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Reading Time: 5 minutesFor years, enterprise data architecture has relied on collecting, transforming, and replicating data across multiple systems to serve different analytical and operational needs. This approach has delivered value, but it has also introduced unwanted side effects, over time: unnecessarily duplicated…
Reading Time: 3 minutesFor years, enterprises have invested heavily in cloud-native lakehouses like Snowflake and Databricks to unify analytics at scale. These platforms are powerful—the culmination of decades of innovation in analytic technology stacks, starting with enterprise data warehouses, continuing through the “big…
Reading Time: 2 minutesAn edited version of this blog is also posted on InsightJam. The recent announcement that Salesforce is acquiring Informatica has sent waves throughout the data management community. This follows ServiceNow’s acquisition of data.world, a cloud-native data catalog platform, raising questions in…
Reading Time: 3 minutesA few months ago, I spoke with the head of data architecture at a leading European bank. They’d just completed a multi-year investment in a modern data lakehouse platform — a combination of Databricks on Azure, paired with legacy systems…
Reading Time: 5 minutesIn my previous post, I described the data fabric architectural pattern. Before turning to the data lakehouse, let’s first look at what I mean by an architectural pattern. The Power of a Pattern An architectural pattern, or logical architecture, describes…
