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What is Azure Table Storage?

Leverage Azure Table storage for the efficient management of large volumes of semi-structured data while keeping costs low. Unlike other data storage options, whether they are hosted on-site or in the cloud, Table storage offers effortless scalability, eliminating the need for any manual dataset sharding. Additionally, worries about data availability are alleviated thanks to geo-redundant storage, which ensures that your information is duplicated three times within a single region and another three times in a distant region. This service is particularly beneficial for a variety of datasets, including user information from online platforms, contacts, device specifications, and assorted metadata, empowering you to develop cloud applications without being tied to rigid data schemas. Different rows can have unique structures within the same table—such as one row containing order information and another holding customer details—granting you the flexibility to modify your application and table schema without experiencing downtime. Furthermore, Azure Table storage maintains a strong consistency model, which guarantees dependable data access and integrity. This makes it an excellent option for enterprises aiming to effectively manage evolving data needs, while also providing the opportunity for seamless integration with other Azure services.

What is Apache Druid?

Apache Druid stands out as a robust open-source distributed data storage system that harmonizes elements from data warehousing, timeseries databases, and search technologies to facilitate superior performance in real-time analytics across diverse applications. The system's ingenious design incorporates critical attributes from these three domains, which is prominently reflected in its ingestion processes, storage methodologies, query execution, and overall architectural framework. By isolating and compressing individual columns, Druid adeptly retrieves only the data necessary for specific queries, which significantly enhances the speed of scanning, sorting, and grouping tasks. Moreover, the implementation of inverted indexes for string data considerably boosts the efficiency of search and filter operations. With readily available connectors for platforms such as Apache Kafka, HDFS, and AWS S3, Druid integrates effortlessly into existing data management workflows. Its intelligent partitioning approach markedly improves the speed of time-based queries when juxtaposed with traditional databases, yielding exceptional performance outcomes. Users benefit from the flexibility to easily scale their systems by adding or removing servers, as Druid autonomously manages the process of data rebalancing. In addition, its fault-tolerant architecture guarantees that the system can proficiently handle server failures, thus preserving operational stability. This resilience and adaptability make Druid a highly appealing option for organizations in search of dependable and efficient analytics solutions, ultimately driving better decision-making and insights.

Media

Media

Integrations Supported

Acryl Data
Amazon Web Services (AWS)
Apache Kafka
Apache Superset
Astro
Auth.js
Azure Marketplace
DQ Studio
Data Sentinel
DataHub
Deep.BI
Emgage
Gravity Data
Metabase
Microsoft Azure
NXLog
Preset
SSIS Integration Toolkit
StrongDM

Integrations Supported

Acryl Data
Amazon Web Services (AWS)
Apache Kafka
Apache Superset
Astro
Auth.js
Azure Marketplace
DQ Studio
Data Sentinel
DataHub
Deep.BI
Emgage
Gravity Data
Metabase
Microsoft Azure
NXLog
Preset
SSIS Integration Toolkit
StrongDM

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

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/services/storage/tables/#features

Company Facts

Organization Name

Druid

Date Founded

2013

Company Website

druid.apache.org/technology

Categories and Features

NoSQL Database

Auto-sharding
Automatic Database Replication
Data Model Flexibility
Deployment Flexibility
Dynamic Schemas
Integrated Caching
Multi-Model
Performance Management
Security Management

Categories and Features

Big Data

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

Data Warehouse

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

Relational Database

ACID Compliance
Data Failure Recovery
Multi-Platform
Referential Integrity
SQL DDL Support
SQL DML Support
System Catalog
Unicode Support

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