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Modernize Data Analytics with Cloud Data Lake

Transparently modernize your data analytics infrastructure towards open cloud data lake storage, cost-efficiently and without disruption to your existing analytics workloads. Move beyond the complexity, performance limitations and higher cost of on-prem and cloud data warehouses and data marts.

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The future is open cloud data lake storage

With the advent of cloud data lake storage on AWS S3 or Azure ADLS, enterprises can harness the agility of cloud to store and analyze data without the lock-in and added expense of enterprise data warehouses.

Move from on-prem to the cloud

You have options: Begin by leveraging the benefits of Dremio for your existing on-prem data. Then add Dremio in the cloud for new data—you’ll enjoy the same Dremio experience on-prem and in the cloud thanks to our exportable semantic layer (no re-write needed). Once you’re ready, seamlessly migrate your on-prem data to the cloud to maximize the agility and savings of Dremio cloud data lake storage.

Move off of data warehouses

Enterprise data warehouses lock your data into proprietary formats that require ETL/ELT—and come with significant added expense. Enter Dremio. Reduce your data warehouse loads—and costs—by leveraging Dremio data lake storage for new workloads while maintaining joins to data in your data warehouse. You’ll be increasing analytics performance, reducing dependency on data engineering and lowering analytics spend.

Migrate data warehouse workloads

Take your analytics modernization step by step. Lower your spend on an enterprise data warehouse by gradually moving workloads on to a data lake. Leverage Dremio to put new workloads on the data lake and then gradually migrate existing workloads—eventually eliminating the need for your data warehouse. Lower costs, increase performance and reduce dependence on data engineering, all at the same time.

Modernization presents management challenges

Enterprises wanting to modernize to open cloud data lake storage face two complex tasks: staging data migration(s) and, perhaps more complicated, moving and rebuilding the analytical business logic—the data pipeline—that connects existing BI and data science tools. Dremio helps solve these problems with a semantic layer that enables seamless operation of analytics workloads during migration and eliminates the need to reconstruct data pipelines on cloud data lake storage.

Simplify with a semantic layer

Dremio’s self-service semantic layer abstracts the virtual datasets being queried from the underlying physical datasets and storage. Thus, even as the location of the physical data changes during migrations, queries continue against the same virtual dataset without disruption.

Take the semantic layer with you

In preparing to migrate data, data engineers can simply transfer Dremio’s semantic layer to the new cloud data lake storage—with no interruptions, and no rebuilding of data pipelines. This radically speeds up both the modernization process and enterprise access to data post-migration.

Operate identically on-prem and in the cloud

Run the exact same SQL query with the same experience, the same performance and the same easy semantic operations, both on-prem and in the cloud. Dremio provides a consistent, best-in-class analytics experience regardless of whether your data is on-prem or in the cloud.

Move data on your own schedule

Migrate your workloads and data to the cloud at your own pace. Dremio enables queries against multiple datasets, both on-prem and in the cloud, so your analytics workloads won’t be slowed by ongoing migrations.

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