The Dremio Blog

Auto Ingest Pipes is here to meet that need with a suite of unique features that set a new standard in data ingestion technology.

The Unified Apache Iceberg Lakehouse, powered by Dremio, offers a compelling solution for unified analytics. By connecting to a wide range of data sources and minimizing data movement, you can achieve faster, more efficient analytics, improve AI model training, and enhance data enrichment processes. Dremio's advanced processing capabilities and performance features make it a standout choice for any organization looking to unify and accelerate their data analytics platform.

Integrating Snowflake with the Dremio Lakehouse Platform offers a powerful combination that addresses some of the most pressing challenges in data management today. By unifying siloed data, optimizing analytics costs, enabling self-service capabilities, and avoiding vendor lock-in, Dremio complements and extends the value of your Snowflake data warehouse.

Platforms like Dremio facilitate this hybrid approach by connecting to various data sources and utilizing the Apache Iceberg format, ensuring that your data is always accessible and performant, regardless of where it resides. Whether you are looking to optimize costs, enhance performance, or achieve greater agility, a hybrid data lakehouse could be the perfect solution for your data needs.

Apache Iceberg's open and extensible design empowers users to achieve optimized query performance while maintaining flexibility and compatibility with a wide range of tools and platforms. Iceberg is indispensable in modern data architectures, driving efficiency, scalability, and cost-effectiveness for data-driven organizations.

For businesses seeking a competitive edge, Dremio's superior query performance, self-service experience, cost effectiveness, flexibility and developer-friendliness make it the clear choice over Denodo. While Denodo has its merits, the complexity and hidden costs can hinder an organization's ability to maximize data's value fully.

For Snowflake users, incorporating Dremio and Apache Iceberg means unifying disparate data sources and achieving unprecedented data acceleration and efficiency. These tools create a powerful, integrated platform for advanced data analytics and AI, making them an invaluable addition to any data strategy.

Dremio's approach removes primary roadblocks to virtualization at scale while maintaining all the governance, agility, and integration benefits.

The recent adoption of the Apache Iceberg REST catalog specification by Nessie not only broadens its accessibility and usability across different programming environments but also cements its position as a cornerstone in the data architecture landscape.

As we look forward to further advancements, Dremio's Reflections are set to redefine the data processing standards, proving that the quest for speed and efficiency in data analytics can be successfully achieved

Central to the functionality of Apache Iceberg tables is their catalog mechanism, which plays a crucial role in the evolution of how these tables are used and their features are developed. In this article, we will take a deep dive into the topic of Apache Iceberg catalogs.

Dremio offers a powerful solution for unifying data across Snowflake, Azure, AWS, and Google based data marketplaces while mitigating egress costs and simplifying data management. By leveraging Dremio's reflections and advanced lakehouse capabilities, you can enhance your analytics without the hassle of complex data movements. We invite you to get hands-on and explore the full potential of Dremio through the tutorials listed below. Discover how Dremio can transform your data operations and take your analytics to the next level.

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.