Unfreeze Apache Iceberg to Thaw Your Data Lakehouse
The original inspiration behind data lakes was simple: Developers needed a centralized location to store any type of data at scale, even if that data didn’t fit in the relational schema required by traditional data warehouses.
Without this centralized location, developers were left with two options for where to store their data — data lakes and warehouses, neither of which were an ideal fit. So along came the data lakehouse, which merged the Python and machine learning functionality of data lakes with the robust SQL capabilities of warehouses. In other words, the best of both worlds for developers.
Unfortunately, the promised satisfaction has yet to arrive for many users who have turned to data lakehouse-powered products and services. Why? Because their lakehouses have frozen over.
Wait, What Happened to the Lakehouses?
What do I mean by a lakehouse being frozen? It’s actually a dual metaphor. The first meaning is a literal one — Apache Iceberg has become the standard storage format for lakehouses, and ice is frozen.
The second meaning describes the subpar user experience of applications and the difficulty of powering operational workloads, such as machine learning, on lakehouse data. Often the data platform used to write the data to Iceberg is not the best one to meet these needs. To do this, you need a modernized data platform with the following capabilities:
- Subsecond point reads and writes.
- Generative AI, which requires both vector search and full text search.
- Subsecond analytical queries for insights on what happened in the past on relational and JSON data.
- Ultra-fast ingestion to power analytical queries and real-time use cases.
What’s more, you need these capabilities at scale, with high concurrency and the high-quality performance experience application users and operational workloads demand.
Unfreezing Data Lakehouses for Good
With these targets in mind, our team developed a new native integration with Apache Iceberg, which opens up a range of possibilities for “unfreezing” Apache Iceberg data.
Our integration with Apache Iceberg is only the first step in the long journey many developers face in navigating complex data sets. The data landscape will continue to grow more unwieldy as businesses look for new ways to source and leverage their data — and that’s not even considering the rise of generative AI applications and their impact on data management and access.
Regardless of how complex data ingestion and management becomes, a strong open source foundation will remain valuable, which is why we’re excited about this new integration.
To learn how SingleStore’s real-time data platform can drive innovation at your company and unfreeze your most valuable enterprise data, try a free trial of SingleStore today.
