3 Data Pitfalls to Avoid with Data Warehouses and Data Lakes

The COVID-19 crisis has highlighted the need for effective information sharing across organizations, especially in dealing with critical equipment like ventilators. As companies and governments modernize their data architecture to bridge the gap between information and outcomes, they face choices such as using data warehouses, data lakes, or a combination of both. The evolution of data warehouses has changed evaluation criteria from price/performance metrics to handling diverse data types. There is no one-size-fits-all solution for data warehouses and data lakes, with cloud data lakes adding SQL analytics capabilities and cloud data warehouses incorporating more data lake-like features. Key challenges in implementing these solutions include not aligning with an organization's data transformation journey, lack of collaboration from the ground up, and insufficient rigor in tackling enterprise-wide data integrity. To address these issues, some organizations are leveraging data observability to monitor and alert for problems in their data pipelines.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.