NoSQL, What it is and how our projects can benefit from it

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A very high level introduction to NoSQL. Introducing the main categories of NoSQL database and the business drivers that could lead you to use it.

NoSQL
What it is and
how Our projects can benefit from it

The Plan
Not Only SQL
Relational Love
Polyglot Persistence
The Range

Driving Forces
Volume Velocity Variety Cost
Agility MismatchProductivity Availability

Volume
My application need to be able
to handle large volumes of
data.

Volume
Simple Horizontal Scaling
Automatic Sharding
Distributed Querying

Velocity
My application needs to store
and serve relevant information
to users in real-time to
provide a high-quality user
experience

Velocity
Distributed Load
Store as Used
Real-time Analytics
Integrated Caching

Variety
My application needs to be able
to handle market data from
multiple internal and external
systems each with its own
schema

Variety
Handles Sparsely Populated Data
Well
Dynamic Schema
No need to define one schema for all
Easier integration of new system’s data

Cost
We are a public sector
organisation which need to
account to the local
community for our
spending.

Cost
Commodity Hardware
Open Source
Lower Support Fees

Agility
My application's data models
need to be updated weekly
to keep pace with business
and market developments.

Agility
Dynamic Schemas
OO Like Structures
Easy to Scale Out

Productivity
My application deals with
patient health records and I’m
finding it complex to model
in my relational database.

Productivity
Data Complex to Model in Relational
Easier to Adopt
Avoid Complex Reads/Writes
No Upfront Schema Design

Mismatch
The team developing my
application are not database
experts

Mismatch
Store as Used
Familiar Formats
ORM Redundant

Availability
My application is critical it needs
to be available 24 x 7. I
can’t afford a lot of
downtime for upgrade.

Availability
Dynamic Schema
Upgrade Without Downtime
Simple Replication
Simple Load Balancing

Key Value
Familiar
Simple API
Just a BLOB

 The Good
Familiar
Simple to Build
Easy to Scale
Great
Performance
X The Bad
XQueries
XComplex Data
XNo Schema
XNot ACID

Uses
Media Stores
User profiles
User sessions
Shopping carts

Vs
Document
{
name: “NoSQL”,
categories: [“document”,
“columnar”,
“graph”,
“key-value”]
}

 The Good
No Mismatch
Productivity
Flexible Schema
Agile
Availability
X The Bad
X Querying
X Transactions
X Multiple Uses
X Responsibility
X Flexible Schema

Uses
• High Volume Data Feeds
• Customer Facing Dashboards
• News Sites/Blogs

 The Good
Relationships
Whiteboard
Graph Algorithms
Schema Free
Performance
Acid Transactions
X The Bad
XSpecialised
XImmature
XPartitioning

Uses
• Recommendations
• Social Networking
• Business Intelligence
• Fraud Detection

Column Orientated
BigTable
Column, Column Families
Ragged

 The Good
Aggregate Column
Fast lookups
Distributed storage
MapReduce
Write Loads
X The Bad
X API
X No Idea of Use

Uses
Large Deployments
Statistics & Analysis

Use Relational
Transactions
Unsure
Flexibility

Use Relational
Integrity
Standardisation
Tried & Tested
Knowledge

Use Relational
Help
Tooling
Security & Compliance
The Hybrid

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So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation. In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.

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Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf

NoSQL, What it is and how our projects can benefit from it

1. NoSQL What it is and how Our projects can benefit from it

2. What this is Not ... SQL

3. What it is ...

4. The Plan Not Only SQL Relational Love Polyglot Persistence The Range

5. What is NoSQL?

6. You have a Choice!

7. It’s not just about ...

8. Driving Forces Volume Velocity Variety Cost Agility MismatchProductivity Availability

9. Volume My application need to be able to handle large volumes of data.

10. Volume Simple Horizontal Scaling Automatic Sharding Distributed Querying

11. Velocity My application needs to store and serve relevant information to users in real-time to provide a high-quality user experience

12. Velocity Distributed Load Store as Used Real-time Analytics Integrated Caching

13. Variety My application needs to be able to handle market data from multiple internal and external systems each with its own schema

14. Variety Handles Sparsely Populated Data Well Dynamic Schema No need to define one schema for all Easier integration of new system’s data

15. Cost We are a public sector organisation which need to account to the local community for our spending.

16. Cost Commodity Hardware Open Source Lower Support Fees

17.

18.

19. Agility My application's data models need to be updated weekly to keep pace with business and market developments.

20. Agility Dynamic Schemas OO Like Structures Easy to Scale Out

21. Productivity My application deals with patient health records and I’m finding it complex to model in my relational database.

22. Productivity Data Complex to Model in Relational Easier to Adopt Avoid Complex Reads/Writes No Upfront Schema Design

23. Mismatch The team developing my application are not database experts

24. Mismatch Store as Used Familiar Formats ORM Redundant

25. Availability My application is critical it needs to be available 24 x 7. I can’t afford a lot of downtime for upgrade.

26. Availability Dynamic Schema Upgrade Without Downtime Simple Replication Simple Load Balancing

27. Key Value

28. Key Value Familiar Simple API Just a BLOB

29. Examples MemcacheDB

30.  The Good Familiar Simple to Build Easy to Scale Great Performance X The Bad XQueries XComplex Data XNo Schema XNot ACID

31. Uses Media Stores User profiles User sessions Shopping carts

32. Document

33. Vs Document { name: “NoSQL”, categories: [“document”, “columnar”, “graph”, “key-value”] }

34. Examples

35.  The Good No Mismatch Productivity Flexible Schema Agile Availability X The Bad X Querying X Transactions X Multiple Uses X Responsibility X Flexible Schema

36. Uses • High Volume Data Feeds • Customer Facing Dashboards • News Sites/Blogs

37. Graph

38. Graph Relations over Data

39.

40. Examples

41.  The Good Relationships Whiteboard Graph Algorithms Schema Free Performance Acid Transactions X The Bad XSpecialised XImmature XPartitioning

42. Uses • Recommendations • Social Networking • Business Intelligence • Fraud Detection

43. Column Orientated

44. Column Orientated BigTable Column, Column Families Ragged

45. Examples

46.  The Good Aggregate Column Fast lookups Distributed storage MapReduce Write Loads X The Bad X API X No Idea of Use

47. Uses Large Deployments Statistics & Analysis

48. Polyglot Persistence

49. Polyglot Persistence

50. Don’t Dismiss Relational

51. Use Relational Transactions Unsure Flexibility

52. Use Relational Integrity Standardisation Tried & Tested Knowledge

53. Use Relational Help Tooling Security & Compliance The Hybrid