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features
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support

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Description

Apache Doris serves as a cutting-edge data warehouse tailored for real-time analytics, enabling exceptionally rapid analysis of data at scale. It features both push-based micro-batch and pull-based streaming data ingestion that occurs within a second, alongside a storage engine capable of real-time upserts, appends, and pre-aggregation. With its columnar storage architecture, MPP design, cost-based query optimization, and vectorized execution engine, it is optimized for handling high-concurrency and high-throughput queries efficiently. Moreover, it allows for federated querying across various data lakes, including Hive, Iceberg, and Hudi, as well as relational databases such as MySQL and PostgreSQL. Doris supports complex data types like Array, Map, and JSON, and includes a Variant data type that facilitates automatic inference for JSON structures, along with advanced text search capabilities through NGram bloomfilters and inverted indexes. Its distributed architecture ensures linear scalability and incorporates workload isolation and tiered storage to enhance resource management. Additionally, it accommodates both shared-nothing clusters and the separation of storage from compute resources, providing flexibility in deployment and management.

Description

A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon S3
Apache Flink
Apache Hive
Apache Hudi
Apache Kafka
Apache Pulsar
Apache Spark
Baidu Palo
Cognee
Database Mart
Docker
Kubernetes
MinIO
MySQL
OpenMetadata
PostgreSQL
Prompt Security
TapData
VeloDB
Zilliz Cloud

Integrations

Amazon S3
Apache Flink
Apache Hive
Apache Hudi
Apache Kafka
Apache Pulsar
Apache Spark
Baidu Palo
Cognee
Database Mart
Docker
Kubernetes
MinIO
MySQL
OpenMetadata
PostgreSQL
Prompt Security
TapData
VeloDB
Zilliz Cloud

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

doris.apache.org

Vendor Details

Company Name

Zilliz

Founded

2020

Country

United States

Website

milvus.io

Product Features

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

Product Features

Database

Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization

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Alternatives

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