Cloud Data Catalog

Cloud Data Catalog – Zeenea Google Cloud Platform provides users with advanced data management tools to build a single point of trust and comprehensive information for easy and efficient data discovery for large datasets.

Finding data within the Google Cloud Platform can be complex for your teams… Our unique solutions provide all the necessary tools to manage and use the assets stored in the main Google Cloud solutions.

Cloud Data Catalog

Cloud Data Catalog

Zeenea establishes scanners for popular technologies: big data, cloud, relational databases, time series, data warehouse, ETL platforms, reference tools, warehouses, unstructured data and ERP/CRM systems. Learn more

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All significant information collected by our gunsmiths is centralized in Zeenea to provide consumers with complete, reliable and realistic information. Learn more

Zeenea Explorer also includes statistical fingerprints of the data you have cataloged. Using our weapons, your data teams are fully aware of the data they are working with. Learn more

Our scanners provide all the necessary security features to keep sensitive and timely information in your hands. Our platform’s architecture for metadata management, encrypted traffic, and user management is designed to meet the security rules required by the Office of Security.

At Zeenea, we define a data catalog as “a comprehensive inventory of all data objects in an organization and their metadata, designed to help data professionals quickly find the most relevant data for any analytical problem.”

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Universal connection to all technologies Flexible metamodel templates adapted to use cases Smart search Data discovery function Ability to create glossaries or import business

CATALOG is given to everyone! Data must not be stored and used only by certain groups of people or individuals. The data catalog is provided to each user working on their projects.

Zeenea’s value proposition enables every order to be small and fast, using agile, incremental and pragmatic project management. Our data catalog’s agile strategy enables companies to launch bottom-up data-driven projects to maximize ROI within weeks. Google catalog metadata management is fully functional and scalable. It can help your organization quickly identify, identify and manage all of your data with one simple tool. Available through the Google Console, the Data Catalog provides instant access to research data without the need for a final state. Data Catalog works are now available for regionalized service delivery in 23 different countries worldwide. In addition to providing a higher level of protection against potential outages, a regionalized service delivers metadata at rest in each supported region, providing a unified view of all data assets distributed across multiple regions. Most organizations today deal with large and growing amounts of data and want open access to that data so that business users can find the right data items through their service. Earlier approaches could not be scaled, were boring and did not provide easy information to everyone. At Google, we’ve also faced the challenge of big and growing data, and we’ve built internal data catalog services to help provide end-to-end metadata management for all user data. You can see more about the techniques used to build an effective data catalog in Good: Organizing Googles Datasets. Data Catalog builds on that foundation, bringing a scalable managed service to all Google users for data within BigQuery, Pub/Sub and Storage. Here are some details on how the Data Catalog works and how it can help. Metadata technology Metadata technology automatically synchronizes data for all Google BigQuery data assets such as datasets, tables, and views in a continuous Data Catalog database. This way you can start using the Catalog Data right away and don’t have to do any tedious setup. The data catalog also includes technical metadata for auto-sync from Pub/Sub and user-stored user logs from the repository. These movies are easy to create – you just need to provide a template with wildcards and apply it to the bucket. Group files into one all files in the bin that match the wildcard tool.

Cloud Data Catalog

Metadata technology vs. metadata business metadata technical metadata refers to metadata that is available in the source system. Technical metadata for a BigQuery table includes table name, table description, column names, column types, column descriptions, creation date, last date, and more. For Pub/Sub, technical metadata refers to the names of Pub/Sub topics and the time they were created. In a file repository, technical metadata refers to the file name, the model used to create the file, the creation date, and the modification date. Business metadata refers to the collection of metadata that is critical to business and performance goals but is not available in technical metadata. Business metadata may include the person responsible for determining data ownership, whether the data item contains personally identifiable information (PII), if the data is authorized for official use, the data retention policy for the data item, and the stage of the data lifecycle. Assets, data quality assessment, data quality, any known issues or data weediness. The data catalog supports structures built to capture complex business metadata (more below). Data Discovery The data catalog can be used from a Google project just like in that plan. The data catalog finds good data not only in the project where the API is given, but in all projects and in all regions. Support for good data outside of BigQuery, Pub/Sub and Storage can be found in the Data Catalog tab, while support for non-Google data sources is available via open source links (see below). You can use the Data Catalog to search all your data assets by simply typing in a keyword and finding all the relevant data. You can also narrow your search to locate data objects in specific projects, systems, data asset types, or you can locate data objects created at specific time intervals.

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In creating Google tags for business metadata, we believe that simple strings, once widely used, are no longer sufficient to capture the volume of business metadata. With catalog data, you can create tags with a structure such that each tag contains multiple attributes, and each attribute contains one type of string, double, boolean, index, and datetime. The delivery of structured tags is a two-fold process. First, in the tag structure of your tag template, create the metadata text to be attached to the template. You can attach individual tags to individual data objects such as datasets, tables, views, and even columns. As illustrated below, structured text in data assets provides rich metadata to all business users. As a data scientist or data analyst, you can search for specific tags and better understand your data with the business context provided by a collection of tags. You as a data manager or data officer can better manage your data assets by using metadata in data quality and data governance.

Access control for metadata The data catalog is integrated with identity and access management (IAM). All operations, including data retrieval, are performed in accordance with applicable control specifications. If user A has read access to the data object and user B does not have access to the data object, a search by user A points to the data object, while the same search performed by user B returns no data object. . Metadata can be sensitive in nature and data management teams, so you want some business metadata tags to be visible only to selected groups of users. The data catalog provides access control over a template and access control over all tags created using that template. Automatically Tagging PII Data with DLP Data Catalog Integration with Data Loss Prevention (DLP) allows users to run an inspection job in BigQuery DLP and automatically create Data Catalog tags to identify PII data. You can find this in the DLP interface. You can refer to the Google guide Create data catalog tags by inspecting BigQuery data with Data Loss Prevention and using the accompanying source code.

Data Catalog Support for Non-Google Data Assets The Data Catalog API provides technical metadata ingestion from non-Google data assets. Open source links are organized into four Google Github repositories: data catalog—contains common links for all links; datacatalog-connectors-rdbms these connectors for Oracle, SQL Server, Teradata, Redshift, PostgreSQL, MySQL, Vertex, and Greenplum; datacatalog-connectors – two host connectors for Looker and Tableau; and data catalog connections Hive provides a connector for Hive with a live synchronization option.

You can add metadata text to Data Catalog entries for data assets located outside of Google. Discover, analyze and manage all your data from a single Data Catalog tool. The next step with Data Catalog Data Catalog is now GA and is essentially a service that provides discovery at scale to users in all enterprise regions. Getting started with Data Catalog couldn’t be easier, because there’s no need to quickly identify, understand and manage all your Google data and support preloaded non-Google metadata.

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