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Analytics views
Manufacturing Data Engine (MDE) Analytics views is a powerful feature
designed to streamline data analysis and reporting within your
MDE environment. By automatically
generating BigQuery views that consolidate type-specific data, metadata,
and associated dimensions, MDE simplifies
the creation of comprehensive queries and dashboards.
Concepts
An Analytics view is a prebuilt BigQuery view tailored to a
specific type version within your MDE environment. This
view acts as a unified data source, seamlessly joining the type table
(your core fact table) with all relevant metadata buckets (your dimensions)
associated with that type version.
This consolidation simplifies querying by eliminating the need for manual joins
across multiple tables. Analytics views also flatten metadata instances and data
fields based on predefined JSON schemas.
How Analytics views work
Automatic creation: MDE automatically creates a
dedicated Analytics view within the mde_analytics dataset for each unique
type version. Views for new type versions are always updated in-place.
Metadata flattening: The view query intelligently flattens the JSON
metadata instances (dimension members) associated with the type. Each field
within the JSON schema of the metadata bucket version associated with the
type version is mapped to a corresponding BigQuery field,
adhering to the following well-defined type mapping:
String -> String
Array -> JSON
Object -> JSON
Number -> Float64
Integer -> Int64
Boolean -> Bool
null -> null
The resulting BigQuery fields are always prefixed with the
metadata bucket name.
Nested JSON Handling: Nested JSON fields are converted into
BigQuery fields by concatenating their path elements with
underscores. For example, {"location":{"city":"Düsseldorf"}} becomes
location_city.
"Data" Field Flattening: The view extends flattening to the JSON "data"
field for non-numeric types based on the schema defined for the type,
ensuring comprehensive data accessibility. Flattening is performed only when
a schema for the "data" field is defined. For more details about data field
schemas, see data field.
Field Naming and Conflicts: In case of a naming conflict, fields derived
from metadata buckets are prefixed with the bucket name.
View Schema
The final view schema encompasses a rich set of fields:
All fields from the underlying type table: Your core manufacturing data.
All flattened fields from each metadata instance: Detailed contextual
information about your records.
All flattened fields from the "data" field (non-numeric): Additional
data points specific to the type.
Benefits of Analytics views
The following are the benefits of Analytics views:
Simplified Querying: Eliminate complex joins by querying a single view
for comprehensive data.
Accelerated Analysis: Spend less time on data preparation and more time
on extracting insights.
Standardized Structure: Consistent schema across type versions
facilitates standardized reporting.
Automation: MDE handles view creation
and maintenance, avoiding you from manual tasks.
Getting started
Analytics views are ready to use as soon as you start working with types in
MDE. You can explore the automatically
generated views in your Google Cloud project with the following steps:
Select BigQuery.
Click Studio on the left panel.
Select the mde_analytics dataset on the BigQuery explorer window.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[],[],null,["# Analytics views\n===============\n\nManufacturing Data Engine (MDE) Analytics views is a powerful feature\ndesigned to streamline data analysis and reporting within your\nMDE environment. By automatically\ngenerating BigQuery views that consolidate type-specific data, metadata,\nand associated dimensions, MDE simplifies\nthe creation of comprehensive queries and dashboards.\n\nConcepts\n--------\n\nAn Analytics view is a prebuilt BigQuery view tailored to a\nspecific type version within your MDE environment. This\nview acts as a unified data source, seamlessly joining the type table\n(your core fact table) with all relevant metadata buckets (your dimensions)\nassociated with that type version.\n\nThis consolidation simplifies querying by eliminating the need for manual joins\nacross multiple tables. Analytics views also flatten metadata instances and data\nfields based on predefined JSON schemas.\n\nHow Analytics views work\n------------------------\n\n1. **Automatic creation:** MDE automatically creates a\n dedicated Analytics view within the `mde_analytics` dataset for each unique\n type version. Views for new type versions are always updated in-place.\n\n2. **Metadata flattening:** The view query intelligently flattens the JSON\n metadata instances (dimension members) associated with the type. Each field\n within the JSON schema of the metadata bucket version associated with the\n type version is mapped to a corresponding BigQuery field,\n adhering to the following well-defined type mapping:\n\n - String -\\\u003e String\n - Array -\\\u003e JSON\n - Object -\\\u003e JSON\n - Number -\\\u003e Float64\n - Integer -\\\u003e Int64\n - Boolean -\\\u003e Bool\n - null -\\\u003e null\n\nThe resulting BigQuery fields are always prefixed with the\nmetadata bucket name.\n\n1. **Nested JSON Handling:** Nested JSON fields are converted into\n BigQuery fields by concatenating their path elements with\n underscores. For example, `{\"location\":{\"city\":\"Düsseldorf\"}}` becomes\n `location_city`.\n\n2. **\"Data\" Field Flattening:** The view extends flattening to the JSON \"data\"\n field for non-numeric types based on the schema defined for the type,\n ensuring comprehensive data accessibility. Flattening is performed only when\n a schema for the \"data\" field is defined. For more details about data field\n schemas, see [data field](/manufacturing-data-engine/docs/concepts/type#data_field).\n\n3. **Field Naming and Conflicts:** In case of a naming conflict, fields derived\n from metadata buckets are prefixed with the bucket name.\n\nView Schema\n-----------\n\nThe final view schema encompasses a rich set of fields:\n\n- **All fields from the underlying type table:** Your core manufacturing data.\n- **All flattened fields from each metadata instance:** Detailed contextual information about your records.\n- **All flattened fields from the \"data\" field (non-numeric):** Additional data points specific to the type.\n\nBenefits of Analytics views\n---------------------------\n\nThe following are the benefits of Analytics views:\n\n- **Simplified Querying:** Eliminate complex joins by querying a single view for comprehensive data.\n- **Accelerated Analysis:** Spend less time on data preparation and more time on extracting insights.\n- **Standardized Structure:** Consistent schema across type versions facilitates standardized reporting.\n- **Automation:** MDE handles view creation and maintenance, avoiding you from manual tasks.\n\nGetting started\n---------------\n\nAnalytics views are ready to use as soon as you start working with types in\nMDE. You can explore the automatically\ngenerated views in your Google Cloud project with the following steps:\n\n1. Select BigQuery.\n2. Click **Studio** on the left panel.\n3. Select the `mde_analytics` dataset on the BigQuery explorer window."]]
