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What is Pantomath?

Organizations are increasingly striving to embrace a data-driven approach, integrating dashboards, analytics, and data pipelines within the modern data framework. Despite this trend, many face considerable obstacles regarding data reliability, which can result in poor business decisions and a pervasive mistrust of data, ultimately impacting their financial outcomes. Tackling these complex data issues often demands significant labor and collaboration among diverse teams, who rely on informal knowledge to meticulously dissect intricate data pipelines that traverse multiple platforms, aiming to identify root causes and evaluate their effects. Pantomath emerges as a viable solution, providing a data pipeline observability and traceability platform that aims to optimize data operations. By offering continuous monitoring of datasets and jobs within the enterprise data environment, it delivers crucial context for complex data pipelines through the generation of automated cross-platform technical lineage. This level of automation not only improves overall efficiency but also instills greater confidence in data-driven decision-making throughout the organization, paving the way for enhanced strategic initiatives and long-term success. Ultimately, by leveraging Pantomath’s capabilities, organizations can significantly mitigate the risks associated with unreliable data and foster a culture of trust and informed decision-making.

What is Aggua?

Aggua functions as an AI-enhanced data fabric platform aimed at equipping both data and business teams with easy access to their information, building trust, and providing actionable insights for more informed decision-making based on data. With just a few clicks, you can uncover essential details about your organization's data framework instead of remaining unaware of its complexities. Obtain insights into data costs, lineage, and documentation effortlessly, allowing your data engineers to maintain their productivity without interruptions. Instead of spending excessive time analyzing how changes in data types affect your pipelines, tables, and overall infrastructure, automated lineage facilitates your data architects and engineers in reducing the time spent on manual log checks, allowing them to concentrate on implementing necessary infrastructure improvements more effectively. This transition not only simplifies operations but also fosters better collaboration among teams, leading to a more agile and responsive approach to tackling data-related issues. Additionally, the platform ensures that all users, regardless of their technical background, can engage with data confidently and contribute to an organization's data strategy.

Media

Media

Integrations Supported

Amazon Redshift
Google Cloud BigQuery
Snowflake
Amazon S3
Apache Airflow
Astro by Astronomer
Azure Synapse Analytics
Databricks
Fivetran
Google Cloud Data Fusion
Google Workspace
HVR
IBM DataStage
Okta
PagerDuty
Qlik Compose
SQL Server
Slack
Talend Data Integration
dbt

Integrations Supported

Amazon Redshift
Google Cloud BigQuery
Snowflake

API Availability

API Availability

Pricing Information

Pricing not provided

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

Online Training

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

Pantomath

Date Founded

2022

Company Location

United States

Company Website

www.getpantomath.com

Company Facts

Organization Name

Aggua

Date Founded

2020

Company Location

United States

Company Website

www.aggua.io

Categories and Features

Data Observability

Not specified

Data Pipeline

Not specified

Categories and Features

Data Catalog

Not specified

Data Engineering

Not specified

Data Fabric

Not specified

Data Governance

Not specified

Data Lineage

Not specified

Data Management

Not specified

Data Mapping

Not specified

Data Observability

Not specified

Data Quality

Not specified

Metadata Management

Not specified

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