Scale data sizes: Data continues to increase exponentially as new data sources are born out of rapid technological advancements making data preparation more complex. What’s needed is a tool that can automatically generate high-quality code that is native to cloud-based distributed data processing systems like Databricks and avoid losing the ease of use a visual interface provides.
Scale the number of pipelines: As data transformations scale to the thousands, it’s imperative that standards are put in place for repeatable business logic, governance, security, and operational best practices. By developing frameworks, engineering teams can provide the building blocks for business SMEs and data users to easily leverage visual components to build and configure data pipelines in a way that is both standardized and easy to manage.
So, What’s Next? Key Considerations to Finding the Ideal Solution
Self-service is the future of data transformation, with a uk whatsapp number data shift toward increased automation, better analytics, and enhanced collaboration. As organizations strive for greater autonomy in their data transformation processes, there will be a rise of intuitive interfaces, automated data profiling, and augmented insights to enable users to engage in more sophisticated data activities without having to rely heavily on central engineering teams.
Organizations must also be prepared to leverage the latest innovations like generative AI and large language models (LLMs). These capabilities, sometimes branded as “co-pilots,” are revolutionizing the way data is transformed and analyzed and are empowering systems to automate aspects of data transformation and enhance natural language interactions within the data transformation process.