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What is Palantir Gotham?

Integrating, managing, securing, and analyzing all organizational data is essential for modern enterprises. Data represents a crucial asset for businesses, and its sheer volume is staggering. It encompasses both structured formats, like log files, spreadsheets, tables, and charts, as well as unstructured forms, including emails, documents, images, videos, and more. Often, this data is stored across various disconnected systems, leading to a proliferation of types and an escalating volume that complicates its usability over time. Users reliant on this data do not categorize their needs into rows, columns, or mere text; instead, they focus on their organization's objectives and the challenges they encounter. They seek the ability to pose questions about their data and receive insights in a context that resonates with them. The Palantir Gotham Platform offers a robust solution to this problem. By integrating and transforming diverse types of data into a unified asset, Palantir Gotham enhances and categorizes information into clearly defined entities, including objects, individuals, locations, and events, thereby facilitating more informed decision-making. Ultimately, this platform empowers organizations to navigate their data landscape more effectively.

What is Data Lakes on AWS?

A multitude of Amazon Web Services (AWS) users are in search of a data storage and analytics option that outperforms traditional data management systems in terms of flexibility and agility. The emergence of data lakes has proven to be a groundbreaking and increasingly popular approach for data storage and analysis, allowing businesses to manage a wide array of data types from multiple sources within a single repository that supports both structured and unstructured data. AWS Cloud offers vital elements that empower customers to develop a secure, versatile, and cost-effective data lake. These elements include AWS managed services that facilitate the ingestion, storage, discovery, processing, and analysis of diverse data formats. To support clients in building their data lakes, AWS presents a thorough data lake solution that acts as an automated reference implementation, laying out a highly available and economical data lake architecture on the AWS Cloud, complemented by a user-friendly console for searching and accessing datasets. Additionally, this solution not only improves the accessibility of data but also simplifies the entire data management process, ultimately leading to enhanced operational efficiency for organizations. As a result, companies can leverage these advantages to make more informed decisions based on their data insights.

Media

Media

Integrations Supported

Malbek
Palantir Apollo
Palantir Foundry

Integrations Supported

Malbek
Palantir Apollo
Palantir Foundry

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
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

Palantir Technologies

Date Founded

2003

Company Location

United States

Company Website

www.palantir.com/palantir-gotham/

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/solutions/implementations/data-lake-solution/

Categories and Features

Big Data

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

Data Analysis

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

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

Categories and Features

Data Analysis

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

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

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