43 results found

1. Support Online Archives in Charts

   We use Atlas Charts and would like to keep the data moved to Online Archive accessible for reporting/visualizations purposes.

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2. Add Incremental Materialized Views

   Add the ability to create a view where the result is pre-computed and is updated incrementally as more data becomes available.

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3. Add support for Text format files

   I have a custom log format that I'd like to be able query. I imagine I would describe the format of the text files to Atlas Data Lake and then be able to query them.

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4. Simplify interface for query commands

   User friendly data filtering, queries for updating or deleting data from collections.

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5. Support the PDF File Format

   I would like to be able to query PDF files using Atlas Data Lake.

   3 votes

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6. 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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7. 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.

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   0 comments  ·  Storage Configuration  ·  Delete…  ·  Admin →
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8. Make mongodump work with Online Archive

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

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9. 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.

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10. 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)"

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11. 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

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12. Connect an ODBC Driver to Atlas Data Lake

    I'd like to use an ODBC driver to connect to Atlas Data Lake's SQL support.

    2 votes

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13. Specify when you'd like Online Archive to migrate data

    I'd like the ability to specify when a migration from my Atlas cluster to my Online Archive to take place.

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14. 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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15. M0 Support for Evaluation

    Please provide M0 support for evaluation purposes.

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16. 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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17. Add support for XML

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

    2 votes

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18. Specify Delimiter for CSV Files

    I need to specify the delimiter for my CSV files.

    2 votes

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19. 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/

    1 vote

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20. 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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