As we step into 2024, a set of pivotal data engineering trends has assembled to reshape the landscape of how we construct, oversee, and harness data infrastructure.
The wide panorama of data engineering utilizes diverse tools and methodologies to establish a sturdy foundation, ensuring the consistent delivery of insights at scale. This approach is instrumental in overcoming the challenges posed by big data.
Transforming this approach to information management, these trends are there to stay for a while to foster improved decision-making and pave the way for more intelligent and responsive systems to evolve. Let's delve into the top data engineering trends that are shaping the future of this dynamic field.
As a data engineer, here are the top data engineering trends shaping the future of this dynamic field—that you should know:
In the data engineering career, the emergence of cloud-native approaches is becoming increasingly popular for their scalability, agility, and cost-effectiveness. Leading cloud platforms like AWS, Azure, and Google Cloud provide a scalable and economical infrastructure for both data storage and processing.
The year 2024 is poised to witness a significant surge in the adoption of cloud-based solutions for data storage, processing, and analysis. This shift empowers businesses to harness advanced computing capabilities, facilitating quicker data processing and accessibility, all while simplifying infrastructure management.
A rising trend in 2024 is Data Engineering as a Service (DEaaS), offering companies a team of data experts without the need for in-house hiring and management. DEaaS providers are to handle everything from data ingestion to transformation, deployment, and monitoring, allowing businesses to focus on extracting valuable insights from their data. This trend is gaining popularity due to the increasing complexity of handling diverse data sources and types, providing access to experienced data professionals.
Big Data and the Internet of Things (IoT) are inseparable trends in the data engineering landscape. According to Statistica, in the coming years, the market is expected to grow at an annual growth rate of 12.57 percent (CAGR 2024-2028), leading to a significant market volume of USD 2,227.00 billion by 2028.
IoT and big data are intertwined, with IoT devices generating vast amounts of unstructured data. This data is efficiently processed and stored in real time using big data technologies. The integration of artificial intelligence and machine learning further enhances the analysis of IoT data, contributing to improved automation and resource optimization.
Data warehouses and data lakes are to take center stage as essential technologies for storing big data. Often discussed together, these two serve distinct purposes in a data management ecosystem. A data lake acts as a repository for raw, unstructured, or semi-structured data, while a data warehouse store structured, processed data for specific purposes such as reporting and analysis. The coexistence of data warehouses and data lakes enables organizations to collect, manage, and utilize diverse data types efficiently.
A prominent trend for 2024 and beyond is edge computing in data engineering. This approach involves processing data closer to its source, reducing latency, and enhancing efficiency. Edge computing enables devices like smartphones and sensors to perform local data processing tasks, leading to quicker and more efficient real-time analysis and decision-making. As data volumes continue to grow, edge computing emerges as a crucial trend, offering practical solutions for effective data handling at the source.
The integral role of real-time data processing in data engineering remains steadfast for the foreseeable future. A report by Statista states that the global data sphere is poised to experience a substantial increase in real-time data volume— it is projected to grow to more than 180 zettabytes by 2025.
Real-time data processing revolutionizes the collection and analysis of data by providing instantaneous insights, in contrast to the traditional batch processing methods. This approach allows companies to make informed decisions aligned with customer or business needs. Moreover, as real-time data processing is largely automated, it reduces manual intervention, minimizes errors, and proves to be cost-effective.
Augmented analytics, incorporating machine learning and AI-driven capabilities, is set to revolutionize data analysis in 2024. With the market size projected to reach USD 61.27 billion by 2030, indicating substantial growth from over USD 8.9 billion in 2023, augmented analytics stands out as a recent trend in data engineering, showing significant promise.
Augmented analytics enhances the data analysis process by integrating artificial intelligence and machine learning. This automation streamlines data evaluation, identifies patterns, highlights key insights, and generates recommendations for business-driven decisions. The incorporation of augmented analytics promises to play a pivotal role in advancing data engineering practices.
A cutting-edge trend in data engineering, cloud computing, is expected to witness continuous growth in the coming years. Google predicts that by 2027, over 50 percent of enterprises will adopt industry cloud platforms.
The adoption of cloud computing facilitates the efficient generation, storage, and analysis of vast data volumes, leading to improved decision-making processes and streamlined core operations. Embracing multi-cloud and hybrid cloud strategies allows organizations to unlock the full potential of cloud computing, leveraging the strengths of platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform. This approach enhances scalability, security, and operational performance while optimizing costs through a multi-cloud setup.
DataOps and MLOps are pivotal in streamlining and automating data engineering processes, ensuring faster delivery and improved data quality. These methodologies increase data availability, accessibility, and integration, allowing businesses to construct automated data pipelines in private, multi-cloud, or hybrid environments.
The evolving trend of MLOps, combining principles from both data engineering and machine learning, addresses the gap between data science and production environments. MLOps becomes indispensable for the deployment and maintenance of machine learning systems at scale. Both DataOps and MLOps work synergistically to accelerate the implementation and maintenance of analytics and data models.
Data mesh emerges as a current trend in data engineering, emphasizing a domain-oriented approach to data. This approach empowers specific domain teams with ownership and independence over their data assets, enabling each domain to maintain its data infrastructure.
Unlike traditional centralized data architectures, data mesh facilitates decentralized data governance, fostering collaboration and efficiency among teams. The adoption of data mesh principles allows organizations to embrace a more effective and collaborative approach to data governance.
Anticipating the future of data engineering in 2024, here are a few predictions encircling the evolving landscape—
The anticipated valuation of the global data analytics market stood at USD 41.5 billion in 2023, with a projected surge to exceed USD 345.5 billion by 2028. The growing accessibility of analytics, business intelligence, and data warehouses has made data more readily available in formats that seemingly amplify organizations' digital presence–– fanning the increasing need for a data engineer in nearly every field. Their expertise ensures the reliability, scalability, and efficiency of data pipelines, empowering organizations and businesses to make informed decisions and gain a competitive edge.
Venturing into the data engineering career path holds strong appeal as the big data analytics field is poised for substantial growth in the coming years. Dice Media's analysis projects that over the past year, data-related skills have witnessed a remarkable 2-percent average increase in market value, with roles like data engineering, data strategy, and Big Data Analytics commanding increase in pay of 18-20 percent.
Although concerns about AI potentially replacing or disrupting Data Engineer roles are dispelled. While AI can automate certain tasks like data cleaning and transformation, professionals with advanced data skills will remain in demand to develop, fine-tune, and manage complex AI systems. In the foreseeable future, businesses embracing AI are likely to seek more Data Engineers, not fewer. And as we move forward, staying agile and adopting these trends will be vital for staying at the forefront of the data engineering landscape.
