Data sources. Gartner, The State of Data Warehousing in 2012
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3 data warehousing has reached the most significant tipping point since its inception. The biggest, possibly most elaborate data management system in IT is changing. Gartner, The State of Data Warehousing in 2012 Data sources
4 5 2 Real-time data 1 Increasing data volumes New data sources & types Data sources Non-Relational Data 3 4 Cloud-born data
5 Extract Transform Load Original Data ETL Tool (SSIS, etc) Transformed Data EDW (SQL Svr, Teradata, etc) BI Tools Data Marts Data Lake(s) Dashboards Apps
6 Extract Transform Load Original Data ETL Tool (SSIS, etc) Transformed Data EDW (SQL Svr, Teradata, etc) BI Tools Data Marts Data Lake(s) Dashboards Ingest (EL) Original Data Apps
7 Extract Transform Load Original Data ETL Tool (SSIS, etc) Transformed Data EDW (SQL Svr, Teradata, etc) BI Tools Data Marts Data Lake(s) Ingest (EL) Original Data Scale-out Storage & Compute (HDFS, Blob Storage, etc) Dashboards Apps Streaming data Transform & Load
8 Extract Transform Load Original Data ETL Tool (SSIS, etc) Transformed Data EDW (SQL Svr, Teradata, etc) BI Tools Data Marts Data Lake(s) Ingest (EL) Original Data Scale-out Storage & Compute (HDFS, Blob Storage, etc) Dashboards Apps Streaming data Transform & Load
9 Data Sources (Import From) Ingest Data Hub (Storage & Compute) BI Tools Data Marts Move data among Hubs Data Lake(s) Data Sources (Import From) Ingest Data Hub (Storage & Compute) Move to data mart, etc Dashboards Apps Information Production: Connect & Collect Transform & Enrich Publish
10 Data Sources (Import From) Data Connector: Import from source to Hub Data Hub (Storage & Compute) Coordination & Scheduling Monitoring & Mgmt Data Lineage BI Tools Data Connector: Import/Export among Hubs Data Marts Data Lake(s) Data Sources (Import From) Data Connector: Import from source to Hub Data Hub (Storage & Compute) Data Connector: Export from Hub to data store Dashboards Apps Information Production: Connect & Collect Transform & Enrich Publish
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13 Example Scenario: Data warehouse sales to Azure pipeline
14 Raw sales (Custom view on top of DW tables) Sales by category by day Hive processing Qty Unit OrderDate Company Category Sales Order Ordered Price 6/1/2004 Action Bicycle Specialists Accessories SO /1/2004 Action Bicycle Specialists Bikes SO /1/2004 Action Bicycle Specialists Clothing SO /1/2004 Action Bicycle Specialists Components SO /1/2004 Aerobic Exercise Company Components SO /1/2004Action Bicycle Specialists Accessories SO71938
15 Data Factory Walkthrough
16 New-AzureDataFactory -Name DW-Demo2 -Location West-US New-AzureDataFactory -Name HaloTelemetry -Location West-US
17 Azure Data Factory New User View On Premises SQL Server Azure Blob Storage
18 View Of Azure Data Factory New Sales Aggregated sales AdventureWorksLTDW2014 On Premises SQL Server Azure Blob Storage
19 View Of View Of Azure Data Factory Pipeline New Sales Copy NewSales to Blob Storage Cloud New Sales New User Activity New User View On Premises SQL Server Azure Blob Storage
20 View Of Azure Data Factory Pipeline New Sales Copy New Sales to Blob Storage Cloud New Sales Pipeline OnPrem SSIS package Aggregated Sales Cloud New Sales Aggregate AggregatedSales HDInsight New User View On Premises SQL Server Azure Blob Storage
21 "availability": { "frequency": "Day", interval": 6 } Activity: (e.g. Hive): Hourly AggregatesSales
22 Hourly Sales From DW Daily Monday Daily Sales Dataset3 Hive Activity Tuesday Daily other source Dataset2 Wednesday Monday Tuesday Wednesday
23 Is my data successfully getting produced? Is it produced on time? Am I alerted quickly of failures? What about troubleshooting information? Are there any policy warnings or errors?
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26 Easily move data to my existing data marts for consumption by my existing BI tools Azure DB SQL Server on premises Oracle Files Azure Blob content
27 Coordination: Rich scheduling Complex dependencies Incremental rerun Authoring: JSON & Powershell/C# Management: Lineage Data production policies (late data, rerun, latency, etc) Hub: Azure Hub (HDInsight + Blob storage) Activities: Hive, Pig, C# Data Connectors: Blobs, Tables, Azure DB, On Prem SQL Server, Oracle
28 Contact me:
29
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data warehousing has reached the most significant tipping point since its inception. The biggest, possibly most elaborate data management system in IT is changing. Gartner, The State of Data Warehousing
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