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What is DataOps.live?

Design a scalable framework that prioritizes data products, treating them as essential components of the system. Automate and repurpose these data products effectively while ensuring compliance and strong data governance practices are in place. Manage the expenses associated with your data products and pipelines, particularly within Snowflake, to optimize resource allocation. For this leading global pharmaceutical company, data product teams stand to gain significantly from advanced analytics facilitated by a self-service data and analytics ecosystem that incorporates Snowflake along with other tools that embody a data mesh philosophy. The DataOps.live platform is instrumental in helping them structure and leverage next-generation analytics capabilities. By fostering collaboration among development teams centered around data, DataOps promotes swift outcomes and enhances customer satisfaction. The traditional approach to data warehousing has often lacked the flexibility needed in a fast-paced environment, but DataOps can transform this landscape. While effective governance of data assets is essential, it is frequently regarded as an obstacle to agility; however, DataOps bridges this gap, fostering both nimbleness and enhanced governance standards. Importantly, DataOps is not solely about technology; it embodies a mindset shift that encourages innovative and efficient data management practices. This new way of thinking is crucial for organizations aiming to thrive in the data-driven era.

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

Snowflake
Alation
Alteryx
Amazon SageMaker
Amazon Web Services (AWS)
Chartio
DQLabs
DataRobot
Dataiku
Docker
Hunters
Immuta
Informatica Cloud Data Integration
JupyterHub
Microsoft Azure
Monte Carlo
Stitch
Talend Data Catalog
ThoughtSpot

Integrations Supported

Snowflake
Amazon Redshift

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

DataOps.live

Date Founded

2020

Company Location

London, UK

Company Website

www.dataops.live/

Company Facts

Organization Name

Aggua

Date Founded

2020

Company Location

United States

Company Website

www.aggua.io

Categories and Features

Data Collaboration

Not specified

Data Governance

Not specified

Data Pipeline

Not specified

Data Quality

Not specified

DataOps

Not specified

Metadata Management

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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