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Rapid growth in the volume, velocity, and variety of organizational data hinders traditional strategies for data management and analysis and agile enterprises must be able to mine value from data in a short period. In response to these challenges, enterprises invest heavily in developing data management strategies using data warehouses, data lakes, and data lakehouses to improve data management.
A solution that enables data access in its native repository has significant advantages to address these business challenges, including minimizing the latency caused by data copying and reducing expenditure for developer resources required to integrate data into data warehouses. This solution allows developers and data scientists to spend more time mining data and developing insights for the enterprise.
Conventional approaches that leverage technologies are slow in delivering innovation at the pace that business ecosystems are evolving. Red Hat OpenShift addresses a wide range of business challenges related to modern application development, deployment, and management.
Storage is another crucial factor for data analytic workloads. The most common storage for unstructured and semi-structured data is S3-compatible object storage. Procuring the right object storage for data analytic workloads can be complex and time consuming.