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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.

Description

Pandas is an open-source data analysis and manipulation tool that is not only fast and powerful but also highly flexible and user-friendly, all within the Python programming ecosystem. It provides various tools for importing and exporting data across different formats, including CSV, text files, Microsoft Excel, SQL databases, and the efficient HDF5 format. With its intelligent data alignment capabilities and integrated management of missing values, users benefit from automatic label-based alignment during computations, which simplifies the process of organizing disordered data. The library features a robust group-by engine that allows for sophisticated aggregating and transforming operations, enabling users to easily perform split-apply-combine actions on their datasets. Additionally, pandas offers extensive time series functionality, including the ability to generate date ranges, convert frequencies, and apply moving window statistics, as well as manage date shifting and lagging. Users can even create custom time offsets tailored to specific domains and join time series data without the risk of losing any information. This comprehensive set of features makes pandas an essential tool for anyone working with data in Python.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

3LC
Avanzai
Codédex
Coiled
Dagster
Dash
Flyte
Kedro
LanceDB
MLJAR Studio
Microsoft Excel
Microsoft Power BI
Netdata
RunCode
Sliq
Spyder
TeamStation
ThinkData Works

Integrations

3LC
Avanzai
Codédex
Coiled
Dagster
Dash
Flyte
Kedro
LanceDB
MLJAR Studio
Microsoft Excel
Microsoft Power BI
Netdata
RunCode
Sliq
Spyder
TeamStation
ThinkData Works

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

SQLBI

Founded

2004

Country

United States

Website

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

Vendor Details

Company Name

pandas

Founded

2008

Website

pandas.pydata.org

Product Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

Product Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

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