Blog

Cloud Data Analytics - Sep 19 2021

Who Is Using Dremio?

Dremio is a data lake engine that enables lightning fast queries, significantly cutting down on the time needed to reach important business insights. In fact, Dremio can completely mitigate the need to structure the data and move it into a data warehouse before data analysis. In addition, Dremio is an open-source solution, which removes the need to pay expensive licensing costs.

So, who’s using Dremio? Let’s take a look at the functionality Dremio offers and how it can be a powerful tool for data analysts.

What is Dremio used for?

Dremio is a data lake engine that facilitates live and interactive data queries on data lake storage, both on-premise and cloud. For a long time, data lakes were known as a transitional data storage medium with very little potential for meaningful BI work. With Dremio, all those notions are shattered as data lakes became an effective BI tool, even negating the need for data warehouses for certain use cases.

Dremio helps democratize the access to data for data management personnel and teams, including analysts, data scientists, and others. This is done through a polished, governed, self-service user interface. Dremio is the pathway for organizations to achieve lightning fast query speeds at one of the lowest possible costs per query.

However, there are several other benefits organizations can leverage using Dremio. Let’s take a look:

  • Data Migration
    Data migration has always been one of the major concerns regarding data lakes. Traditional legacy data protocols like ODBC and JDBC would often lead to slow migration speeds, resulting in problems in time-critical data requirements. Dremio removes these problems with the use of Apache Arrow, an open-source project that brings columnar data interchange and in-memory data processing to the table. The execution engine in Dremio is built from the ground up to make the best use of Apache Arrow. In terms of migration, Arrow Flight, a feature in Dremio, helps replace legacy migration protocols and can speed up data migration up to 1,000 times.
  • Business Insights Dashboarding
    BI dashboarding is one of the best ways to view, interpret, and understand critical business intelligence. Dremio opens up a world of BI dashboarding opportunities thanks to Data Reflections, a unique feature that optimizes parquet data structures automatically and invisibly. This optimized structure is then incorporated into the data query plan to achieve unparalleled query speeds and rich dashboards.
  • Virtual Datasets
    With Dremio’s virtual datasets, Dremio can help create a semantic layer of data access. This solves one of the most major problems faced by data professionals: Sharing, managing, and curating data without having to copy it multiple times. Copying data can not only be slow and inefficient but also create several crucial governance and security issues. With virtual datasets, it is possible to create semantic data layers that are completely indexed, searchable, and totally virtualized.
  • Connecting to External Sources
    Dremio also gives organizations the opportunity to further enhance and enrich their data analytics by providing the option to join or connect the data lake to a number of external sources. Correlating with this, advanced data manipulation and analytics can be carried out without having to physically move the data. This can drastically improve the time to value, while also leaving other options open regarding data migration.

Does Dremio use SQL? 

Dremio implements full support for SQL and enables its use to perform a multitude of functions. Users can create data views based on one or more physical or virtual datasets through the use of SQL code. In addition to using Apache Arrow and Apache Parquet, Dremio also makes use of Apache Calcite for query optimization and SQL parsing. Traditional cloud storage or Hadoop clusters can work with SQL queries, but are definitely not optimized for it. With Dremio, a variety of SQL queries can be used with exceptional speed and simplicity.

Currently, Dremio is equipped to provide users with comprehensive access to the stored data, wherever it might be stored. As a part of this support, it includes compatibility with several different data types, including Elastisearch, Hive, MongoDB, MySQL, PostgreSQL, Teradata, and Redshift. SQL functions can be used to carry out a variety of operations, including data/time, mathematical, string, nested data, type conversion, aggregate, and conditional operations. 

In the enterprise edition, the SQL editor can be used for simple role-based access control through the sys privileges system table where access can be managed using GRANT and REVOKE commands. Users can also define reflections on any existing dataset, with the ability to create raw, aggregate, external, and dropping reflections. Tables can also be easily created, either in the shared location, or in filesystem sources.

Arguably the most powerful use of SQL in Dremio lies in working with datasets. With simple SQL commands, users can create, replace, or drop virtual datasets and enable default reflections on virtual datasets. SQL can also be used with physical datasets to refresh or forget the dataset metadata. Source statuses can also be managed using SQL. SQL connections can be established in Dremio using their own JDBC and ODBC drivers.

Dremio is also known for featuring the most powerful and potent query pushdown capabilities currently offered. Even with very complex queries, there’s full support for partial and complete pushdowns.

Read More

How Do I Choose a Cloud Data Warehouse?
Is Dremio Right for Enterprise?
The Truth About Dremio vs Snowflake
The Ultimate Guide to Implementing Dremio

 

Have questions? We help companies like yours, every day.

Email us at hello@nextphase.ai

About NextPhase.ai

NextPhase.ai is a data cloud services provider specializing in Snowflake, cloud data management and analytics technologies. We accelerate enterprise digital transformation initiatives by leveraging our innovative cloud data management technology, “NextPhase.ai DATAFLO” to optimize and rationalize disparate enterprise data into relevant insights. “DATAFLO” is designed to automate the lifecycle of data management transformation using AI and ML along with expeditious on-ramps to the Snowflake data cloud infrastructure. NextPhase.ai provides a range of technology consulting services for the Financial Services, Biotech and Technology industry sectors combining our platform-based services, seasoned talent, and industry proven methodology so our customers can harness more from their data. We are a Silicon Valley based company with global presence having delivered high value service engagements for numerous Global 2000 enterprises.

 

Leave a Comment

Your email address will not be published. Required fields are marked *

Get in touch with NextPhase.ai