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Data and metadata stored in Azure Data Lake

  • Straipsnis

Note

Over the past 12 months, we've been working to fill in gaps and add new features that members of the user community have highlighted. Synapse Link for Dataverse service built into Power Apps, the successor to the Export to Data Lake feature in finance and operations apps, is generally available and ready for you. Synapse Link provides one experience for working with data from all Microsoft Dynamics 365 apps.

We want you to benefit from the enhanced performance, flexibility, and improved user experience that Synapse Link offers as soon as possible. Therefore, we've announced the deprecation of the Export to Data Lake feature, effective October 15, 2023. If you're already using the Export to Data Lake feature, you can continue to use it until November 1, 2024. If you're new to the Export to Data Lake feature or are planning to adopt this feature in the coming months, we recommend that you use Synapse Link instead.

We understand that that transition might seem daunting, but we want to provide a smoother experience and offer guidance. To get started, see the Synapse Link transition guide. We're listening closely to the community and are working on multiple features to help make the transition smoother. We'll announce these other improvements to the transition process as we bring new features online. If you want to stay in touch, join the community at https://aka.ms/SynapseLinkforDynamics.

How data is stored in the data lake

Data in Microsoft Azure Data Lake is stored as comma-separated values (CSV) files in a folder structure that is maintained by the system. This folder structure is based on the organization of data in finance and operations apps. For example, you'll find folders that have names such as Finance, Supply Chain, and Commerce. Inside these folders, you'll find subfolders that have names such as Accounts Receivable and Accounts Payable. Further down the hierarchy, you'll find folders that contain the actual data for each table. Inside these table-level folders, you'll find one or more CSV files, and also metadata files that describe the format of the data.

When data in finance and operations apps is updated, corresponding data rows are updated in CSV files inside the table-level folders. When a new row is added to a table in finance and operations apps, or an existing row is deleted, new rows are added or existing rows are deleted in corresponding CSV files.

The number of CSV files in the data lake depends on the size of data rows. The system tries to keep the size of each CSV file at about 200 megabytes (MB) to optimize read and write operations. If the size of a CSV file exceeds 200 MB, the system might split the data into multiple files.

Data files don't contain field headers or any other information. You have to read the metadata to understand the structure of the files.

How metadata is stored in the data lake

Metadata describes the name, data type, size, and nature of data. In addition to the data files in the data lake, you'll notice metadata files at a folder level that corresponds to the data files. Metadata in a data lake is written in a machine-readable format that is described by the Common Data Model (CDM) standard. When you install the Export to Data Lake feature and select data to add to the data lake, the system writes metadata files in addition to the data.

Both Microsoft and third parties provide tools that understand the CDM metadata format. These tools make it easy to work with data in the data lake. Azure Synapse Analytics serverless SQL pools let you use the Transact-SQL (T-SQL) language to consume data in the data lake. T-SQL is widely supported by many tools. You can define a Synapse workspace over the data in the data lake, and then use T-SQL, Spark, or Synapse Pipelines as though you're consuming data from a database. To create a Synapse workspace over your data in the data lake, you can use FastTrack Solutions for Dynamics 365 - CDMUtilSolution. This solution creates external table definitions in Azure Synapse Analytics serverless SQL pools by using metadata in the data lake.

If you select the Enhanced metadata option when you install Export to Data Lake, the system adds even more metadata.

Enhanced metadata

If you're familiar with Dynamics 365 applications such as Dynamics 365 Finance and Dynamics 365 Supply Chain Management, you might be familiar with the rich metadata structures that are present in them. In addition to basic types, enhanced metadata includes the following types:

  • Extended data types (EDTs), which offer richer data types that describe behavior and business rules that are applicable to "business data types," such as bank accounts, ledger accounts, and telephone numbers.
  • Descriptive names, together with translated labels that are available in many languages.
  • Higher-level data abstractions, such as entity definitions.

When you enable the Enhanced metadata option, the system writes more metadata that is derived from Dynamics 365 into the data lake. Client tools that can understand and work with the additional metadata properties can then provide a better experience to users.

Prerequisites for enabling the Enhanced metadata feature

  1. The finance and operations app version must be later than the following versions:

    • Version 10.0.22 with the latest updates (version 10.0.995.146 or later)
    • Version 10.0.23 with the latest updates (version 10.0.1037.133 or later)
    • Version 10.0.24 with the latest updates (10.0.1084.89 or later)
    • Version 10.0.25 and later

  2. When your administrator installs the Export to Data Lake add-in, they must select the Enable enhanced metadata option. You can't enable the enhanced metadata feature unless this option is selected during the installation stage.

  3. In the finance and operations app, you must select Republish metadata on the Manage tab on the Action Pane of the Export to Data Lake page the first time that you open it. You have to complete this step only once. The system then continues to republish metadata as changes happen.

    Note

    In version 10.0.23 and later, the system automatically runs the Republish metadata command the first time that you open the Export to Data Lake page.

Changes introduced by the Enhanced metadata feature

If you're currently using the Export to Data Lake feature, you'll notice the folder structure and metadata in the data lake. When you enable the Enhanced metadata feature by reinstalling the add-in, you might notice several changes. These changes are described in more detail later in this article.

  • Data that was previously stored in the Custom folder might be moved to a folder structure that consists of subject areas. These subject areas are based on the metadata that is defined in Dynamics 365. If you're using the metadata files to navigate and find data, you can go to the correct folder structure. However, if you hard-coded the folder structure in consuming tools, you might have to update those tools.
  • In some cases, the folder structure under the Custom folder remains, together with metadata files. No data files are present in this folder.

Folder structure in the data lake

finance and operations apps have over 10,000 tables and over 2,500 entities. (If extensions and customizations are included, the numbers are larger.) To help secure data in the data lake in a granular way, tables and entities are organized into modules that represent application areas. In the data lake, this organization is reflected in a folder structure that mimics the organization of application areas in Dynamics 365.

Table folder structure

The table folder structure is three levels deep:

  1. Application area – This level is a grouping of modules in finance and operations apps. For example, a folder at this level might be named Finance.
  2. Module – This level is derived by using a table-level Module metadata property in finance and operations apps. For example, a folder at this level might be named General Ledger.
  3. Table type – This level is derived by using the existing TableGroup metadata property. For example, a folder at this level might be named Main.

You can view the application area > module > table type hierarchy for tables that are part of finance and operations apps.

Note

When the Enhanced metadata feature is enabled, tables that are introduced by system integrators and partners (custom tables) follow the same structure, provided that the same metadata properties are added to tables in finance and operations apps. If the Enhanced metadata feature isn't enabled, all custom tables are put in the Custom folder.

The table-level Module metadata property is backed by an extensible enumeration (enum) that is named ModuleAxapta. This enum contains predefined modules that are released by Microsoft. However, you can extend the enum by adding your own module definitions. In this way, you can use your own modules in addition to the predefined modules. The Export to Data Lake feature puts custom tables in appropriate folders, based on the table-level Module property.

The mapping of application areas to modules is defined in finance and operations apps and can't currently be modified. If you define a new Module property, it probably won't be reflected in the application area–to–module mapping. In this case, the Export to Data Lake feature puts the table in an application area folder that is named Custom.

Systems integrators and partners can't modify the Module property for tables that are released by Microsoft. Although you can modify the Module property for custom tables, we recommend that you avoid frequent modifications. Modification of the Module property causes the location of the table in the data lake to change. Therefore, consuming applications that assume a specific location for data files might be affected every time that you modify the Module property.

If the Module property isn't defined for a custom table, that table is put in the Custom folder.

Entity folder structure

There are fewer entities than tables in finance and operations apps. The entity folder structure is only two levels deep:

  1. Application area – This level is a grouping of modules in finance and operations apps. The grouping is similar to the grouping for tables. For example, a folder at this level might be named Finance.
  2. Module – This level is derived by using an entity-level Module metadata property in finance and operations apps. For example, a folder at this level might be named General Ledger.

Just as systems integrators and partners can extend the table-level Module property by introducing custom tables, they can extend the entity-level Module property by introducing custom entities. The entity-level Module property is backed by the same ModuleAxapta extensible enum that backs the table-level Module property.

Changes to metadata

New software updates can cause metadata in finance and operations apps to change. For example, a developer might add a new field to an existing table or entity. In less frequent cases, a developer might remove an existing field from a table or change the data type of an existing field.

If the structure of a table or entity is changed, and especially if a field is removed from the table or entity, consuming applications might have to be updated. The Export to Data Lake feature is designed to minimize the downstream impact but also reflect metadata changes in the data lake. This section explains how metadata changes are reflected in the data lake.

Note

Finance and operations apps include governance processes and developer tools that help developers learn about such changes and their impact. However, users who consume data in the date lake by creating and running a Power BI report, for example, might not be aware of changes in finance and operations apps.

When a new field is added to a table, metadata files in the data lake are updated to reflect the change. All the records in the CSV files that include the newly added data contain the new field. If a CSV file isn't modified, or no new rows are added, the file won't contain the new field. This behavior helps minimize the data writes to the data lake. Most data pipeline tools, and especially those tools that understand the CDM standard, support a feature that can adapt to changes. This feature is known as schema drift.

If the change to the data structure is destructive, the system might repopulate the whole table folder (that is, all the CSV files). For example, in a rare but destructive change, a field might be removed from a table in a finance and operations app. In this case, the whole table folder is repopulated, together with the updated metadata. Destructive changes might require changes to downstream reports, especially if the report is expecting a data field that was removed.

Any questions, feedback?

We're actively working on this and other features. Do you want to stay in touch and ask questions of the product team or your fellow customers or partners? Do you want to provide feedback directly to the product team? If you do, you can join the Preview Yammer group. You can then attend weekly online "office hours" meetings and use the Yammer online forums to stay in touch and ask questions.