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Data Quality Patterns in the Cloud
with Azure Data Factory
Azure Data Week
ADF: Simple and Productive ETL in the Cloud at any Scale
Modern Data Warehouse Pattern Today
Applications
Dashboards
Business/custom apps
(structured)
Logs, files, and media
(unstructured)
r
Ingest storage
Azure Storage/
Data Lake Store
Data
Loading
Azure Data
Factory
Load flat files
into data lake on a
schedule
Data processing
Read data from
files using DBFS
Orchestration
Azure Data
Factory
Extract and
transform
relational data
Azure Databricks
Serving storage
Azure SQL DW
Load processed
data into tables
optimized for
analytics
Clean and
join with
stored data
Load to SQL DW
Databases
Modern Data Warehouse Pattern with ADF Mapping Data Flows
Applications
Dashboards
Business/custom apps
(structured)
Logs, files, and media
(unstructured)
r Azure Storage/
Data Lake Store
Azure Data
Factory
Load files into data
lake on a schedule
Azure Data
Factory
Extract and
transform
relational data
Azure SQL DW
Load processed
data into tables
optimized for
analytics
Clean and
join disparate
data
Databases
Azure Databricks
list of connectors
full list of compute environments data
Data Flow
Data Quality Patterns
http://aka.ms/dataflowvideos
http://aka.ms/dataflowpatterns
https://docs.microsoft.com/en-us/azure/data-
factory/concepts-data-flow-overview
http://aka.ms/dataflowexpressions
https://aka.ms/dfperf
https://aka.ms/dflinks

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Data Quality Patterns in the Cloud with Azure Data Factory

Editor's Notes

  1. Let’s take a look at the process through the visual user interface Orchestration. Directs other services to execute actions as part of the transformation process. Mapping Data Flows. Develop graphical data transformation logic at scale without writing code using Mapping Data Flows (preview). Mapping Data Flows. Develop graphical data transformation logic at scale without writing code using Mapping Data Flows (preview). Monitoring: Monitor pipeline and activity runs with a simple list view interface