{"id":52179,"date":"2023-09-05T10:45:45","date_gmt":"2023-09-05T14:45:45","guid":{"rendered":"https:\/\/sdtimes.com\/?p=52179"},"modified":"2023-09-05T10:45:45","modified_gmt":"2023-09-05T14:45:45","slug":"the-post-modern-data-stack-why-all-roads-lead-to-automation","status":"publish","type":"post","link":"https:\/\/sdtimes.com\/data\/the-post-modern-data-stack-why-all-roads-lead-to-automation\/","title":{"rendered":"The post-modern data stack: Why all roads lead to automation"},"content":{"rendered":"

The modern data stack, once a symbol of streamlined efficiency, is cracking under its own weight. What seemed like a dream come true for engineering teams has become a complexity trap that requires more and more maintenance as it scales.<\/span> Technical debt<\/span><\/a> is piling up and teams are struggling to keep pace, suggesting a future where there will be no capacity left for innovation. Maintaining the modern data stack consumes too many resources, and the only way to free up engineers\u2019 time is to evolve the current architecture. The industry needs a post-modern data stack.<\/span><\/p>\n

To understand what this looks like, it\u2019s helpful to look at how we got here. Data management has come a long way over the past decade, evolving from Teradata and Informatica, to Hadoop, and now to the age of Snowflake, Databricks, and Google BigQuery. Today\u2019s modern, cloud-based architecture has an alluring simplicity, but as the task of managing and maintaining it grows in complexity it undermines the very benefits it has set out to provide. We’ve made progress, but we’re not there yet.<\/span><\/p>\n

An unwieldy mass of tools<\/b><\/h5>\n

Even with a modern cloud architecture, significant complexity remains. Data engineers have been given a vast array of new tools, but there are so many of these tools that they have become unwieldy. Data sources have multiplied, pipelines have become more intricate, and the resulting infrastructure is not self-aware, requiring significant manual maintenance. The challenges of the modern data stack outweigh the benefits, leaving teams disillusioned and overworked.<\/span><\/p>\n

All roads lead to automation<\/b><\/h5>\n

When we look at the history of enterprise technologies, it becomes clear that all roads lead to consolidation, simplification, productization, and ultimately automation. Here are two examples that illustrate this:<\/span><\/p>\n