iomete

At IOMETE, we understand the importance of having full control over your data. With and time travel capabilities, users can easily query data as it existed at any specific point in time. This feature is not only useful for debugging and auditing, but it's also perfect for historical analysis.�Iceberg's feature is designed to give you peace of mind. Every time a table is modified, such as adding or deleting data, a new snapshot is created and assigned a unique identifier. Each snapshot provides a consistent and complete view of the table at a specific time.�To make the most of this feature, we've provided an example below that showcases how to use time travel in the platform. Here is a step-by-step guide: https://iomete.com/docs/time-travel

This post(https://iomete.com/docs/guides/sql-quick-start/tables-from-jdbc-csv-json) provides a comprehensive guide on and external table manipulation in IOMETE, with examples covering , CSV, JSON, Parquet, and ORC. It demonstrates creating tables from various data sources, querying these tables, as well as exporting data back to these sources.
Here is an example of how to create a table from a JDBC source (MySQL):

In this blog post, our co-founder, Piet Jan de Bruin, provides valuable insights into why IOMETE has chosen to position itself as the on-premise lakehouse. Feel free to follow this link https://iomete.com/blog/why-we-choose-to-be-the-on-premise-data-lakehouse if you are:

◼︎ IT decision-makers who are considering deploying a .

◼︎ Data engineers and scientists who are looking for a more secure and cost-effective way to manage their data.

◼︎ Anyone who is interested in learning more about the data lakehouse market.