The Dremio Blog

A data flow design for modern data analytics. The medallion architecture empowers data analysts to access trusted data, collaborate with colleagues, and uncover invaluable insights quickly and efficiently. Analysts can unlock the full potential of their organization's data and drive informed decision-making by understanding the distinct layers of the data lakehouse and its role in unifying data analytics.

The demand for flexible and fast data-driven decision-making is critical for modern business strategy. Semantic layers are designed to bridge the gap between complex data structures and business-friendly terminology, enabling self-service analytics. However, traditional approaches often struggle to meet performance and flexibility demands for today’s business insights. This is where a data lakehouse-powered semantic layer […]

By unifying data sources, simplifying governance, providing an intuitive UI, and supporting flexible data access methods, Dremio empowers users to independently explore and analyze data.

By leveraging these technologies, Dremio ensures that the Unified Apache Iceberg Lakehouse provides top-notch performance while keeping costs low. This combination allows organizations to unify their data, minimize data movements, and achieve high performance without breaking the bank.

Dremio offers a versatile and powerful platform for data sharing, whether through integrating with existing data marketplaces, providing shared compute resources, or enabling independent data access via catalogs. By leveraging these capabilities, you can maximize the value of your data, streamline collaboration, and create new opportunities for revenue and partnerships. Dremio’s comprehensive approach to data sharing ensures that you can meet your organization’s needs while maintaining control and governance over your data assets.

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.

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.

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

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.

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.