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Average Ratings 0 Ratings

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features
design
support

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Write a Review

Description

Google Cloud Lakehouse is a modern data storage and management solution that combines the capabilities of data warehouses and data lakes into a unified platform. It enables organizations to store, access, and analyze data in open formats like Apache Iceberg, Parquet, and ORC without duplication. By maintaining a single source of truth, the platform eliminates the need for complex data movement and reduces operational overhead. It offers fine-grained security controls, allowing organizations to manage access and governance policies effectively. The Lakehouse runtime catalog provides centralized metadata management and simplifies resource organization. The platform supports scalable analytics and integrates seamlessly with tools like Apache Spark for advanced data processing. It is designed to handle large-scale data workloads while maintaining high performance and reliability. Built-in best practices and guides help users optimize their data architecture. It also supports replication and disaster recovery for enhanced resilience. Overall, Google Cloud Lakehouse provides a flexible and efficient way to unify and analyze enterprise data.

Description

Upsolver makes it easy to create a governed data lake, manage, integrate, and prepare streaming data for analysis. Only use auto-generated schema on-read SQL to create pipelines. A visual IDE that makes it easy to build pipelines. Add Upserts to data lake tables. Mix streaming and large-scale batch data. Automated schema evolution and reprocessing of previous state. Automated orchestration of pipelines (no Dags). Fully-managed execution at scale Strong consistency guarantee over object storage Nearly zero maintenance overhead for analytics-ready information. Integral hygiene for data lake tables, including columnar formats, partitioning and compaction, as well as vacuuming. Low cost, 100,000 events per second (billions every day) Continuous lock-free compaction to eliminate the "small file" problem. Parquet-based tables are ideal for quick queries.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS IoT SiteWise No 
Amazon S3 Yes 
Apache Iceberg Yes 
Apache Spark Yes 
Azure Data Lake Yes 
Eco No 
Google Cloud BigQuery Yes 
Google Cloud Storage Yes 
Hive No 
Micromerce No 
Presto Yes 
PuppyGraph No 

Integrations

AWS IoT SiteWise Yes 
Amazon S3 No 
Apache Iceberg No 
Apache Spark No 
Azure Data Lake No 
Eco Yes 
Google Cloud BigQuery No 
Google Cloud Storage No 
Hive Yes 
Micromerce Yes 
Presto No 
PuppyGraph Yes 

Pricing Details

$5 per TB
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

docs.cloud.google.com/lakehouse/docs

Vendor Details

Company Name

Upsolver

Founded

2014

Country

Israel

Website

www.upsolver.com

Product Features

Data Warehouse

Ad hoc Query No 
Analytics No 
Data Integration No 
Data Migration No 
Data Quality Control No 
ETL - Extract / Transfer / Load No 
In-Memory Processing No 
Match & Merge No 

Product Features

Big Data

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

Data Mining

Data Extraction No 
Data Visualization No 
Fraud Detection No 
Linked Data Management No 
Machine Learning No 
Predictive Modeling No 
Semantic Search No 
Statistical Analysis No 
Text Mining No 

Data Preparation

Collaboration Tools No 
Data Access No 
Data Blending No 
Data Cleansing No 
Data Governance No 
Data Mashup No 
Data Modeling No 
Data Transformation No 
Machine Learning No 
Visual User Interface No 

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