How to catalog big data in Azure Data Catalog
New Azure Data Catalog accounts can no longer be created.
For data catalog features, please use the Microsoft Purview service, which offers unified data governance for your entire data estate.
If you are already using Azure Data Catalog, you'll need to create a migration plan for your organization to move to Microsoft Purview by August 2025.
Microsoft Azure Data Catalog is a fully managed cloud service that serves as a system of registration and system of discovery for enterprise data sources. It's all about helping people discover, understand, and use data sources, and helping organizations to get more value from their existing data sources, including big data.
Azure Data Catalog supports the registration of Azure Storage blobs and directories as well as Hadoop HDFS files and directories. The semi-structured nature of these data sources provides great flexibility. However, to get the most value from registering them with Azure Data Catalog, users must consider how the data sources are organized.
Directories as logical data sets
A common pattern for organizing big data sources is to treat directories as logical data sets. Top-level directories are used to define a data set, while subfolders define partitions, and the files they contain store the data itself.
An example of this pattern might be:
\vehicle_maintenance_events \2013 \2014 \2015 \01 \2015-01-trailer01.csv \2015-01-trailer92.csv \2015-01-canister9635.csv ... \location_tracking_events \2013 ...
In this example, vehicle_maintenance_events and location_tracking_events represent logical data sets. Each of these folders contains data files that are organized by year and month into subfolders. Each of these folders could potentially contain hundreds or thousands of files.
In this pattern, registering individual files with Azure Data Catalog probably doesn't make sense. Instead, register the directories that represent the data sets that be meaningful to the users working with the data.
Reference data files
A complementary pattern is to store reference data sets as individual files. These data sets may be thought of as the 'small' side of big data, and are often similar to dimensions in an analytical data model. Reference data files contain records that are used to provide context for the bulk of the data files stored elsewhere in the big data store.
An example of this pattern might be:
\vehicles.csv \maintenance_facilities.csv \maintenance_types.csv
When an analyst or data scientist is working with the data contained in the larger directory structures, the data in these reference files can be used to provide more detailed information for entities that are referred to only by name or ID in the larger data set.
In this pattern, it makes sense to register the individual reference data files with Azure Data Catalog. Each file represents a data set, and each one can be annotated and discovered individually.
The patterns described in the preceding sections are two possible ways a big data store may be organized, but each implementation is different. Regardless of how your data sources are structured, when registering big data sources with Azure Data Catalog, focus on registering the files and directories that represent the data sets that are of value to others within your organization. Registering all files and directories can clutter the catalog, making it harder for users to find what they need.
Registering data sources with Azure Data Catalog makes them easier to discover and understand. By registering and annotating the big data files and directories that represent logical data sets, you can help users find and use the big data sources they need.
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