The document discusses the operational challenges and complexities of big data systems, emphasizing the need for effective integration and management across various applications. It highlights scenarios like price management in a hypermarket, focusing on rule validation, concurrency control, and transaction handling using a compensation mechanism. Additionally, it outlines the advantages of a multi-datacenter (multi-dc) approach in terms of resilience and simplified deployment within a platform-as-a-service (PaaS) environment.

1. Connecting the dots
Operationalizing Big Data

2. 1
2
3
4
5
6
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Agenda
Where are we now?
SAGA and CQRS
Usage example
Why multi-datacenter?
Multi-DC technical dive
Questions

3. Where are we now?

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Once upon a time...
An application will handle its processes and rely on a database to handle the data persistency.
App DB
ACID
Welcome to Strong consistency systems!

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Data and systems grew...
App
DB
node 4
App
App
App
DB
node 3
DB
node 2
DB
node 1
1. write
2.replica
3.replica
4.OK 5. OK
6. OK

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And we grew more...
Welcome to Eventual consistency systems!
App
DB
node 4
App
App
App
DB
node 3
DB
node 2
DB
node 1
1. write
3.replica
4.replica
6.OK 5. OK
2. OK

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Now, a company looks like...
App
App
App
App
DB
node 1
DB
node 2
DB
node 3
Operational
applications
App
App
DB
node 3
DB
node 2
DB
node 1
Informational
applications
(Big Data!)
Data Science
Business Intelligence
...
Operations personnel
Content managers
...
Integration is (on most
cases) on people,
not on systems!

8. The situation is...
- Company does not explode benefits of Big Data/Informational systems
- Data analysis depends on dumps/historical data
- Integrating analytics results with operational applications is a nightmare
- There are not built-in mechanisms to facilitate it
- Custom integrations are required and are usually expensive
- Governance and security are splitted in two parts
- This makes much harder its maintenance

9. USAGE SCENARIO

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USAGE SCENARIO
Price
New
Price
€3.00
€3.00€2.10
Business rules:
● Number → OK
● Greater than zero → OK
● Maximum variation less than or equal to 15% → OK
A user is managing prices of articles in the portfolio of a
hypermarket. In daily operations he queries article prices
recovering the information stored at that time.
The user can modify prices and after validating the new figure
meets the established business rules, the new value shall be
stored. In the event of not meeting business rules, the
previous value shall be kept.
Business rules:
● Number → OK
● Greater than zero → OK
● Maximum variation less than or equal to 15% → KO
Final price
Final price
€3.20
Manual
process

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USAGE SCENARIO
If two or more users are simultaneously working on the price of the same article, the system will
guarantee correct isolation and versioning of the data item, i.e. the modified information is carried out
on the latest version, therefore only one user can apply the changes.
Current
price
€3.20
New Price
€3.20€3.15 €3.18
The current
price has
changed
Current
price
New Price
Manual
process
Manual
process

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USAGE SCENARIO
Price
€11.70
€18.25
1 L
Price
per Litre
€4.20 €4.20
/l
€3.90
/l
€3.65
/l
Through a compensation mechanism of Stratio Datacentric it is possible to carry out operations with
distributed transactions and later compensate the operations that were not correctly completed. For
example, during price validation of a family of articles, when prices do not meet the family coherence:
Manual
process
3 L
5 L
The family coherence validation is performed after the
prices have been modified. The compensation
mechanism will launch the necessary operations to
execute a roll-back

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USAGE SCENARIO
Price
New
Price
€5.40
€9.60
New Price
per Litre
€ 1.00 €1.00 /l
€0.85 /l
€0.87 /l
€5.10
€10.44
Final
price
New
Price
€5.40
€9.60
New Price
per Litre
€ 1.00 €1.00 /l
€0.85 /l
€0.87 /l
€5.10
€10.44
Manual
process
1 L
3 L
5 L
Through a compensation mechanism of Stratio Datacentric the transaction will be completed or
aborted compensating, in the event of the latter, with the necessary operations to restore the initial
state. For example, during price validation of a family of articles, when prices do not meet the family
coherence

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USAGE SCENARIO
In daily operations, many users will work concurrently in Stratio Datacentric, but their user experience
must not be affected.
...
...

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USAGE SCENARIO
In the event of applying multiple price modifications, these changes are made through an automatic
batch process. The file shall contain numerous records that Stratio Datacentric must process following
the transactional or operational flow depending on the established rule.
Batch
process
Date of new price
newer than the
one stored?
Article Price Date
Validate business
rules
Business rules:
● Number → OK
● Greater than zero → OK
● Maximum variation less than or equal to 15% → OK
YES
Save past
records
NO

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USAGE SCENARIO
Finally, the two aforementioned operations coexist in Stratio
Datacentric:
● Manual (online price change by users)
● Automatic (price changes through a batch file)
And all changes are consolidated in the
Operational DB and replicated to the
Analytical Datastore, giving the user the
capacity to query and analyse all past
information included in Stratio Datacentric.
Batch
process
Manual
process

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Manual Process
Validate the coexistence of manual processes (user action) with automatic processes (batch) in Stratio
Datacentric, also facilitating querying and analysis of information included by both operations
Manual
process
MANUAL PROCESS
The data included manually by the
user is sent to Stratio Datacentric,
validating the fulfilment of
established business rules for
storage.
In addition, this information will be
replicated to the analytical datastore
for query and analysis.
Business rule
validation
HDFS
Analytics
Architecture
Microservices

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Automated process
Rule-based information division
The batch-processed information is
derived in Sparta, based on defined
rules, to:
· the Operational DB through the
transactional flow, also replicated
to the Analytical datastore
· the Analytical datastore directly
Analytics
Batch
process
Business rule
validation
Architecture
Microservices
Validate the coexistence of manual processes (user action) with automatic processes (batch) in Stratio
Datacentric, also facilitating querying and analysis of information included by both operations
Business rule
validation
HDFS

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Operational and Analytics coexistence
Batch
process
Finally, the three operations
(transactional, batch and analytical)
must be validated to coexist in
Stratio Datacentric
Manual
process
Analytics
Architecture
Microservices
Business rule
validation
Validate the coexistence of manual processes (user action) with automatic processes (batch) in Stratio
Datacentric, also facilitating querying and analysis of information included by both operations
HDFS
Rule-based information division

20. User Interface
Streaming
EVENT
MICRO-SERVICES
FRAMEWORK
Event Bus
Elastic
SAGA
Service
Query
Service
Command
Service
Command Bus
HDFS
COMMAND
SECURITY AND GOVERNANCE
DCDATA-CENTRIC
DC
DC
DC

21. User Interface
MICRO-SERVICES
FRAMEWORK
Event Bus
Elastic
SAGA
Service
Query
Service
Command
Service
Command Bus
HDFS
SECURITY AND GOVERNANCE
DC
DC

22. SAGA and CQRS

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CQRS PATTERN

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MICROSERVICES -> MICROTRANSACTIONS

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SAGA Pattern

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SAGA compensation
Product
Invoicing
Emailing
...

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SAGA compensation
Product
Invoicing
Emailing
...
An error ocurred on preparing
the invoice for our customer…
Email was sent in parallel… We need to trigger a compensation mechanism for email:
- Send another email asking for missing data
- Cancel the purchase and send an email of apologise
- ...

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ROLL-OUT ARCHITECTURE

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ROLL-OUT ARCHITECTURE - CONCURRENCE

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ROLL-OUT ARCHITECTURE - ONLINE

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ROLL-OUT ARCHITECTURE - BATCH

32. simplicity

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What happens with Critical apps?

34. “ “An application important to
keep the business running

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What is Multi-DC?
App App...
DB DB...
App App...
DB DB...
Location 1 Location 2

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Applications on Multi-DC
App App... App App...
Location 1 Location 2
Depending on the application we can guarantee different execution scenarios:
- Active - Active: application is replicated and executed in parallel
- Active - Passive: there is an active part and a stand-by part keeping the same state in case of failover
- Active with failover mechanisms: applications can self-heal and achieve resiliency
Coordination

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Multi-DC datastores
DB DB... DB DB...
Location 1 Location 2
A multi-dc setup needs to guarantee a copy of our data in both datacenters
Data
replication

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Example Cassandra
Client Write
replication
replication
It offers the possibility to modify the configuration to achieve different consistency levels

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Stratio EOS (PaaS)

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Multi-DC PaaS
App App...
DB DB...
App App...
DB DB...
Location 1 Location 2

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Multi-DC PaaS - Traffics
Location 1 Location 2
Master 1 Master 2 Master 3Gosec 1 Gosec 2 Gosec 3
... ...
Management & Control
Security
Data & Application traffic
Agent 1 Agent 2 Agent N Agent N+1 Agent N+MAgent N+2

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Logical network setup - Real
scenario

43. So… Multi-DC is exactly the
same as one data center?

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Reality...
App App...
DB DB...
App App...
DB DB...
Location 1 Location 2
... ...Bandwidth/
latency?

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Distributed Applications and latency
Location 1 Location 2
Master 1 Master 2 Master 3Gosec 1 Gosec 2 Gosec 3
... ...
Management & Control
Security
Data & Application traffic
Agent 1 Agent 2 Agent N Agent N+1 Agent N+MAgent N+2

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Considerations
Data locality
Failover on disaster:
- Fail on container
- Fail on node
- Fail on datacenter
Location 1 Location 2
Master 1 Master 2 Master 3Gosec 1 Gosec 2 Gosec 3
... ...
Management & Control
Security
Data & Application traffic
Agent 1 Agent 2 Agent N Agent N+1 Agent N+MAgent N+2

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Advantages of using a PaaS with Multi-DC
- Simplicity
- Recovery mechanisms
- Application, Node and DC
- Network defined infrastructure
- ACLs to define network
- Upgrades and new deployments are easy to setup
- Security:
- Communication encrypted
- Centralized access policies definition
- Enforcement of accessing rules
- Multi-tenant with resource isolation

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Conclusions
- Multi datacenter is complex
- Requires coordination
- Requires awareness of the topology on the PaaS and in some cases on applications
- Having a PaaS to manage and handle the deployment and failure logic is
necessary for easier operations.
- Understanding and applying different consistency levels we can use the right
one to cover needs
- Strong consistency
- Eventual consistency
- We can combine operational and informational processes and do it with Multi-DC