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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Cloud Datalab is a user-friendly interactive platform designed for data exploration, analysis, visualization, and machine learning. This robust tool, developed for the Google Cloud Platform, allows users to delve into, transform, and visualize data while building machine learning models efficiently. Operating on Compute Engine, it smoothly integrates with various cloud services, enabling you to concentrate on your data science projects without distractions. Built using Jupyter (previously known as IPython), Cloud Datalab benefits from a vibrant ecosystem of modules and a comprehensive knowledge base. It supports the analysis of data across BigQuery, AI Platform, Compute Engine, and Cloud Storage, utilizing Python, SQL, and JavaScript for BigQuery user-defined functions. Whether your datasets are in the megabytes or terabytes range, Cloud Datalab is equipped to handle your needs effectively. You can effortlessly query massive datasets in BigQuery, perform local analysis on sampled subsets of data, and conduct training jobs on extensive datasets within AI Platform without any interruptions. This versatility makes Cloud Datalab a valuable asset for data scientists aiming to streamline their workflows and enhance productivity.

Description

The VertiPaq Analyzer is a powerful tool for examining the storage structures within a data model in Power BI and Analysis Services Tabular. It includes measures for various segments and partitions, such as pageable, resident, refresh date, and last access. Analysis Services offers a range of Dynamic Management Views (DMVs) to gather insights about the memory utilized by a data model. For instance, the DISCOVER_OBJECT_MEMORY_USAGE DMV reveals details about all objects currently residing in memory. This type of DMV can also be employed to monitor a Multidimensional instance of Analysis Services effectively. A notable contribution by Kasper de Jonge is the BISM Memory Report, a sample model that presents this information hierarchically, allowing users to easily identify the most resource-intensive databases, tables, and columns on a server. Should you wish to delve deeper into a specific database, additional detailed insights are accessible through other DMVs designed for this purpose. Understanding these tools and their capabilities can significantly enhance your data management strategies.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

DataLab Yes 
Google Cloud Platform Yes 
Google Workspace Yes 
Jupyter Notebook Yes 
Microsoft 365 No 
Microsoft Excel No 
Microsoft Power BI No 

Integrations

DataLab No 
Google Cloud Platform No 
Google Workspace No 
Jupyter Notebook No 
Microsoft 365 Yes 
Microsoft Excel Yes 
Microsoft Power BI Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
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 No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Customer Support

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

Customer Support

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

cloud.google.com/datalab

Vendor Details

Company Name

SQLBI

Founded

2004

Country

United States

Website

www.sqlbi.com/tools/vertipaq-analyzer/

Product Features

Data Analysis

Data Discovery No 
Data Visualization No 
High Volume Processing No 
Predictive Analytics No 
Regression Analysis No 
Sentiment Analysis No 
Statistical Modeling No 
Text Analytics No 

Data Visualization

Analytics No 
Content Management No 
Dashboard Creation No 
Filtered Views No 
OLAP No 
Relational Display No 
Simulation Models No 
Visual Discovery No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Product Features

Data Analysis

Data Discovery No 
Data Visualization No 
High Volume Processing No 
Predictive Analytics No 
Regression Analysis No 
Sentiment Analysis No 
Statistical Modeling No 
Text Analytics No 

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