51 results found

1. Add support to $out to S3 for Standard JSON

   I'd like to be able to use $out but output to Standard JSON instead of Extended JSON as the tool I'm using needs to consume standard JSON.

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   0 comments  ·  File Formats  ·  Delete…  ·  Admin →
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2. Connect Data Lake to Self Managed MongoDB Clusters

   Connect your Atlas Data Lake to Self Managed MongoDB clusters in private data centers, self managed in a public cloud, or locally hosted.

   (This would also allow certain cloud services like Charts and a component of Realm Scheduled Triggers to work with these clusters.)

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   0 comments  ·  Infrastructure Options  ·  Delete…  ·  Admin →
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3. M0 Support for Evaluation

   Please provide M0 support for evaluation purposes.

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4. The "Date field to archive on" option under Archiving Rule tab should also accept date in timestamp format.

   The "Date field to archive on" option under Archiving Rule tab in Online Archive should also accept date field having timestamp format instead of only having date format.

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   0 comments  ·  Query Functionality  ·  Delete…  ·  Admin →
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5. Add support for XML

   I would like to be able to query XML files using my Atlas Data Lake

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6. AWS IAM AuthN for Atlas SQL

   Support AWS IAM Authentication mechanism in JDBC and ODBC drivers (Atlas SQL)

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   0 comments  ·  Connectors  ·  Edit…  ·  Delete…  ·  Admin →
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7. Create a read/write Data Federation connection string

   Some customers need a connection string both to the cluster and to Online Archive with the ability to write to the cluster only.

   So far, the only option is to use more than a connection string in the application.

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   0 comments  ·  Query Functionality  ·  Edit…  ·  Delete…  ·  Admin →
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8. 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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   0 comments  ·  Reporting  ·  Edit…  ·  Delete…  ·  Admin →
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9. 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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   0 comments  ·  Storage Configuration  ·  Edit…  ·  Delete…  ·  Admin →
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10. 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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    0 comments  ·  Query Functionality  ·  Edit…  ·  Delete…  ·  Admin →
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11. 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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    0 comments  ·  Edit…  ·  Delete…  ·  Admin →
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