The Dremio Blog

Dremio's `COPY INTO` command, and the soon-to-be-released Auto Ingest feature provide robust solutions for importing these files into Apache Iceberg tables. By leveraging Dremio, ingesting and maintaining data in Apache Iceberg becomes manageable and efficient, paving the way for performant and flexible analytics directly from your data lake. In this article, we’ll do a hand-on exercise you can do in the safety of your local environment to see these techniques at work.

Dremio enables directly serving BI dashboards from Apache Druid or leveraging Apache Iceberg tables in your data lake. This post will explore how Dremio's data lakehouse platform simplifies your data delivery for business intelligence by doing a prototype version that can run on your laptop.

This exercise hopefully illustrates that setting up a data pipeline from Kafka to Iceberg and then analyzing that data with Dremio is feasible, straightforward, and highly effective. It showcases how these tools can work in concert to streamline data workflows, reduce the complexity of data systems, and deliver actionable insights directly into the hands of users through reports and dashboards.

The most straightforward area to address in terms of reducing costs is your BI Dashboards. Whenever someone interacts with a BI dashboard that uses Snowflake as the data source, queries are sent to Snowflake, increasing your expenditure. Imagine if you could significantly cut the costs of serving dashboards from your Snowflake data by drastically reducing the amount of Snowflake compute resources needed.

Moving data from source systems like MySQL to a dashboard traditionally involves a multi-step process: transferring data to a data lake, moving it into a data warehouse, and then building BI extracts and cubes for acceleration. This process can be tedious and costly. However, this entire workflow is simplified with Dremio, the Data Lakehouse Platform. Dremio enables you to directly serve BI dashboards from MySQL or leverage Apache Iceberg tables in your data lake.

Moving data from source systems like Elasticsearch to a dashboard traditionally involves a multi-step process: transferring data to a data lake, moving it into a data warehouse, and then building BI extracts and cubes for acceleration. This process can be tedious and costly. However, this entire workflow is simplified with Dremio, the Data Lakehouse Platform. Dremio enables direct serving of BI dashboards from Elasticsearch or leveraging Apache Iceberg tables in your data lake.

ical use cases. While you can deploy Dremio as self-managed software in a Kubernetes environment, you can get some nice bonuses when working with a Dremio Cloud Managed environment

Tim Hurski, Prashanth Badari, Sonal Chavan, Dexin Zhu and Dmitry Chirkov

Dremio's version 25 is not just an update; it's a transformative upgrade that redefines the standards for SQL query performance in lakehouse environments. By intelligently optimizing query processing and introducing user-friendly features for data management, Dremio empowers organizations to harness the full potential of their data, driving insightful business decisions and achieving faster time-to-value. With these advancements, Dremio continues to solidify its position as a leader in the field of data analytics, offering solutions that are not only powerful but also practical and cost-effective.

Dremio version 25 represents a significant leap forward in making lakehouse analytics more accessible and manageable. With its enhanced monitoring capabilities, seamless third-party integrations, and a suite of additional features, Dremio is setting a new industry standard for ease of administration. These improvements streamline the monitoring process and empower administrators to proactively manage their environments, ensuring that Dremio continues to be an optimal choice for companies seeking advanced, user-friendly analytics solutions.

Dremio's version 25 is not just an incremental update; it's a transformative release that redefines stability and durability in SQL analytics. By introducing sophisticated memory management, spillable hash joins, and a proactive memory arbiter, Dremio ensures businesses can rely on its engine for their most critical and complex data workloads.

With version 25, Dremio redefines the analytics landscape for Apache Iceberg, offering a robust, efficient, and user-friendly platform. By enhancing its native support for Iceberg and integrating features like real-time data ingestion and simplified migration, Dremio empowers organizations to harness the full potential of their data, enabling insightful analytics and informed decision-making in a modern data environment.

Dremio enables direct serving of BI dashboards from SQLServer or leveraging Apache Iceberg tables in your data lake. This post will explore how Dremio's data lakehouse platform simplifies your data delivery for business intelligence by doing a prototype version that can run on your laptop.

Business Intelligence (BI) dashboards are invaluable tools that aggregate, visualize, and analyze data to provide actionable insights and support data-driven decision-making. Serving these dashboards directly from the data lake, especially with technologies like Apache Iceberg, offers immense benefits, including real-time data access, cost-efficiency, and the elimination of data silos. Dremio as a data lakehouse platform, […]

Moving data from source systems like Postgres to a dashboard traditionally involves a multi-step process: transferring data to a data lake, moving it into a data warehouse, and then building BI extracts and cubes for acceleration. This process can be tedious and costly. However, this entire workflow is simplified with Dremio, the Data Lakehouse Platform.