The Autonomous Data Engine Description

Today there is a constant buzz about how top companies are using big data to gain competitive advantage. Your company is trying to be one of these market-leading companies. The reality is that more than 80% of big-data projects fail to go to production. This is because project implementation can be complex and resource-intensive. It can take months, if not years, to complete. The technology is complex and the people with the right skills are difficult to find. Automates all data workflows, from source to consumption. Automates the migration of data and workloads between legacy Data Warehouse systems and big data platforms. Automates the orchestration and management complex data pipelines in production. Alternative methods, such as custom development or stitching together multiple points solutions, are more expensive, inflexible and time-consuming, and require specialized skills to assemble.

The Autonomous Data Engine Alternatives
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Qrvey is the only solution for embedded analytics with a built-in data lake. Qrvey saves engineering teams time and money with a turnkey solution connecting your data warehouse to your SaaS application. Qrvey’s full-stack solution includes the necessary components so that your engineering team can build less software in-house. Qrvey is built for SaaS companies that want to offer a better multi-tenant analytics experience. Qrvey's solution offers: - Built-in data lake powered by Elasticsearch - A unified data pipeline to ingest and analyze any type of data - The most embedded components - all JS, no iFrames - Fully personalizable to offer personalized experiences to users With Qrvey, you can build less software and deliver more value.
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Domo puts data to work for everyone so they can multiply their impact on the business. Underpinned by a secure data foundation, our cloud-native data experience platform makes data visible and actionable with user-friendly dashboards and apps. Domo helps companies optimize critical business processes at scale and in record time to spark bold curiosity that powers exponential business results.
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Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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Illuminate dark data and accelerate data-driven transformation with intelligent data operations to enable an edge-to-cloud data fabric. Pentaho products automate onboarding, integrating, governing, and publishing trusted data, with an intelligent composable data platform to automate data management needs.
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Pricing

Free Trial:
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Company Details

Company:
Infoworks
Year Founded:
2014
Headquarters:
United States
Website:
www.infoworks.io

Media

The Autonomous Data Engine Screenshot 1
The Autonomous Data Engine Screenshot 1
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The Autonomous Data Engine Features and Options

Big Data Software

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

The Autonomous Data Engine User Reviews

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