decube
Octopai
Alation
Monte Carlo Data
IBM InfoSphere Information Governance Catalog
Digna AI
Metaplane
MarkovML
FirstEigen Databuck
Monte Carlo Data
DQLabs.ai
Collibra
Bigeye
decube is an all-in-one package solution for data observability, catalog and governance. We provide a feature-rich solution for organizations who want a unified solution for their data stack for different groups of users to discover, curate and collaborate on data assets all in one place.
Here's what you can expect after using decube: - Spend less time fire-fighting data incidents and build trust with internal stakeholders from reliable data. - Get complete visibility and understanding of data going into AI/ML models to develop accurate models for real business impact. - Discover quickly the data you need to make informed decisions and break down data silos between teams. - Protect your sensitive and PII data by implementing strict access controls to only allow access to teams that need to know.
Databuck is a robust solution designed to enhance data accuracy and trustability through advanced machine learning and automated data matching. As a leader in the data trustability field, Databuck offers: Comprehensive Data Verification: With 14 data checks, our tool surpasses the industry standard. Automated Data Matching: Ensuring data consistency and accuracy with minimal manual intervention. Real-Time Monitoring: Providing actionable insights and alerts to maintain data quality. It supports cloud platforms such as GCP and BigQuery, making it an essential tool for organizations aiming to ensure the accuracy and integrity of their data in real-time.
FirstEigen DatabuckNo features have been listed yet.
FirstEigen Databuck's answer:
FirstEigen primarily targets small to mid-sized companies in the USA. The key decision-makers include data engineers, data managers, and CTOs responsible for ensuring data accuracy, trustability, and observability in cloud environments. These professionals seek solutions that simplify and automate data quality management and cross-platform reconciliation, especially when dealing with large, complex data pipelines in environments like Google Cloud Platform (GCP) and BigQuery. The audience values data observability, trustability, and high levels of automation to reduce the risk of data leakage and operational inefficiencies.
FirstEigen Databuck's answer:
While specific customer names are not disclosed, FirstEigen serves a range of mid-sized companies across various sectors in the USA covering all sectors. These companies typically have revenues between $50-100 million and are heavily reliant on data-driven operations, making Databuck an ideal solution for data engineers, managers, and CTOs looking to streamline their data quality and observability processes.
FirstEigen Databuck's answer:
FirstEigen Databuck uses AI/ML to perform 14 automated data checks, exceeding competitors' 6-10 checks. It ensures real-time data quality monitoring, cross-platform reconciliation, and strengthens data observability and trustability. With AI-driven capabilities, Databuck improves decision-making and prevents data errors.
FirstEigen Databuck's answer:
FirstEigen’s Databuck offers distinct advantages over its competitors in terms of data accuracy and validation by measuring Data Trustability with AI/ML. Databuck performs 14 comprehensive data checks—significantly more than the 6-10 checks provided by competitors like Anomalo and Monte Carlo. Additionally, Databuck specializes in automated cross-platform data reconciliation, which ensures data trustability and observability across structured and semi-structured data sources. By automating data matching and validation, Databuck reduces manual intervention and prevents costly data errors, thereby enhancing decision-making and analytics. These features make Databuck particularly valuable for businesses managing complex, cloud-native data environments like GCP and BigQuery.
FirstEigen Databuck's answer:
FirstEigen developed Databuck in response to the growing challenges of managing complex, multi-source data environments. With AI/ML at its core, Databuck autonomously validates data, preventing costly errors that lead to lost revenue and inefficiencies. As data accuracy becomes more critical, Databuck ensures observability, trustability, and quality across platforms. Its ability to perform more extensive data checks than competitors, combined with automated reconciliation and matching, makes it a vital tool for optimizing reporting, analytics, and decision-making in any AI-powered data strategy.
FirstEigen Databuck's answer:
FirstEigen’s Databuck uses advanced AI/ML algorithms to autonomously verify data accuracy across both structured and semi-structured environments. Designed for cloud-native platforms like Google Cloud Platform (GCP) and BigQuery, Databuck provides real-time data quality monitoring and observability. Using AI-driven technologies, it automates data matching and cross-platform reconciliation, ensuring the efficient handling of large data volumes with exceptional accuracy.
I tried their trial version, absolutely stunned how they think from data engineer point of view. Their recon is quite unique and fast to process. I was amaze to see SSH connection selection with Postgresql.
Based on our record, decube seems to be more popular. It has been mentiond 9 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Just deploy - decube.io or soda.io and go to sleep. Source: about 2 years ago
I went with a managed service which is way cheaper (decube.io) . Its way cheaper too <10k / year. Source: about 2 years ago
Decube was the one we boiled down to - fairly a new company based in Singapore and provides hosting option too on all clouds. Source: about 2 years ago
Agreed, my reservation with GE,dbt_GE or Soda is that it doesn't help in Metadata Lineage. So if jobs fail I don't know what the downstream impact. We can use Datahub, but hosting cost is around 6-10k per year. I would rather buy something like decube.io or Metaplane. Source: about 2 years ago
Alternatively to GE, I prefer soda.io (cloud) or decube.io (community edition). Source: about 2 years ago
Octopai - An automated, centralized, cross-platform metadata search engine that enables BI groups to quickly and precisely discover and govern shared metadata.
Monte Carlo Data - Monte Carlo’s Data Observability platform increases trust in data by eliminating data downtime, so engineers innovate more and fix less.
Alation - Alation is a platform that makes data more accessible to individuals across an organization.
DQLabs.ai - The Modern Data Quality Platform.
Collibra - Collibra automates data management processes by providing business-focused applications where collaboration and ease-of-use come first.
IBM InfoSphere Information Governance Catalog - IBM InfoSphere Information Governance Catalog enables you to catalog your data, understand its meaning and track its usage all in one place.
