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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 Apache Flume?
Flume serves as a powerful service tailored for the reliable, accessible, and efficient collection, aggregation, and transfer of large volumes of log data across distributed systems. Its design is both simple and flexible, relying on streaming data flows that provide robustness and fault tolerance through multiple reliability and recovery strategies. The system features a straightforward and extensible data model, making it well-suited for online analytical applications. The Apache Flume team is thrilled to announce the launch of Flume 1.8.0, which significantly boosts its capacity to handle extensive streaming event data effortlessly. This latest version promises enhanced performance and improved efficiency in the management of data flows, ultimately benefiting users in their data handling processes. Furthermore, this update reinforces Flume's commitment to evolving in response to the growing demands of data management in modern applications.
Integrations Supported
Apache Doris
Apache Flink
Apache Kafka
Apache Phoenix
Apache Spark
MySQL
Observo AI
Onum
Yandex Data Proc
dbt
Integrations Supported
Apache Doris
Apache Flink
Apache Kafka
Apache Phoenix
Apache Spark
MySQL
Observo AI
Onum
Yandex Data Proc
dbt
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
VeloDB
Date Founded
2023
Company Location
Singapore
Company Website
www.velodb.io
Company Facts
Organization Name
Apache Software Foundation
Date Founded
1999
Company Location
United States
Company Website
flume.apache.org
Categories and Features
Data Warehouse
Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge
Categories and Features
Data Warehouse
Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge