Consolidate data sources into standard data models
Large volumes of decentralized datasets living in data lakes lose value over time as data owners and purpose change. Tamr’s approach streamlines data integration with powerful Spark transformations and machine learning to ensure data in lakes is findable, accessible, interoperable, and reusable.
Save time and money since you don’t have to re-create duplicative data sets or build complex ETL conversions just to get usable data.
Deduplicate, clean, and enrich key data entities
Within vast data lakes, Tamr quickly consolidates relevant data attributes for key entities such as customers, suppliers, assets and products. By doing so, Tamr enables organizations to focus all data sources available in the lake around specific value-driven goals.
Identify linkage between datasets to uncover new insights and analytics.
Track your data and keep the data lake clean
Tamr provides the data lake management tools necessary for building out best-in-breed data operation pipelines to ensure that new data sources and records being added continue to be consolidated and unified over time with little effort.
Build out carefully curated data repositories to reduce operational costs and drive trustworthy outcomes.
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Consolidate large varieties of related data sources into standard data schema models
De-duplicate and clean key entity values with machine learning
Identify key entities across data sources and join data sources together
Maintain auditability of data lineage while consolidating data sources
Build out a robust data operation pipeline to streamline the ingestion of data overtime
Data lakes often contain decentralized data sets brought on for specific purposes. Tamr provides a platform to manage and maintain custom data schema models that can be mapped against datasets throughout the data lake. With the assistance of human-guided machine learning, data mappings are maintained and replicable across datasets with little manual effort.
De-duplicate and clean
Within data lakes, duplicated data of varying quality runs rampant as the volume and variety of data constantly increases. Rather than rely on hundreds or thousands of rules to de-duplicate and clean data, Tamr provides an agile approach. As new data, with potentially different qualities that break existing rules comes in, Tamr uses human-guided machine learning to de-duplicate and cleanse the data.
In order to ensure that all data flowing into the data lake is accessible, interoperable, and valuable, Tamr joins datasets together across any entity. This provides data-driven insights around specific entities from different data sources.
With the large volume of data moving in and out of data lakes, ensuring data lineage and governance may be difficult at the record level. Tamr provides the ability to track records moving in and out of the data curation process using persistent ids which provide clear auditability of the data pipeline.
Without the right systems in place to ensure incoming data is managed, data lakes quickly become data swamps. Data governance becomes labor and process intensive over time. Tamr provides a machine learning approach to help build best-in-breed data operation pipelines to streamline and automate the data curation process. This means that data cleanliness can be maintained overtime as data volume and variety grows.