Unlock your data's potential
No-code integrations with the leading data tools
With easy to use search, you can easily activate your metadata and find answers to common questions with a modern data catalog.
Secoda automatically ingests metadata from across your stack to give you a single view of your entire data landscape.
Start free trialAnyone, regardless of technical ability, can ask questions about your data and receive a contextual response.
Secoda lets you manage questions and search your data right from Slack where you’ll be notified of any changes that are relevant to you.
Secoda provides a centralized location for collecting and organizing data requests so data teams never have to answer the same question twice.
Give your team insight into your data relationships with automated column and table level lineage functionality. Add any additional context to lineage using the Secoda API and notify others when lineage changes impact their work.
Certify and share commonly used data assets for internal access. Secoda's AI data catalog identifies frequently used data and sensitive resources, empowering confident data usage within the company.
Automatically discover, categorize, tag, and regulate sensitive data across your resources. Securely collaborate with any team member using Secoda, while ensureing data governance.
With Secoda, data teams save time and ship faster by making it easier to work together.
Get startedMultiple users can collaborate on the same documentation, data cataloging, or data tagging task in real-time with an AI-powered data catalog tool
Secoda integrates with Git so you can easily track changes, collaborate, and ensure data integrity.
Teams, collections, and documents make it easy to curate and organize your data knowledge
Admins can grant appropriate data access to individuals and teams to ensure each business unit sees only what they need to.
No-code integrations with the leading data tools
"Secoda AI lets me reduce the time my team spends on documentation by 90%"
—
Chief Data Officer
Secoda is SOC 2 Type 1 and 2 compliant. The way we process and store client data is secure and protected, based on standards set by the AICPA.
You can host Secoda in a self-hosted environment, behind your own VPN, and in your own VPC. Deploy via Terraform or Docker.
Sign in with the services you already use, including Google and Microsoft SSO, Okta, MFA and SAML
Securely move data from your private databases to Secoda with SSH tunneling.
Get control to remove or leave out sensitive datasets from your syncs or mark it automatically in Secoda.
Data managed with Secoda is fully encrypted in transit and at rest. We do not see the data we are moving.
Built for data teams, designed for everyone so you can get more from your data stack.
A data catalog is used as a centralized repository or inventory of metadata about various datasets within an organization. It provides a structured and organized way to manage and discover data assets. Data catalogs help users understand what data is available, its attributes, quality, lineage, and usage, facilitating data governance, collaboration, and efficient data discovery for analysis and decision-making.
Firstly, it should offer robust metadata management capabilities, allowing teams to annotate and document data comprehensively. This metadata should include information about data lineage, quality, and usage, providing a holistic view of the data's lifecycle. See the full set of criteria in the Ultimate Data Catalog Buyer's Guide.
Creating a data catalog can greatly help you with organizing the data they collect, therefore making it easier to find what you need when you need it. The 5 steps involve: gathering sources, assigning owners to resources, getting support and buy-in, integrating workflows, and upkeeping the catalog.
AI data catalogs offer a wide range of benefits for data-driven businesses such as improved data discovery and management, data quality, data governance, and better insights.
An enterprise data catalog is a centralized repository that organizes and manages an organization's data assets based on their metadata. It provides a single point of reference for discovering, understanding, and using data at scale.
A data catalog helps analytics engineers easily discover and locate relevant datasets for their analytics projects, saving time and effort in searching for data. Secondly, the catalog provides essential metadata about the datasets, such as data definitions, schemas, and quality metrics, enabling engineers to understand and evaluate the data's suitability for analysis. Lastly, a data catalog promotes collaboration and knowledge sharing among analytics teams by providing a centralized platform to document, annotate, and share insights or best practices related to the datasets, enhancing the overall efficiency and effectiveness of analytics workflows.
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