ow that we’re in 2024, it’s vital to do not forget that data engineering is a critical discipline for any organization that wishes to profit from its data. These data professionals are chargeable for constructing and maintaining the infrastructure that permits organizations to gather, store, process, and analyze data.

And as the quantity of information that organizations generate continues to grow and the demand to utilize this data grows with it, the demand for data engineers is just going to extend. So let’s dive in and explore 10 data engineering topics which are expected to shape the industry in 2024 and beyond.

Data Engineering for Large Language Models

LLMs are artificial intelligence models which are trained on massive datasets of text and code. They are used for a wide range of tasks, comparable to natural language processing, machine translation, and summarization. As LLMs turn out to be more powerful and as more organizations move toward domain-specific LLMs, the demand for data engineers who can construct and maintain the infrastructure to support these models will increase. The growth of complexity will bring greater demand for the talent that may handle the infrastructure needs of LLM use.

Real-Time Data

Real-time data is data that’s processed and analyzed as soon because it is generated. This is in contrast to batch processing, where data is collected and processed at regular intervals. Real-time data is becoming increasingly vital as organizations look to make faster and more informed decisions. Data engineers might want to develop the abilities and tools to gather, store, and process real-time data. This will turn out to be more vital as the quantity of this data grows in scale.

Data Governance

Data governance is the strategy of managing data to make sure its quality, accuracy, and security. Data governance is becoming increasingly vital as organizations turn out to be more reliant on data. Data engineers will must be involved in data governance initiatives to be certain that the information they’re working with is reliable and trustworthy. Data engineers act as gatekeepers that be certain that internal data standards and policies stay consistent

EVENT – ODSC East 2024

In-Person and Virtual Conference

April twenty third to twenty fifth, 2024

Join us for a deep dive into the newest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI.

Data Observability and Monitoring

Data observability is the flexibility to observe and troubleshoot data pipelines. Data monitoring is the strategy of collecting and analyzing data about data pipelines to discover and resolve problems. Data observability and monitoring are essential for ensuring the reliability and performance of information pipelines. This includes having the ability to discover and troubleshoot data issues, in addition to track and monitor data usage. Tools might help data engineers gain visibility into their data pipelines and discover potential problems. Monitoring tools might help data engineers to trace data usage and discover trends. By being proficient in these tools and techniques, data engineers might help to be certain that data is accurate, reliable, and available to be used.

Democratization of Data and Self-Service Analytics

The democratization of information is the strategy of making data more accessible to users across the organization. Self-service analytics is the flexibility for users to investigate data without the assistance of a knowledge scientist or data engineer. The democratization of information and self-service analytics are vital trends because they permit organizations to make higher use of their data.

Multi-cloud and Hybrid Cloud Adoption

Multi-cloud and hybrid cloud adoption is the trend of using multiple cloud providers or a mix of on-premises and cloud-based infrastructure. Multi-cloud and hybrid cloud adoption is becoming increasingly popular as organizations look to get the perfect of each worlds: the flexibleness and scalability of the cloud and the control and security of on-premises infrastructure. Data engineers will must be acquainted with multiple cloud providers and the challenges of managing data in a multi-cloud or hybrid cloud environment.

Data Privacy

Data privacy is the protection of private data from unauthorized access, use, or disclosure. Data privacy is becoming increasingly vital as regulations comparable to the GDPR and CCPA come into effect. Data engineers will need to pay attention to data privacy regulations and the way to design and implement data pipelines that protect user privacy.

Development of Data Fabrics and Data Mesh Architectures

Data fabrics and data mesh architectures are recent approaches to data management which are designed to enhance scalability, flexibility, and resilience. Data fabrics are a centralized approach to data management, while data mesh architectures are a decentralized approach.

Focus on Automation and DevOps Practices

Automation and DevOps practices have gotten increasingly vital in data engineering. Automation might help to scale back the time and price of information engineering tasks, while DevOps practices might help to enhance the reliability and scalability of information pipelines. So as you’ll be able to imagine, the flexibility to concentrate on each automation and best practices for DevOps will turn out to be increasingly vital as organizations look to enhance efficiency.

Ethical Data Engineering and Algorithmic Bias

Ethical data engineering is the practice of designing and implementing data pipelines in a way that’s fair, just, and transparent. Algorithmic bias is the unintentional or intentional discrimination that happens when algorithms are used to make decisions. Keeping pipelines freed from bias is a very important responsibility that can fall on data engineers. And because the demand for greater AI-integrated tools grows, firms will push to scale back the chance of algorithmic bias with the intention to maintain their ethical standards.


It’s clear that 2024 goes to be a tremendous yr for data engineering. If these trends, and even others, move forward, your complete field will likely see some pretty big movement. And as any data engineering skilled knows, the perfect method to stay ahead of the curve is by maintaining with the newest in all things related to data and data engineering. The best method to try this is by joining us at ODSC’s Data Engineering Summit and ODSC East.

At the ODSC Data Engineering Summit on April twenty fourth, you’ll be on the forefront of all the key changes coming before it hits. So get your pass today, and keep yourself ahead of the curve.

This article was originally published at summit.ai