"},{"ID":293902,"user_firstname":"Nana","user_lastname":"Dei","nickname":"Nana Y. Dei","user_nicename":"ndeiesri-com_esriinc","display_name":"Nana Dei","user_email":"ndei@esri.com","user_url":"","user_registered":"2021-11-24 22:03:56","user_description":"Nana is a Product Manager on the ArcGIS Pro team, focusing on the product's data management capabilities. In his free time, he enjoys travelling, playing Scrabble, and shooting pool.","user_avatar":"
"},{"ID":360602,"user_firstname":"Nolan","user_lastname":"Reichmann","nickname":"Nolan Reichmann","user_nicename":"nreichmann","display_name":"Nolan Reichmann","user_email":"NReichmann@esri.com","user_url":"","user_registered":"2024-08-23 22:39:09","user_description":"Nolan Reichmann is a Senior Product Marketing Manager at Esri, focusing on the value of Data Management and ArcGIS. With over a decade of experience in the field of GIS, he is dedicated to driving customer success through the adoption and use of Esri software and technology.","user_avatar":"
"}],"short_description":"Discover how ArcGIS connects and integrates your data, powers smarter workflows, and brings spatial insights to your data stack. ","flexible_content":[{"acf_fc_layout":"content","content":"In this second entry in <\/span>The Science of Where in the Modern Data Stack<\/span><\/i>\u00a0blog series,\u00a0we will detail the common approaches to data integration that can be used to further your knowledge base and inform your design decisions.<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2980170,"id":2980170,"title":"Table","filename":"Table-2.png","filesize":141890,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/data-management\/the-science-of-where-in-the-modern-data-stack-data-integration-approaches-and-methods-in-arcgis\/table-26","alt":"Data Integration Method","author":"325202","description":"","caption":"","name":"table-26","status":"inherit","uploaded_to":2979730,"date":"2026-08-13 20:39:37","modified":"2026-08-13 20:39:45","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1205,"height":590,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2.png","medium-width":464,"medium-height":227,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2.png","medium_large-width":768,"medium_large-height":376,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2.png","large-width":1205,"large-height":590,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2.png","1536x1536-width":1205,"1536x1536-height":590,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2.png","2048x2048-width":1205,"2048x2048-height":590,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2-826x404.png","card_image-width":826,"card_image-height":404,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/Table-2.png","wide_image-width":1205,"wide_image-height":590}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" To jump to a specific method, use these links:\u00a0<\/strong><\/p>\n While this blog is focused on integrating data with ArcGIS, it\u2019s important to highlight the enterprise geodatabase at the start. <\/span>\u00a0<\/span><\/p>\n Geodatabases are the native data structure of ArcGIS and provide a foundation for data management, editing, and spatial analysis workflows. Enterprise geodatabases, sometimes referred to as a multiuser geodatabase, offer a comprehensive set of functionalities to ArcGIS Pro and ArcGIS Enterprise users beyond traditional relational databases and include features for modeling, editing, and maintaining GIS datasets. They also support versioning, topology, relationship classes, and other advanced GIS data modeling capabilities.<\/span>\u00a0<\/span><\/p>\n They are used by thousands of organizations today as the geospatial system of record for their authoritative GIS datasets, including utility networks, road networks, parcel fabrics, and indoor information models. While the data is stored in the geodatabase, referenced web services can be published from them to ArcGIS Enterprise allowing for web editing, field data collection, and visualization and analytics on the web. <\/span>\u00a0<\/span><\/p>\n Enterprise geodatabases deliver specialized functionality for modeling, editing, and maintaining your spatial datasets. What differentiates enterprise geodatabases from file and mobile geodatabases is that they\u2019re designed for multiuser editing workflows, where multiple people can edit the same data simultaneously without interfering with one another, and with any conflicts flagged for review and resolution. <\/span>\u00a0<\/span><\/p>\n Data from an enterprise geodatabase can be published as web services to ArcGIS Enterprise either by reference from ArcGIS Pro or using bulk publishing, and be used to support visualization, analytics, and editing via the web and mobile applications. <\/span>\u00a0<\/span><\/p>\n While you can publish layers from an enterprise geodatabase to ArcGIS Online, the data is copied during publishing, so data updates will need to be managed through periodic republishing or overwriting of the content in ArcGIS Online. This process can be automated using a Python script, an ArcGIS Data Interoperability flow, or a third-party ETL tool. Publishing data from an enterprise geodatabase as a copy to ArcGIS Enterprise is also possible, but is less common, as data can be shared by reference, maintaining a live connection to the database.<\/span>\u00a0<\/span><\/p>\n Best used for:<\/span><\/b> organizations looking for a robust multiuser geodatabase to create, update, and manage their spatial system of record.\u00a0<\/span>\u00a0<\/span><\/p>\n If you are new to ArcGIS and are unfamiliar with what enterprise geodatabases are, the benefits they offer, and how organizations use them to store their GIS system of record, check out the following resources:<\/span>\u00a0<\/span><\/p>\n Now that\u00a0we\u2019ve\u00a0highlighted integrating data from an enterprise geodatabase,\u00a0let\u2019s\u00a0get into the approaches which allow us to integrate data from\u00a0<\/span>external sources<\/span><\/b>, that are being\u00a0maintained\u00a0outside of ArcGIS, with the platform.<\/span><\/p>\n"},{"acf_fc_layout":"content","content":" Need to integrate tables from a database not natively supported in ArcGIS Pro?<\/strong><\/p>\n ArcGIS Pro supports OLE DB connections, allowing you to access external tables from data sources beyond those supported through native database connections. Once connected, the tables can be viewed, queried, and joined with spatial data to support mapping, analysis, and sharing workflows.<\/p>\n To learn more, read the documentation<\/a> and review this ArcGIS Blog<\/a>.<\/p>\n","image_reference":false,"layout":"standard","image_reference_figure":"","snippet":"","spotlight_name":"","section_title":"","position":"Right","spotlight_image":false},{"acf_fc_layout":"content","content":" Query layers are a layer type in ArcGIS Pro that allows users to define a feature layer or stand-alone table using SQL after a connection to a database has been established. The list of supported databases for query layers includes many commonly used relational databases, cloud data warehouses, and document databases. \u00a0<\/span><\/p>\n The SQL statement used to define the query layer can filter, aggregate, join, and otherwise shape the data that is returned, leveraging the SQL functions supported by the underlying database. Because the data remains in the source system, updates made to the data are immediately reflected in the layer in ArcGIS Pro.<\/p>\n ArcGIS Enterprise users can also share query layers from ArcGIS Pro as referenced feature layers and map image layers that can be used in web maps, web apps, and other ArcGIS clients. Like query layers in ArcGIS Pro, these web layers maintain a live connection to the underlying database without requiring the data to be moved or copied into ArcGIS.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2875092,"id":2875092,"title":"2-Pro_2","filename":"2-Pro_2.png","filesize":1720668,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-enterprise\/data-management\/ways-to-integrate-data-with-arcgis-enterprise\/2-pro_2","alt":"Screenshot of ArcGIS Pro showing the Share As Web Layer pane, with a world map displaying sea surface temperature data in a hexagon grid overlay.","author":"356032","description":"","caption":"Query layer in ArcGIS Pro showing average sea surface temperature from a Google BigQuery table with 358 million data points, aggregated into feature bins with the Share as Web Layer pane open.","name":"2-pro_2","status":"inherit","uploaded_to":2874942,"date":"2025-07-02 18:57:43","modified":"2026-08-10 18:24:36","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1864,"height":1022,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2.png","medium-width":464,"medium-height":254,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2.png","medium_large-width":768,"medium_large-height":421,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2.png","large-width":1864,"large-height":1022,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2-1536x842.png","1536x1536-width":1536,"1536x1536-height":842,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2.png","2048x2048-width":1864,"2048x2048-height":1022,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2-826x453.png","card_image-width":826,"card_image-height":453,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/07\/2-Pro_2.png","wide_image-width":1864,"wide_image-height":1022}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" To share web layers by reference to ArcGIS Enterprise, the source database must first be added to ArcGIS Enterprise as a data store item.<\/p>\n Best used for<\/span><\/b>: visualization and analysis of data stored in external databases and cloud data warehouses.<\/span>\u00a0<\/span><\/p>\n Integrate data from:<\/span><\/b> Microsoft SQL Server, Oracle, PostgreSQL, Snowflake, Google BigQuery, Amazon Redshift, SAP HANA, IBM Db2, Teradata, Dameng, Elasticsearch, and OpenSearch.<\/span><\/p>\n Learn more about query layers:<\/span><\/b><\/p>\n In the future, you will be able to create query layers in Map Viewer in ArcGIS Enterprise. We\u2019re also working to expand the list of supported cloud data warehouses in ArcGIS Pro and ArcGIS Enterprise to include Databricks SQL Warehouse.<\/span><\/p>\n Note: Query layers that reference data in relational databases can be shared as a copy to ArcGIS Online or ArcGIS Enterprise. However, to share data from a cloud data warehouse or document database as a copy, you must first export it to a geodatabase feature class and then publish the layer.<\/p>\n ArcGIS<\/span>\u00a0Data Pipelines is a web app designed for data preparation and integration that is available in ArcGIS Online and ArcGIS Enterprise. It provides users with a visual diagramming app that makes it fast and easy to build,\u00a0<\/span>maintain<\/span>, and hand off data preparation workflows.<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2979784,"id":2979784,"title":"DataPipelinesSnowflakewwebapp","filename":"DataPipelinesSnowflakewwebapp.png","filesize":1072467,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis\/data-management\/the-science-of-where-in-the-modern-data-stack-data-integration-approaches-and-methods-in-arcgis\/datapipelinessnowflakewwebapp","alt":"ArcGIS Data Pipeline","author":"325202","description":"","caption":"A data pipeline workflow that integrates and transforms data from Snowflake to support downstream mapping and analytics.","name":"datapipelinessnowflakewwebapp","status":"inherit","uploaded_to":2979730,"date":"2026-08-10 19:12:03","modified":"2026-08-10 19:12:14","menu_order":0,"mime_type":"image\/png","type":"image","subtype":"png","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1922,"height":1002,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp.png","medium-width":464,"medium-height":242,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp.png","medium_large-width":768,"medium_large-height":400,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp.png","large-width":1920,"large-height":1001,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp-1536x801.png","1536x1536-width":1536,"1536x1536-height":801,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp.png","2048x2048-width":1922,"2048x2048-height":1002,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp-826x431.png","card_image-width":826,"card_image-height":431,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/08\/DataPipelinesSnowflakewwebapp-1920x1001.png","wide_image-width":1920,"wide_image-height":1001}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" Connect to input data sources from databases (Snowflake, Google BigQuery, Databricks), cloud storage (Amazon S3 and Microsoft Azure Storage), URLs, files, and ArcGIS feature services. Use tools to build, blend, and transform data, then configure outputs that create or update content for downstream mapping and analytics. Add notes to the canvas to document decisions and enhance usability, and preview data as you engineer it by reviewing the attributes, geometry, and schema of your data<\/span>.<\/p>\n Because workflows are saved as reusable data pipeline items, they can be shared, updated, and\u00a0maintained\u00a0over time, making it easier for teams to standardize recurring integration processes across an organization.<\/span>\u00a0<\/span><\/p>\n Leverage built-in scheduling to automate data updates as often as every 15 minutes or run data pipelines as part of broader automation workflows using the ArcGIS REST API, ArcGIS API for Python, or Microsoft Power Automate.<\/span>\u00a0<\/span><\/p>\n ArcGIS Data Pipelines is available to all ArcGIS Online organizations and charges credits while in use. In ArcGIS Enterprise, ArcGIS Data Pipelines comes with ArcGIS Enterprise Advanced on Windows and Linux.\u00a0<\/span>\u00a0<\/span><\/p>\n Best used for:<\/span><\/b>\u00a0integrating data from the cloud and transforming it into an authoritative layer in ArcGIS Online and ArcGIS Enterprise.<\/span>\u00a0<\/span><\/p>\n Integrate data from:\u00a0<\/span><\/b>files and feature layers from your ArcGIS content, Amazon S3, Microsoft Azure Storage, Google\u00a0BigQuery, Snowflake, Databricks, URLs and APIs, and file shares.\u00a0<\/span>\u00a0<\/span><\/p>\n To learn more about ArcGIS Data Pipelines:<\/span><\/b><\/p>\n In the future,\u00a0we\u2019re\u00a0planning to add connectors to ArcGIS Data Pipelines in ArcGIS Enterprise to Microsoft SQL Server, PostgreSQL, Oracle, and Databricks, and\u00a0we\u2019re\u00a0also working to bring ArcGIS Data Pipelines to ArcGIS Enterprise on Kubernetes. <\/span><\/p>\n If you\u2019re interested in staying informed about upcoming beta testing opportunities available for ArcGIS Data Pipelines in ArcGIS Enterprise on Kubernetes, please submit your interest via <\/span>this form<\/span><\/a>.<\/span><\/p>\n\n
Manage<\/span>\u00a0your<\/span>\u00a0authoritative GIS data with enterprise geodatabases\u00a0<\/span><\/span>\u00a0<\/span><\/strong><\/h3>\n"},{"acf_fc_layout":"content","content":"
\n
Use query layers to access external databases without moving your data\u00a0<\/span><\/span>\u00a0<\/span><\/strong><\/h3>\n"},{"acf_fc_layout":"sidebar","content":"
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![](\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2023\/02\/back.png\")
<\/a><\/p>\n"},{"acf_fc_layout":"content","content":"Connect, transform, and automate data integration with\u00a0<\/span>ArcGIS Data Pipelines<\/span><\/strong><\/h3>\n"},{"acf_fc_layout":"content","content":"
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