridge to this brave new world.

2.2.1 Intermediate representation

While IR is a relatively recent idea in data analytics, it has been around for a while in other software engineering domains like compilers and machine learning. For example, LLVM and more recently the MLIR project defined a common IR to unify the infrastructure to run machine learning models across frameworks like TensorFlow and PyTorch.

LLVM includes an "intermediate representation" (IR), a special language designed for machines to read and write (instead of for people), which has enabled a huge community of software to work together to provide better programming language functionality across a wider range of hardware.

- Jeremy Howard, founder of fast.ai

The rationale for an IR in those domains was pretty similar to the situation in the data analytics space now:

1. Developing any new front-end framework meant writing loads of glue code to target every back-end framework
2. Developing any new back-end framework meant writing loads of glue code to target every front-end framework
3. Developing any holistic system meant writing loads of glue code to add or swap any front-end or back-end frameworks

Instead of targeting new compilers and libraries for every new hardware type and device, what if we create a middle man to bridge frameworks and platforms? Framework developers will no longer have to support every type of hardware, only need to translate their framework code into this middle man. Hardware vendors can then support one intermediate framework instead of supporting many?

- Chip Huyen, founder of Claypot

2.2.2 IR in data systems

In a data system, any components that produce and consume the same IR are interoperable. This means they can readily exchange information about what the user wants to actually do with the data, without writing bespoke connector code by the user or the engine developers.

What happens without IR? Same thing we have now: lots of language silos and glue-infra to enable interoperability.

You'll find yourself duplicating a lot of things… It's very hard to DRY (don't repeat yourself) up your code.

If you do the IR, then you end up having all those syntactical constructs take the same path, which is easier to maintain…

Finally, if you want to do any kind of optimization…I think that is what they have programmers doing in hell: trying to do optimization on an AST (abstract syntax tree) rather than on an intermediate representation.

- Andrew Kelley, President and Lead Developer of Zig Software Foundation

But, what happens with IR? We improve the quality of life for different developers in a composable data system: those who develop UIs, those who develop execution engines, and those who design and develop data systems.

Again, IR is not code that anyone should write. Ideally, data people never have to touch IR. IR is a specific kind of code that is passed from one machine to another. But this is exactly why IR is the bridge between the language divide: if languages can communicate to a translator (the IR), the translator talks to all engines, then all languages are on a level playing field within the data system.

2.2.3 Composing with Substrait

Substrait is an open IR standard for UIs and engines to represent analytical plans. Substrait is a cross-language specification for data compute operations, also known as an IR for relational algebra. Substrait plans are focused on defining and standardizing data manipulation operations.

To me, this is the right way to go, this is the clear direction, this is sort of what's coming next. In the same way as Parquet and ORC, you would have a system that supports Substrait and it would allow you to integrate into a big data ecosystem in a way that existing systems don't do right now... You'll see a lot more of this in the future.

- Andy Pavlo

Instead of managing a network of connections between interfaces and engines, a composable data system need only specify that:

a) UIs ultimately produce a Substrait plan

b) Engines can operate by accepting a Substrait plan

It might seem counterintuitive to add yet another layer to your system. However, in reality, all systems already have an IR layer that acts as the glue between the UI and the engine. In the world of data systems, there are two flavors of IR:

1. A closed IR: In proprietary systems, the IR layer is internal. It can only be understood by the bundled engine. It is not documented or reusable by engine developers outside that system. Often, it only offers one UI choice for users.

2. An open IR standard like Substrait: This is more common in a composable data system. This would mean that:

   • Any UI could take a query and produce a Substrait plan
   • Any engine can compute by accepting a Substrait plan

2.3 Composable user interfaces

While adopting an IR standard helps to bridge language divides for system developers, people will still need to actually use the data system. To do that, they will need functional UIs available in multiple programming languages.

Any UI needs to check a lot of boxes to actually win friends and influence people. Functionality, features, and usability are usually top of mind when choosing UIs, but in a composable system, users are able to make those choices themselves. For those designing the system, the UI is a critical component that can help climb the data system hierarchy of needs.

Not so much "don't make me think" but "don't make me think about the wrong things."

- Kathy Sierra

We will use Ibis, a Python-based UI, as a case study in composable UIs to climb the data systems hierarchy of needs.

2.3.1 Modular

Here's the world I want to live in:

• The value of choosing one tool over another doesn't affect the end result. Rather, it's a structure used to facilitate the describing of instructions, much like tabs and spaces
• Moving between data transformation APIs is as easy as possible
• New interfaces can spring up, gain adoption, evolve, and influence other [data transformation] APIs.

- Anders Swanson, The Analytics Engineering Roundup

We want to live in this composable world, too. The first level in our data system hierarchy of needs is modularity. How does a UI snap into a modular system?

1. First, it should “do no harm”: choosing one UI should not change the end result, should not require changes in other layers of the system like the engine or storage layers, and should not limit your future choices.

2. Second, it should be “plug and play”: the hurdles to entry and exit should be minimal. A UI needs to support two system changes:

   • Adopting the UI
   • Migrating away from the UI

Here are some features of Ibis that enable modularity and pave the way for a composable world you will want to live in:

2.3.2 Interoperable

Following our mouse up the hierarchy of needs, a UI needs to lean on standards for system interoperability. This means that users will not get saddled with extra development work like converting data formats or query code into what the other parts of the system need. A UI needs to support:

1. A standard plan to pass to the engine. A UI needs to pass the user’s instructions to an engine. Without an IR, the way to do this is through a SQL query. Without a true SQL standard, the engine has to understand any number of SQL dialects. With a standard IR, the UI simply translates user input into an IR. Any engine that can consume the IR plan will be able to execute it.
2. A standard data format to return from the engine. Without standards, different engines will use different in-memory formats for processing, and this translation between engines can be costly. But, if for example, the engine operates on Arrow data, the UI should keep it in the Arrow format. This simplifies passing data from the engine to downstream tools, like a machine learning or visualization library. If the downstream tool accepts Arrow-formatted data, passing the data between processes is simpler and faster with a shared, standard in-memory data format.

Think about having a pipeline of data that's Arrow in, Arrow out all through the pipeline. If one day you want to remove a system and replace it with a new system - if that system also can generate Arrow - you don't have to change that ETL process between those two points.

- Josh Patterson, CEO @ Voltron Data, on the Imply podcast

2.3.3 Customizable

In Standards Over Silos, we said that developing with standards is a lot like using anchors while rock climbing: they help you chart a course to your final destination, and offer protection when changes need to happen in the future.

A UI can help you do just that with features that allow you to shop for and swap out other system components. Being able to switch system components easily means that you can test how your queries perform without rewrites, or switch engines without changing queries. This flexibility gives you better visibility into your holistic system: what is working, what you can change, what would break, and which options might best fit your needs.

2.3.4 Extensible

An extensible UI enables downstream development work, but the good kind: the kind that is well-scoped, builds on top of standards, and keeps users in the flow of coding with an API that is consistent with the built-in library functions. In Chapter 01, we introduced piggybacked and greenfield extensions. A composable UI provides two important ways to extend its functionality, should you have specific needs and resources for extra development work:

1. Piggybacked extensions

   When the UI library uses a license that allows developers to reuse the code, especially if the codebase is designed for extensibility, it opens the door for others to build support for specialized backends, or to add features that might be specific to a backend. Standards make the development of these extensions possible and keep the development of the library active.

   As an example, several companies have piggybacked on top of Ibis:

   • Starburst
   • Google Cloud
   • Google BigQuery DataFrames
   • Heavy.AI
   • DuckDB
   • b.telligent

2. Greenfield extensions

   User-defined functions (UDFs) are important because they allow users to go beyond the existing functions of the user interface to support niche workflows. Without UDFs, these niche workflows would need to run outside of the execution engine where the rest of the data analysis takes place, slowing down the materialization of the results.

   

   Without UDFs, you'd need to do something like:

   • dump your data from Redshift into S3
   • download it into your server or your local machine
   • transform and evaluate the data
   • upload into S3
   • load into Redshift

   
   

   - Bence Faludi

2.4 What is next?

You’ve officially made it halfway through The Composable Codex. If you landed on the Bridging Language Divides chapter first, we encourage you to go back and read Chapter 00: A New Frontier and Chapter 01: Standards Over Silos. These chapters cover the foundation and fundamentals of composable data systems.

2.4.1 How can I hear about upcoming chapter releases?

Sign up to receive email notifications when new chapters drop. You can also follow Voltron Data on LinkedIn and X (formerly Twitter) for updates.

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2.4.2 Who wrote The Composable Codex?

The Codex is written by engineers, data scientists, and leaders at Voltron Data. You can find out more about what we do at Voltron Data here: https://voltrondata.com/product

2.4.3 How can I cite The Codex?

For attribution, please cite this work as:

Voltron Data. 2023. The Composable Codex. https://voltrondata.com/codex

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    author = {Voltron Data},
    title = {"The Composable Codex"},
    year = {2023},
    url = {https://voltrondata.com/codex},
    langid = {en}
}