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Description

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

Description

Voldemort does not function as a relational database, as it does not aim to fulfill arbitrary relations while adhering to ACID properties. It also does not operate as an object database that seeks to seamlessly map object reference structures. Additionally, it does not introduce a novel abstraction like document orientation. Essentially, it serves as a large, distributed, durable, and fault-tolerant hash table. For applications leveraging an Object-Relational (O/R) mapper such as ActiveRecord or Hibernate, this can lead to improved horizontal scalability and significantly enhanced availability, albeit with a considerable trade-off in convenience. In the context of extensive applications facing the demands of internet-level scalability, a system is often comprised of multiple functionally divided services or APIs, which may handle storage across various data centers with their own horizontally partitioned storage systems. In these scenarios, the possibility of performing arbitrary joins within the database becomes impractical, as not all data can be accessed within a single database instance, making data management even more complex. Consequently, developers must adapt their strategies to navigate these limitations effectively.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Acryl Data Yes 
Amundsen Yes 
Apache Superset Yes 
Astro by Astronomer Yes 
Azure Marketplace Yes 
CelerData Cloud Yes 
Cloudera Data Warehouse Yes 
DataHub Yes 
Deep.BI Yes 
Emgage Yes 
Gravity Data Yes 
Hue Yes 
Imply Yes 
Manticore Search Yes 
OpenMetadata Yes 
Stackable Yes 
StrongDM Yes 
WisdomAI Yes 

Integrations

Acryl Data No 
Amundsen No 
Apache Superset No 
Astro by Astronomer No 
Azure Marketplace No 
CelerData Cloud No 
Cloudera Data Warehouse No 
DataHub No 
Deep.BI No 
Emgage No 
Gravity Data No 
Hue No 
Imply No 
Manticore Search No 
OpenMetadata No 
Stackable No 
StrongDM No 
WisdomAI No 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Druid

Founded

2013

Website

druid.apache.org/technology

Vendor Details

Company Name

Voldemort

Website

www.project-voldemort.com/voldemort/

Product Features

Big Data

Collaboration No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

Data Warehouse

Ad hoc Query No 
Analytics No 
Data Integration No 
Data Migration No 
Data Quality Control No 
ETL - Extract / Transfer / Load No 
In-Memory Processing No 
Match & Merge No 

Relational Database

ACID Compliance No 
Data Failure Recovery No 
Multi-Platform No 
Referential Integrity No 
SQL DDL Support No 
SQL DML Support No 
System Catalog No 
Unicode Support No 

Product Features

Data Replication

Asynchronous Data Replication No 
Automated Data Retention No 
Continuous Replication No 
Cross-Platform Replication No 
Dashboard No 
Instant Failover No 
Orchestration No 
Remote Database Replication No 
Reporting / Analytics No 
Simulation / Testing No 
Synchronous Data Replication No 

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