Top Data Engineering Trends to Watch in 2024 and Beyond

In today’s data-driven world, where real-time data pipelines and real-time data processing systems fuel innovation and drive decision-making, the role of data engineering has never been more pivotal. Data engineering uses different tools and methods to create a robust foundation for consistently delivering insights at scale and overcoming big data challenges that companies face today.

As we head into 2024, several key data engineering trends help reshape how we build, manage, and utilize data infrastructure. These trends unveil exciting developments that revolutionize how we handle information, paving the way for more streamlined processes, enhanced decision-making, and more intelligent, responsive systems.

Table of Contents:

• Importance of Data Engineering and Market Overview
• Top Data Engineering Trends 2024
• Top Data Engineering Predictions 2024
• Why Choose Rishabh Software as Your Trusted Data Engineering Partner?
• Takeaway: The Future of Data Engineering is Dynamic

Due to the increasing diversity and exponential growth of data, Data engineering is indispensable today for automating and orchestrating data pipelines and ETL (Extract, Transform, Load) processes. Data engineers design, build, and maintain these data pipelines, ensuring that the data is meticulously collected, cleansed, transformed, and made available across the organization in a structured and reliable manner. Data engineering thus allows seamless access to data to help enterprises get value out of their data faster and at scale.

Whether efficiency, managing data influx, or bridging the gap between information and informed decision-making, data engineering helps your business make informed decisions.

The global big data and data engineering market will likely grow from $39.50 billion in 2020 to $87.37^[1] billion by 2025 at an annual growth rate (CAGR) of 17.6%.

These stats clearly show the growth of data engineering in the upcoming years. As companies adopt digital transformation and evolve in this digital ecosystem, quality data engineering plays a crucial role in the success of your business operations.

The latest trends in data engineering for 2024 underline the increasing importance of scalable, agile, and innovative data management strategies. Understanding and embracing these trends will be crucial for organizations aiming to stay competitive in an increasingly data-centric business landscape, shaping the future of data engineering and its applications across industries.

Cloud-Native Data Engineering

Cloud-native data engineering will emerge as a preferred trend for the ability to offer scalability, agility, and cost-effectiveness. Leading cloud platforms such as AWS, Azure, and Google Cloud offer a scalable and budget-friendly infrastructure for storing and processing data.

2024 will likely see a rise in migration towards cloud-based data storage, processing, and analysis solutions. This shift empowers businesses to leverage advanced computing power, enabling faster data processing and accessibility while reducing infrastructure complexities.

Data Warehouse and Data Lake

The convergence of data warehouses and data lakes is fast gaining momentum, providing a unified platform for storing and analyzing structured and unstructured data. This integration simplifies data management, allowing for seamless data exploration and insights generation. Organizations are expected to invest more in cohesive data architectures that combine the strengths of both data warehousing and data lakes.

Data Mesh

Data mesh, with its focus on agility, scalability and distributed, decentralized data architecture, is intended to influence data engineering practices in 2024. By promoting domain-oriented, self-serve data platforms, data mesh enables efficient data access while maintaining data quality and governance. This trend aims to decentralize data ownership and facilitate cross-functional collaboration within organizations while also making the system more resilient to failures as data volume grows.

Data Engineering as a Service

DEaaS is a rising trend in data engineering for 2024. It’s like having a team of data experts available without the need to hire and oversee them in your company. Instead of building and maintaining your own data pipelines, data lakes, and complex data infrastructure in-house, you rent access to a managed data engineering platform. DEaaS providers take care of everything from data ingestion and transformation to deployment and monitoring, freeing you to focus on what matters most – extracting insights from your data. DEaaS benefits companies lacking internal capabilities to construct and oversee their data pipelines.

DEaaS is gaining popularity because handling data is getting more complex with more data sources and types. Also, finding skilled people to manage all this data takes a lot of work. DEaaS helps by giving access to experienced data engineers and data scientists without the hassle of hiring them.

Edge Computing

Edge computing is a prominent and increasingly popular data engineering trend for 2024 and beyond. This trend involves processing data closer to where it’s created rather than sending it all to a centralized location. This approach helps reduce latency and enhances efficiency in handling vast data. Using edge computing, devices like smartphones, sensors, and other smart devices can perform data processing tasks locally, making real-time analysis and decision-making quicker and more efficient. As the volume of data generated continues to soar, edge computing stands out as a vital trend in data engineering, offering practical solutions to handle data more effectively at the source.

Augmented Analytics

Augmented analytics incorporates machine learning and AI-driven capabilities to assist data engineers and analysts derive more profound insights from complex datasets. The prominent features of augmented analytics include automated data visualization, anomaly detection, and predictive insights. The integration of expanded analytics tools is expected to democratize data analysis, streamline data processing, automate insights generation, and enhance decision-making processes across industries.

Data Automation and AI

Automation connected with AI is set to revolutionize data engineering workflows. Organizations can enhance efficiency and accuracy by automating repetitive tasks like data pipeline orchestration, quality checks, and testing while allowing data professionals to focus on higher-value tasks like analysis and strategy. Automation and AI accelerate data analysis while simultaneously identifying and correcting data anomalies and inconsistencies, enabling real-time insights for faster decision-making.

Big Data Insights

As the adoption of the Internet of Things (IoT) continues to increase, vast amounts of data are being generated by its diverse devices. In 2024 and beyond, enterprises will increasingly embrace big data insights to leverage predictive and prescriptive analytics and AI to extract meaningful patterns for data-driven decisions. This trend is also driving a surge in big data engineering technologies. They are crucial for storing, processing, and analyzing the massive influx of data, enabling organizations to extract valuable insights and optimize operations. In fact, the big data engineering services market size is expected to reach $140.60 ^[2] billion by 2028, growing at a CAGR of 15.38%.

DataOps and MLOps

DataOps and MLOps methodologies continue to gain traction in 2024, emphasizing collaboration, automation, and continuous integration in data and machine learning workflows. These practices enable faster development cycles, ensuring efficiency reliability and scalability of AI and machine learning models in production environments. DataOps and MLOps together represent a fundamental shift in how organizations can unlock full potential of their data to gain a competitive edge in 2024.

The upcoming years are expected to bring key advancements and transformative shifts in data engineering. These predictions outline the significant forecasts anticipated in 2024 and beyond.

#Prediction 1 – Focus on Data Security and Cybersecurity

Intensified concerns about data security will lead to a greater emphasis on cybersecurity measures within data engineering. Strengthening data encryption access controls and implementing robust security protocols will be critical to safeguarding sensitive information against evolving cyber threats.

#Prediction 2 – Data Governance and Ethical Considerations

Enhanced focus on data governance and ethical considerations is anticipated to become a foundation of data engineering best practices. With the increasing emphasis on privacy regulations and ethical use of data, data engineers must integrate robust governance frameworks into their processes to ensure compliance and build trust with consumers and stakeholders.

#Prediction 3 – AI Becomes the Data Engineer’s Copilot

As AI co-pilots become common in enterprises in 2024, Artificial intelligence is fast becoming a helpful partner for data engineers. It can automate repetitive tasks such as data ingestion, cleaning, and automate data pipelines, generate code snippets and identify problems in data pipelines. This helps engineers focus on solving critical issues and making their data systems better. AI working alongside engineers makes managing data more accessible and more efficient.

#Prediction 4 – Embracing Data Contracts

Data contracts help improve transparency, trust, and collaboration in Data Management. In today’s complex data ecosystem, data contracts have the power to revolutionize data management, establishing clear, well-defined formal agreements between data producers and consumers, enabling trust, and enhancing collaboration. These contracts offer clear guidelines on ownership, quality standards, and usage terms, ensuring everyone understands expectations and operates on the same page. It helps in reliable data production, streamlines efficient data exchange, promotes cross-team collaboration, and mitigates risks of misuse.

Although data contracts are still new for the industry, their usage is expected to significantly increase within the next year or so, becoming more prevalent and widely implemented.

#Prediction 5 – Real-time Data Streaming

Event stream processing is a top prediction in 2024 due to its real-time data processing capabilities, scalability, and role in event-driven architectures. Facilitating the handling of massive data volumes, streaming frameworks like Apache Kafka and Apache Flink enable businesses to process information as it flows in, which is crucial for timely decision-making.

Its significance extends to IoT and edge computing, where real-time analysis of data generated by these devices becomes essential. Moreover, integrating real-time data streaming with AI/ML fosters adaptive systems, while its impact on various sectors, including healthcare, e-commerce, and retail, helps deliver personalized experiences to consumers.

As your trusted partner in data engineering, Rishabh Software offers extensive expertise and a comprehensive range of services. We cover the full cycle of data management, including acquisition, cleansing, conversion, interpretation, and deduplication. This ensures that your data is handled by experienced data engineers comprehensively from start to finish, guaranteeing accuracy and reliability. With hands-on expertise in AWS and Azure platforms, Rishabh Software is well-equipped to harness the benefits of cloud technologies for efficient data processing and management.

Our extensive experience, and proficiency in cloud technologies, comprehensive data management capabilities, and expertise in modernizing data infrastructure make us a reliable and trusted data engineering company for all your data engineering needs. We apply an efficient and smart approach to migrate business data from on-prem legacy systems to modern databases, including cloud storage infrastructure. This ensures that your data environment is optimized and transformed for enhanced performance and real-time exploration and analysis.

The data engineering trends 2024 mentioned above show a move towards using more innovative technologies like AI and machine learning to handle information more efficiently.

An even more significant shift is seen in how data is processed more quickly through edge computing, making things quicker and more responsive. The future of data engineering is data-driven, faster, more innovative, and easily accessible to all.

Footnotes:

1. https://www.marketdataforecast.com/market-reports/big-data-engineering-services-market

2. https://www.globenewswire.com/news-release/2023/08/10/2722722/0/en/Big-Data-Engineering-Services-Market-Size-Share-Analysis-Growth-Trends-Forecasts-2023-2028.html