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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 Pinot?
Pinot is designed to optimize the handling of OLAP queries with low latency when working with static data. It supports a variety of pluggable indexing techniques, such as Sorted Index, Bitmap Index, and Inverted Index. Although it does not currently facilitate joins, this can be circumvented by employing Trino or PrestoDB for executing queries. The platform offers an SQL-like syntax that enables users to perform selection, aggregation, filtering, grouping, ordering, and distinct queries on the data. It comprises both offline and real-time tables, where real-time tables are specifically implemented to fill gaps in offline data availability. Furthermore, users have the capability to customize the anomaly detection and notification processes, allowing for precise identification of significant anomalies. This adaptability ensures users can uphold robust data integrity while effectively addressing their analytical requirements, ultimately enhancing their overall data management strategy.
Integrations Supported
Amazon S3
Apache Kafka
Astro by Astronomer
Auth.js
Autymate
Azure Data Lake
DQ Studio
Data Sentinel
Hadoop
Hue
Integrations Supported
Amazon S3
Apache Kafka
Astro by Astronomer
Auth.js
Autymate
Azure Data Lake
DQ Studio
Data Sentinel
Hadoop
Hue
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
Apache Corporation
Date Founded
1954
Company Location
United Statess
Company Website
pinot.apache.org
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