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What is Dimodelo?

Focus on crafting meaningful and influential reports and analytics instead of getting overwhelmed by the intricacies of data warehouse coding. It's essential to prevent your data warehouse from devolving into a disorganized collection of numerous challenging pipelines, notebooks, stored procedures, tables, and views. Dimodelo DW Studio significantly reduces the effort required for the design, construction, deployment, and management of a data warehouse. It supports the creation and implementation of a data warehouse tailored for Azure Synapse Analytics. By establishing a best practice architecture that integrates Azure Data Lake, Polybase, and Azure Synapse Analytics, Dimodelo Data Warehouse Studio guarantees the provision of a high-performing and modern cloud data warehouse. Additionally, the use of parallel bulk loads and in-memory tables further enhances the efficiency of Dimodelo Data Warehouse Studio, allowing teams to prioritize extracting valuable insights over handling maintenance tasks. This shift not only streamlines operations but also empowers organizations to make data-driven decisions with greater agility.

What is Apache Hudi?

Hudi is a versatile framework designed for the development of streaming data lakes, which seamlessly integrates incremental data pipelines within a self-managing database context, while also catering to lake engines and traditional batch processing methods. This platform maintains a detailed historical timeline that captures all operations performed on the table, allowing for real-time data views and efficient retrieval based on the sequence of arrival. Each Hudi instant is comprised of several critical components that bolster its capabilities. Hudi stands out in executing effective upserts by maintaining a direct link between a specific hoodie key and a file ID through a sophisticated indexing framework. This connection between the record key and the file group or file ID remains intact after the original version of a record is written, ensuring a stable reference point. Essentially, the associated file group contains all iterations of a set of records, enabling effortless management and access to data over its lifespan. This consistent mapping not only boosts performance but also streamlines the overall data management process, making it considerably more efficient. Consequently, Hudi's design provides users with the tools necessary for both immediate data access and long-term data integrity.

Media

Media

Integrations Supported

Azure Data Lake
AWS Marketplace
Alluxio
Amazon Redshift
Apache Cassandra
Apache Flink
Apache Hive
Apache Spark
Azure DevOps
Azure SQL Database
Azure Synapse Analytics
CelerData Cloud
Microsoft Azure
MySQL
PostgreSQL
Presto
PuppyGraph
SQL Server
e6data

Integrations Supported

Azure Data Lake
AWS Marketplace
Alluxio
Amazon Redshift
Apache Cassandra
Apache Flink
Apache Hive
Apache Spark
Azure DevOps
Azure SQL Database
Azure Synapse Analytics
CelerData Cloud
Microsoft Azure
MySQL
PostgreSQL
Presto
PuppyGraph
SQL Server
e6data

API Availability

Has API

API Availability

Has API

Pricing Information

$899 per month
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

Dimodelo

Company Location

Australia

Company Website

www.dimodelo.com

Company Facts

Organization Name

Apache Corporation

Date Founded

1954

Company Location

United States

Company Website

hudi.apache.org

Categories and Features

Data Warehouse

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

Categories and Features

Data Warehouse

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

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