- June 5, 2023
- Posted by: Aanchal Iyer
- Category: Data Science
Data Engineering has been helping businesses gain a competitive edge by offering real-time insights and data-driven strategies at scale over the past decade. This blog presents the path-breaking data engineering trends for 2023.
As organizations are becoming AI-driven, collecting, processing, and analyzing vast amounts of data becomes crucial. Augmented adoption of cloud platforms and democratization of data are now resulting in the sudden requirement for data engineering skills.
Data Engineering Trends for 2023
Data engineering is constantly evolving, with innovative technologies and practices emerging faster than ever. Let’s explore the top 5 trends that data engineering consulting services should look out for:
Data Lakehouses are a new method of data storage and processing that integrates the best features of data warehouses and data lakes. They combine the functionality, performance, and governance of a data warehouse as well as the use and cost benefits of a data lake. With a data lakehouse, engines can manipulate and access data directly from a data lake storage without having to copy the data into expensive proprietary systems while using Extract, Transform, and Load (ETL) pipelines. The data lakehouse architecture is a popular trend as it offers a single view of all enterprise data, that can be easily accessed and analyzed in real-time. This makes it easier to extract data insights and gain a competitive advantage.
One of the most fascinating data engineering trends for the year is the data mesh which focuses on decentralized data ownership across business functions. In this approach, multiple decentralized data repositories are used to analyze data, which enables self-service while removing various operational bottlenecks. The main principles of data mesh are:
- Data as a product: The approach values the data as a product and allows data monetization.
- Self-service data infrastructure: Data users can leverage self-service analytics tools and reporting to explore real-time insights. This improves efficiency and fosters a culture of data-driven decision-making.
- Governance: Data mesh ensures that the data adheres to industry standards and compliance requirements.
Generally, developers are not aware of how the consumption of data happens. Thus, data consumers cannot easily reach out to data producers to prioritize their use case resulting in dependencies and delays. Data contracts can remove these dependencies with API-based agreements between IT teams and data consumers to create high-quality, reliable, and real-time data. Data contracts can be designed to reflect the semantic nature of events, business entities, and attributes. In a nutshell, data contracts help in:
- Increasing the quality of produced data
- Maintenance of data
- Application of governance and standardization over a data platform
Generative AI is a new field of AI that helps machines create content, such as images, text, and videos. This technology has crucial implications for data engineering, as it can be used to create dictionaries, semantics, and synthetic data, which can be used to train ML models. Data science engineers must learn how to develop and work with generative AI models. Also, they must learn how to integrate generative AI into prevailing data pipelines and ensure that the models are generating relevant content.
Organizations see a huge value if data is readily available and accessible to various stakeholders. This data democratization is more valuable amongst the data engineering trends in 2023 as it is cost-efficient. When data is readily accessible, duplication efforts reduce when different stakeholders require the same underlying data. You can also reduce the investment in IT and technical teams as laymen can also access the data.
Data engineering consulting companies can gain a competitive advantage by leveraging the full potential of the data available by staying up to date with the latest trends and adopting new technologies and practices.