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Current Architecture (Conceptual) The current Dremio Software architecture often uses a fixed pool of executor engines. While this provides stability for baseline workloads, it struggles to handle predictable spikes in demand, leading to performance bottlenecks during peak periods, and overallocation of engines during quieter periods. Overallocation can have an impact on Cloud costs. This document’s […]
Overview Dremio provides out-of-the-box methods of handling complex data types in, for example JSON and parquet datasets. Common characteristics are embedded “columns within columns” and “rows within columns”. In this blog, we will demonstrate how Dremio can discover and handle these types of data. The examples have been tested on the following Dremio versions: Preparation […]
Introduction Dremio can automatically take advantage of partitioning on parquet data sets (or derivatives such as Iceberg or Delta Lake). By understanding the dataset’s partitioning, Dremio can perform partition pruning, the process of excluding irrelevant partitions of data during the query optimisation phase, to boost query performance. (See Data Partition Pruning). Partition bucketing provides a […]
Introduction Dremio now offers the capability to both query and manipulate data stored in Apache Iceberg table format. Apache Iceberg is rapidly becoming the industry-standard open table format, allowing data management within the data lake — see here for more details on Apache Iceberg. In this blog, we’ll show how Dremio can be used to […]
Intro Dremio Dremio AWS Edition, a production-grade, high-scale data lake engine highly optimized for AWS, delivers cost savings and makes it very easy to deploy a Dremio cluster in AWS. Learn more about the Dremio AWS Edition and check out the onboarding videos to get started. Whether you are a current user of Dremio AWS […]
