The concept of data mesh has gained traction over the last three years. It brings two key components to the forefront. First, it introduces the idea of “data as a product,” which involves packaging data in a well-defined, discoverable format that can be used in a self-service fashion, without direct involvement from the data producer. This concept includes not only raw data but also analytical models, such as those used for customer churn or fraud prevention.
Secondly, the use of self-service platforms for producing malaysia whatsapp number data data products, not for business intelligence, enables various business units to create data products without the need for separate data platforms. This reduces costs and increases efficiency.
Major technology providers, including cloud services like Azure and AWS, are catching up and offering solutions to manage distributed data and analytics platforms in a data mesh fashion. This helps to connect data across various platforms and technologies, providing a centralized view of the data landscape.
Trend: LLMs Will Play a Crucial Role in Enhancing Data Engineering and Data Operations
Generative AI and large language models (LLM) have the potential to transform the data space. This transformation includes deploying GenAI models within existing data infrastructures for tasks like data engineering and data operations.