AI for data engineering: Use cases and key benefits 

Artificial Intelligence is making a robust advent into multiple contemporary industries—from banking and insurance to logistics, retail, and software engineering. This technology’s progress is so fast that it is predicted to increase its market size more than fourfold between 2024 and 2032, displaying a spectacular CAGR of 19%. 

AI performs various functions across various sectors, streamlining and facilitating workflow routine and improving customer satisfaction. No matter what economic field AI is applied in, one of the use cases where it excels is data analysis.  

This article explores the role of AI in data processing and engineering, the benefits of using this technology in the data science domain, its most popular use cases, the challenges data engineers face in harnessing AI, and the prospects of blending AI and data engineering.  

Let’s begin with the fundamentals and understand the meaning of AI-propelled data engineering. 

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What is AI data engineering?

Classical data engineering services focus on creating IT systems employed for collecting, storing, organizing, monitoring, and processing information to turn it into a databank of accessible, usable, and meaningful records. Such dossiers are further leveraged by companies as actionable insights instrumental in making business decisions.  

Today, these routine data engineering tasks undergo revolutionary transformations caused by the exponential growth in the volume of datasets. As IBM experts claim, approximately 90% of the real-world data is, in fact, new data that has been generated within the last two years, threatening to overwhelm traditional data management and analytics mechanisms and rendering their performance inadequate and highly time-consuming. The demand for more efficient big data handling necessitated the involvement of cutting-edge technologies and tools, such as cloud data warehouses, natural language processing, machine learning algorithms, generative AI models, and other know-how nestling under the umbrella term of artificial intelligence. 

Thus, AI engineering specialists process huge volumes of information by employing the power of AI. They create intelligent systems that can retrieve data from various sources, analyze it, identify patterns, predict outcomes, and assist in making data-driven decisions, developing and getting more sophisticated, thanks to the machine learning models that fuel them.  

What are the critical components of AI-powered data platforms? 

Zooming in on the structure of AI-based data engineering systems

Having the good old ETL pipelines as their backbone, AI data engineering solutions perform the following functions. 

• Data collection. Relevant information is extracted from different sources and databases (both historical and real-time) via a network of APIs.  
• Data storage. Utilizing state-of-the-art software (for example, AWS S3, Spark, or Hadoop), AI data engineers employ data storage facilities (data lakes, warehouses, and various cloud environments) to store all structured and unstructured data they have obtained. 
• Data processing. Raw data is unsuitable for analysis. To prepare it, data engineers leverage specialized tools (like Talend or Apache Kafka) that clean and transform data, bring it to readable formats, eliminate data inconsistencies, and load it into analytics platforms grounded on AI models.  
• Data quality and governance. Ensuring the data under analysis is accurate, up-to-date, consistent, complete, coherent, and intelligible plays a crucial role in the adequate data processing workflow. AI-fueled large language models implement validation techniques, pinpoint errors, supply missing values by understanding the context, and check the conformity with compliance norms.  
• Data integration. In many organizations, the data landscape is a collection of siloed data banks that hardly interact with each other. With the help of integration tools and techniques, AI data engineers unite them into a transparent ecosystem, enabling data to move freely across all departments and branches and ensuring a unified view.  

Where are these capabilities employed? 

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Use cases of artificial intelligence for data engineering

AI data engineering solutions find wide application in many shop floor activities of companies.  

• Automation of data collection and integration. AI algorithms crawl databases, APIs, and websites to discover and download relevant information. They can be trained to recognize different data formats, adapt to changing data volumes, and build real-time data ingestion pipelines. 
• Data transformation and cleansing. AI mechanisms can identify anomalies and unusual patterns in data that can be the sign of errors and missing points. Plus, AI can be highly instrumental in automating string manipulation, format conversion, unit transformation, and other data-cleaning jobs. 
• Lineage tracking and data profiling. AI can track data movement down to its origin, allowing personnel to see the transformations applied to it. Plus, it can conduct data versioning and rollback processes, providing a clear audit trail and boosting troubleshooting in case issues crop up. Besides, AI mechanisms can promote extracting insights for various use cases by identifying data types, pre-set statistical properties, and potential biases.  
• Self-optimizing data pipelines. The usage of deep learning mechanisms allows AI solutions to monitor data pipeline performance on a regular basis, detect bottlenecks, register delays, and anticipate potential failures.  

They can also optimize resource allocation, reduce expenditures, and suggest adjustments. The latest trend in this area is self-healing pipelines that automatically identify malfunctions and address issues, thus minimizing downtime and guaranteeing data flow continuity. 

• Data augmentation. Specialized data model development performed by generative AI tools provides the ability to produce synthetic data that mirrors the characteristics of existing data but exceeds it in volume and diversity. Such newly generated data can be employed to train ML algorithms and simulate real-world scenarios during QA and testing procedures. 
• Code generation. The data artificial intelligence produces encompasses software code. It can save the developers’ time and effort they spend on coding because the lion’s share of software syntax is repetitive. That is why AI’s predictive mechanisms can anticipate and complete code lines without involving human personnel who would otherwise do it manually.  

While performing these and other tasks, AI data systems usher in numerous boons for organizations that employ them.  

The benefits of AI for data engineering made plain

Having an in-depth knowledge of AI-driven data engineering, we can pinpoint the following advantages of embracing this technology. 

1. Boosted efficiency. Comprehensive automation of all aspects of data handling routine allows organizations to cut down on manual effort, accelerate data processing, and increase overall efficiency, which is mission-critical in working with huge data volumes. 
2. Improved consistency and accuracy. No matter how high the level of people’s skills might be, human labor is error-prone, leading to inconsistencies and inaccuracies across datasets. AI techniques rule out the negative impact of human factors, contributing to more reliable data analysis outcomes. 
3. Adaptability and scalability. The rapid increase in data volume calls for employing data processing mechanisms that can step up their capacity, accommodate new data sources, and evolve with the company’s expansion. AI mechanisms tick all these boxes, producing solutions with the utmost scalability and flexibility potential.  
4. Shorter time-to-insights. In the contemporary, fast-paced business world, response time is vital. AI tools can provide key decision-makers with relevant analytics and insights on short notice, enabling companies to take changes in their stride and adapt to fluctuating market conditions and consumer preferences. 
5. Enhanced customer satisfaction. With all the organization’s elements functioning like well-greased cogs, enterprises can provide their clientele with top-notch personalized customer experience and tailored support. Such first-rate services hone your competitive edge, leave the clients satisfied, and foster brand loyalty among them.  

To enjoy all the assets of AI-powered data engineering, you should overcome obstacles that are symptomatic of this field.  

Top challenges in implementing AI-based data engineering

As a vetted vendor that has delivered multiple projects in data science and artificial intelligence, DICEUS is aware of the pitfalls and bottlenecks that await data engineering initiatives. 

• The dearth of a qualified workforce. To implement any AI data engineering project, you need competent IT staff well-versed in AI practices and data science. The breakneck speed of these domains’ development makes it hard for technicians to keep abreast of the latest trends in the niche.  
• Data variability and complexity. You should ensure AI algorithms can handle disparately structured and formatted data retrieved from diverse sources. To do that, you need to conduct careful data validation and testing. 
• Embracing ethical practices. While developing and using AI-powered products, you should keep your eyes skinned for their adherence to ethical principles. To develop solutions that align with moral guidelines and norms, you should ensure their transparency, accountability, fairness, and the absence of algorithmic bias.  
• Data privacy and security. A large portion of the data AI handles is sensitive. You should institute and uphold stringent protection measures (such as encryption, multifactor authentication, access control, and more) to eliminate or at least minimize the chances of data breaches, unauthorized access, and potential misuse.  
• Regulatory compliance. In such industries as banking, insurance, and healthcare, organizations deal with personal and financial data whose safety is protected by various legal standards (HIPAA, GDPR, CCPA, etc.). AI data engineers should see to it that the solutions they develop adhere to these norms and provide continuous compliance monitoring of their systems.  

Successfully addressing these challenges is possible when development teams are aware of the domain’s prospects. 

Data engineering and AI: A glimpse into the future

AI technology and approaches to data engineering are constantly evolving. What are these realms likely to witness in the nearest future?  

• Lower access threshold. The contemporary reliance on SQL and BI dashboards in data analytics will give way to AI-powered chat-like interfaces. They will unlock data processing for a non-tech audience who will be able to ask data-related questions in natural language and enable more people to participate in data handling tasks.  
• Massive advent of cloud technologies and SaaS products. The first data processing products were created for on-premises environments, forcing engineers to spend more time on the configuration of their systems than on creating business value. By switching over to cloud computing and SaaS solutions, they can focus on organizations’ business needs and leave technical moments to service providers. 
• Edge computing integration. Today, an ever-growing bulk of data is generated by IoT devices. With the increase in the amount of such data, engineers will focus on bringing AI data processing systems closer to data sources, which will help to use bandwidth more efficiently and drastically reduce latency.  

Evidently, implementing AI-fueled data engineering solutions is a complex task with multiple niceties to consider, which requires fundamental theoretical knowledge of the niche and mastery of numerous hands-on IT skills. Seasoned experts of DICEUS possess both to successfully accomplish an AI data engineering project of any scope and complexity, delivering a top-notch product that will add value to your organization. Contact us to pave the way to embracing disruptive data handling practices and revolutionizing your data processing pipeline. 

Conclusion

Nowadays, artificial intelligence is revolutionizing many areas, and data handling is no exception. AI solutions are massively leveraged for data collection, storage, processing, governance, and integration, helping organizations to automate their data pipelines, conduct data profiling and lineage tracking, generate software code, perform data transformation and cleansing, and more. As a result, companies obtain highly adaptable and scalable systems that improve data consistency and accuracy, boost organizational efficiency, reduce time-to-insights, and augment the customer experience of their clientele. 

To get a first-rate AI-powered data engineering platform, you should watch for pitfalls, understand the current and future trends in the area, and hire a seasoned team of qualified professionals to implement your project. 

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