Introduction to GCP Data Catalog
GCP Data Catalog is rapidly taking over the metadata management services, availability being on the google cloud. The google cloud platform is quite widely used for metadata management.
Why has it become so necessary? What are the related terms to GCP Data Catalog?
A data catalog is responsible for the maintenance of data assets. It records discovery, organization, and dataset description. It enables the data analysts, scientists, and other consumers to query and use data from the datasets and understand. A data catalog is responsible for maintaining an inventory of data assets through the discovery, description, and organization of datasets. It provides a meaningful context to enable data analysts, scientists, and other data consumers to search and to be able to understand a relevant dataset to extract business value.
Demand for data catalogs is soaring as organizations struggle to inventory distributed data assets to facilitate data monetization and conform to regulations.
Source: Gartner, Inc
Data catalogs provide context to stakeholders to find and understand data. They also automate data managing and thus make it collaborative. A good data catalog must be chosen with key capabilities filtered out as all data catalogs are not the same. Most famous data catalogs rely upon the key components that help to make the data strategy successful.
Capabilities of a Data Catalog
A Data Catalog can be seen as one of the data governance framework components and has integrated data quality and analytics capabilities. Given below are listed some key capabilities that a data catalog inherits:
- Automation helps in the incremental process for efficiency, agility, and speed.
- Ability to perform analysis of root cause.
- Super fast and powerful search for exploration of Datasets.
- Ability to add business context to data.
- Reduction of data pollution by profiling.
Thus, a good data catalog provides clarity into data definitions to understand and leverage their Data assets in a better way.
GCP Data Catalog
In the case of GCP, the data catalog is managed by Google Cloud and is a centralized service. It builds and manages an optimized index for searching data assets such as datasets, views, tables, files, streams, and spreadsheets. It uses the metadata of these assets to build up the index.
Update or storage of assets causes the metadata to be updated or created and later be changed in the source systems. First-class citizens here include Privacy and information in the index. Go through some terms related to GCP Data Catalog below:
1. Search Catalog
This can be seen as the first contact point with the cataloging process’s Data catalog. Search Catalog in GCP is quite simple to use and very powerful. When there is a search query to the catalog, a result set is built and returned to the user.
These are actually just summaries of the actual assets that are being indexed. These result sets include searchresultSubtype, relative resource name, and linked resource fields for the indexed assets. ENTRY, TAG_TEMPLATE are some main search result types in the result set.
2. Get Entry
This operation is performed to retrieve even more information related to a given data asset. Here, we receive a relativeResourceName field that includes a name parameter being represented by a SearchResult. There would be one or more catalog entries for each result returned by the Search Catalog.
Schema field stores a table column schema for an entry referring to a table, but it is available in the entries that refer to datasets.
3. Lookup Entry
Say we already know the name of the Data asset that we want to fetch data related to. Here, we perform a catalog search with Lookup Entry, which allows for going from the asset’s name to the catalog entry in just one step.
4. Tags and Templates
Tag is the native entity of the Data catalog. It is responsible for allowing users and automating processes to attach more metadata to any given data asset index using the catalog, making it easy to find them in any future query.
Technical and Business Metadata
When managing Metadata on google cloud, one must know the type of metadata they are working on. It is mainly categorized into Technical and Business Metadata.
Technical Metadata refers to the data about data related to the technical aspect of the equation here. The organization of various data sources and attributes related to them is the main focus. This may include the following aspects:
- Data Source and the metadata Incorporation
- Credentials such as ODBC or JDBC username/password, IAM creds, or accessibility creds for certain areas.
- Location of the files, assets are also handled by the technical metadata managers.
- Mapping of objects in a defined manner.
- Schemas related to the data business Metadata structures.
- Attributes related to data and metadata.
In contrast to technical metadata, business metadata is more focused on the meaning of the available data for the organization itself. It includes the following aspects:
- The relationship between the various objects in the catalog is also an important aspect of the cataloging process.
- Ownership is to be tracked so that users can find, query, access the data.
- Classification is important to discover wanted data in a clean and orderly manner with the least or no latency.
Why use Data Catalog?
A Data catalog not only helps an organization to be able to handle the data more efficiently but also gives the data a new and refined structure. Some key aspects of having an enterprise data catalog are:
1. Immediate Search and Access to Relevant Data
The enterprise does not have to worry about managing all its users and handlers to be aware of all the data that there is, as the analyst would only be able to figure that out after searching that data.
2. Speed and Ability of Self-Service
The analyst can now search for data themselves and does not need to be dependent on a team of IT professionals to do so for them.
3. Faster Metadata Operations
To preview the data and profile it, analysts can debug and resolve the data faster and easier. This improves the confidence and trust of the data available to the analysts.
4. Having a Meaningful Context
For a data, analyst to find out relevant data and have description access, viewing business metadata and terms definitions make it a smoother analytical process.
5. Increased Metadata Protection
Instead of having a professional mask in each data region, columns now, the rules are automatically run based on the stored data classification.
A data catalog is a very detailed inventory of data assets designed to easily and efficiently search the most appropriate data for any analysis or business purpose.
Having a data Catalog set up in an organization helps the organization to be able to grow by providing it the ability to handle and manage data better, which results in smoother and way more efficient store and access to data.
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