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SQL vs NoSQL and moving data from
MongoDB to Azure data lake by
using Azure Data Factory
Diponkar Paul
@Baton Rouge User Groups
Father and
Husband
Blogger &
Speaker
Profession:
Data Engineer
Working with
BI, data
warehouse 12
years
Diverse
background:
South Asia,
Nordic
region, North
America
Community:
Lead Toronto
Data
Professionals
Community
Twitter: @Paulswengrr
Blog: www.allaboutdata.ca
What we
cover
Refresh our memory with traditional SQL
Know about NoSQL (MongoDB)
Demo: No SQL
Comparison
Azure data factory: Copy data from MongoDB
Demo: MongoDB with ADF
SQL Syntax
SELECT Id, Product, Price
From Product
Where ProductCategory=’Bikes’
Join, Insert, Update, Delete
Well defined
Schema
CREATE TABLE [Production].[Product](
[ProductID] [int] IDENTITY(1,1) NOT NULL,
[Name] [nvarchar](100) NOT NULL,
[ProductNumber] [nvarchar](25) NOT NULL,
[MakeFlag] [dbo].[Flag] NOT NULL,
[FinishedGoodsFlag] [dbo].[Flag] NOT NULL,
[Color] [nvarchar](15) NULL,
[SafetyStockLevel] [smallint] NOT NULL,
[StandardCost] [money] NOT NULL,
[ListPrice] [money] NOT NULL,
[Size] [nvarchar](5) NULL)
Relationship/Normalization
Customer Bridge table (Order)
Product
Id Name Price Description
1 “Mountain Bike “ 2500 “Bike for mountain trek”
2 “City Bike” 1000 “Best fit to roam around city”
Id Customer_ID Product_ID
1 2 1
2 2 2
3 1 1
Id Name Email
1 Morten
Sorenson
m.s@outlook.com
2 Andersen Lu al@yahoo.com
3 Derek Paul dp@outlook.com
Type of
relationships
NoSQL
• MongoDB
• Azure Cosmos DB
• Amazon Document DB
• Oracle NoSQL
• Google BigTable
Not Only SQL!!
NoSQL- MongoDB
“MongoDB” derives from the word “humongous”
How we call them?
Database E-Commerce
Collections Table –Customer, Product…
Documents {“Name”: ”Anders”, age:36}
{“Name”: “Carsten”, age:42}
No defined Schema
Id:1 Age:36
Name: ‘Anders’ …..
Id:2
Age:36
Name: ‘Carsten’ …..
Id:3 …..
NoSQL –No relation
Profession
{id:1,profession:’Developer’}
{id:2, profession: ’Data Engineer’}
{id:3, profession: ’Actor’}
Users
{id:1,name:’Tom Hanks’, age:20}
{id:2,name:’Casper Ruther’, age:42}
{id:3,name:’Paul Anders’, age:63}
db.Users.insert(
{
id:"01",
name:"Tom Hanks",
age:20
email:"th@hollywood.com",
Profession:["Developer","Data
Engineer","Actor"]
}
)
Usersprofession
{id:1,userId:1,professionId:1}
{id:2,userId:1, professionId: 2}
{id:3,userId: 1, professionId: 3}
{id:4,userId: 2, professionId: 2}
Tools: MongoDB
https://www.mongodb.com/products/compass
Robo 3T: https://robomongo.org/
https://docs.mongodb.com/manual/core/data-model-design/
https://docs.mongodb.com/manual/reference/method/db.collection.update/
Languages
• MONGO SHELL
• Python
• java
• C#
• Scala
• GO and many more.
Demo
SQL vs NoSQL
SQL NoSQL
Data uses Schema Schema-less (Schema Agnostic)
Maintain Relationship No relations– though you can design relationship
Data distributed in multiple tables Data in one table (embedded)
Move your NoSQL data from OnPrem to Data
Lake Gen2
Azure Data Lake
Azure Data Lake is a scalable data storage
and analytics service
-Fully HDFS compliance file system
-Azure AD integrated
-Microsoft’s PAAS service big data solution
Azure Data Factory
-ETL/ELT Tool
-Code free
-Azure Cloud
-a lot more…
Pre-requisite
• azure account
• Azure data factory resource
• Linked services (Source and
target connection)
• Integration run time
Integration
Runtime
Linked Service
Demo
Be cautious!
• MongoDB version supported for ADF copy activity (V 3.4)
*https://docs.microsoft.com/en-us/azure/data-factory/connector-mongodb
Questions
@paulswengrr
Diponkarpaul

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Baton rouge - sql vs no sql and azure data factory

  • 1. SQL vs NoSQL and moving data from MongoDB to Azure data lake by using Azure Data Factory Diponkar Paul @Baton Rouge User Groups
  • 2. Father and Husband Blogger & Speaker Profession: Data Engineer Working with BI, data warehouse 12 years Diverse background: South Asia, Nordic region, North America Community: Lead Toronto Data Professionals Community Twitter: @Paulswengrr Blog: www.allaboutdata.ca
  • 3. What we cover Refresh our memory with traditional SQL Know about NoSQL (MongoDB) Demo: No SQL Comparison Azure data factory: Copy data from MongoDB Demo: MongoDB with ADF
  • 4. SQL Syntax SELECT Id, Product, Price From Product Where ProductCategory=’Bikes’ Join, Insert, Update, Delete
  • 5. Well defined Schema CREATE TABLE [Production].[Product]( [ProductID] [int] IDENTITY(1,1) NOT NULL, [Name] [nvarchar](100) NOT NULL, [ProductNumber] [nvarchar](25) NOT NULL, [MakeFlag] [dbo].[Flag] NOT NULL, [FinishedGoodsFlag] [dbo].[Flag] NOT NULL, [Color] [nvarchar](15) NULL, [SafetyStockLevel] [smallint] NOT NULL, [StandardCost] [money] NOT NULL, [ListPrice] [money] NOT NULL, [Size] [nvarchar](5) NULL)
  • 6. Relationship/Normalization Customer Bridge table (Order) Product Id Name Price Description 1 “Mountain Bike “ 2500 “Bike for mountain trek” 2 “City Bike” 1000 “Best fit to roam around city” Id Customer_ID Product_ID 1 2 1 2 2 2 3 1 1 Id Name Email 1 Morten Sorenson m.s@outlook.com 2 Andersen Lu al@yahoo.com 3 Derek Paul dp@outlook.com
  • 8. NoSQL • MongoDB • Azure Cosmos DB • Amazon Document DB • Oracle NoSQL • Google BigTable Not Only SQL!!
  • 9. NoSQL- MongoDB “MongoDB” derives from the word “humongous”
  • 10. How we call them? Database E-Commerce Collections Table –Customer, Product… Documents {“Name”: ”Anders”, age:36} {“Name”: “Carsten”, age:42}
  • 11. No defined Schema Id:1 Age:36 Name: ‘Anders’ ….. Id:2 Age:36 Name: ‘Carsten’ ….. Id:3 …..
  • 12. NoSQL –No relation Profession {id:1,profession:’Developer’} {id:2, profession: ’Data Engineer’} {id:3, profession: ’Actor’} Users {id:1,name:’Tom Hanks’, age:20} {id:2,name:’Casper Ruther’, age:42} {id:3,name:’Paul Anders’, age:63} db.Users.insert( { id:"01", name:"Tom Hanks", age:20 email:"th@hollywood.com", Profession:["Developer","Data Engineer","Actor"] } ) Usersprofession {id:1,userId:1,professionId:1} {id:2,userId:1, professionId: 2} {id:3,userId: 1, professionId: 3} {id:4,userId: 2, professionId: 2}
  • 13. Tools: MongoDB https://www.mongodb.com/products/compass Robo 3T: https://robomongo.org/ https://docs.mongodb.com/manual/core/data-model-design/ https://docs.mongodb.com/manual/reference/method/db.collection.update/
  • 14. Languages • MONGO SHELL • Python • java • C# • Scala • GO and many more.
  • 15. Demo
  • 16. SQL vs NoSQL SQL NoSQL Data uses Schema Schema-less (Schema Agnostic) Maintain Relationship No relations– though you can design relationship Data distributed in multiple tables Data in one table (embedded)
  • 17. Move your NoSQL data from OnPrem to Data Lake Gen2
  • 18. Azure Data Lake Azure Data Lake is a scalable data storage and analytics service -Fully HDFS compliance file system -Azure AD integrated -Microsoft’s PAAS service big data solution
  • 19. Azure Data Factory -ETL/ELT Tool -Code free -Azure Cloud -a lot more…
  • 20. Pre-requisite • azure account • Azure data factory resource • Linked services (Source and target connection) • Integration run time Integration Runtime Linked Service
  • 21. Demo
  • 22. Be cautious! • MongoDB version supported for ADF copy activity (V 3.4) *https://docs.microsoft.com/en-us/azure/data-factory/connector-mongodb