In the world today, everyone is producing more data than ever before and with this daily production comes a lot of challenges that give rise to rules and regulations thereby making it harder to find the right type of data at the right time.

Data Access and Data Governance has become a major challenge to businesses as they try to secure ways in which these challenges can be overcome using data cataloging tools. With a Data Catalog in place, you can gain access to your data, tell the kind of data that you have now, know who is moving it, what the data is being used for, and how the data needs to be protected from harmful practices. 

This article is to differentiates the key components between two of the industry-leading data catalog platforms, Apache Atlas vs Google Data Catalog, using highlights of their features to show how they differ from one another.

What is Data Catalog?

Data Catalog simply put can be defined as an organized structure or inventory of data assets within an organization. Data catalog uses metadata to help businesses manage the data they produce to aid data discovery and data governance thereby helping data professionals such as data engineers, data scientists, data stewards, chief data officers, etc. to collect, access, organize, and manage data.

Using a data catalog gives you a single, deeper view into how all your data is stored at any time by helping you to understand the kind of data you have, who is viewing or moving the data, and what the data being moved is used for.

In recent times, the idea of a data catalog has caught up with data professionals because of the ever-increasing amounts of data generated which has to be managed and a determination has to be made about how access will be granted to users. The introduction of the Cloud, big data analytics, AI, and machine learning has changed the way data is viewed hence, the need to manage and leverage whatever insights can be gotten from data and ultimately how the data needs to be protected to avoid harm.

Using a data catalog effectively will result in cost savings, improved operational efficiency,  having a competitive advantage, offering a better customer experience, and lots more. 

What is Metadata?

It has been explained earlier that a Data Catalog uses Metadata to organize and manage data so what is metadata? Metadata is defined as data that provides information about one or more aspects of your data, that is, data information about data. It does not give the content of the data such as the text of a message or an image but rather, the detailed information of the data.

Metadata is divided into two major parts namely technical and business metadata. Technical metadata comprises schemas, tables, columns, file names, report names, etc. This kind of data is generally referred to as anything that is documented in the source system while business metadata is known as the business knowledge that users have about the assets in the organization that includes business descriptions, comments, annotations, classifications, ratings, etc.

Another part of the metadata is the operational data and can be seen as the operations that have been carried out on data such as when was it last refreshed, what sort of ETL jobs created it, the number of times the tables has been accessed by users, and which particular user accessed these data.

What is Data Governance?

Data Governance is a very important concept in data catalog as it provides organizations with information about data sources, schemas for the data sources, processes involved in reading the data,  the classification of the data, the data transformation undertaken, when the data was last updated, and the restrictions placed on the data.

From the explanation above, it can be deduced that Data Governance provides the ability for you to comprehend your metadata thereby allowing you to make and take appropriate actions when required to.

What is Apache Atlas?

Apache Atlas is a platform that offers solutions to data governance and metadata management. It enables the gathering, processing, and maintenance of metadata by monitoring the data processes, data stores, files, and any updates in the metadata repository.

Apache Atlas is conventionally used within the Hadoop environment though, it can be used with other environments as it allows integration with other enterprise data ecosystems. Atlas has a scalable and extensive set of core foundational governance services thereby enabling you to effectively and efficiently use its metadata and governance capabilities to organize and build catalogs for your data assets, classify, and manage them for your team of data professionals.

Apache Atlas has unique capabilities such as lineage, entities, etc. which will be discussed next to show its key differences vis-a-vis Google Data Catalog.

Apache Atlas vs Google Data Catalog: Metadata Types and Instances

Apache Atlas allows users to define a model for metadata objects to be managed and these models are composed of definitions known as types. A type represents one or a collection of attributes that defines the properties for the metadata objects. You can define new types for the metadata to be managed and the metadata types can be pre-defined for various Hadoop and non-Hadoop metadata.

The metadata types in Apache Atlas can have primitive and enum attributes, complex attributes, object references, and can also inherit other metadata types. 

Apache Atlas vs Google Data Catalog: Entities

Entities on Apache Atlas can be defined as instances of types that capture metadata object details and their relationships. They represent an asset’s technical metadata, though they do not come with pre-defined structures, they have pre-defined entity types for various Hadoop and non-Hadoop metadata. REST Application Programming Interface (APIs) can also be used to work with types and instances to allow for easier integration.

Apache Atlas vs Google Data Catalog: Classifications

Entities are associated with Classifications to enable easier discoveries of assets and promote effective data management. Apache Atlas does not create a different object but uses Classifications objects to apply them to entities and have superTypes attributes such as expiry_data attribute in EXPIRES_ON.

The capabilities of Classification allow you to create dynamic classifications such as PII, EXPIRES_ON, DATA_QUALITY, SENSITIVE, etc. Classification on Apache Atlas can be propagated through lineage, therefore, ensuring that classifications follow the data as it goes through different processing.

Apache Atlas vs Google Data Catalog: Lineage

Apache Atlas has a unique feature called Lineage. Lineage allows you to view the history/trajectory of your data as it moves from one process to another using an intuitive User Interface (UI) and REST APIs to either access or update it. Lineage is carried out as Atlas reads the content of your metadata to build relationships among the data assets by noting the input and output of the query information it receives and ultimately generates a lineage map that shows all usage and transformations done on the assets over time.

It is very helpful to data professionals as you can quickly identify the sources of data and understand the impact of data and schema changes.

Apache Atlas vs Google Data Catalog: Search/Discovery

This is used to search entities by type, classification, attribute value, or free text through an intuitive UI. It also has SQL-like query language to search entities, Domain Specific Language (DSL), and uses REST APIs to search with complex criteria.

Apache Atlas vs Google Data Catalog: Security and Data Masking

Apache Atlas is integrated with Apache Ranger to enable authorization/ data-masking on data access based on classifications associated with entities and offers security for metadata access controls on access to entity instances and operations such as add/update/remove classifications.

What is Google Data Catalog?

Google Data Catalog is a fully managed and scalable metadata management service provided by Google with which you can organize, discover, and manage your data stored in Google Cloud. In other words, Data Catalog is a centralized service that is fully managed by Google Cloud with privileges that enables you to build an optimized search index for your data assets such as datasets, tables, views, spreadsheets, data streams, text/CSV files, etc. This index is built on Data Catalog using the assets’ metadata like name, description, column definition, etc.

The metadata on Google Data Catalog comes with pre-defined structures and allows users to add more attributes using templates to their assets. It also stores metadata for assets managed by other Google Cloud Platform (GCP) services and details about them can be gotten using Data Catalog’s UI or API.

Google Data Catalog has specific components that will be discussed next to show how it is distinct from Apache Atlas stating its usefulness in the management of your metadata and data governance in general.

Apache Atlas vs Google Data Catalog: Search Catalog

The search catalog is simply the search feature found in Data Catalog where you can search the catalog for results. A result set is returned when you search the catalog summarizing the details about the indexed assets where each SearchResult may have a small set of fields such as SearchResultType, SearchResultSubtype, relativeResourceName, and LinkedResource.

The search result is usually divided into two, ENTRY and TAG_TEMPLATE. ENTRY refers to data assets managed by other Google Cloud Services. Services such as BigQuery and Pub/Sub are automatically indexed by Data Catalog. TAG_TEMPLATE on the other hand refers to data assets that are native to Data Catalog.

Apache Atlas vs Google Data Catalog: Entry Group

An entry group is used to keep related entries together and it can be also used to determine users who can create, edit, and view entries within the group. The related data is kept in the group using Cloud Identity and Access Management (IAM). As stated in the previous section, services such as BigQuery and Pub/Sub are automatically indexed by Data Catalog to form entry groups.

Apache Atlas vs Google Data Catalog: Get Entry

The Get Entry operation is used to retrieve information regarding a given data asset, it receives a name parameter and returns one catalog Entry for each Search Catalog operation carried out. The Entry represents a native Data Catalog entity that describes the asset’s technical metadata and contains fieldsets that change according to its type.

Apache Atlas vs Google Data Catalog: Lookup Entry

Lookup Entry is used to find a catalog Entry associated with a data asset without having to perform a catalog search as you can go from an asset’s name to its catalog entry in one step. With Lookup Entry in Data Catalog, users can know the type of data stored in  Google Cloud and also know where it is stored.

Apache Atlas vs Google Data Catalog: Templates and Tags

Tags on Google Data Catalog are used to improve data governance. A Tag is a native Data Catalog entity that is used to automate processes and attach additional metadata to any data asset indexed by the catalog. A Tag can be attached to an Entry, its flexibility enables you to attach multiple fields as required to get a classification job done properly, and it must be created according to a user-defined Tag Template. For example, a Tag Template can be used to classify assets to search and troubleshoot all tables which have a failed status.

Apache Atlas vs Google Data Catalog: On-Prem Connectors

With Data Catalog, you can ingest technical metadata from non-Google Cloud data assets to Data Catalog for a unified view of all your data assets.


This article has shows the key differences between Apache Atlas and Google Data Catalog. It has postulated that if you want a platform where you will install and manage your data catalog yourself then Apache Atlas is it, whereas, Google Data Catalog is a fully managed and server-less product, where you do not need to manage anything as it is hosted on the Google Cloud Platform (GCP).

It further showed that with Apache Atlas, you have features such as entities, classification, lineages, etc. while entries, tags, and templates are components synonymous with Google Data Catalog.

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Ofem Eteng
Technical Content Writer, Hevo Data

Ofem Eteng is a dynamic Machine Learning Engineer at Braln Ltd, where he pioneers the implementation of Deep Learning solutions and explores emerging technologies. His 9 years experience spans across roles such as System Analyst (DevOps) at Dagbs Nigeria Limited, and as a Full Stack Developer at Pedoquasphere International Limited. With a passion for bridging the gap between intricate technical concepts and accessible understanding, Ofem's work resonates with readers seeking insightful perspectives on data science, analytics, and cutting-edge technologies.

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