A robust data catalog model for a single source of truth
Enterprise-Wide Data Catalog and Data Lineage to empower data-driven enterprises to quickly discover, understand, and manage all their data and keep it up-to-date.
Data Catalog for a strong data foundation
Gartner describes the data catalog as “A data catalog maintains an inventory of data assets through the discovery, description, and organization of datasets. The catalog provides context to enable data analysts, data scientists, data stewards, and other data consumers to find and understand a relevant dataset for the purpose of extracting business value.”
Lyftrondata’s use of AI and machine learning for metadata collection, semantic inference, and tagging, provides maximum value from automation and minimize manual effort. Lyftrondata robust data catalog provides many other capabilities including support for data curation and collaborative data management, intelligent dataset recommendations, and a variety of data governance features.
Work efficiently and effectively
Simplify data discovery at all scales
Transform messy, unstructured data in order to facilitate and improve analysis. Find, comprehend, and deal with every piece of data via a robust visual interface. Search by object types, such as database, table, schema, view, column, and pipeline, with a few clicks.
Achieve compliance with data privacy best practices
Maintain high security for your data in the cloud. Be up-to-date on privacy requirements, identify and remediate any gaps in your compliance, and optimize your overall data governance strategy.
Be in control of your data
Enable data governance that helps the framework validate and improve data quality while protecting it from misuse. Experience up-to-date and accessible data at all times. Anti-corruption layer allows nested tagging by resource groups to ensure effective and efficient data usage.
Spend less time organizing your data
Spend time and energy on your projects as per your needs. Organize and tag your tables and transform them into groups, then by project, team, or status.
Hear how Lyftrondata helped accelerate the
data journey of MOL Group
data loading
saving
productivity
reduction
Lyftrondata enables instant analytics on WNI weather data that helped us
streamline shipping lines 
Koichi Tsuji
Director at Consulting Partner at MOL Group
From data to analytics, in a
matter of seconds
Centralized data dictionary
Simplify data search at any scale or volume. Just use our powerful visual interface and search by Object Types like databases, schemas, tables, rows, or columns.
Data governance
Achieve continuous intelligence with advanced security analytics. Stay in control of your data, reduce the risk and experience a secured distribution of actionable intelligence.
Search & discovery
A simple way to do an enterprise-level search based on the keyword and easily get the results categorized by object types like database, table, schema, view, column, pipeline.
Tagging & categorization
Easily categorize your datasets based on your data classification or departments like HIPAA, PII, GDPR, Sales, Marketing etc and empower your team to get the insights in minutes.
Statistics discovery
It's easier to find out which pipeline, database, or table is contributing to a higher number of records. Simply click on the statistics categories and get the desired results.
Relationship identification
Fully managed way to identify parent and child relationship between the table and pipeline. Just select the include parents and include children option and get the top to bottom and bottom to top relationship.
Become a dossier maestro
FAQs
What is a Data Catalog?
Organizing data so that it can be easily found and understood is known as data cataloging. Work more efficiently if you tag, label, and document all data assets. It is also simpler to scale your data team when knowledge about your data is widely available rather than locked in someone's head.
How does Data Catalog benefit businesses?
A data catalog is a critical component in any data strategy for organizations looking to get more out of their data. They provide a centralized location for monitoring data flow while also providing audible lineage to improve data protection and governance. Furthermore, they are required for the deployment of actionable machine learning and artificial intelligence.
Easily build end-to-end data pipelines for breakthrough results
