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What is VeloDB?

VeloDB, powered by Apache Doris, is an innovative data warehouse tailored for swift analytics on extensive real-time data streams. It incorporates both push-based micro-batch and pull-based streaming data ingestion processes that occur in just seconds, along with a storage engine that supports real-time upserts, appends, and pre-aggregations, resulting in outstanding performance for serving real-time data and enabling dynamic interactive ad-hoc queries. VeloDB is versatile, handling not only structured data but also semi-structured formats, and it offers capabilities for both real-time analytics and batch processing, catering to diverse data needs. Additionally, it serves as a federated query engine, facilitating easy access to external data lakes and databases while integrating seamlessly with internal data sources. Designed with distribution in mind, the system guarantees linear scalability, allowing users to deploy it either on-premises or as a cloud service, which ensures flexible resource allocation according to workload requirements, whether through the separation or integration of storage and computation components. By capitalizing on the benefits of the open-source Apache Doris, VeloDB is compatible with the MySQL protocol and various functions, simplifying integration with a broad array of data tools and promoting flexibility and compatibility across a multitude of environments. This adaptability makes VeloDB an excellent choice for organizations looking to enhance their data analytics capabilities without compromising on performance or scalability.

What is Dremio?

Dremio offers rapid query capabilities along with a self-service semantic layer that interacts directly with your data lake storage, eliminating the need to transfer data into exclusive data warehouses, and avoiding the use of cubes, aggregation tables, or extracts. This empowers data architects with both flexibility and control while providing data consumers with a self-service experience. By leveraging technologies such as Apache Arrow, Data Reflections, Columnar Cloud Cache (C3), and Predictive Pipelining, Dremio simplifies the process of querying data stored in your lake. An abstraction layer facilitates the application of security and business context by IT, enabling analysts and data scientists to access and explore data freely, thus allowing for the creation of new virtual datasets. Additionally, Dremio's semantic layer acts as an integrated, searchable catalog that indexes all metadata, making it easier for business users to interpret their data effectively. This semantic layer comprises virtual datasets and spaces that are both indexed and searchable, ensuring a seamless experience for users looking to derive insights from their data. Overall, Dremio not only streamlines data access but also enhances collaboration among various stakeholders within an organization.

Media

Media

Integrations Supported

Apache Doris
Apache Flink
Apache Spark
MySQL

Integrations Supported

AccessOwl
Apache Superset
BI Book
Codd AI
DataClarity Unlimited Analytics
Emgage
HPE Ezmeral
Hex
Looker
Microsoft Power BI
Microsoft Power Query
Preset
SQL
Tableau
Yurbi
witboost

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Not specified

Company Facts

Organization Name

VeloDB

Date Founded

2023

Company Location

Singapore

Company Website

www.velodb.io

Company Facts

Organization Name

Dremio

Date Founded

2015

Company Location

United States

Company Website

www.dremio.com

Categories and Features

Data Warehouse

Not specified

Query Engines

Not specified

Categories and Features

Big Data

Not specified

Data Engineering

Not specified

Data Lake

Not specified

Data Lineage

Not specified

Data Virtualization

Not specified

Data Warehouse

Not specified

Query Engines

Not specified

Semantic Layer

Not specified

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