As a data scientist, you know the importance of efficient data management in your work. But sometimes, the need to write "glue code" bogs us down. Glue code is the infrastructural code connecting software components, and surrounding the ML code that builds models and creates predictions. Glue code is a concept from computer programming that has been applied to ML and data science in a frequently cited paper published by Google called the “Hidden Technical Debt in Machine Learning Systems.” As the paper describes, glue code can be a major burden, taking an unpredictable amount of time away from your work in building and tuning models, as well as creating predictions. Glue code can also be prone to errors and maintenance issues, which can further hinder your progress. As expectations and stakes for data teams soar, it’s important to operationalize and systematize redundant and costly. In this article, we'll explore the role of glue code in data science and machine learning, and how it can slow you down.
Glue code in data science and machine learning
If you’ve worked in computer programming or software engineering, you're likely familiar with the concept of glue code. Glue code refers to code that connects otherwise incompatible software components together. When applied to data science and machine learning projects, one can think of glue code as the code that reconciles different parts of the data ecosystem, ensuring that all the pipelines run so that you can get your final models and predictions. This can be a common task, as you often need to bring together data from multiple sources, different databases, data warehouses, or data lakes, integrate them into your workflows, transform and clean the data for your use-case, all before actually building and improving upon your model.
Although glue code is not the direct predecessor to your model or predictions, it is an absolutely critical part of the data science workflow, and can take up a lot of your workday. As many of us know, even the best model cannot save incomplete or inaccurate data. For example, glue code can be used to describe these critical processes:
Extracting data from a database or API
On an enterprise level, your data may be stored in a variety of places, each system and application or API may have a different format or other peculiarities. For example, you might use glue code to query a database for specific data, or to retrieve data from an API that provides access to a particular service or platform. This can be useful when you need to access data that is stored in a specific format or location, or when you need to retrieve data in real-time.
Transforming and cleaning data
Big data can make big messes. Transforming and cleaning data, making it suitable for analysis, is foundational for any data scientist’s work. For example, you might use SQL to join different tables, and then use Python to convert data types, handle missing values or outliers. This can be a crucial step in the data preparation process, as it ensures that your data is in a consistent and usable format.
Loading data into a data warehouse or other storage system
Once you've extracted and cleaned your data, you may need to load it into a data warehouse or other storage system. This can be a laborious process that requires highly customized code to ensure all of the data you need is stored properly.
Integrating data into a single view or dataset
Another common task is integrating data from different sources into a single view or dataset. For example, you might use glue code to join data from different tables in a database, or to merge data from different sources into a single file or dataset. But those tables may have the same variables represented in slightly different ways, or have slightly different missing data or outliers. Consistency and data quality can then become an issue.
Connecting to external libraries or tools
Finally, glue code can also connect to external libraries or tools that provide specialized functionality. For example, you might use glue code to access machine learning libraries or data visualization tools, or to connect to external APIs that provide access to specific services or data. This can be useful when you need to access specialized functionality that is not available in your current tools or systems.
While glue code is a necessary part of many data science and machine learning projects, it can also present several challenges. For one, writing and maintaining glue code can be time-consuming, as you need to ensure that your code is accurate and efficient every time. Glue code can also be prone to errors and maintenance issues, which can slow down your progress and affect your productivity. In addition, if you're working on a large or complex project, you may need to write and maintain a significant amount of glue code, which can be a significant burden. This code can then become outdated, or difficult to apply to new data and projects.
How writing glue code slows you down
Time spent on non-core tasks
One of the most obvious ways that writing glue code slows you down in unpredictable ways. As a data scientist, you have the benefit and burden of having a wide skill set–from programming to statistics and business acumen–you are expected to leverage all three to do your job. But your primary goal is to use data to answer questions, make predictions, and drive decision-making. But if you're spending a significant portion of your time writing code to connect different systems or sources together, you're not able to focus on those core tasks. This can be frustrating, and it can be challenging reporting to stakeholders when there are infrastructural maintenance issues interfering with time that could be spent moving the needle on deliverables.
Glue code can also be time-consuming to write and maintain. Depending on the complexity of your project, you may need to spend hours or even days writing code to connect different systems and sources. We could better spend this on tasks that directly contribute to your project goals, such as exploring data, building and testing models, productionalizing results or communicating your findings.
Context switching can lead to decreased productivity
Besides taking time away from core tasks, glue code can also negatively affect your overall productivity. When you're constantly switching between different tasks and contexts, it's difficult to maintain focus and momentum. This can lead to decreased productivity and reduced efficiency, as you're not able to fully immerse yourself in any one task.
Glue code can be difficult to maintain, as bug fixes that worked for one dataset or situation may not be easily applied in a different context, which can further decrease your productivity. If you're constantly debugging and refactoring your code, you're not able to make progress on other tasks. Writing glue code can also be a drain on your motivation and enjoyment of your work. If you're constantly dealing with frustrating and time-consuming tasks, you may lose passion for your work and become less engaged. This can have long-term effects on your career and overall satisfaction with your job.
How Einblick gets rid of the need for glue code
But what if there was a way to streamline this process and eliminate the glue code burden? That's where Einblick comes in. Einblick is a collaborative platform for data scientists that uses visual data computing to facilitate faster decision-making and streamline the entire data science workflow. With Einblick, you can focus on your core mission as a data scientist without the distractions and inefficiencies of repetitive code.
One of the major benefits of using Einblick is that it brings together many parts of your data science workflow in the same space. You’re able to
- Use no-code cells instead of glue code for tedious tasks
- Connect easily to your database or data storage system of choice
- Toggle between SQL and Python
- Use AutoML on a progressive computation engine to quickly prototype models so you can focus on tuning them
- And much more
By providing a single platform for all of your data science needs, Einblick simplifies integrating data and tools, and makes it easier to access and manage your data. This can help you focus on your core mission as a data scientist, and to make the most of your skills and expertise.
Einblick is a one-stop-shop for data scientists, providing everything you need to manage your data, build and test models, and communicate your findings. It's built on a collaborative platform, which means you can work with your team members in real-time, sharing data, models, dashboards, and insights as you go. This can help to increase efficiency and productivity, as you don't need to switch back and forth between different tools and systems.
Besides being a collaborative platform, Einblick also uses visual data computing to facilitate faster decision-making. With visual data computing, you can see the relationships and patterns in your data in a more intuitive and interactive way, making it easier to understand and explore. You’re also able to compare visualizations, evaluation metrics, and models easily side-by-side due to the canvas environment. This can help you identify trends and insights, and to make more informed decisions based on your data.
Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.