lakeFS

Classical Data Lakes are often reduced to basic but cheap raw data storage, neglecting significant aspects like transformation, data quality and security. These topics are left to data scientists, who end up spending up to 80% of their time acquiring, understanding and cleaning data before they can start using their core competencies. In addition, classical Data Lakes are often implemented by separate departments using different standards and tools, which makes it harder to implement comprehensive analytical use cases. Smart Data Lakes solve these various issues by providing architectural and methodical guidelines, together with an efficient tool to build a strong high-quality data foundation. Smart Data Lakes are at the core of any modern analytics platform. Their structure easily integrates prevalent Data Science tools and open source technologies, as well as AI and ML. Their storage is cheap and scalable, supporting both unstructured data and complex data structures.

Cribl Search delivers next-generation search-in-place technology, empowering users to explore, discover, and analyze data that was previously impossible – directly at its source, across any cloud, even data locked behind APIs. Effortlessly search your Cribl Lake or sift through data in major object stores like AWS S3, Amazon Security Lake, Azure Blob, and Google Cloud Storage, and enrich your insights by querying dozens of live API endpoints from various SaaS providers. The power of Cribl Search lies in its strategic approach: forward only the critical data to your systems of analysis, thus avoiding the cost of expensive storage. With native support for platforms such as Amazon Security Lake, AWS S3, Azure Blob, and Google Cloud Storage, Cribl Search delivers a first-of-its-kind ability to seamlessly analyze all data right at its source. Cribl Search allows users to search and analyze data wherever it is located, from debug logs at the edge to archived data in cold storage.