5 AI prompts to boost productivity for Data Engineers

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Dear Reader…

In the practice of Data Engineering, we’re constantly seeking ways to optimise and refine data models. With the advent of Generative AI, you can now leverage the power of LLM’s to streamline different workflows and improve the accuracy of different models.

This week we’ve researched five useful prompts you can experiment with as you tinker with data models and use AI to boost your day-to-day productivity.

#1. Data Schema Suggestions

"I need to design a data schema for this use case: <USE_CASE_DESCRIPTION>. Can you suggest an efficient schema for it?"

Bonus tip: Always start your prompt with “You are an expert in Data Engineering, Data Warehouse Architecture and Data Modelling”. Many platforms allow you to pre-configure prompt threads or create custom GPT’s that save you having to enter this in every time you prompt.

#2. Feature Engineering

"I want you to act as a data scientist and perform feature engineering. I am working on a model that predicts <FEATURE_NAME>. There are columns: <COLUMN_NAMES>. Can you suggest features that we can engineer for this machine learning problem?"

This prompt enables you to identify relevant features that can improve the accuracy of your model. By providing the column names and feature name, you can get a list of engineered features that can enhance your model's performance.

#3. Data Transformation Code Generation

"I need to transform data from this source schema: <SOURCE_SCHEMA> to this target schema: <TARGET_SCHEMA>. Could you generate a Python script or SQL query that does this?"

Bonus tip: Don’t expect a “zero-shot” answer, sometimes it can take several iterations to get code or a query right. Always check the answer and compare it to what you might have written. Another way to approach this is to ask the LLM “Suggest ways to improve <YOUR QUERY OR CODE>, sometimes this can can yield better results.

#4. Data Quality Measurement

"I want to measure the data quality of the attached sample dataset. Can you suggest metrics and methods to evaluate the quality of my data?"

This prompt enables you to assess the quality of your data using metrics and methods suggested by the AI. By evaluating your data quality, you can identify areas for improvement and refine your data model to ensure it's accurate and reliable. Remember to be prepared to iterate if you don’t get quite the right answer to begin with.

#5. Model Deployment

"I have this machine learning model: <MODEL_DESCRIPTION>. Can you suggest best practices for deploying it to production, including model versioning, A/B testing, and monitoring?"

By providing a description of your model, you can get recommendations on model versioning, A/B testing, and monitoring.

Bonus Tip: Try adding including your particular production environment as a reference point, or prompt for alternatives such as “Suggest three alternative deployment options, listing advantages and disadvantages for each one”

Remember to customise the prompts to your specific use case and provide clear descriptions of your data and models to get the most accurate and relevant results. Happy prompting!

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Times have changed…

And so is how we are learning these days. Embarking on a Data Engineering career has drastically altered with the proliferation of online content and courses. If you were starting out or retraining in 2024 there are credible alternatives to tertiary level education. The Seattle Data Guy is an example of one alternative approach. With a whopping 5 million views on his Data Engineering channel Ben Rogojan is in the top 5 of YouTube educators on Data Science.

This week we wanted to pose the question: If you had 100 days to reinvent yourself as a Data Engineer, what would you do? Chances are you would turn to YouTube and find this video guide…

Embark on Your Data Engineering Journey: A 100-Day Crash Course

Here’s what you need to do…

1. Laying the Groundwork (Days 1-10): Kickstart your journey by the foundational skills of SQL, Python, and data modelling create a solid base to build from.

2. Deepening Your Expertise (Continuing from Day 11): Adopting daily practice to master more complex SQL queries, enhance your Python scripting abilities, and explore data warehousing concepts. This phase is about building robust technical skills that are crucial to data engineering.

3. Project-Based Learning (Every 30-50 Days): Put your skills to the test by undertaking mini-projects. These practical assignments involve real-world data sets and focus on creating tangible outputs like dashboards. It’s an opportunity to apply what you’ve learned in a controlled, impactful way.

4. Tool Mastery and Technological Fluency (Up to Day 70): Discover and master a variety of tools and technologies essential for any data engineer. From Snowflake to Docker and cloud solutions, you want to navigate the landscape of data engineering tools with confidence.

5. Capstone Project and Community Engagement (Final Phase): Conclude your learning expedition by designing and executing a capstone project. Choose your data sets and tools, then craft a unique project that showcases your skills. Share your progress and final products within the community to gain feedback and further your learning through engagement.

This is a simple enough framework for self-directed learning, using the proliferation of free materials and videos available. The real challenge is having the motivation, and discipline to direct yourself on a journey like this. We want to improve your chances of success with the Data Innovators Exchange, where you will find a host of materials and a community of professionals to help guide you on your Data and AI engineering career. Come on in if you would like to see what’s on offer.

Thank you

That’s a wrap for this week.

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