As we approach the second quarter of 2024, Artificial Intelligence (AI) is driving significant changes in the field of data engineering. Integrating data engineering with AI has led to the popularity of modern data integration and the expertise required. I want to highlight here how the rise of AI will change data engineering trends in 2024 compared to previous years.
First thing first, a quick recap on traditional data engineering
Let's quickly recap how the data engineering role is born and grows with the passage of time. Almost a decade ago, businesses realised the importance of data-driven decisions. That opened roles like BI/ETL Developers with expertise in Microsoft SSIS/SSRS, Talend, Informatica, and other similar tools. However, with the emergence of social media applications, these traditional ETL development tools faced limitations in processing billions of records. The idea of distributed storage and processing came into the field of play, such as Spark and NoSQL databases. As a result, the demand for Hadoop/Spark Big Data Developers has increased. Exciting Hadoop times ahead!
In 2006, AWS launched its cloud services S3 and EC2, followed by Microsoft and Google launching Azure and Google Cloud in 2008. This led to an increase in the use of cloud technologies. The cloud technologies are based on a pay-as-you-use model that avoids buying expensive servers and operating system licenses. These cloud technologies have now been called "Modern Data Warehouses" since the union of Big Data and Cloud. In early 2010, Cloud Developer jobs rose in the market with the release of AWS EMR, etc. Because now, each and every company wants to migrate from on-premise to the cloud. With the passage of time, modern cloud-based data warehousing solutions have become costly and inefficient in handling unstructured data, leading to the birth of file-based data lake solutions.
Different Variations of Data Engineers
The Data Engineering Landscape is quite complex and vast. Engineers and businesses struggle to keep up with the latest tools and technologies to build and maintain a scalable data platform. Each business creates its definition of data engineers, and some require data engineers to focus primarily on creating pipelines. Some require software engineering and reverse engineering expertise or the ability to build KPI models as an analytics engineer along with different cloud-based specializations.
This is the current state of Data Engineering variation and demand, but this is not stopping here. I want to highlight some other trends that are rising this year.
AI Meets Data Engineers
Recently, I saw on LinkedIn that many positions are open for AI Data Engineers. They mostly wrote that the responsibilities are to build LLM Models or prepare the batch/streaming data for these AI-based models. This includes regular data engineering tasks to build pipelines for product feature enhancement and enable data-driven business with a centralized data warehouse or data lake solution.
Quality Assurance Engineer Merge with Data Engineer
As a Data Engineer by profession, I realise that a data team should have at least one Quality Assurance Engineer. The QA takes all the responsibility for testing data quality, as they used to do web or mobile application testing. That person is always up-to-date with business domain knowledge. While the engineers are mostly focusing on technical aspects. With the rising demand for Gen AI solutions like GitHub Codepilot and ChatGPT, there is no guarantee that their solutions are always reliable; hence, it increases data quality problems with their use in production. Quality is one of the biggest challenges for small to enterprise companies. You are definitely going to see a lot more job openings as Data Quality Engineer in the coming days. This role is going to be the next demanding job in the coming months.
Batch Data Processing Could Obsolete Soon
Stream data integration and processing keep adding value daily for the business allowing them to make faster decisions and offer better product features. The CDC (Change Data Capture) is already taking place for batch pipeline integration, primarily for the main source database. However, Due to less demand for batch processing and the high output of stream processing architecture, it may slowly become obsolete or less required in the future.
Lakehouse Architecture Will Remain The First Choice
Hundreds and thousands of companies have already taken advantage of building Lakehouse Architecture. They moved from the costly “Modern cloud data warehouse“ to the file-based Lakehouse Architecture either using Delta Lake, Apache Hudi or Apache Iceberg open storage format, as its ability to manage large-scale data with ACID transactions and schema enforcement will remain the first choice for next some year(s) unless we see any other new tools or architecture in the market.
The popularity of No Code Data Integration Tools
Modern Data Integration Tools like Airbyte, Mage.ai, Stitch, and Fivetran simplify data integration and workflow and reduce the need for custom Python-based data pipeline development. These tools speed up the process of pipeline development, but I will come up with the cost of the complexity of debugging these tools if something breaks; it will not be very straightforward to fix it. On the other hand, expert engineers will be required to kick-start the pipeline integration in one click. I would like to see how long we can go to adapt these tools, no-code-based tools.