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What is DataLakeHouse.io?

DataLakeHouse.io's Data Sync feature enables users to effortlessly replicate and synchronize data from various operational systems—whether they are on-premises or cloud-based SaaS—into their preferred destinations, mainly focusing on Cloud Data Warehouses. Designed for marketing teams and applicable to data teams across organizations of all sizes, DLH.io facilitates the creation of unified data repositories, which can include dimensional warehouses, data vaults 2.0, and machine learning applications. The tool supports a wide range of use cases, offering both technical and functional examples such as ELT and ETL processes, Data Warehouses, data pipelines, analytics, AI, and machine learning, along with applications in marketing, sales, retail, fintech, restaurants, manufacturing, and the public sector, among others. With a mission to streamline data orchestration for all organizations, particularly those aiming to adopt or enhance their data-driven strategies, DataLakeHouse.io, also known as DLH.io, empowers hundreds of companies to effectively manage their cloud data warehousing solutions while adapting to evolving business needs. This commitment to versatility and integration makes it an invaluable asset in the modern data landscape.

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

Aiven for PostgreSQL
Auth0
Bullhorn
ConnectWise RMM
Dropbox
Facebook Ads
Jira
Meta Ads
Salesforce
Shift4
Square Loyalty
Workday Extend

Integrations Supported

AWS Marketplace
Alluxio
Amazon Athena
Apache Cassandra
Apache Hive
Apache Spark
PuppyGraph

API Availability

API Availability

Pricing Information

$99
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

DataLakeHouse.io

Date Founded

2019

Company Location

United States

Company Website

datalakehouse.io

Company Facts

Organization Name

Apache Corporation

Date Founded

1954

Company Location

United States

Company Website

hudi.apache.org

Categories and Features

Data Engineering

Not specified

Data Lake

Not specified

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Security

Data Replication

Asynchronous Data Replication
Automated Data Retention
Continuous Replication
Dashboard
Orchestration
Reporting / Analytics
Synchronous Data Replication

Data Warehouse

Ad hoc Query
Analytics
Data Migration
ETL - Extract / Transfer / Load

Categories and Features

Data Warehouse

Not specified

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