Introduction

Dremio is a self-service data platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure.

Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others. Many of the newer datastores are often more agile and provide improved scalability, but at a cost to speed and ease of access via traditional SQL-based analysis tools. Additionally, raw data found in these stores is often too complex or inconsistent for analysis to use with business intelligence tools.

ETL pipelines that load a subset of data into relational databases provide one answer, but aside from the burden these solutions impose on data engineers and IT staff, data becomes stale by the time it is available to analysts and data scientists.

Self-service Analytics on Modern Data

Dremio solves these problems by providing an experience that is easy and immediately usable by analysts, with integrations to BI tools, R, Python, and any SQL-based tool. This frees your business from potentially costly data workflows that depend on custom solutions from IT and data professionals, while simultaneously granting instant compatibility with industry standard tools that are already deployed across millions of desktops.

Dremio allows analysts to easily cut through complicated source data with nested structures or mixed types, creating a new virtual dataset that represents only the most useful version of your available information. Virtual datasets can join data across multiple systems, including Elasticsearch, MongoDB, Amazon S3, Hadoop and legacy RDBMSs.

Interactive Analysis Regardless of the Source

Dremio brings speed to your analysis tasks by leveraging the Apache Arrow columnar in-memory data technology, and an intelligent caching feature called the Dremio Accelerator. With acceleration enabled, queries can be run interactively. This capability is a game changer in the realm of Big Data exploration and analysis, which has traditionally struggled with the challenge of creating an interactive experience.


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