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This article looks at how legacy databases have evolved over time and how they compare to Amazon Redshift. It also takes you through Adastra's methodology for migrating to Redshift.
Organizations are growing increasingly reliant on fresh, timely insights for decision-making and this has highlighted the crucial need for data. Early adoption of analytics started in the late 90s and the earliest forms of legacy databases came into use. While customers undoubtedly benefited from these early solutions, they came at a phenomenal cost and posed many implementation challenges.
Some of these legacy platforms, like Teradata, are still around, but have undergone significant overhaul and upgradation. Today’s business needs, however, are quite different compared to what they were a few decades ago. The digital transformation wave has led to exponential growth of data volumes and drastic changes in the ways that organizations collect, store, transform, and analyze data. The business use cases are different, and both data types and user needs are evolving at an unprecedented pace. Moreover, the rapidly changing business landscape demands faster time-to-value, and in many ways, legacy databases can no longer offer the value they once did.
This article explores how legacy databases have evolved since their early days and how the challenges associated with these platforms have changed. It then makes a case for migrating to the cloud and compares the legacy platforms of old and today with Amazon Redshift. Finally, we discuss in some detail the value Redshift offers and Adastra’s approach for migrating to Redshift.