A modern data architecture acknowledges the idea that taking a one-size-fits-all approach to analytics eventually leads to compromises. It is not simply about integrating a data lake with a data warehouse, but rather about integrating a data lake, a data warehouse, and purpose-built stores, enabling unified governance and easy data movement. With a modern data architecture on AWS, customers can rapidly build scalable data lakes, use a broad and deep collection of purpose-built data services, ensure compliance via a unified data access, security, and governance, scale their systems at a low cost without compromising performance, and easily share data across organizational boundaries, allowing them to make decisions with speed and agility at scale.
Why you need a modern data architecture
Data volumes are increasing at an unprecedented rate, exploding from terabytes to petabytes and sometimes exabytes. Traditional on-premises data analytics approaches can’t handle these data volumes because they don’t scale well enough and are too expensive. Many companies are taking all their data from various silos and aggregating all that data in one location, what many call a data lake, to do analytics and ML directly on top of that data. At other times, these same companies are storing other data in purpose-built data stores to analyze and get fast insights from both structured and unstructured data. This data movement can be “inside-out”, “outside-in”, “around the perimeter” or “sharing across” because data has gravity.
