The DataOps Blog

Whether you are building your very first pipeline or you’re an old pro, these best practices for building data pipelines can help you make pipelines that are easy to understand and therefore easy to maintain and extend.   Design Data Pipelines for Simplicity Reduce complexity in the design wherever possible. This is a concept borrowed from software development. When reviewing your…

One can imagine the catastrophe of using inaccurate machine learning models in business – accidents, investment losses, and erroneous analysis. However, because the use cases for machine learning algorithms are numerous and can be positively and negatively impactful, a lot relies on the data quality fed into these models. Before machine learning engineers build machine learning models, the data must…

Every organization will ‌inevitably migrate data between locations at some point. Data migration refers to the movement of data between storage locations and data platforms. For example, you might need data migration when you introduce new database systems or migrate applications from on-premises to the cloud. Before the evolution of data migration tools, the data migration process was inefficient, lengthy,…

Data lacks value until organizations can gain business intelligence and insights from it. The ability to transform and maximize the value of an organization's data can be challenging for most businesses. Data storage options must hold company data and be available for querying as needed. Why? Statistics indicate that fast and easy data access increases business performance by up to…