Making the right decisions for your data architecture is crucial for efficient performance and optimal cost. Open lakehouse architectures have become popular due to their increased flexibility by storing data in open source storage formats. Additionally, open data lakehouses leverage a data catalog to allow multiple, governed access points and manage schemas across data sources. Apache Iceberg is an open table format that many organizations are choosing to implement.
Multiple vendors are offering Iceberg as a managed service as part of their larger data architecture portfolio. Providers are touting that implementing open source friendly software will allow buyers to avoid vendor lock-in down the road. However, the functionality provided around Iceberg is not created equal and should be evaluated critically in order to truly determine true openness.
For example, many organizations have already chosen to adopt Snowflake and are interested in expanding their data into Iceberg with Snowflake. However, Snowflake’s Iceberg offerings force users to choose between a plethora of significant trade-offs. In some scenarios external Iceberg clients are unable to write to Snowflake Iceberg tables and read is only possible by using Spark. Additionally partitioning is not supported which will severely impact performance. Some of this can be remediated by implementing Apache Polaris but will come with increased cost and resources.
IBM’s watsonx.data is highly flexible with the ability to configure services on premise, or a public or private cloud environment. External data sources are easily integrated and open source tooling like Iceberg remains true to its intended ease of use. Because of the out of the box multi-engine functionality, managing governance, performance, and cost can be fine tuned and transparent.
Every watsonx.data customer can tailor their architecture to their specific data needs and trust that the future of their applications will be scalable and resilient.
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