Amazing Data Engineering Project Created by ChatGPT
Table of Contents:
- Introduction
- The Threat of Chat GPT to Creative Jobs
- Chat GPT and the Engineering World
- Creating an End-to-End Data Engineering Project with Chat GPT
4.1. Step 1: Creating a Cloud Composer Environment
4.2. Step 2: Creating a Chat GPT Bucket
4.3. Step 3: Uploading Files to the Chat GPT Bucket
4.4. Step 4: Creating Empty Tables in BigQuery
4.5. Step 5: Writing a Python Script for Data Processing
4.6. Step 6: Granting Access to GCS and BigQuery
4.7. Step 7: Scheduling the Script with Cloud Composer
4.8. Step 8: Quality Checks and Data Validation
- Limitations of Chat GPT in Data Engineering Projects
5.1. Limitation 1: Slow Code Generation
5.2. Limitation 2: Outdated Commands and Information
5.3. Limitation 3: Withholding Information and Options
5.4. Limitation 4: Providing Incorrect Code Suggestions
- The Promise of Chat GPT in Functional Data Engineering Use Cases
- Embracing AI as a Tool in Data Engineering
- The Future of AI in Data Engineering
- Upscaling in Data Engineering with Praxis Business School and Board Infinity
- Special Surprise for Subscribers: Free Mentoring Sessions
Article
Is Chat GPT a Threat to Data Engineering Jobs?
As the advancement of AI technology continues to accelerate, concerns about job displacement have also risen. In the creative industry, AI like Chat GPT is already capable of replacing jobs such as content Creators and scriptwriters. However, the impact of AI on technical roles, like data engineering, has remained a subject of debate. In this article, we will explore the implications of Chat GPT on data engineering jobs and assess whether it has the potential to replace data engineers.
The Threat of Chat GPT to Creative Jobs
With its ability to generate human-like text, Chat GPT has posed a significant threat to jobs in the creative department. Content creators and scriptwriters, who rely on their writing skills and creativity, now face the risk of being replaced by AI algorithms. To demonstrate the capabilities of Chat GPT, a LinkedIn post was made using the AI-generated content. While some personal touches were added, the majority of the post was generated by Chat GPT itself. This experiment highlights the ease with which an AI can replace human creative roles.
Chat GPT and the Engineering World
The threat of AI is not limited to the creative industry. In the field of engineering, the rise of Chat GPT has raised concerns about the potential replacement of engineers and developers. As a data engineer, I decided to investigate the extent to which Chat GPT can contribute to an end-to-end data engineering project. By following the suggestions provided by Chat GPT, I aimed to uncover the limitations and functional aspects that a data engineer might encounter.
Creating an End-to-End Data Engineering Project with Chat GPT
To understand the capabilities of Chat GPT in a data engineering Context, I initiated the creation of a sample project. The project involved using Composer, GCS, and BigQuery on the Google Cloud Platform (GCP). Through a series of steps recommended by Chat GPT, I examined the feasibility and effectiveness of relying on AI for project creation.
Step 1: Creating a Cloud Composer Environment
The first step in the project was to Create a Cloud Composer environment. By naming and selecting the region for the environment, I set up the foundation for the upcoming tasks.
Step 2: Creating a Chat GPT Bucket
In order to store the data that needed processing, a Chat GPT bucket was created. This bucket would serve as the storage location for the files required by the project.
Step 3: Uploading Files to the Chat GPT Bucket
Once the Chat GPT bucket was set up, the next step involved uploading the necessary files to the bucket. This ensured that the project had access to the data required for processing.
Step 4: Creating Empty Tables in BigQuery
To load the processed data into BigQuery, two empty tables were created within a designated dataset. This step involved creating the dataset and then generating the tables.
Step 5: Writing a Python Script for Data Processing
With the infrastructure in place, the next focus was on writing a Python script to process the data from GCS and load it into BigQuery. Chat GPT provided an example script using the Composer environment Package, which was used as a starting point.
Step 6: Granting Access to GCS and BigQuery
To ensure that the Cloud Composer environment had access to GCS and BigQuery, the necessary permissions were granted. Chat GPT guided me through the process of enabling access, including the steps required to configure the service account.
Step 7: Scheduling the Script with Cloud Composer
To automate the data processing, the Python script was scheduled to run regularly using Cloud Composer. Chat GPT suggested using a Python operator and provided a code sample that demonstrated how to implement the scheduling.
Step 8: Quality Checks and Data Validation
In a data engineering project, ensuring the quality and integrity of the data is vital. With the assistance of Chat GPT, I discovered various types of data quality checks that could be performed on the dataset. These checks ranged from identifying outliers to ensuring data completeness and consistency.
Limitations of Chat GPT in Data Engineering Projects
While Chat GPT showcased its capabilities in assisting with the creation of an end-to-end data engineering project, several limitations were observed. These limitations highlight the challenges of relying solely on AI algorithms for complex and real-life data engineering projects.
Limitation 1: Slow Code Generation
One notable drawback of Chat GPT was the slow generation of code samples. When presented with lengthy code, the AI algorithm took considerable time to provide a response. In instances where a simple code sample was required, it was more efficient to search for the code on platforms like Stack Overflow.
Limitation 2: Outdated Commands and Information
Chat GPT's data training only extends until 2021, which can lead to outdated commands and information being provided. This limitation was evident when encountering commands that had evolved since the training period. In such cases, resorting to external sources, like search engines, was necessary to find accurate and up-to-date solutions.
Limitation 3: Withholding Information and Options
Another limitation observed with Chat GPT was its tendency to withhold information and options. Instead of providing a comprehensive range of solutions, the AI algorithm often suggested only one option, limiting the user's ability to explore alternative approaches. This can be frustrating for users who rely solely on Chat GPT and are unaware of other available methods.
Limitation 4: Providing Incorrect Code Suggestions
In certain instances, Chat GPT provided incorrect or problematic code suggestions. When encountering errors in the provided code, relying solely on the AI's guidance led to further issues. While Chat GPT was able to provide a solution upon questioning, the initial incorrect suggestion raised concerns about the accuracy of its recommendations.
The Promise of Chat GPT in Functional Data Engineering Use Cases
Despite its limitations, Chat GPT showcased its potential in addressing functional aspects of data engineering projects. When asked about joining tables or applying quality checks to a dataset, Chat GPT provided valuable insights and suggestions. The AI's ability to generate SQL syntax for quality checks and identify outliers demonstrated its usefulness in simplifying certain aspects of data engineering.
Embracing AI as a Tool in Data Engineering
Rather than perceiving AI as a threat, it is crucial to view it as a tool in the data engineering field. AI algorithms like Chat GPT have the potential to streamline certain tasks and free up time for data engineers to focus on more complex and strategic aspects of their work. By embracing AI as an assistant rather than a replacement, data engineers can leverage the benefits of AI technology while retaining their expertise and critical thinking.
The Future of AI in Data Engineering
The launch of Chat GPT and other AI products has set the stage for future advancements in the field of data engineering. With Microsoft's recent acquisition of a stake in Chat GPT and the potential involvement of Google and Apple in the AI race, the development and improvement of AI products in data engineering are expected to Continue. As AI technology evolves, data engineers must remain adaptable and open to integrating AI Tools into their workflows.
Upscaling in Data Engineering with Praxis Business School and Board Infinity
To stay ahead in the rapidly evolving field of data engineering, professionals need access to continuous upskilling opportunities. Institutions like Praxis Business School and Board Infinity offer comprehensive programs and resources to help individuals enhance their skills and knowledge in data engineering. Through webinars and master classes, participants can gain insights into various aspects of data engineering and network with industry experts.
Special Surprise for Subscribers: Free Mentoring Sessions
In appreciation of the support from subscribers, I have a special surprise. I am offering free mentoring sessions to three lucky subscribers. To take part, simply subscribe to my YouTube Channel, leave a comment explaining why You need to connect with me, and include your email ID. If selected, I will reach out to you to schedule the mentoring session. Don't miss out on this opportunity to gain personalized guidance and support in your data engineering Journey.
In conclusion, while Chat GPT showcases promising AI capabilities and has the potential to simplify certain aspects of data engineering projects, its limitations must be acknowledged. Real-life data engineering projects are often more complex and extensive, requiring domain expertise and critical thinking that AI algorithms cannot fully replicate. The key lies in embracing AI as a tool in the data engineering toolbox, enabling professionals to leverage AI's strengths while adding value through human expertise.
FAQs
Q: Can Chat GPT completely replace the role of a data engineer?
A: No, Chat GPT cannot fully replace the role of a data engineer. While it can assist in certain tasks, data engineering requires domain expertise, critical thinking, and an understanding of complex data pipelines that AI algorithms cannot replicate.
Q: How can Chat GPT be useful in data engineering projects?
A: Chat GPT can be useful in providing suggestions, code samples, and insights for specific aspects of data engineering projects, such as data processing, quality checks, and SQL syntax. It can help streamline certain tasks and enhance productivity.
Q: What are the limitations of relying solely on Chat GPT in data engineering projects?
A: Some limitations of Chat GPT in data engineering projects include slow code generation, outdated commands and information, the withholding of information and options, and the potential for incorrect code suggestions. It is important to cross-verify and use other reliable sources for accurate solutions.
Q: How can data engineers adapt to the advancement of AI in their field?
A: Data engineers can adapt to the advancement of AI by embracing it as a tool rather than viewing it as a threat. By integrating AI into their workflows, data engineers can leverage its benefits while focusing on more complex and strategic aspects of their work.
Q: What are the future prospects of AI in data engineering?
A: The future prospects of AI in data engineering are promising. With the continuous development and improvement of AI products, data engineers can expect enhanced tools and technologies to augment their work. This includes advancements in areas like natural language processing and automated data pipelines.