Dataedo VS Purview
In this article, we would like to showcase key features that differentiate Dataedo from Microsoft Purview.
Dataedo and Purview present unique methods for discovering, documenting, and managing metadata, each boasting its own set of strengths. We grouped features and differences by Personas using Data Catalog and their specific needs.
Dataedo Overview
Dataedo excels with some convenient features for different Personas
Business Users will appreciate a user-friendly web interface with features like Profiling, Lineage, Data Models, or the ability to collaborate with the community.
Data Stewards can use tools like Steward Hub, Workflows, AI Assistant, and the ability to track curation to boost their productivity.
In contrast to Purview, it supports open-source databases like MySQL, MariaDB, and PostgreSQL on all cloud platforms.
It is also worth mentioning that it supports better automated lineage for even Microsoft technologies like SQL Server, Microsoft Dataverse, and modern Data Management Platforms like Microsoft Fabric, DBT, and Databricks with Unity Catalog.
A more detailed comparison is below.
Purview Overview
Purview, on the other hand, is a Microsoft product that integrates well with various Microsoft and Azure stacks, offering automated data classification using a reach list of built-in rules and sensitive data identification.
However, its support for metadata, lineage, or classification for many technologies is limited and requires additional manual effort, which is slowing down Catalog adoption in organizations.
Dataedo distinctive features grouped by Persona
Data Steward Features
Data Steward , and Data Curator - play a pivotal roles in documenting data ecosystems, adding business context to raw metadata harvested automatically. The features below were designed to help them be more productive and deliver better documentation.
Feature | Dataedo | Purview |
---|---|---|
Entity Relationship Diagrams | Automated Discovery (Reverse Engineering) and modeling of Data Models Entity Relationship Diagrams and Primary, Foreign Keys, to keep documentation up to date | In Purview Schema is discovered automatically but not harvesting PK, FK nor relationships to other tables, so Stewards or experts need to document it separately i.e. in the description as tables or adding links to external documentation with data model ERD which is usually stale |
Documentation Curation | AI Auto Documentation help data steward automatically document metadata Power Users can self-document assets editing description directly from Dataedo Web, facilitating Crowdsourcing knowledge from experts |
Documentation can be edited only by data steward (curators) so it's hard to collect knowledge from experts | AI assistant not available |
Documentation Export | Dataedo offers alternative ways of sharing documentation in the formats Html, Pdf & Excel | Documentation can't be exported in standard format |
Documentation Format | Dataedo description provides full, rich text support, including attachments, pictures, hyperlinks, and code blocks | In Purview, Rich text editor has limited options for edition, i.e., can't attach code blocks, attachments, or pictures |
Collaboration | Collaborate with Users Data Community, answering their comments or raised warnings in easy-to-manage threads | User can rate asset and leave simple comments but can't raise an issue or start thread |
Steward Portal | Dataedo offering Steward Hub, which uses built-in rules and GenerativeAI models, automatically recommends areas of improvement, suggesting documentation fill-ins and potential entity links. | ❌ |
Steward Reports | Offering Tracker to see the progress of the documentation process per source directly in the catalog explorer highlighting areas that need attention | In Purview Data Estate Insights has a couple of dashboards showing KPI's about curation of assets or catalog adoptions, classification and allow to drill down to see the details |
Data Quality | Automatically generate Data Profiles so users can better understand data and quality Avoid surprises, troubleshoot faster by tracking Schema changes and quickly identify what has changed using code diff Instead of Excel, better manage your reference data (lookups, LoV) with ease using dataedo reference data management |
❌ |
Data Workers Features
Data workers - can play different roles in the organization. They can be Business users looking for the right report. Data Analyst or BI Developer who is looking for data sources for the report creation or analysis. These Personas will use the Dataedo web portal to find & understand data assets and collaborate with stewards and owners in case of any doubts.
Feature | Dataedo | Purview |
---|---|---|
User Interface | Provides Business User-friendly Web Portal UI | Purview recently improved UI. However, less technical users might find features like search, browse, or lineage a bit overwhelming |
Search & Browse | Find Popular keywords for your data source | |
Report Catalog | Easily find reports helpful for your use case, using business-friendly UI showing visualizations, business glossaries, and datasets cataloged in one place. | In Purview, users can find reports among other assets using filters, but the browsing experience might be difficult |
Data Lineage | trace back the source of the data used in the report dataset in user-friendly end-to-end showing object or column level lineage. | The Purview lineage graph only shows objects. Mapping between columns is in the table view below. Navigation between objects is clunky |
Collaboration | Using Data Community for data asset or lineage, you can ask questions or start discussion with experts to solve doubts, provide ratings or raise warnings to improve the trustworthiness of data | ❌ |
Learning | Users can use Micro-Learning approach, leveraging provided Video Tutorials or | | Bootcamp |❌ |
Data Profiling | Better understand and increase confidence in your data using Data Profiling | ❌ |
Data Quality checks | Are on the roadmap | ❌ |
Data Catalog Owner Features
Feature | Dataedo | Purview |
---|---|---|
Customer Success Manager | Will help you to achieve goals, answer all your questions, and provide initial training and ongoing support | ❌ |
Customer Support | You can report issues directly from the application, and Dataedo Support will help to solve it | Microsoft Support does not react to provided feedback |
Product Roadmap | Dataedo has transparent Product Vision & Roadmap | Microsoft do not provide roadmap so hard to plan |
Releases | Dataedo has regular minor releases of improving existing product features and major releases with new features | Users are informed about new features if they become available for public preview |
Data Source Admin Features
Data Source Administrator is usually an IT person like Cloud engineer or Database admin, who manages the data stores or applications.
Feature | Dataedo | Purview |
---|---|---|
Native Connectors | 68 | 46 |
Bring Your own Connector | Offering customers generic connectors can be used to configure custom connectors if a source is not yet supported | ❌ |
Data Catalog API | REST API to easily integrate with Dataedo is on the roadmap | provides Apache Atlas API |
Dataedo vs Purview Connectors Differences
Category | Connector Name | Dataedo | Purview |
---|---|---|---|
Data Store | Azure Database for MySQL | ✅ | ⚠️Limited |
Azure Database for PostgreSQL | ✅ | ⚠️Limited | |
Azure Synapse Analytics (SQL Data Warehouse) | ✅ | ⚠️Limited | |
Amazon RDS for PostgreSQL | ✅ | ⚠️Limited | |
Amazon RDS for SQL Server | ✅ | ⚠️Limited | |
Amazon Redshift | ✅ | ⚠️Limited | |
SQL Server | ✅ | ⚠️Limited | |
Microsoft Dataverse | ✅ | ⚠️Limited | |
Databricks Unity Catalog | ✅ | ⚠️Limited | |
Microsoft Fabric? | ✅ | ⚠️Limited | |
MariaDB | ✅ | ❌ | |
Astra DB | ✅ | ❌ | |
Amazon Keyspaces | ✅ | ❌ | |
Elasticsearch | ✅ | ❌ | |
Neo4J | ✅ | ❌ | |
SAP ASE (Sybase) | ✅ | ❌ | |
Vertica | ✅ | ❌ | |
Amazon RDS for MariaDB | ✅ | ❌ | |
Amazon RDS for MySQL | ✅ | ❌ | |
Amazon RDS for Oracle Database | ✅ | ❌ | |
Amazon Relational Data Store (RDS) | ✅ | ❌ | |
Azure Database for MariaDB | ✅ | ❌ | |
Google Cloud SQL for MySQL | ✅ | ❌ | |
Google Cloud SQL for PostgreSQL | ✅ | ❌ | |
Google Cloud SQL | ✅ | ❌ | |
IBM Db2 Warehouse on Cloud | ✅ | ❌ | |
Amazon Athena | ✅ | ❌ | |
Amazon Aurora MySQL | ✅ | ❌ | |
Amazon Aurora PostgreSQL | ✅ | ❌ | |
Amazon DynamoDB | ✅ | ❌ | |
Azure Cosmos DB - Cassandra API | ✅ | ❌ | |
Azure Cosmos DB - SQL/Core API | ✅ | ❌ | |
IBM Db2 Big SQL | ✅ | ❌ | |
IBM Db2 LUW | ✅ | ❌ | |
IBM Db2 Warehouse | ✅ | ❌ | |
Netsuite | ✅ | ❌ | |
Percona Server for MySQL | ✅ | ❌ | |
Apache HBase | ✅ | ❌ | |
Apache Hive | ✅ | ❌ | |
Apache Impala | ✅ | ❌ | |
Firebird | ✅ | ❌ | |
IBM Informix | ✅ | ❌ | |
Microsoft Access | ✅ | ❌ | |
SAP IQ | ✅ | ❌ | |
SQLite | ✅ | ❌ | |
Amazon DocumentDB | ✅ | ❌ | |
Apache Impala | ✅ | ❌ | |
Apache Spark (with Hive Metastore) | ✅ | ❌ | |
ClickHouse | ✅ | ❌ | |
Cloudera | ✅ | ❌ | |
Hortonworks | ✅ | ❌ | |
IBM Db2 for iSeries (DB2 for IBM i) | ✅ | ❌ | |
InterBase | ✅ | ❌ | |
Azure Blob Storage | ❌ | ✅ | |
Azure Data Explorer | ❌ | ✅ | |
Azure Data Share | ❌ | ✅ | |
Azure Files | ❌ | ✅ | |
Azure SQL Managed Instance | ❌ | ✅ | |
SAP Business Warehouse | ❌ | ✅ | |
SQL Server on Azure-Arc | ❌ | ✅ | |
HDFS | ❌ | ✅ | |
ETL | DBT | ✅ | ❌ |
SQL Server Integration Services | ✅ | ❌ | |
Airflow | ❌ | ✅ | |
Azure Machine Learning | ❌ | ✅ | |
Business Intelligence | Redash | ✅ | ❌ |
Looker | ❌ | ✅ | |
Data Catalogs | Apache Atlas | ✅ | ❌ |
AWS Glue Data Catalog | ✅ | ❌ | |
Apps | SAP ECC | ❌ | ✅ |
SAP S/4 HANA | ❌ | ✅ | |
Data Modeling | Erwin | ❌ | ✅ |