"},{"ID":325202,"user_firstname":"Sarah","user_lastname":"Hanson","nickname":"Sarah Hanson","user_nicename":"shanson","display_name":"Sarah Hanson","user_email":"shanson@esri.com","user_url":"","user_registered":"2023-01-11 15:54:54","user_description":"Sarah is a Product Manager on the ArcGIS Enterprise team, focused on data management. She also manages ArcGIS Data Pipelines, a no-code data engineering app in ArcGIS. In her free time, Sarah loves to explore new places and spend time outdoors.","user_avatar":"
"}],"short_description":"Parquet feature layers (Beta) bring fast, scalable, read-only mapping to ArcGIS Online, optimized for large datasets and fast visualization.","flexible_content":[{"acf_fc_layout":"content","content":"Esri is excited to introduce a new type of layer in the June 2026 release of ArcGIS Online, Parquet feature layers (Beta). This new layer type provides fast visualization and client-side querying capabilities, bringing the benefits of Parquet to your maps and apps.<\/span> It\u2019s especially optimized for read-only reference data and large datasets where quick filtering and drawing time will help improve the end-user experience. Using ArcGIS Data Pipelines<\/a>, a no-code data engineering capability available to all ArcGIS organizations, existing data can be converted into Parquet feature layers, making it easy to integrate this high-performance file format into your maps.<\/p>\n This blog will cover what Parquet is and how it is extended with the new spatially optimized Parquet feature layers. It will also review how to create and work with Parquet feature layers in Map Viewer and other ArcGIS apps as well as recommendations on when to use them.<\/p>\n Parquet is an open-source columnar file format designed for efficient storage and analytics of large tabular datasets. It\u2019s cloud-friendly and popular among data scientists, becoming an increasingly common data interchange format. Instead of storing data by row, Parquet organizes data by column. Client applications can make requests directly to the Parquet file, and the Parquet metadata provides clients with a way to identify and retrieve only the portions of data needed.<\/span><\/p>\n For example, to query the number of records in CA (California) in row-based database storage, the system must scan every row and all the attributes before returning the rows that meet the query.<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2972990,"id":2972990,"title":"Row-based DB","filename":"Row-based-DB.png","filesize":42552,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/announcements\/scaling-your-gis-workflows-with-the-new-parquet-feature-layer-beta-in-arcgis-online\/row-based-db","alt":"In row-based database storage, the system must scan every row and all the attributes before returning the rows that meet the query.","author":"437412","description":"","caption":"","name":"row-based-db","status":"inherit","uploaded_to":2969554,"date":"2026-06-25 01:11:56","modified":"2026-06-25 01:12:23","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":522,"height":527,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB.png","medium-width":259,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB.png","medium_large-width":522,"medium_large-height":527,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB.png","large-width":522,"large-height":527,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB.png","1536x1536-width":522,"1536x1536-height":527,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB.png","2048x2048-width":522,"2048x2048-height":527,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB-461x465.png","card_image-width":461,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Row-based-DB.png","wide_image-width":522,"wide_image-height":527}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" In columnar storage<\/span>,<\/span>\u00a0the logic is flipped<\/span>.\u00a0<\/span>Instead of structuring data\u00a0<\/span>using rows, the data is organized by columns.<\/span>\u00a0<\/span>This\u00a0<\/span>allows for faster queries by only\u00a0<\/span>evaluating\u00a0<\/span>columns that store the data<\/span>, resulting in better performance.<\/span><\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2971662,"id":2971662,"title":"Parquet File","filename":"Parquet-File.png","filesize":46778,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File.png","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/announcements\/scaling-your-gis-workflows-with-the-new-parquet-feature-layer-beta-in-arcgis-online\/parquet-file","alt":"Columnar datasets only query the column where the data of interest is stored and returns the results.","author":"437412","description":"","caption":"","name":"parquet-file","status":"inherit","uploaded_to":2969554,"date":"2026-06-22 16:30:40","modified":"2026-06-22 16:31:49","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":519,"height":526,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File-213x200.png","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File.png","medium-width":258,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File.png","medium_large-width":519,"medium_large-height":526,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File.png","large-width":519,"large-height":526,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File.png","1536x1536-width":519,"1536x1536-height":526,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File.png","2048x2048-width":519,"2048x2048-height":526,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File-459x465.png","card_image-width":459,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet-File.png","wide_image-width":519,"wide_image-height":526}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" In addition to columnar data storage,\u00a0Parquet\u00a0files\u00a0use\u00a0other\u00a0properties\u00a0and metadata\u00a0to improve query speed. This includes grouping data by type and using metadata\u00a0like\u00a0min\/max values\u00a0and\u00a0null count\u00a0to\u00a0skip\u00a0entire\u00a0groups\u00a0of the data\u00a0if the\u00a0metadata\u00a0doesn\u2019t\u00a0meet the query request.\u00a0<\/span>\u00a0<\/span><\/p>\n Finally,\u00a0Parquet uses compression to reduce\u00a0the overall\u00a0file size, helping minimize storage while improving read speed and overall scalability. All these properties result in\u00a0making Parquet a\u00a0highly efficient\u00a0file format\u00a0with faster queries and better performance, especially with large datasets.<\/span>\u00a0<\/span><\/p>\n GeoParquet builds on the Parquet data storage standard by adding geospatial metadata and geometry types (point, line, and polygon). The geospatial metadata includes key information like the coordinate system, the spatial extent, and the geometry type<\/span>\u00a0of the data<\/span>. The geometry is stored in a column encoded using WKB (Well-Known Binary). Geospatial metadata and geometry enable GeoParquet datasets to support mapping the data and other GIS workflows.<\/span>\u00a0<\/span><\/p>\n Esri leveraged the efficiency and scalability of Parquet data and extended it to support faster spatial queries and visualization through the development of a spatially optimized Parquet in a new layer type called Parquet feature layers. In the June 2026 update to ArcGIS Online, Parquet feature layers are introduced in beta and can be created using ArcGIS Data Pipelines.<\/span>\u00a0<\/span><\/p>\n The spatial optimization includes the addition of two key characteristics:<\/span><\/p>\n Parquet feature layers can be used in many of the same ways as hosted feature layers and in some cases will perform better, especially <\/span>with large datasets with complex polygon or polyline geometries<\/span>. However, there are current limitations that should be considered when evaluating if Parquet feature layers are the right layer type for your downstream workflows.<\/span><\/p>\n Here are a few important notes:<\/span>\u00a0<\/span><\/p>\n Parquet feature layers are best suited for large, read-only datasets where scalability, efficient storage, and optimized visualization performance are critical. These layers are highly effective when dealing with very large datasets because they offer fast-drawing times like vector tile layers but provide the customization and attribute support of a feature layer. When determining whether a Parquet feature layer is the best format for your workflow, consider both the goals of the solution and the characteristics of your data.<\/span><\/p>\n Parquet feature layers are recommended for:<\/span>\u00a0<\/span><\/p>\n Parquet feature layers can be created using ArcGIS Data Pipelines<\/a>, a visual data engineering app in ArcGIS Online that makes it easy to integrate and prepare data.<\/span><\/p>\n Using the\u00a0Data Pipelines\u00a0editor, you\u00a0can\u00a0connect to\u00a0data\u00a0from\u00a0a variety of\u00a0sources, including URLs and APIs, cloud stores (Amazon S3 and Microsoft Azure Storage), cloud databases like Snowflake, Databricks, and Google\u00a0BigQuery, and more.\u00a0Then, apply common data preparation tools to enhance the quality and readiness of your data, with the resulting data written out to layers in your content.<\/span>\u00a0<\/span><\/p>\n In the past, Data Pipelines only supported writing prepared data to feature layers, but in this release, it\u2019s been enhanced to also offer support for writing data out to the new Parquet feature layer (Beta)<\/a>. Data Pipelines also supports scheduled or on-demand updates to Parquet feature layers by using workflows to overwrite existing datasets.<\/span>\u00a0<\/span><\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2971742,"id":2971742,"title":"Parquet_Blog","filename":"Parquet_Blog.gif","filesize":7753866,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-online\/announcements\/scaling-your-gis-workflows-with-the-new-parquet-feature-layer-beta-in-arcgis-online\/parquet_blog","alt":"ArcGIS Data Pipelines can be used to create Parquet feature layers","author":"325202","description":"","caption":"A data pipeline is configured to write the prepared data to a new Parquet feature layer (Beta).","name":"parquet_blog","status":"inherit","uploaded_to":2969554,"date":"2026-06-22 18:07:33","modified":"2026-06-23 00:22:49","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":1068,"height":522,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog.gif","medium-width":464,"medium-height":227,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog.gif","medium_large-width":768,"medium_large-height":375,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog.gif","large-width":1068,"large-height":522,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog.gif","1536x1536-width":1068,"1536x1536-height":522,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog.gif","2048x2048-width":1068,"2048x2048-height":522,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog-826x404.gif","card_image-width":826,"card_image-height":404,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2026\/06\/Parquet_Blog.gif","wide_image-width":1068,"wide_image-height":522}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":" While ArcGIS Data Pipelines is a robust solution that supports a comprehensive set of data engineering needs and automation, in the future there will be more pathways to publish Parquet feature layers in ArcGIS Online.<\/span><\/p>\n There were many other exciting updates to ArcGIS Data Pipelines in the June 2026 update in ArcGIS Online, which you can review in this blog:\u00a0<\/span>What\u2019s new in ArcGIS Data Pipelines (June 2026)<\/span><\/a>.\u00a0If\u00a0you\u2019re\u00a0new to Data Pipelines,\u00a0review\u00a0<\/span>the documentation<\/span><\/a>\u00a0and consider starting with\u00a0<\/span>this tutorial<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n Once a Parquet feature layer is created, it will be available in your content. Like other data types, there are overview, usage, and settings tabs that can be used to set properties, download, and learn more about the layer. The owner of the layer can control the layer sharing, and settings including delete protection, item extent, and cache control.<\/span>\u00a0<\/span><\/p>\n A Parquet feature layer contains one or more underlying spatially optimized Parquet files. They can be downloaded and accessed directly from the Overview tab using the Download button. It provides a zipped file containing the Parquet file(s) which can be used in any external system that supports Parquet.<\/p>\n From the Overview tab or the content search in Map Viewer, the Parquet feature layer can be added to a web map. Once the layer is added to a web map, the display settings (styles, filters, effects, aggregation, pop-ups, fields, labels) can be used to control and customize how the data appears on the map.<\/span><\/p>\nWhat is Parquet?<\/span><\/b>\u00a0<\/span><\/h3>\n
What is\u00a0GeoParquet?<\/span><\/b>\u00a0<\/span><\/h3>\n
What are Parquet feature layers? <\/span><\/b>\u00a0<\/span><\/h3>\n
\n
Key considerations for using Parquet feature layers<\/strong><\/h3>\n
\n
When to use Parquet feature layers<\/span><\/b>\u00a0<\/span><\/h3>\n
\n
Creating Parquet\u00a0feature layers<\/span><\/b>\u00a0<\/span><\/h3>\n
Working with Parquet\u00a0feature\u00a0layers\u00a0in ArcGIS Online<\/span><\/b>\u00a0<\/span><\/h3>\n