44 results found

1. Implement a feature to track data download volume per DB user

   In order to enhance data security and prevent unauthorized data exfiltration, our team proposes the implementation of a metric within MongoDB Atlas that allows administrators to monitor and measure the amount of data downloaded by each database user over a specified period. This feature would provide critical insights into user behavior, helping to identify unusual data access patterns or potential data breaches. By tracking network data usage at the user level, we can more effectively audit data access and transfer, ensuring that data is used appropriately and in compliance with organizational data governance policies. This granularity in monitoring would be a significant step forward in data management and security within MongoDB Atlas, offering a proactive tool for administrators in safeguarding sensitive data.

   In order to enhance data security and prevent unauthorized data exfiltration, our team proposes the implementation of a metric within MongoDB Atlas that allows administrators to monitor and measure the amount of data downloaded by each database user over a specified period. This feature would provide critical insights into user behavior, helping to identify unusual data access patterns or potential data breaches. By tracking network data usage at the user level, we can more effectively audit data access and transfer, ensuring that data is used appropriately and in compliance with organizational data governance policies. This granularity in monitoring would be… more

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2. Support to export backups to Azure Blob Storage in Atlas

   I would like the capability to export my cloud snapshots to Azure blob storage.

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3. Support for Superset and other Python DB-API / SQLAlchemy connections to SQL Atlas

   Superset uses SQL Alchemy and/or Python DB-API drivers, not JDBC or ODBC drivers. Superset is the most popular, open-source Apache visualization tool.

   Others have made it work like this: https://preset.io/blog/building-database-connector/

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4. Add support region at south korea for federated database
   6 votes

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5. Combine data lake snapshots into a single federated collection

   A common use case for data analytics is to analyse how your data evolve over time.
   For example, imagine you have an e-commerce database and your products have their price change every day. You may only store the price in your database but you'd like to make a chart that shows the evolution of your product prices over time (price y axis and time for x axis).

   It is possible today to make this happen with the combination of Data Lake and Data Federation, but the Storage Configuration JSON need to be manually updated like this:

   {
     "databases": [
       {
         "collections": [
           {
             "name": "collectionName",
             "dataSources": [
               {
                 "datasetName": "v1$atlas$snapshot$Cluster0$env$collectionName$20230814T050424Z",
                 "provenanceFieldName": "provenance",
                 "storeName": "..."
               },
               {
                 "datasetName": "v1$atlas$snapshot$Cluster0$env$collectionName$20230813T050415Z",
                 "provenanceFieldName": "provenance",
                 "storeName": "..."
               },
               {
                 "datasetName": "v1$atlas$snapshot$Cluster0$env$collectionName$20230812T050424Z",
                 "provenanceFieldName": "provenance",
                 "storeName": "..."
               },
               ...,
               ...,
               ...,
               ...,
               ...,
               ...,
             ]
           }
         ],
   
       }
     ],
     ...
   }
   

   The Data Federation configuration json is going to make thousands of lines and need to be maintained daily or maybe using a script + API. (3 lines of json per collection * 365 snapshots a year * 20 collections = 22'000 lines of json a year)

   One idea could be to use a simple wildcard instead of the timestamp like this:

   {
     "databases": [
       {
         "collections": [
           {
             "name": "collectionName",
             "dataSources": [
               {
                 "datasetName": "v1$atlas$snapshot$Cluster0$env$collectionName$*",
                 "provenanceFieldName": "provenance",
                 "storeName": "..."
               }
             ]
           }
         ]
       }
     ]
   }
   

   P.S: I know that time series collection could be useful in the specific example I just gave. But, sometime, you may want to analyse various properties over time, that's where a Data Lake solutions make sense.

   A common use case for data analytics is to analyse how your data evolve over time.
   For example, imagine you have an e-commerce database and your products have their price change every day. You may only store the price in your database but you'd like to make a chart that shows the evolution of your product prices over time (price y axis and time for x axis).

   It is possible today to make this happen with the combination of Data Lake and Data Federation, but the Storage Configuration JSON need to be manually updated like this:

   {
     "databases": [

   … more
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6. Support AWS IAM for Data Federation Authentication

   We would like to be able to connect to the Federated Database Instance using AWS IAM for Authentication just like you can for Atlas Clusters.

   4 votes

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7. readPreference=Secondary for Federated Data Store

   We're using Online Archives to ensure our cluster data size stays manageable, while at the same time enabling our data extraction process to have access to older date on an exception basis.

   Not being able to set the read preference on our mongoexport connection string for our federated data source ( https://www.mongodb.com/docs/atlas/app-services/mongodb/read-preference/) is a significant issue for our use case.

   12 votes

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8. Add last modified timestamp to Data Federation Provenance for S3

   It would be great to have the last modified timestamp of a file in S3 returned with the provenance functionality in Atlas Data Federation.

   2 votes

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9. Schema inference

   Schemaless is flexible but it has a big impact for the downstreams especially for data exchange and DW/AI.

   It is a must-have effort to derive & infer the schema from the actual documents, so that we can understand/track/evolve/translate the document schema.

   https://www.mongodb.com/blog/post/engblog-implementing-online-parquet-shredder is a great article.

   I'd like to propose an additional feature in ADL/ADF to make schema inference as a 1st-class citizen with faster turnaround & less operation cost.

   After the $out operation of ADL/ADF, please collect the Parquet schema from each data files and union/unify them into a single schema. This schema will be stored in a .schema.json or .schema.txt file in the same S3/GCS location.

   Add a new flag/parameter for $out to scan through all the documents based on the filter condition in the queries, but instead of writing the Parquet files, the $out only writes out .schema.json or .schema.txt file to s3. This can be a quite useful operation routine to run every week with or without a rough datetime incremental filter to infer the schema and then update the corporate/enterprise schema repository/central.

   I've elaborated this idea with MGM and benjamin.flast

   Thank you.

   Schemaless is flexible but it has a big impact for the downstreams especially for data exchange and DW/AI.

   It is a must-have effort to derive & infer the schema from the actual documents, so that we can understand/track/evolve/translate the document schema.

   https://www.mongodb.com/blog/post/engblog-implementing-online-parquet-shredder is a great article.

   I'd like to propose an additional feature in ADL/ADF to make schema inference as a 1st-class citizen with faster turnaround & less operation cost.

   After the $out operation of ADL/ADF, please collect the Parquet schema from each data files and union/unify them into a single schema. This schema will be stored in a .schema.json… more

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10. Make mongodump work with Online Archive

    Update mongodump so that it can be used against an Online Archive.

    2 votes

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11. Support Geo Queries on Object Storage

    I'd like to be able to query using the Geo functionality inside of MongoDB Query Language on data stored in Object Storage.

    Maybe using a format like: https://github.com/opengeospatial/geoparquet

    3 votes

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12. Filtered Data Lake Ingestions

    Our immediate need is that our applications are multi-tenant, so it would be very useful if we could create tenant-specific data lakes, by setting particular constraints in the ingestion configuration (ex. only ingest the documents with tenantId = 'specificTenantId').
    However, the usefulness of filtered data lake ingestions can be multifaceted. The ingestion could be done only for archived=false documents, documents with status=ACTIVE, etc.

    2 votes

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13. Atlas Data Explorer to support using Aggregation Builder against Atlas Data Lake

    You can use the Atlas Data Explorer and Aggregation Builder in the MongoDB Atlas web dashboard on regular collections and views. Unfortunately there appears to be no way to use them against a Data Lake within the web dashboard, either directly or while constructing new Data Sources for Charts. Attempting to use Aggregation Builder on a Data Lake while defining a Data Source forwards to a URL that returns 404.

    It would be great if the same functionality was available for Data Lake as well.

    3 votes

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14. Data Uploading process Is Little bit Difficult for new users. Upload a demo vedio of Uploading.

    Overall I Found one of the Interesting Software and Friendly use

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15. Add eu-north-1 as a option for AWS hosting

    Sweden is a very innovative country with many startups and scaleups and AWS is used very often for hosting of services and data. Sweden is also very strict on rules where and how to store data and that is why AWS has eu-north-1 as a location to choose for storing data (which is in Sweden). Currently Data Lake doesn't support that option, the closest one is Germany. It would be great to support eu-north-1 as well, so that we don't have to live with the unnecessary latency.

    3 votes

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16. Import and Export archiving rules

    Ability to import and export archiving rules to be able to restore them if/when we need to restore the cluster. Also useful when replicating prod clusters to our stage environment

    3 votes

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17. On-line Archive survives region outage

    I understand that even with a geo-replicated cluster if that cluster is configured with an online archive and there's a region outage, access to the online archive data is lost. It is still unclear to me if queries against collections configured with online would fail in this scenario. In any case, it would make sense to me to enable the S3 bucket backing the on-line archive to itself be replicated using "Amazon S3 Cross-Region Replication (CRR)"

    2 votes

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18. Cross Project Access to Atlas Clusters from Data Lake

    I would like by Data Lake in Project A to be able to query data in a Cluster in Project B.

    8 votes

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19. Connect Atlas Data Lake to my self managed cloud object storage (S3)

    I'd like to be able to connect Atlas Data Lake to my self managed cloud object storage (S3 Compatible) in my data center or private cloud

    2 votes

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20. Ability to use GUID field as a partition field for online archive

    Hi,

    Today there is no way to partition the archive data based on a field that is of type GUID (legacy GUID). For example, I tried selecting a field which had Binary('0TfYLb3Qg0WT2mZu0wbq8Q==', 3) as the value but I got an error saying that the field is not supported to be a partition field. It makes sense to do this because archived data is usually old and at that time most people were using legacy guids as opposed to object ids.

    3 votes

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