The Dremio Blog

Dremio stands as a formidable solution to the pervasive challenge of data silos. Unifying disparate data sources enables organizations to leverage their data assets fully, enhancing decision-making and operational efficiency. As the data landscape evolves, tools like Dremio will be critical in shaping a more integrated and insightful approach to data analytics.

Whether through direct PyArrow library usage or leveraging the dremio-simple-query library for simplified querying and data manipulation, the synergy of these tools opens up new possibilities for data analysis and processing. The ability to convert data streams into different formats ensures compatibility with a wide array of data processing and analytics tools, making this approach highly versatile.

this is a product release announcement regarding new ingestion capabilities for Apache Iceberg. Customers can now use COPY INTO to get data in parquet format into Iceberg tables.

The demand for insightful and high-performance dashboards has never been greater. As organizations accumulate vast amounts of data, the challenge lies in visualizing this data efficiently, especially when dealing with large datasets. In this article, we will delve into the realm of BI dashboards, exploring the hurdles that hinder their performance for sizable datasets. Traditionally, […]

This month, we’re excited to announce automated table cleanup!

The concept of data marts has long been a pivotal strategy for organizations seeking to provide specialized access to critical data for their business units. Traditionally, data marts were seen as satellite databases, each carved out from the central data warehouse and designed to serve specific departments or teams. These data marts played a vital […]

The Databricks platform is widely used for extract, transform, and load (ETL), machine learning, and data science. When using Databricks, it's essential to save your data in a format compatible with the Databricks File System (DBFS). This ensures that either the Databricks Spark or Databricks Photon engines can access it. Delta Lake is designed for […]

We're always looking for ways to better handle and save money on our data. That's why the "data lakehouse" is becoming so popular. It offers a mix of the flexibility of data lakes and the ease of use and performance of data warehouses. The goal? Make data handling easier and cheaper. So, how do we […]

In the age of data-centric applications, storing, accessing, and managing data can significantly influence an organization's ability to derive value from a data lakehouse. At the heart of this conversation are data lakehouse table formats, which are metadata layers that allow tools to interact with data lake storage like a traditional database. But why do […]

Using Airbyte, you can easily ingest data from countless possible data sources into your S3-based data lake, and then directly into Apache Iceberg format. The Apache Iceberg tables are easily readable directly from your data lake using the Dremio data lakehouse platform, which allows for self-service data curation and delivery of that data for ad hoc analytics, BI dashboards, analytics applications, and more.

In the last decade, a lot of innovation in the data space took place to address the need for more storage, more compute, hybrid environments, data management automation, self-service analytics, and lower TCO to operate business at scale.

Dremio Arctic bring new features to Dremio Cloud, including Apache Iceberg table optimization and Data as Code.

With its capabilities in on-prem to cloud migration, data warehouse offload, data virtualization, upgrading data lakes and lakehouses, and building customer-facing analytics applications, Dremio provides the tools and functionalities to streamline operations and unlock the full potential of data assets.

By implementing these data quality checks, you can trust the accuracy of your data and make reliable decisions based on high-quality information. Data quality is an ongoing process, and these techniques serve as essential tools to ensure the integrity and usefulness of your datasets.

This blog presents how to get started with Flink SQL and Apache Iceberg for streaming analytical workloads.