Microsoft
Microsoftkumarneeraj , Thanks for your feedback much appreciated.
1) Followers cluster is an in place data sharing functionality and it can be used in multiple scenarios, in my article I explained that it will be useful for workload isolation and consumer/department paying their own cluster bill.
2) We can definitely use the primary cluster for all workload and many customers are using this approach. In some cases data scientist/Analyst do quick testing and POC and bring their own datasets to join with production data. bringing un-managed dataset in the primary production cluster might be a concern in some scenario that why the proposed architecture.
3) Having a very large cluster which can serve both read/write requirements(Autoscale might work as well) might come out to be expensive. If the read and write data retention policy is different it might not be possible in the same cluster as well.
4) Thanks for your feedback i will update the .ingest section. The dynamic .ingest in the blog will hide the secrets, avoid duplicate file loading and also can set a custom creation time if the file arrive a few days late(which is common in prod scenario).
5) Azure Data Explorer is a highly available service with 99.9% uptime. I have not covered the DR scenario in the blog but it is well documented. Also Availability zone is currently only available in few regions worldwide so I might not available in all Azure regions.
Clickstream analysis is the process of collecting, analysing and reporting aggregated data about user’s journey on a website. User’s interactions with websites are collected, with applications like Adobe analytics and Tealium. Bringing clickstream data into a centralised place and combining it with other data sources for rich analytics is often required.
\nTypically clickstream data can reach around 2-3TB/day in size. Using Relational databases to analyse such data might not be suitable or cost effective.
\nAzure Data Explorer is a fast, fully managed data analytics service for real-time analysis on large volumes of Telemetry, Logs, Time Series data streaming from applications, websites, IoT devices, and more.
\n\n
Proposed Architecture:
\nAn end to end proposed architecture how you can use Azure Data Explorer to bring Clickstream historical and incremental data into Azure Data Explorer and analyse, aggregate and visualise the data.
\n\n
Source System:
\n1.a) Adobe Analytics dataset usually contains about 1000+ fields. Adobe Analytics provides Data feed functionality which can be used to extract the data to Azure Data Lake Store. Data can be extracted with different configurations:
\n1.b) Tealium datasets are extracted in JSON structure with 100’s of nested fields. The data can be sent to Azure Event hub in near realtime.
\n\n
Bringing Data into ADX:
\nCreating the table structure and mapping for 1000+ column is a cumbersome task, it can be made easy with 1 Click Ingestion.
\n\n
There are multiple ways to load the data.
\n3) Historical Load using Lightingest:
\nLightIngest is a command-line utility for ad-hoc data ingestion into Azure Data Explorer.
\nSimplest and efficient way to load all the historical data, with just one command.
\n\n
\n
\n
\n
\n
\n
Although adobe files are in TSV format but due to special characters in the files, you should use the format TSVE in ADX.
\nSimilarly, for Tealium historical load, use Json format.
\n\n
Incremental Load:
\n3.a)
\nFor Adobe batch file loading subscribe to eventgrid.
\nAlternatively, ADF is useful for loading because you can easily control the loading flow, and manage all your ETL or ELT pipelines from a single tool. Example pipeline (Template attached)
\n\n
.ingest command:
\nRead more about .ingest command. The below sample .ingest command is created dynamically using ADF parameters. It will avoid duplicate loading of same file, encrypt the secrets in logs and create custom creation date.
\n\n
\n
\n
\n
\n
\n
3.b) For Tealium real time streaming connect Eventhub to ADX table:
\n\n
4) Expected Compression ratio:
\n\n
| \n Source \n | \n\n Data Size \n | \n\n Input Format \n | \n\n ADX Mapping Format \n | \n\n Expected Data Size in ADX \n | \n
| \n Adobe Analytics \n | \n\n XX - TB \n | \n\n TSV \n | \n\n TSVE \n | \n\n XX/10 TB (10x Compression) \n | \n
| \n Tealium \n | \n\n XX - TB \n | \n\n JSON \n | \n\n JSON \n | \n\n XX/3.5 TB (3.5x Compression) \n | \n
\n
\n
\n
5) Querying & Analytical workload:
\nThe Analytical work requires more compute but is usually required at working hours. Using a follower cluster will make it easy to pause/resume and optimise for read workload. This will be useful for charge back to different groups and will provide workload isolation.
\n\n
Cluster pause/resume can be done using Logic app or Azure Automation.
\n\n
Estimated Cluster Size:
\nThe cluster size will depend on the daily data ingestion and how many days needs to be retained in hot cache. You can estimate your cluster size based on your requirements.
\n\n
Costing for 2TB daily load with 10x compression.
\nYou can monitor your workload and adjust your cluster accordingly.
\n","kudosSumWeight":5,"postTime":"2020-11-03T14:04:13.232-08:00","images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyNWk5MDRFQzRGNzU0NUIyNUUx?revision=22\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyM2kwMTBGRDdFNzA5MjhGNTA4?revision=22\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyNmk5MTZFNjg3RDYwN0M1NTU0?revision=22\"}"}}],"totalCount":3,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"attachments":{"__typename":"AttachmentConnection","pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null},"edges":[{"__typename":"AttachmentEdge","cursor":"MjUuMXwyLjF8b3w1fF9OVl98MQ","node":{"__ref":"Attachment:{\"id\":\"attachment:message1835942AttachmentNumber1\",\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/attachments/gxcuf89792/AzureDataExplorer/130/1/LoadAdobeFileWithTrigger.zip\"}"}}]},"tags":{"__typename":"TagConnection","pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null},"edges":[{"__typename":"TagEdge","cursor":"MjUuMXwyLjF8b3wxMHxfTlZffDE","node":{"__typename":"Tag","id":"tag:ADX","text":"ADX","time":"2018-01-27T01:22:32.124-08:00","lastActivityTime":null,"messagesCount":null,"followersCount":null}},{"__typename":"TagEdge","cursor":"MjUuMXwyLjF8b3wxMHxfTlZffDI","node":{"__typename":"Tag","id":"tag:Azure Data Explorer","text":"Azure Data Explorer","time":"2019-01-31T03:19:30.535-08:00","lastActivityTime":null,"messagesCount":null,"followersCount":null}},{"__typename":"TagEdge","cursor":"MjUuMXwyLjF8b3wxMHxfTlZffDM","node":{"__typename":"Tag","id":"tag:kusto","text":"kusto","time":"2018-07-08T06:30:26.789-07:00","lastActivityTime":null,"messagesCount":null,"followersCount":null}}]},"timeToRead":3,"rawTeaser":"
Cost effective technology to ingest 1000+ columns, billions of rows and can query in sub-second
","introduction":"","coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""},"currentRevision":{"__ref":"Revision:revision:1835942_22"},"latestVersion":{"__typename":"FriendlyVersion","major":"5","minor":"0"},"metrics":{"__typename":"MessageMetrics","views":7363},"visibilityScope":"PUBLIC","canonicalUrl":null,"seoTitle":null,"seoDescription":null,"placeholder":false,"originalMessageForPlaceholder":null,"contributors":{"__typename":"UserConnection","edges":[]},"nonCoAuthorContributors":{"__typename":"UserConnection","edges":[]},"coAuthors":{"__typename":"UserConnection","edges":[]},"blogMessagePolicies":{"__typename":"BlogMessagePolicies","canDoAuthoringActionsOnBlog":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.blog.action_can_do_authoring_action.accessDenied","key":"error.lithium.policies.blog.action_can_do_authoring_action.accessDenied","args":[]}}},"archivalData":null,"customFields":[],"body":"Clickstream analysis is the process of collecting, analysing and reporting aggregated data about user’s journey on a website. User’s interactions with websites are collected, with applications like Adobe analytics and Tealium. Bringing clickstream data into a centralised place and combining it with other data sources for rich analytics is often required.
\nTypically clickstream data can reach around 2-3TB/day in size. Using Relational databases to analyse such data might not be suitable or cost effective.
\nAzure Data Explorer is a fast, fully managed data analytics service for real-time analysis on large volumes of Telemetry, Logs, Time Series data streaming from applications, websites, IoT devices, and more.
\n\n
Proposed Architecture:
\nAn end to end proposed architecture how you can use Azure Data Explorer to bring Clickstream historical and incremental data into Azure Data Explorer and analyse, aggregate and visualise the data.
\n\n
\n
Source System:
\n1.a) Adobe Analytics dataset usually contains about 1000+ fields. Adobe Analytics provides Data feed functionality which can be used to extract the data to Azure Data Lake Store. Data can be extracted with different configurations:
\n1.b) Tealium datasets are extracted in JSON structure with 100’s of nested fields. The data can be sent to Azure Event hub in near realtime.
\n\n
Bringing Data into ADX:
\nCreating the table structure and mapping for 1000+ column is a cumbersome task, it can be made easy with 1 Click Ingestion.
\n\n
There are multiple ways to load the data.
\n3) Historical Load using Lightingest:
\nLightIngest is a command-line utility for ad-hoc data ingestion into Azure Data Explorer.
\nSimplest and efficient way to load all the historical data, with just one command.
\n\n
\n
\n
LightIngest.exe \"https://ingest-<<ClusterName.ClusterRegion>>.kusto.windows.net;Fed=True\" -database:<<DatabaseName>> -table:<<TableName>> -source:\" https://<<StorageName>>.blob.core.windows.net/<<ContainerName>>;<<StorageAccountKey>> \" -prefix:\"<<FolderPath>>\" -pattern:*.tsv.gz -format:tsve\n
\n
\n
Although adobe files are in TSV format but due to special characters in the files, you should use the format TSVE in ADX.
\nSimilarly, for Tealium historical load, use Json format.
\n\n
Incremental Load:
\n3.a)
\nFor Adobe batch file loading subscribe to eventgrid.
\nAlternatively, ADF is useful for loading because you can easily control the loading flow, and manage all your ETL or ELT pipelines from a single tool. Example pipeline (Template attached)
\n\n
\n
.ingest command:
\nRead more about .ingest command. The below sample .ingest command is created dynamically using ADF parameters. It will avoid duplicate loading of same file, encrypt the secrets in logs and create custom creation date.
\n\n
\n
\n
.ingest into table @{pipeline().parameters.tableName} (h'abfss://@{variables('containerName')}@{pipeline().parameters.storageName}.dfs.core.windows.net/@{variables('folderPath')}/@{pipeline().parameters.fileName};@{activity('GetAccountKeyFromKV').output.value}') with (format='tsve',ingestIfNotExists = '[\"@{pipeline().parameters.fileName}\"]', tags = '[\"ingest-by:@{pipeline().parameters.fileName}\"]',creationTime='@{variables('inputFileDateTime')}')\n
\n
\n
3.b) For Tealium real time streaming connect Eventhub to ADX table:
\n\n
4) Expected Compression ratio:
\n\n
| \n Source \n | \n\n Data Size \n | \n\n Input Format \n | \n\n ADX Mapping Format \n | \n\n Expected Data Size in ADX \n | \n
| \n Adobe Analytics \n | \n\n XX - TB \n | \n\n TSV \n | \n\n TSVE \n | \n\n XX/10 TB (10x Compression) \n | \n
| \n Tealium \n | \n\n XX - TB \n | \n\n JSON \n | \n\n JSON \n | \n\n XX/3.5 TB (3.5x Compression) \n | \n
\n
\n
\n
5) Querying & Analytical workload:
\nThe Analytical work requires more compute but is usually required at working hours. Using a follower cluster will make it easy to pause/resume and optimise for read workload. This will be useful for charge back to different groups and will provide workload isolation.
\n\n
Cluster pause/resume can be done using Logic app or Azure Automation.
\n\n
Estimated Cluster Size:
\nThe cluster size will depend on the daily data ingestion and how many days needs to be retained in hot cache. You can estimate your cluster size based on your requirements.
\n\n
Costing for 2TB daily load with 10x compression.
\n\n
You can monitor your workload and adjust your cluster accordingly.
\n","revisions({\"constraints\":{\"isPublished\":{\"eq\":true}},\"first\":1})":{"__typename":"RevisionConnection","totalCount":22}},"Conversation:conversation:1835942":{"__typename":"Conversation","id":"conversation:1835942","solved":false,"topic":{"__ref":"BlogTopicMessage:message:1835942"},"lastPostingActivityTime":"2020-11-14T22:24:46.922-08:00","lastPostTime":"2020-11-14T22:24:46.922-08:00","unreadReplyCount":3,"isSubscribed":false},"ModerationData:moderation_data:1835942":{"__typename":"ModerationData","id":"moderation_data:1835942","status":"APPROVED","rejectReason":null,"isReportedAbuse":false,"rejectUser":null,"rejectTime":null,"rejectActorType":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyNWk5MDRFQzRGNzU0NUIyNUUx?revision=22\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyNWk5MDRFQzRGNzU0NUIyNUUx?revision=22","title":"AdobeUseCase.jpg","associationType":"BODY","width":1841,"height":1934,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyM2kwMTBGRDdFNzA5MjhGNTA4?revision=22\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyM2kwMTBGRDdFNzA5MjhGNTA4?revision=22","title":"ADF Pipeline.JPG","associationType":"BODY","width":1439,"height":1603,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyNmk5MTZFNjg3RDYwN0M1NTU0?revision=22\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0xODM1OTQyLTIzMDMyNmk5MTZFNjg3RDYwN0M1NTU0?revision=22","title":"Costing.JPG","associationType":"BODY","width":1660,"height":825,"altText":null},"Attachment:{\"id\":\"attachment:message1835942AttachmentNumber1\",\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/attachments/gxcuf89792/AzureDataExplorer/130/1/LoadAdobeFileWithTrigger.zip\"}":{"__typename":"Attachment","id":"attachment:message1835942AttachmentNumber1","filename":"LoadAdobeFileWithTrigger.zip","filesize":73929,"contentType":"application/x-zip-compressed","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/attachments/gxcuf89792/AzureDataExplorer/130/1/LoadAdobeFileWithTrigger.zip"},"Revision:revision:1835942_22":{"__typename":"Revision","id":"revision:1835942_22","lastEditTime":"2020-11-04T22:42:55.471-08:00"},"BlogReplyMessage:message:1855881":{"__typename":"BlogReplyMessage","id":"message:1855881","conversation":{"__ref":"Conversation:conversation:1835942"},"author":{"__ref":"User:user:208794"},"revisionNum":1,"uid":1855881,"depth":1,"hasGivenKudo":false,"subscribed":false,"board":{"__ref":"Blog:board:AzureDataExplorer"},"subject":"Re: Clickstream analytics in Azure Data Explorer","readOnly":false,"editFrozen":false,"moderationData":{"__ref":"ModerationData:moderation_data:1855881"},"parent":{"__ref":"BlogTopicMessage:message:1835942"},"body":"
kumarneeraj , Thanks for your feedback much appreciated.
\n\n
1) Followers cluster is an in place data sharing functionality and it can be used in multiple scenarios, in my article I explained that it will be useful for workload isolation and consumer/department paying their own cluster bill.
\n2) We can definitely use the primary cluster for all workload and many customers are using this approach. In some cases data scientist/Analyst do quick testing and POC and bring their own datasets to join with production data. bringing un-managed dataset in the primary production cluster might be a concern in some scenario that why the proposed architecture.
\n3) Having a very large cluster which can serve both read/write requirements(Autoscale might work as well) might come out to be expensive. If the read and write data retention policy is different it might not be possible in the same cluster as well.
\n4) Thanks for your feedback i will update the .ingest section. The dynamic .ingest in the blog will hide the secrets, avoid duplicate file loading and also can set a custom creation time if the file arrive a few days late(which is common in prod scenario).
\n5) Azure Data Explorer is a highly available service with 99.9% uptime. I have not covered the DR scenario in the blog but it is well documented. Also Availability zone is currently only available in few regions worldwide so I might not available in all Azure regions.
","body@stringLength":"1988","rawBody":"\n
1) Followers cluster is an in place data sharing functionality and it can be used in multiple scenarios, in my article I explained that it will be useful for workload isolation and consumer/department paying their own cluster bill.
\n2) We can definitely use the primary cluster for all workload and many customers are using this approach. In some cases data scientist/Analyst do quick testing and POC and bring their own datasets to join with production data. bringing un-managed dataset in the primary production cluster might be a concern in some scenario that why the proposed architecture.
\n3) Having a very large cluster which can serve both read/write requirements(Autoscale might work as well) might come out to be expensive. If the read and write data retention policy is different it might not be possible in the same cluster as well.
\n4) Thanks for your feedback i will update the .ingest section. The dynamic .ingest in the blog will hide the secrets, avoid duplicate file loading and also can set a custom creation time if the file arrive a few days late(which is common in prod scenario).
\n5) Azure Data Explorer is a highly available service with 99.9% uptime. I have not covered the DR scenario in the blog but it is well documented. Also Availability zone is currently only available in few regions worldwide so I might not available in all Azure regions.
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