Past few years we have witnessed digital transformation of an unprecedented level and this trend will continue into 2023. Organizations are embracing new ways to evolve work processes and find efficient ways of working. This paradigm shift involves heavy usage of AI and automation along with using data tools and machine learning technologies.
According to Saxon, around 80% of the work in AI and analytics is focused on data engineering which shows how valuable this sector is. As businesses continue to enter new markets and look for enhanced ways to sell their products and services, the usage of data in multiple formats and structures will grow. From blockchain to customers finding recommendations in their movie and shopping lists, the role of data engineering will become more complicated and important.
Here are some top trends that technologies in data engineering have to watch out for!
There Will Be a Boost in Data Services
According to LinkedIn, the Software as a service (SaaS) market has jumped from 30 Billion to 200 Billion in 2023 and this indicates that data-producing tools which are already indispensable will become more diverse in the future.
Popular applications that we use such a Zoom, Gmail and others depend on SaaS delivery models. As the volume of data continuous to increase, there will be increased usage of SaaS tools as they are scalable, cost effective, accessible, and many SaaS software applications integrate well with other software solutions.
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Data privacy regulations have grown worldwide in recent years and it is necessary to protect data from falling into the wrong hands. This has prompted organizations to increase their investments in data engineering to address data privacy concerns.
Given these developments, data engineering continues to be a top priority for businesses. This is why data regulation is among the top trends in data engineering for 2023. While organizations usually prioritize long-term needs over short-term trends, it's crucial to consider the changes expected in the next 6-12 months as well.
Making Data Catalogs
Data pipelines have a crucial role in data engineering. Traditionally, these pipelines focused on extracting, transforming, and loading data. However, they were prone to failures and unavailability when changes occurred in the data structure or values.
To address this challenge, the data catalog comes into play. It enables internal and external users to easily access and discover data. By effectively managing and organizing data, any changes in structure or format won't disrupt the data pipelines.
Low Code and No Code Technologies
Data has become an indispensable part of the business world and offers various job opportunities. So, it’s no surprise that non-technical professionals are pulled to this field. Though, they are required to learn the technical aspect of the work to perform their duties well. According to LinkedIn, nearly 58% of non-technical professionals need to become more data-savvy to grow in their existing job role.
New applicants in the field of data engineering will be relying low-code’ and ‘no-code’ technologies which will empower them to build data solutions without any obstacle. So those from non-technical background can easily work without the need to code.