Top Data Engineering & AI Trends for 2024

The world of  data engineering and AI is evolving rapidly. You may overlook something significant if you're not attentive. Are you interested in the latest developments in AI and data engineering? Each year, we have a conversation with a top data specialist to get their outlook on the future, and we also add some of our own. Here are our top 10 trends for data engineering and AI in 2024. Let's get started and see what lies ahead!

Data Engineering & AI Trends

LLMs are revolutionizing all fields

In the last year, large language models—AI that can comprehend and utilize human language—have significantly impacted technology. Numerous businesses, both large and small, are attempting to use AI technology for various objectives.

This trend will continue into 2024 and beyond. It's creating a greater need for data and necessitating the development of new methods for storing and using it, such as vector databases, a novel kind of AI technology setup. Additionally, it is altering how we manage and use data for our product users.

We anticipate that automated data analysis, in which a computer does the necessary tasks on your behalf, will be a standard feature of all products and data handling processes. How can we ensure these new AI technologies will be more than dazzling talking points in 2024? That is a critical issue.

Data team will function similarly to an app team

Top data engineering teams are beginning to handle their data like a genuine product. This implies that they offer their consumers a specific quality of service, operate in short cycles (sprints), develop thorough plans, and establish explicit targets.

Data engineering and ai teams will be regarded more like crucial product development teams, with all the structure and expectations accompanying that position, as businesses begin to see the increasing value in their data.

Data will also be the focus of software teams

Usually, when developers attempt to build AI or data products without having a thorough grasp of the data, bad things happen.

As AI becomes more prevalent, the distinction between engineering and data processing will become increasingly blurred. It is essential to consider AI when creating critical software, and actual, meaningful data is essential when working on big AI projects.

This implies that to create AI solutions that are beneficial and continue to bring advantages over time, developers will need to focus more on data—that is, understand it and know how to utilize it efficiently.

Retrieval-augmented generation (RAG) is going to gain traction

After a few significant AI failures, it's become evident that high-quality, trustworthy data appropriately selected is necessary for AI products to function well.

Teams with unique data will use RAG and other fine-tuning methods much more as we uncover holes in AI's learning and learn more about the technology. They will take these steps to improve their AI tools and provide genuine, distinctive value to their customers.

Standardizing AI technologies for commercial use

AI is unquestionably one of the data products that are part of the ongoing primary trend in data products.

In 2024, the goal will be to integrate AI technologies into company processes, while in 2023, the main emphasis will be on investigating AI. AI Sonata teams will use data teams across all industries to use solutions prepared for large-scale commercial applications. A critical issue is whether these AI technologies will be genuinely ready for the major leagues.

We'll get to the point of simply adding AI features for kicks. Teams are expected to become more astute in their AI tool development by 2024. Rather than merely creating more complicated devices, they will use AI to provide value and address actual issues.

Data engineering is essential to the development of AI

According to an AWS report, data quality is the main issue for businesses when using AI.

Like other data-driven techniques, generative AI depends on high-quality data to function successfully. Manual data verification cannot guarantee that larger-than-large models (LLMs) function as required.

To assist data teams intrepidly in identifying and resolving data problems, sophisticated monitoring tools tailored to AI will be necessary. As businesses manage more data and more complicated tasks, they can maintain the dependability of their AI systems in this manner. In 2024, it will be critical to have tools that prioritize problem-solving, optimize data pipelines, and enable the new types of databases used in artificial intelligence.

Managing extensive data in more intelligent and compact ways

Having a personal computer was considered very significant in the past. Our laptops nowadays are just as powerful as the large servers used by businesses a few years ago for intensive data processing. This indicates that it is becoming more difficult to distinguish between standard consumer technology and business-grade solutions.

According to Tomasz Tunguz, data teams will begin using more effective techniques, such as processing data directly in the computer's memory, since many jobs are relatively simple. With cloud technology, this method may be quickly set up and readily scaled to suit company objectives.

Achieving the ideal balance while using data engineering with AI

Today's data executives have a challenging task: they must utilize more data, use it more effectively, employ more AI, and pay less for aws services.

According to experts, the objectives established for data engineering and AI officers could be more achievable. For instance, the amount spent on cloud services has increased dramatically; according to one source, in only the first quarter of 2023, expenditures hit $21.5 billion. According to a different survey, cloud prices are increasing annually for businesses by as much as 30%.

Finding more effective methods to employ cloud and data resources will be crucial in 2024. Monitoring data consumption and adjusting data usage using tools would be very beneficial.

Apache Iceberg is gaining popularity

The AI and data engineering teams at Netflix developed Apache Iceberg, an open-source format that speeds up and simplifies handling massive volumes of data. It enables practical SQL analysis of massive data tables.

In contrast to conventional data storage options, which also handle data processing and storage, Iceberg focuses on offering reasonably priced, neatly structured storage. This storage is adaptable to various organizational demands and may be utilized with different data processing tools, such as Apache Spark and Presto.

Two significant suppliers of data platforms, Databricks and Snowflake, have recently begun to support the Iceberg format, indicating the format's increasing significance. Apache Iceberg is anticipated to grow in popularity as more businesses embrace the lakehouse model, which combines elements of data lakes and data warehouses.

Want to make the most of your data? Our data engineering and AI services are here to help! Get in touch with us now to see how we can help your business grow with the power of data and AI!