10 Data Engineering Topics and Trends You Need to Know in 2024 10 Data Engineering Topics and Trends You Need to Know in 2024
Now that we’re in 2024, it’s important to remember that data engineering is a critical discipline for any organization that wants... 10 Data Engineering Topics and Trends You Need to Know in 2024

Now that we’re in 2024, it’s important to remember that data engineering is a critical discipline for any organization that wants to make the most of its data. These data professionals are responsible for building and maintaining the infrastructure that allows organizations to collect, store, process, and analyze data. 

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

Data Engineering for Large Language Models

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

Real-Time Data

Real-time data is data that is processed and analyzed as soon as 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 important as organizations look to make faster and more informed decisions. Data engineers will need to develop the skills and tools to collect, store, and process real-time data. This will become more important as the volume of this data grows in scale.

Data Governance

Data governance is the process of managing data to ensure its quality, accuracy, and security. Data governance is becoming increasingly important as organizations become more reliant on data. Data engineers will need to be involved in data governance initiatives to ensure that the data they are working with is reliable and trustworthy. Data engineers act as gatekeepers that ensure that internal data standards and policies stay consistent.

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Data Observability and Monitoring

Data observability is the ability to monitor and troubleshoot data pipelines. Data monitoring is the process of collecting and analyzing data about data pipelines to identify and resolve problems. Data observability and monitoring are essential for ensuring the reliability and performance of data pipelines. This includes being able to identify and troubleshoot data issues, as well as track and monitor data usage. Tools can help data engineers gain visibility into their data pipelines and identify potential problems. Monitoring tools can help data engineers to track data usage and identify trends. By being proficient in these tools and techniques, data engineers can help to ensure that data is accurate, reliable, and available for use.

Democratization of Data and Self-Service Analytics

The democratization of data is the process of making data more accessible to users across the organization. Self-service analytics is the ability for users to analyze data without the help of a data scientist or data engineer. The democratization of data and self-service analytics are important trends because they allow organizations to make better 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 combination of on-premises and cloud-based infrastructure. Multi-cloud and hybrid cloud adoption is becoming increasingly popular as organizations look to get the best of both worlds: the flexibility and scalability of the cloud and the control and security of on-premises infrastructure. Data engineers will need to be familiar 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 personal data from unauthorized access, use, or disclosure. Data privacy is becoming increasingly important as regulations such as the GDPR and CCPA come into effect. Data engineers will need to be aware of data privacy regulations and how 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 new approaches to data management that are designed to improve 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 are becoming increasingly important in data engineering. Automation can help to reduce the time and cost of data engineering tasks, while DevOps practices can help to improve the reliability and scalability of data pipelines. So as you can imagine, the ability to focus on both automation and best practices for DevOps will become increasingly important as organizations look to improve efficiency. 

Ethical Data Engineering and Algorithmic Bias

Ethical data engineering is the practice of designing and implementing data pipelines in a way that is fair, just, and transparent. Algorithmic bias is the unintentional or intentional discrimination that occurs when algorithms are used to make decisions. Keeping pipelines free of bias is an important responsibility that will fall on data engineers. And as the demand for greater AI-integrated tools grows, companies will push to reduce the risk of algorithmic bias in order to maintain their ethical standards. 


It’s clear that 2024 is going to be an amazing year for data engineering. If these trends, or even others, move forward, the entire field will likely see some pretty big movement. And as any data engineering professional knows, the best way to stay ahead of the curve is by keeping up with the latest in all things related to data and data engineering. The best way to do that is by joining us at ODSC’s Data Engineering Summit and ODSC East. 

At the ODSC Data Engineering Summit on April 24th, you’ll be at the forefront of all the major changes coming before it hits. So get your pass today, and keep yourself ahead of the curve.



ODSC gathers the attendees, presenters, and companies that are shaping the present and future of data science and AI. ODSC hosts one of the largest gatherings of professional data scientists with major conferences in USA, Europe, and Asia.