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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.

Media

Media

Integrations Supported

Auth.js
Autymate
DQ Studio
Data Sentinel
Microsoft Azure
NXLog
SSIS Integration Toolkit
StarfishETL

Integrations Supported

Amazon S3
Apache Kafka
Astro by Astronomer
Azure Data Lake
Hadoop
Hue
Kestra
Onehouse
OpenMetadata
StarTree

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

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

Columnar Databases

Not specified

Key-Value Databases

Not specified

NoSQL Database

Not specified

Categories and Features

Columnar Databases

Not specified

OLAP Databases

Not specified

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