Present Trends and Future of Data Engineering: 2023-2024


With the advancement of technology, the volume of data also increased. In the early 2010s, with the rise of the internet, these numerous increases in the data volume, velocity, and variety have led to the term big data to describe the data itself, and data-driven tech companies like Facebook and Airbnb started using the phrase data engineer for the people performing these tasks.

Data has been part of our identity as humans since the time of the ancient Romans. We’ve gone from 0% of the world on the internet to 59.5% of the world. 4.32 billion people with cell phones generate quite a huge data feed. How has humanity dealt with the need to analyze this extraordinary amount of data? We’ve come up with punch cards, relational databases, the cloud, Hadoop, distributed computing, and even real-time stream processing to try and manage this data.

In recent years data silos have come out as a big issue for companies. Every big organization consumes a large amount of data to perform decision-making. It becomes possible with the assistance of a data engineer. Data engineers play a crucial role in examining the infrastructure and performing relatable actions on it. 

Current Trends of Data Engineering:

Data engineering is a constantly evolving field with new technologies and practices emerging faster than ever before. In recent years, several trends have appeared in the world of data engineering that are shaping the way data is stored, processed, and analyzed. Let’s explore some of the top trends in data engineering: Data Lake-houses, Open Table Formats, Data Mesh, DataOps, and Generative Artificial Intelligence.

Role of Engineering

·         Data Lake-houses

Data Lakehouses represent a novel paradigm in the realm of data storage and processing, integrating the most advantageous attributes inherent in both data lakes and data warehouses. A Data Lakehouse essentially fuses the high-performance, comprehensive functionality, and stringent governance associated with data warehouses, with the scalability and cost-efficiency advantages offered by data lakes. This transformative architecture empowers data engines to directly access and manipulate data residing in data lake storage, avoiding the need for costly, specialized systems or the intermediary use of ETL pipelines for data replication.

The ascendance of the Data Lakehouse architecture is primarily attributed to its growing popularity as it furnishes organizations with a unified, singular vantage point encompassing all enterprise data. This holistic view is readily accessible and amenable to real-time analysis, thereby affording organizations a heightened capacity to extract valuable insights from their data reservoirs, thereby enabling them to gain a distinctive competitive edge in their respective domains.

·         Open table formats

Open Table Formats represent a pioneering standard in data storage and processing that champions the cause of seamless interoperability across diverse tools and platforms. In the past, each tool or platform adhered to its proprietary data format, thereby posing formidable challenges when it came to the transfer of data between systems or the analysis of data across different platforms. This led to vendor lock-in and the creation of data silos.

Notably, Open Table Formats such as Apache Iceberg, Delta Lake, and Hudi introduce a table format that is precisely engineered for optimal performance and encompasses a broad spectrum of data types. This pivotal development streamlines the task of working with data from various sources and facilitates the utilization of assorted tools for data processing and analysis.

Open table formats enable data lakes to be as approachable as databases, by providing a framework for interaction using an array of tools and programming languages. A table format empowers the abstraction of disparate data files into a unified dataset, effectively transforming data lakes into more structured and manageable entities resembling tables.

Data Engineering

Open Table Format

·         Data mesh

Data Mesh is a new approach to data architecture that emphasizes the decentralization of data ownership and management. In a traditional data architecture, data is centralized in a single repository and managed by a central team. In a Data Mesh architecture, data is owned and managed by individual teams or business units, and access to data is governed by a set of shared standards and protocols.

Data Mesh enables organizations to scale their data architecture by allowing different teams to manage their data and build their data products. This reduces the burden on the central data team and enables faster data processing and analysis.

·         DataOps

DataOps and MLOps are methodologies that bring together data engineering, data science, and machine learning. These practices streamline the process of developing, deploying, and monitoring data pipelines and machine learning models. By automating and orchestrating these workflows, organizations can accelerate their data-driven initiatives and improve collaboration between data engineers and data scientists.

The DataOps approach will enable organizations to implement automated data pipelines in their private, multi-cloud, or hybrid environments. The main objective of DataOps and MLOps is to accelerate the development and maintenance cycle of analytics and data models.


DataOps is an Agile approach to designing, implementing, and maintaining a distributed data architecture that will support a wide range of open-source tools and frameworks in production.

·         Generative AI

Generative AI is a new field of AI that enables machines to create content, such as text, images, and videos. This technology has significant implications for data engineering, as it can be used to generate semantics, dictionaries, and synthetic data, which can be used to train ML models. This automation not only accelerates the data engineering process but also reduces the risk of human error, thereby increasing overall data quality and reliability.

Modern data engineering is shifting towards decentralized and flexible approaches, with the emergence of concepts like Data Mesh, which advocates for federated data platforms partitioned across domains. 


Future Trends:

The future trend of the world is going to be automated. In the next 5 years, the data is going to be fully automated and the data is going to become the end product. the data gaps between the organizations and users will be reduced. As a result, there will be an increased use and demand for fully automated clouds, and hybrid infrastructures, which will highly affect the future of data engineering.

It is predicted that the future of technology is Artificial Intelligence. Such that all the applications nowadays are self-generating and automated. In the field of data engineering, there is also going to be a rise in the use of AI, AI-powered tools can analyze large volumes of data, identify patterns, and generate optimized ETL workflows automatically. This automation not only accelerates the data engineering process but also reduces the risk of human error, thereby increasing overall data quality and reliability.

 That will help in speeding up the data manipulation processes. The data engineers will be more specialized and will give a variety of services to the organizations.


Dot Labs is an IT outsourcing firm that offers a range of services, including software development, quality assurance, and data analytics. With a team of skilled professionals, Dot Labs offers nearshoring services to companies in North America, providing cost savings while ensuring effective communication and collaboration.

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