Data engineers are easily the unsung heroes of modern business operations. Just think of all the people who create and maintain the data pipelines and infrastructures that store and analyze the ever-increasing waves of data. Without data engineers, many digital products or services simply wouldn’t be possible.
Luckily, the data engineer’s life is changing to match the increasing pace of innovation. Generative AI has revolutionized day-to-day data management, freeing engineers’ time and attention by automating many tedious but time-consuming tasks.
What role does AI stand to play in enabling data engineering best practices? Keep reading to learn how data engineers can benefit from AI solutions in their daily jobs.
Understanding AI in Data Engineering
Generative AI is making a big splash in data engineering by reducing or even eliminating time-consuming manual work. As they advance, AI models can take on more difficult data engineering activities, such as schema generation and feature engineering. By automating much of the technical drudgery of data work—for example, coding or system maintenance—GenAI allows data engineers to devote more of their time and creativity to high-value work and more abstract thinking.
AI can also help data engineers better manage the flow of old data while simultaneously creating new data. The value of this may not be clear to an organization already drowning in existing data, such as one striving to convert an unmanageable “data swamp” into a less intimidating “data lake. ” However, there are certain critical areas where fresh data may directly drive growth and inform decision-making.
Every data engineer’s pet peeve is an incomplete dataset. Just as GPT-4 can produce lifelike, human-like text, Generative AI models use advanced machine learning techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to generate realistic, high-quality data.
By training many neural networks to work together, the resulting output can be tweaked until it is functionally identical to the missing data. This breakthrough, which eliminates the need for human data imputation, has the potential to significantly streamline the data engineering process and reduce the time spent on data cleaning and preparation.
Another use case is data anonymization. In the age of rigorous data privacy standards like GDPR and CCPA, organizations must secure the privacy of sensitive customer information. Generative AI models can generate synthetic data that keeps the statistical qualities of the original data but removes any personally identifiable information. This data can then be used for data analysis and other purposes while remaining compliant with privacy standards.
How AI Benefits Data Engineering
Increased efficiency
GenAI can automate tedious and time-consuming data engineering operations like data extraction, transformation, and loading (ETL), data integration, and pipeline development. By automating these tasks, teams can drastically minimize manual work, speed up data processing, and enhance overall efficiency when dealing with enormous amounts of data.
Automation for faster insight delivery
GenAI accelerates data engineering processes, resulting in faster insight delivery. Organizations may streamline data pipeline automation, reduce bottlenecks, and speed up converting raw data into usable insights by decreasing manual intervention. This provides decision-makers with timely and relevant information to make data-driven decisions.
Improved accuracy and consistency
Manual data engineering procedures are prone to human mistakes, resulting in data inconsistencies and inaccuracies. GenAI approaches, with their capacity to handle data consistently and precisely, can increase data correctness, decrease errors, and assure consistency in data engineering workflows. This leads to more dependable and trustworthy data analysis.
Scalability and adaptability
As data quantities rise dramatically, scalability becomes increasingly important for data engineers. GenAI-driven automation allows teams to scale their data engineering activities efficiently. This flexibility and scalability are key for handling larger datasets, accommodating new data sources, and responding to changing business needs.
AI in Data Engineering Use Cases
Data Cleaning and Preprocessing Automation
AI processes can detect anomalies and odd patterns in data, which could indicate inaccuracies or missing points. Moreover, AI solutions can be extremely useful in automating string manipulation, format conversion, unit transformation, and other data cleanup tasks.
Advanced Data Integration Techniques
One of the most powerful AI use cases is data collection and integration. AI algorithms explore databases, APIs, and web pages to find and retrieve important data. They may learn to recognize various data types, adapt to changing data quantities, and create real-time data input pipelines.
Predictive Data Pipeline Management
Deep learning processes enable AI solutions to continuously monitor data pipeline performance, discover bottlenecks, validation delays, and predict probable breakdowns.
Challenges of Integrating AI in Data Engineering
By carefully analyzing these challenges and applying suitable fixes, teams can maximize the benefits of automation while minimizing potential downsides:
| Challenge | Fix |
|---|---|
| Data Security and Privacy | Automation increases productivity while simultaneously raising worries about data security and privacy. With AI automating sensitive data handling operations, enterprises must implement strong security measures to prevent unwanted access, data breaches, and potential misuse. Implementing encryption, access limits, and monitoring systems is critical to ensuring data privacy and security. |
| Data Complexity and Variability | Data engineering involves many data sources, formats, and structures. AI programs must be able to understand and adapt to this complexity. However, assuring the correctness and dependability of automated procedures in the face of diverse data sources can be challenging. To account for the differences between datasets, rigorous validation, and testing are required. |
| Algorithmic Bias and Fairness | AI systems use algorithms that learn from past data. Automated methods may unintentionally reinforce bias if the training data is skewed or reflects existing inequities. Teams should do their best to evaluate and mitigate algorithmic bias. |
| Legal and Regulatory Compliance | As GenAI advances, legal and regulatory frameworks may need to change. Organizations must keep up with growing legislation governing data privacy, security, and algorithmic transparency. Compliance with these standards guarantees that GenAI implementation meets legal requirements while mitigating potential dangers. |
| Skills and Expertise Requirements | Using GenAI to automate data engineering processes requires a competent team. Organizations need data engineers who are knowledgeable with GenAI technologies and can effectively apply them. Upskilling and reskilling initiatives are critical to closing the skills gap and allowing data engineering teams to fully realize the potential of GenAI. |
Best Practices for Implementing AI in Data Engineering
Building a Robust Data Architecture
Data engineering relies heavily on architecture. A well-thought-out design not only meets present data requirements but also scales to meet future demands with minimal rework. Create a modular architecture where components can be independently developed, replaced, or scaled.
Today’s cloud systems enable this paradigm, but it is critical to consider the data flows, data subsystem interfaces, and ingress/egress costs that may apply in a multi-cloud context. And the world of AI is moving towards standardization as well – for example, via AI frameworks.
Ensuring Data Privacy and Security
Security is critical and must be prioritized at all stages of data engineering. Strong data protection safeguards must be deployed both at rest and in transit. This includes encryption technologies like TLS for data in transit and AES for data at rest to protect data from unauthorized access.
Then there’s compliance. Data engineering methods must closely follow applicable legal and regulatory standards, such as GDPR, HIPAA, and CCPA, which control data protection and security.
Compliance with these rules entails implementing correct data access controls, conducting frequent audits, and maintaining complete documentation of data handling activities to ensure transparency and accountability. Implementing these security standards not only helps to protect sensitive and vital data but also fosters trust among users and stakeholders by demonstrating a commitment to data privacy and security.
Data governance refers to the processes, regulations, standards, and measurements that assure the effective and efficient use of information. It’s essential that teams create a clear data governance framework to manage data access, security, compliance, and quality. Implementing data governance frameworks raises corporate knowledge of the availability of specific technologies and reduces duplication of effort.
Implement continuous integration and deployment (WAP)
Using Write-Audit-Publish (WAP) approaches in data engineering is based on integrating methodologies that promote automation and continuous delivery of data operations.
All pipeline configurations need to be tracked, starting with version control to ensure effective change management. This process can include additional procedures to ensure appropriate reviews and QA controls are in place. Automated tests, such as unit, integration, and data validation tests, illustrate this. They guarantee that each component works correctly alone and in conjunction with others while maintaining data integrity.
These tests run automatically after every code contribution using CI systems like Jenkins or GitHub Actions, ensuring rapid feedback on errors. Such actions are critical in protecting the business data landscape from flaws that could compromise reporting layers and decision-making processes. CD techniques use configuration management tools such as Ansible or Terraform to automate the provisioning and deployment of infrastructure changes, reducing manual errors and speeding up the process.
WAP not only streamlines workflows in data engineering but also considerably reduces the chance of errors in production environments, resulting in more reliable and efficient data operations.
Top AI Solutions for Data Engineering
1. lakeFS
lakeFS is an open-source data version control system that sits on top of data lakes and lets users take advantage of Git-like mechanisms to version control data. lakeFS manages data versions using metadata. Its versioning engine is very scalable and has minimal influence on storage performance.
The system is format-agnostic, supporting structured, unstructured, open table, and other formats. lakeFS supports data in all object stores, including all major cloud providers: S3, Azure Blob, and GCP, as well as on-premises MinIO, Ceph, Dell EMC, and any other S3-compliant storage.
Use cases for lakeFS include:
| Use Case | Solution |
|---|---|
| Isolated dev/test environments | You can use lakeFS branches to create isolated dev/test environments, reducing testing time by 80%. Perform data cleansing, outlier handling, missing value filling, and other pre-processing tasks to guarantee that your data pipelines are robust and deliver high quality. |
| Promote only high-quality data to production | You can implement Write-Audit-Publish for data using lakeFS Hooks, which allows for automation checks and validation of data on branches. |
| Fix bad data with production rollback | Commits allow you to save whole, consistent snapshots of your data and roll back to earlier commits in the event of faulty data. |
2. TensorFlow
TensorFlow is an open-source library that supports numerical computation, large-scale machine learning, deep learning, and other statistical and predictive analytics tasks. This solution accelerates and simplifies the implementation of machine learning models by assisting with data acquisition, large-scale prediction serving, and refinement of future outcomes.
TensorFlow can train and run deep neural networks for tasks such as handwritten digit classification, picture recognition, word embedding, and natural language processing (NLP). The code supplied in its software libraries can be integrated into any application to assist it in learning these tasks.
TensorFlow applications can run on traditional CPUs (central processing units) and GPUs (high-performance graphics processing units). Because Google created TensorFlow, it also uses the company’s tensor processing units (TPUs), which are specifically built to accelerate TensorFlow workloads.
One of the greatest advantages of TensorFlow is the open-source community of developers, data scientists, and data engineers who contribute to its repository.
3. Kubeflow
Kubeflow makes AI and machine learning simple, portable, and scalable. The solution is a Kubernetes-based ecosystem that supports each AI/ML lifecycle stage, including popular open-source tools and frameworks.
Kubeflow Pipelines is a tool for creating, deploying, and managing whole ML processes on Kubernetes. It lets users create, deploy, and monitor machine learning pipelines using a simple and straightforward YAML-based syntax. It also provides a comprehensive collection of tools and integrations to support the full machine learning process.
Kubeflow Pipelines is built on Kubernetes, which provides a scalable and flexible platform for running machine learning pipelines and models. This lets teams scale up and down your pipelines effortlessly while ensuring they are performant and stable. It includes built-in support for TensorFlow, PyTorch, and XGBoost, as well as a robust ecosystem of integrations and plugins for additional tools and services.
4. DeepCode AI
DeepCode AI powered by Snyk, uses artificial intelligence to conduct code reviews. It was designed to help developers generate better, higher-quality code faster. With its continual learning and adaptation capabilities, DeepCode AI has verified the code of over four million developers.
DeepCode AI uses machine learning algorithms to learn from millions of software development repositories. This dataset enables it to comprehend and identify any flaws in your code.
The platform’s main feature is artificial intelligence-driven code analysis. It outperforms typical static analysis tools by comprehending the context of the code. This results in more precise and detailed feedback, allowing developers to improve their work more efficiently.
Versatility is another advantage of this instrument. It supports a variety of programming languages, including C and C++. This broad interoperability makes it a versatile alternative for various coding projects.
These critical capabilities make this tool an invaluable ally for developers looking to increase code quality, productivity, and security.
5. GitHub Copilot
GitHub Copilot is an AI-powered code suggestion tool created by GitHub and OpenAI. Copilot’s real-time code suggestions can help data engineers save time and enhance productivity.
The solution uses machine learning methods to recommend code snippets and auto-complete code in real time.
Check out more tools in our annual State of Data Engineering in 2024 report.
Key Trends Shaping AI in Data Engineering
AI-Augmented ETL Processes
The rising volume and complexity of data in modern companies present major hurdles to traditional Extract, Transform, and Load (ETL) methods. Thanks to advances in efficiency, accuracy, and scalability, AI can help teams address these challenges by automating ETL processes.
AI is changing ETL operations and paving the way for more agile and adaptive data management, promising to transform the relationship between AI and data processing.
Low-Code/No-Code AI Data Tools
The tools that data engineers employ can make or break workflows. Low-code and no-code solutions have emerged as game changers, causing controversy among data engineers.
Low-code platforms provide a visual approach to data and AI pipeline development, allowing users to build data pipelines using graphical interfaces instead of traditional hand-coded programming. This method reduces the effort required to perform repeated operations, allowing less skilled data professionals to perform their own duties, and improves the development process.
One key benefit of using low-code tools is the speed with which pipelines can be built and deployed. Traditional data engineering operations, such as data intake, transformation, and integration, can require creating and debugging large amounts of code. These tasks can be completed using low-code technologies’ accessible visual interfaces. This not only speeds up the development process but also dramatically saves the time spent on maintenance.
Low-code technologies also help to bridge the gap between technical and non-technical team members throughout the firm. Low-code tools allow data analysts, business intelligence specialists, and marketing teams to contribute more effectively without diving into technical specifics.
The Rise of AI-Driven Data Observability
AI improves data observability by automating monitoring tasks, detecting anomalies quickly, and anticipating possible issues before they affect the business. Such automation enables businesses to optimize data handling in real time, reducing costly errors and increasing overall efficiency.
Ensuring data quality and cost-effectiveness has become a top responsibility for most organizations. High-quality data leads to better decision-making, and effective cost management ensures that resources are used efficiently. Businesses may address both of these challenges simultaneously by implementing AI-driven data observability.
AI improves data observability by using machine learning algorithms to examine large volumes of data in real time. These algorithms learn from historical trends to predict anomalies and deviations, reducing downtime and improving data accuracy. AI-powered observability tools automatically identify fundamental causes of problems, delivering actionable insights without requiring human participation.
AI observability platforms also optimize resource allocation by monitoring system performance and recommending enhancements to query optimization, storage management, and data pipeline efficiency.
This not only improves data quality but also increases cost efficiency by allowing firms to manage resources better. AI-powered data observability enables enterprises to maintain consistent, accurate, high-performing data environments even as data volume and complexity grow.
Conclusion
Early data processing tools were designed for on-premises environments, which required engineers to spend more time configuring their systems rather than delivering business value. By transitioning to AI-powered tools based on cloud computing and SaaS (or even RAG as a Service!) solutions, teams can concentrate on their objectives while leaving technical tasks to suppliers.
