SlideShare a Scribd company logo
1 of 131
Download to read offline
5 Levels of High Availability
From Multi-instance to Hybrid Cloud
Rafał Leszko
@RafalLeszko
rafalleszko.com
Hazelcast
About me
● Cloud Software Engineer at Hazelcast
● Worked at Google and CERN
● Author of the book "Continuous Delivery
with Docker and Jenkins"
● Trainer and conference speaker
● Live in Kraków, Poland
About Hazelcast
● Distributed Company
● Open Source Software
● 140+ Employees
● Products:
○ Hazelcast IMDG
○ Hazelcast Jet
○ Hazelcast Cloud
@Hazelcast
● Introduction
● High Availability Levels
○ Level 0: Single Instance
○ Level 1: Multi Instance
○ Level 2: Multi Zone
○ Level 3: Multi Region
○ Level 4: Multi Cloud
○ Level 5: Hybrid Cloud
● Summary
Agenda
Introduction
application service
micro-service
application service
micro?
application service
application service
application service
application service
Stateless
application service
application servicedata store
application servicedata store
queue
application servicedata store
queue
other service
Data is the problem!
● Introduction ✔
● High Availability Levels
○ Level 0: Single Instance
○ Level 1: Multi Instance
○ Level 2: Multi Zone
○ Level 3: Multi Region
○ Level 4: Multi Cloud
○ Level 5: Hybrid Cloud
● Summary
Agenda
Level 0: Single Instance
application service data store
Level 0: Single Instance
machine
request
Level 0: Single Instance
YOLO!
LATENCY EXPERIMENT
What does "Level 0: Single Instance" mean to You?
No high availability!
No scalability!
Super low latency:
● in-process memory
● no network
● local file system
Data consistency
● Introduction ✔
● High Availability Levels
○ Level 0: Single Instance ✔
○ Level 1: Multi Instance
○ Level 2: Multi Zone
○ Level 3: Multi Region
○ Level 4: Multi Cloud
○ Level 5: Hybrid Cloud
● Summary
Agenda
Level 1: Multi Instance
If one machine is down,
the system is still available
application
data
Level 1: Multi Instance
request
data
load balancer
application
application
application
application
machine 1
machine 2
machine 3
machine 4
application
data
Level 1: Multi Instance
data
application
application
application
application
machine 1
machine 2
machine 3
machine 4
data
Level 1: Multi Instance
data
machine 3
machine 4
data store
Level 1: Multi Instance
machine 1
data store
machine 2
Assumptions:
● Local network
● Fast
● Reliable
For example:
● EC2 Instances in the
same availability
zone
● GCP VM instances in
the same zone
● Your on-premises
server machines
connected with LAN
data store
Level 1: Multi Instance
machine 1
data store
machine 2
Data replication
data store
Option 1: Active-Passive (Master-Slave) Replication
machine 1
data store
machine 2
application service
application service
application service
active
passive
SQL
data store
Option 1: Active-Passive (Master-Slave) Replication
machine 1
data store
machine 2
application service
application service
application service
active
passive
data store
Option 1: Active-Passive (Master-Slave) Replication
machine 1
data store
machine 2
application service
application service
application service
down
active
data store
Option 1: Active-Passive (Master-Slave) Replication
machine 1
data store
machine 2
application service
application service
application service
active
passive
SQL
data store
[A-G]
Option 2: Clustering
machine 1
data store
[H-S]
machine 2
data store
[T-Z]
machine 3
NoSQL
data store
[A-G]
Option 2: Clustering
machine 1
data store
[H-S]
machine 2
application service
application service
application service
data store
[T-Z]
machine 3
data store
[A-G]
Option 2: Clustering
machine 1
data store
[H-S]
machine 2
application service
application service
application service
data store
[T-Z]
machine 3
1
Hazelcast Member 1
data
partitions
4
7
data
partition
backups
3 5
9
3
Hazelcast Member 3
data
partitions
6
9
data
partition
backups
2 4
8
2
Hazelcast Member 2
data
partitions
5
8
data
partition
backups
1 6
7
data store
[A-G]
Option 2: Clustering
machine 1
data store
[H-S]
machine 2
data store
[T-Z]
machine 3
NoSQL
Synchronous
vs
Asynchronous
Synchronous (Consistency) or Asynchronous (Latency)?
data
store
machine 1
machine 2 machine 3
data
store
machine 1
data
store
machine 2
active
passive
data
store
data
store
LATENCY EXPERIMENT
Synchronous (Consistency) or Asynchronous (Latency)?
data
store
machine 1
machine 2 machine 3
data
store
machine 1
data
store
machine 2
active
passive
data
store
data
store
synchronous
Synchronous (Consistency) or Asynchronous (Latency)?
data
store
machine 1
machine 2 machine 3
data
store
machine 1
data
store
machine 2
active
passive
data
store
data
store
synchronous?
What does "Level 1: Multi Instance" mean to You?
Data consistency!
Most tools supported
Cloud-specific toolkit (e.g. AWS SQS)
Simple setup (even on-premises)
High latency if accessed multi regions
● Introduction ✔
● High Availability Levels
○ Level 0: Single Instance ✔
○ Level 1: Multi Instance ✔
○ Level 2: Multi Zone
○ Level 3: Multi Region
○ Level 4: Multi Cloud
○ Level 5: Hybrid Cloud
● Summary
Agenda
Level 2: Multi Zone
If one availability zone is down,
the system is still available
application
data
Level 2: Multi Zone
request
data
load balancer
application
application
application
application
zone 1
zone 2
Is multi-zone deployment any
different?
No
No… but Yes
LATENCY EXPERIMENT
No… but Yes
Level 2: Multi Zone
zone 1
zone 2
data data
data data
Level 2: Multi Zone
zone 1
zone 2
data data
data data
Assumptions:
● Machines in at
least 2 AZ
● Fast and
reliable
network
For example:
● EC2 Instances
in 2 AWS
Availability
Zones
● Azure VM
instances in 2
Availability
Sets
1
Hazelcast Member 1
data
partitions
4
7
data
partition
backups
3 5
9
3
Hazelcast Member 3
data
partitions
6
9
data
partition
backups
2 4
8
2
Hazelcast Member 2
data
partitions
5
8
data
partition
backups
1 6
7
Level 2: Multi Zone
machine 1 machine 2
Hazelcast Member 2
zone 1
zone 2
Hazelcast Member 1
machine 3 machine 4
Hazelcast Member 4Hazelcast Member 3
Hazelcast configuration:
hazelcast:
partition-group:
enabled: true
group-type: ZONE_AWARE
Hazelcast Zone Aware Feature
Hazelcast Zone Aware Feature
Level 2: Multi Zone
machine 1 machine 2
Hazelcast Member 2
zone 1
zone 2
Hazelcast Member 1
machine 3 machine 4
Hazelcast Member 4Hazelcast Member 3
No… but Yes
Make sure your
data store is
ZONE AWARE
What does "Level 2: Multi Zone" mean to You?
Currently top 1 choice!
Data consistency!
Cloud-specific toolkit (e.g. AWS SQS)
High latency if accessed multi regions
Not all tools are "zone aware"
● Introduction ✔
● High Availability Levels
○ Level 0: Single Instance ✔
○ Level 1: Multi Instance ✔
○ Level 2: Multi Zone ✔
○ Level 3: Multi Region
○ Level 4: Multi Cloud
○ Level 5: Hybrid Cloud
● Summary
Agenda
Level 3: Multi Region
If one region is down,
the system is still available
application
data
Level 3: Multi Region
geo
load balancer
region 1
load balancer
load balancer
region 2
application
application
application
application
application
application
application
data
data
data
data
data
geo replication
data
Level 3: Multi Region
region 1
region 2
data
data
data
data
data
geo replication
Assumptions:
● Machines in at
least 2
geographical
regions
● Network may be
slow and unreliable
For example:
● EC2 Instances in
regions: eu-central-
1 and us-west-2
10 000 km
Speed of light: 300 000 km/s
Distance: 10 000 km
RTT (Round Trip Time) = 60 ms
Level 3: Multi Region
Geo-replication
data
data
data
geo replication
Level 3: Multi Region (Geo-replication)
data
data
data
Geo-replication
● It's asynchronous
● Your data store must support it
● You must be prepared for data loss
● Two modes:
○ Active-Passive
○ Active-Active
data
data
data
geo replication
Active-Passive Geo-replication
data
data
data
active passive
data
data
data
geo replication
Active-Passive Geo-replication
data
data
data
active passive
● data loss possible
● (eventual) consistency
data
data
data
geo replication
Active-Active Geo-replication
data
data
data
active active
data
data
data
geo replication
Active-Active Geo-replication
data
data
data
active active
● data loss possible
● eventual consistency
● conflict resolution
Hazelcast WAN Replication
hazelcast:
wan-replication:
batch-publisher:
target-endpoints: 35.184.122.109
Do I really need to lose
consistency?
LATENCY EXPERIMENT
Strong Consistency in Multi Region
● NewSQL (Spanner, CockroachDB)
● Multi-region distributed transactions
● Consensus algorithms (Paxos, Raft)
● Always a trade-off: consistency vs latency
What does "Level 3: Multi Region" mean to You?
Super high available!
Low latency if accessed from multi regions
Sometimes possible to use Cloud-specific
toolkit (e.g. Google Spanner - yes, AWS
Elasticache - no)
Geo-replication (asynchronous)!
Eventual consistency (conflict resolution)
● Introduction ✔
● High Availability Levels
○ Level 0: Single Instance ✔
○ Level 1: Multi Instance ✔
○ Level 2: Multi Zone ✔
○ Level 3: Multi Region ✔
○ Level 4: Multi Cloud
○ Level 5: Hybrid Cloud
● Summary
Agenda
Level 4: Multi Cloud
If one cloud provider is down,
the system is still available
app
data
Level 4: Multi Cloud
global
load balancer
load
balancer
app
app
data
data
replication
app
data
load
balancer
app
app
data
data
data
Level 4: Multi Cloud
data
data
replication
data
data
data
cloud provider 1
cloud provider 2
Assumptions:
● Machines in at
least 2 cloud
providers
● Network may be
slow and unreliable
● Machines may be
in different geo
regions
For example:
● EC2 Instances in
eu-central-1 and
GCP VM Instances
in us-west1-a
What's different from multi-
region?
Level 4: Multi Cloud
● No Cloud-specific tools
● No VPC Peering across Cloud providers
○ Latency
○ Security
● Cost
Is High Availability the only
reason for Multi-Cloud?
Reasons for Multi-Cloud
● High Availability / Disaster Recovery
● Avoiding vendor lock-in
● Cloud cost optimization
● Risk Mitigation
● Low latency
● Data Protection / Regulations / Compliance
● Best-Fit Technology (Cloud-specific portfolios)
What does "Level 4: Multi Cloud" mean to You?
No vendor lock-in!
Cloud cost negotiations
Low latency if accessed from multi-cloud
Complex setup!
No Cloud toolkit (e.g. AWS SQS)
● Introduction ✔
● High Availability Levels
○ Level 0: Single Instance ✔
○ Level 1: Multi Instance ✔
○ Level 2: Multi Zone ✔
○ Level 3: Multi Region ✔
○ Level 4: Multi Cloud ✔
○ Level 5: Hybrid Cloud
● Summary
Agenda
Level 5: Hybrid Cloud
If all cloud providers are down,
the system is still available
Is it possible that
all cloud providers
are down?
No!
Reasons for Hybrid Cloud
● Data requirements / regulations
● Data security
● Moving to Cloud
● Cost reduction
● All mentioned already in Multi-Cloud
Level 5: Hybrid Cloud
global
load balancer
On-Premises
What does "Level 5: Hybrid Cloud" mean to You?
No Cloud lock-in!
Low latency if accessed from custom
networks
Super complex setup!
Usually extra layer needed (e.g.
Kubernetes, OpenShift)
Costs a fortune!
● Introduction ✔
● High Availability Levels
○ Level 0: Single Instance ✔
○ Level 1: Multi Instance ✔
○ Level 2: Multi Zone ✔
○ Level 3: Multi Region ✔
○ Level 4: Multi Cloud ✔
○ Level 5: Hybrid Cloud ✔
● Summary
Agenda
Summary
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
distributed system
(data replication)
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
distributed system
(data replication)
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
zone aware
distributed system
(data replication)
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
long distance
(geo-
replication)
zone aware
distributed system
(data replication)
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
no cloud
tools
long distance
(geo-
replication)
zone aware
distributed system
(data replication)
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
no cloud
tools
own
infrastructure
long distance
(geo-
replication)
zone aware
Which
High Availability Level
is for me?
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
Single Instance Multi Instance Multi Zone Multi Region Multi Cloud Hybrid Cloud
Rafał Leszko
@RafalLeszko
rafalleszko.com
Thank You!

More Related Content

What's hot

Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleRafał Leszko
 
Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleRafał Leszko
 
Architectural patterns for caching microservices
Architectural patterns for caching microservicesArchitectural patterns for caching microservices
Architectural patterns for caching microservicesRafał Leszko
 
Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...
Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...
Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...StreamNative
 
How THINQ runs both transactions and analytics at scale
How THINQ runs both transactions and analytics at scaleHow THINQ runs both transactions and analytics at scale
How THINQ runs both transactions and analytics at scaleMariaDB plc
 
Deploying MariaDB databases with containers at Nokia Networks
Deploying MariaDB databases with containers at Nokia NetworksDeploying MariaDB databases with containers at Nokia Networks
Deploying MariaDB databases with containers at Nokia NetworksMariaDB plc
 
ClustrixDB: how distributed databases scale out
ClustrixDB: how distributed databases scale outClustrixDB: how distributed databases scale out
ClustrixDB: how distributed databases scale outMariaDB plc
 
How Alibaba Cloud scaled ApsaraDB with MariaDB MaxScale
How Alibaba Cloud scaled ApsaraDB with MariaDB MaxScaleHow Alibaba Cloud scaled ApsaraDB with MariaDB MaxScale
How Alibaba Cloud scaled ApsaraDB with MariaDB MaxScaleMariaDB plc
 
How we switched to columnar at SpendHQ
How we switched to columnar at SpendHQHow we switched to columnar at SpendHQ
How we switched to columnar at SpendHQMariaDB plc
 
Kafka on Kubernetes—From Evaluation to Production at Intuit
Kafka on Kubernetes—From Evaluation to Production at Intuit Kafka on Kubernetes—From Evaluation to Production at Intuit
Kafka on Kubernetes—From Evaluation to Production at Intuit confluent
 
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...Learnings from the Field. Lessons from Working with Dozens of Small & Large D...
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...HostedbyConfluent
 
Scylla Summit 2016: Scylla at Samsung SDS
Scylla Summit 2016: Scylla at Samsung SDSScylla Summit 2016: Scylla at Samsung SDS
Scylla Summit 2016: Scylla at Samsung SDSScyllaDB
 
QCon NYC: Distributed systems in practice, in theory
QCon NYC: Distributed systems in practice, in theoryQCon NYC: Distributed systems in practice, in theory
QCon NYC: Distributed systems in practice, in theoryAysylu Greenberg
 
Mongo DB Monitoring - Become a MongoDB DBA
Mongo DB Monitoring - Become a MongoDB DBAMongo DB Monitoring - Become a MongoDB DBA
Mongo DB Monitoring - Become a MongoDB DBASeveralnines
 
MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...
MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...
MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...MongoDB
 
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafka
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache KafkaStrata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafka
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafkaconfluent
 
Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.
Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.
Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.Raghavendra Prabhu
 
Workshop - How to benchmark your database
Workshop - How to benchmark your databaseWorkshop - How to benchmark your database
Workshop - How to benchmark your databaseScyllaDB
 
MySQL Cluster (NDB) - Best Practices Percona Live 2017
MySQL Cluster (NDB) - Best Practices Percona Live 2017MySQL Cluster (NDB) - Best Practices Percona Live 2017
MySQL Cluster (NDB) - Best Practices Percona Live 2017Severalnines
 

What's hot (20)

Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by example
 
Where is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by exampleWhere is my cache? Architectural patterns for caching microservices by example
Where is my cache? Architectural patterns for caching microservices by example
 
GCP for AWS Professionals
GCP for AWS ProfessionalsGCP for AWS Professionals
GCP for AWS Professionals
 
Architectural patterns for caching microservices
Architectural patterns for caching microservicesArchitectural patterns for caching microservices
Architectural patterns for caching microservices
 
Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...
Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...
Replicated Subscriptions: Taking Geo-Replication to the Next Level - Pulsar S...
 
How THINQ runs both transactions and analytics at scale
How THINQ runs both transactions and analytics at scaleHow THINQ runs both transactions and analytics at scale
How THINQ runs both transactions and analytics at scale
 
Deploying MariaDB databases with containers at Nokia Networks
Deploying MariaDB databases with containers at Nokia NetworksDeploying MariaDB databases with containers at Nokia Networks
Deploying MariaDB databases with containers at Nokia Networks
 
ClustrixDB: how distributed databases scale out
ClustrixDB: how distributed databases scale outClustrixDB: how distributed databases scale out
ClustrixDB: how distributed databases scale out
 
How Alibaba Cloud scaled ApsaraDB with MariaDB MaxScale
How Alibaba Cloud scaled ApsaraDB with MariaDB MaxScaleHow Alibaba Cloud scaled ApsaraDB with MariaDB MaxScale
How Alibaba Cloud scaled ApsaraDB with MariaDB MaxScale
 
How we switched to columnar at SpendHQ
How we switched to columnar at SpendHQHow we switched to columnar at SpendHQ
How we switched to columnar at SpendHQ
 
Kafka on Kubernetes—From Evaluation to Production at Intuit
Kafka on Kubernetes—From Evaluation to Production at Intuit Kafka on Kubernetes—From Evaluation to Production at Intuit
Kafka on Kubernetes—From Evaluation to Production at Intuit
 
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...Learnings from the Field. Lessons from Working with Dozens of Small & Large D...
Learnings from the Field. Lessons from Working with Dozens of Small & Large D...
 
Scylla Summit 2016: Scylla at Samsung SDS
Scylla Summit 2016: Scylla at Samsung SDSScylla Summit 2016: Scylla at Samsung SDS
Scylla Summit 2016: Scylla at Samsung SDS
 
QCon NYC: Distributed systems in practice, in theory
QCon NYC: Distributed systems in practice, in theoryQCon NYC: Distributed systems in practice, in theory
QCon NYC: Distributed systems in practice, in theory
 
Mongo DB Monitoring - Become a MongoDB DBA
Mongo DB Monitoring - Become a MongoDB DBAMongo DB Monitoring - Become a MongoDB DBA
Mongo DB Monitoring - Become a MongoDB DBA
 
MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...
MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...
MongoDB .local Bengaluru 2019: Using MongoDB Services in Kubernetes: Any Plat...
 
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafka
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache KafkaStrata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafka
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafka
 
Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.
Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.
Clusternaut: Orchestrating Percona XtraDB Cluster with Kubernetes.
 
Workshop - How to benchmark your database
Workshop - How to benchmark your databaseWorkshop - How to benchmark your database
Workshop - How to benchmark your database
 
MySQL Cluster (NDB) - Best Practices Percona Live 2017
MySQL Cluster (NDB) - Best Practices Percona Live 2017MySQL Cluster (NDB) - Best Practices Percona Live 2017
MySQL Cluster (NDB) - Best Practices Percona Live 2017
 

Similar to 5 levels of high availability from multi instance to hybrid cloud

Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2aspyker
 
Open stack HA - Theory to Reality
Open stack HA -  Theory to RealityOpen stack HA -  Theory to Reality
Open stack HA - Theory to RealitySriram Subramanian
 
Dragonflow Austin Summit Talk
Dragonflow Austin Summit Talk Dragonflow Austin Summit Talk
Dragonflow Austin Summit Talk Eran Gampel
 
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...javier ramirez
 
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...Athens Big Data
 
Unified Batch & Stream Processing with Apache Samza
Unified Batch & Stream Processing with Apache SamzaUnified Batch & Stream Processing with Apache Samza
Unified Batch & Stream Processing with Apache SamzaDataWorks Summit
 
Glusterfs for sysadmins-justin_clift
Glusterfs for sysadmins-justin_cliftGlusterfs for sysadmins-justin_clift
Glusterfs for sysadmins-justin_cliftGluster.org
 
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kevin Lynch
 
Intro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming
Intro to Apache Apex (next gen Hadoop) & comparison to Spark StreamingIntro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming
Intro to Apache Apex (next gen Hadoop) & comparison to Spark StreamingApache Apex
 
A Tour of Apache Kafka
A Tour of Apache KafkaA Tour of Apache Kafka
A Tour of Apache Kafkaconfluent
 
Using Kubernetes to deliver a “serverless” service
Using Kubernetes to deliver a “serverless” serviceUsing Kubernetes to deliver a “serverless” service
Using Kubernetes to deliver a “serverless” serviceDoKC
 
Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)
Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)
Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)Rick Hwang
 
Demystifying the Distributed Database Landscape (DevOps) (1).pdf
Demystifying the Distributed Database Landscape (DevOps) (1).pdfDemystifying the Distributed Database Landscape (DevOps) (1).pdf
Demystifying the Distributed Database Landscape (DevOps) (1).pdfScyllaDB
 
Fully-managed Cloud-native Databases: The path to indefinite scale @ CNN Mainz
Fully-managed Cloud-native Databases: The path to indefinite scale @ CNN MainzFully-managed Cloud-native Databases: The path to indefinite scale @ CNN Mainz
Fully-managed Cloud-native Databases: The path to indefinite scale @ CNN MainzQAware GmbH
 
Ambedded - how to build a true no single point of failure ceph cluster
Ambedded - how to build a true no single point of failure ceph cluster Ambedded - how to build a true no single point of failure ceph cluster
Ambedded - how to build a true no single point of failure ceph cluster inwin stack
 
Scylla on Kubernetes: Introducing the Scylla Operator
Scylla on Kubernetes: Introducing the Scylla OperatorScylla on Kubernetes: Introducing the Scylla Operator
Scylla on Kubernetes: Introducing the Scylla OperatorScyllaDB
 
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAwareLeveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAwareLucidworks
 
Leveraging the Power of Solr with Spark
Leveraging the Power of Solr with SparkLeveraging the Power of Solr with Spark
Leveraging the Power of Solr with SparkQAware GmbH
 
Blackray @ SAPO CodeBits 2009
Blackray @ SAPO CodeBits 2009Blackray @ SAPO CodeBits 2009
Blackray @ SAPO CodeBits 2009fschupp
 

Similar to 5 levels of high availability from multi instance to hybrid cloud (20)

Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2Netflix Open Source Meetup Season 4 Episode 2
Netflix Open Source Meetup Season 4 Episode 2
 
Open stack HA - Theory to Reality
Open stack HA -  Theory to RealityOpen stack HA -  Theory to Reality
Open stack HA - Theory to Reality
 
Dragonflow Austin Summit Talk
Dragonflow Austin Summit Talk Dragonflow Austin Summit Talk
Dragonflow Austin Summit Talk
 
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
Como creamos QuestDB Cloud, un SaaS basado en Kubernetes alrededor de QuestDB...
 
MYSQL
MYSQLMYSQL
MYSQL
 
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
 
Unified Batch & Stream Processing with Apache Samza
Unified Batch & Stream Processing with Apache SamzaUnified Batch & Stream Processing with Apache Samza
Unified Batch & Stream Processing with Apache Samza
 
Glusterfs for sysadmins-justin_clift
Glusterfs for sysadmins-justin_cliftGlusterfs for sysadmins-justin_clift
Glusterfs for sysadmins-justin_clift
 
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
Kubernetes @ Squarespace (SRE Portland Meetup October 2017)
 
Intro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming
Intro to Apache Apex (next gen Hadoop) & comparison to Spark StreamingIntro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming
Intro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming
 
A Tour of Apache Kafka
A Tour of Apache KafkaA Tour of Apache Kafka
A Tour of Apache Kafka
 
Using Kubernetes to deliver a “serverless” service
Using Kubernetes to deliver a “serverless” serviceUsing Kubernetes to deliver a “serverless” service
Using Kubernetes to deliver a “serverless” service
 
Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)
Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)
Study Notes - Architecting for the cloud (AWS Best Practices, Feb 2016)
 
Demystifying the Distributed Database Landscape (DevOps) (1).pdf
Demystifying the Distributed Database Landscape (DevOps) (1).pdfDemystifying the Distributed Database Landscape (DevOps) (1).pdf
Demystifying the Distributed Database Landscape (DevOps) (1).pdf
 
Fully-managed Cloud-native Databases: The path to indefinite scale @ CNN Mainz
Fully-managed Cloud-native Databases: The path to indefinite scale @ CNN MainzFully-managed Cloud-native Databases: The path to indefinite scale @ CNN Mainz
Fully-managed Cloud-native Databases: The path to indefinite scale @ CNN Mainz
 
Ambedded - how to build a true no single point of failure ceph cluster
Ambedded - how to build a true no single point of failure ceph cluster Ambedded - how to build a true no single point of failure ceph cluster
Ambedded - how to build a true no single point of failure ceph cluster
 
Scylla on Kubernetes: Introducing the Scylla Operator
Scylla on Kubernetes: Introducing the Scylla OperatorScylla on Kubernetes: Introducing the Scylla Operator
Scylla on Kubernetes: Introducing the Scylla Operator
 
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAwareLeveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
Leveraging the Power of Solr with Spark: Presented by Johannes Weigend, QAware
 
Leveraging the Power of Solr with Spark
Leveraging the Power of Solr with SparkLeveraging the Power of Solr with Spark
Leveraging the Power of Solr with Spark
 
Blackray @ SAPO CodeBits 2009
Blackray @ SAPO CodeBits 2009Blackray @ SAPO CodeBits 2009
Blackray @ SAPO CodeBits 2009
 

More from Rafał Leszko

Mutation Testing with PIT
Mutation Testing with PITMutation Testing with PIT
Mutation Testing with PITRafał Leszko
 
Architectural caching patterns for kubernetes
Architectural caching patterns for kubernetesArchitectural caching patterns for kubernetes
Architectural caching patterns for kubernetesRafał Leszko
 
Mutation testing with PIT
Mutation testing with PITMutation testing with PIT
Mutation testing with PITRafał Leszko
 
Architectural caching patterns for kubernetes
Architectural caching patterns for kubernetesArchitectural caching patterns for kubernetes
Architectural caching patterns for kubernetesRafał Leszko
 
Build your operator with the right tool
Build your operator with the right toolBuild your operator with the right tool
Build your operator with the right toolRafał Leszko
 
Where is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by exampleWhere is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by exampleRafał Leszko
 
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...Rafał Leszko
 
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019Rafał Leszko
 
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018Rafał Leszko
 
Mutation Testing - Voxxed Days Cluj-Napoca 2017
Mutation Testing - Voxxed Days Cluj-Napoca 2017Mutation Testing - Voxxed Days Cluj-Napoca 2017
Mutation Testing - Voxxed Days Cluj-Napoca 2017Rafał Leszko
 
Continuous Delivery - Voxxed Days Cluj-Napoca 2017
Continuous Delivery - Voxxed Days Cluj-Napoca 2017Continuous Delivery - Voxxed Days Cluj-Napoca 2017
Continuous Delivery - Voxxed Days Cluj-Napoca 2017Rafał Leszko
 
Continuous Delivery - Voxxed Days Bucharest 2017
Continuous Delivery - Voxxed Days Bucharest 2017Continuous Delivery - Voxxed Days Bucharest 2017
Continuous Delivery - Voxxed Days Bucharest 2017Rafał Leszko
 
Mutation Testing - Voxxed Days Bucharest 10.03.2017
Mutation Testing - Voxxed Days Bucharest 10.03.2017Mutation Testing - Voxxed Days Bucharest 10.03.2017
Mutation Testing - Voxxed Days Bucharest 10.03.2017Rafał Leszko
 
Continuous Delivery - Devoxx Morocco 2016
Continuous Delivery - Devoxx Morocco 2016Continuous Delivery - Devoxx Morocco 2016
Continuous Delivery - Devoxx Morocco 2016Rafał Leszko
 
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016Rafał Leszko
 

More from Rafał Leszko (15)

Mutation Testing with PIT
Mutation Testing with PITMutation Testing with PIT
Mutation Testing with PIT
 
Architectural caching patterns for kubernetes
Architectural caching patterns for kubernetesArchitectural caching patterns for kubernetes
Architectural caching patterns for kubernetes
 
Mutation testing with PIT
Mutation testing with PITMutation testing with PIT
Mutation testing with PIT
 
Architectural caching patterns for kubernetes
Architectural caching patterns for kubernetesArchitectural caching patterns for kubernetes
Architectural caching patterns for kubernetes
 
Build your operator with the right tool
Build your operator with the right toolBuild your operator with the right tool
Build your operator with the right tool
 
Where is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by exampleWhere is my cache architectural patterns for caching microservices by example
Where is my cache architectural patterns for caching microservices by example
 
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
[DevopsDays India 2019] Where is my cache? Architectural patterns for caching...
 
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
Stream Processing in the Cloud - Athens Kubernetes Meetup 16.07.2019
 
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
Stream Processing with Hazelcast Jet - Voxxed Days Thessaloniki 19.11.2018
 
Mutation Testing - Voxxed Days Cluj-Napoca 2017
Mutation Testing - Voxxed Days Cluj-Napoca 2017Mutation Testing - Voxxed Days Cluj-Napoca 2017
Mutation Testing - Voxxed Days Cluj-Napoca 2017
 
Continuous Delivery - Voxxed Days Cluj-Napoca 2017
Continuous Delivery - Voxxed Days Cluj-Napoca 2017Continuous Delivery - Voxxed Days Cluj-Napoca 2017
Continuous Delivery - Voxxed Days Cluj-Napoca 2017
 
Continuous Delivery - Voxxed Days Bucharest 2017
Continuous Delivery - Voxxed Days Bucharest 2017Continuous Delivery - Voxxed Days Bucharest 2017
Continuous Delivery - Voxxed Days Bucharest 2017
 
Mutation Testing - Voxxed Days Bucharest 10.03.2017
Mutation Testing - Voxxed Days Bucharest 10.03.2017Mutation Testing - Voxxed Days Bucharest 10.03.2017
Mutation Testing - Voxxed Days Bucharest 10.03.2017
 
Continuous Delivery - Devoxx Morocco 2016
Continuous Delivery - Devoxx Morocco 2016Continuous Delivery - Devoxx Morocco 2016
Continuous Delivery - Devoxx Morocco 2016
 
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
Continuous Delivery - Voxxed Days Thessaloniki 21.10.2016
 

Recently uploaded

Unveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML DiagramsUnveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML DiagramsAhmed Mohamed
 
Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...Rob Geurden
 
Cyber security and its impact on E commerce
Cyber security and its impact on E commerceCyber security and its impact on E commerce
Cyber security and its impact on E commercemanigoyal112
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...Technogeeks
 
Salesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZSalesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZABSYZ Inc
 
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdfExploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdfkalichargn70th171
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationBradBedford3
 
Real-time Tracking and Monitoring with Cargo Cloud Solutions.pptx
Real-time Tracking and Monitoring with Cargo Cloud Solutions.pptxReal-time Tracking and Monitoring with Cargo Cloud Solutions.pptx
Real-time Tracking and Monitoring with Cargo Cloud Solutions.pptxRTS corp
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringHironori Washizaki
 
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...OnePlan Solutions
 
Balasore Best It Company|| Top 10 IT Company || Balasore Software company Odisha
Balasore Best It Company|| Top 10 IT Company || Balasore Software company OdishaBalasore Best It Company|| Top 10 IT Company || Balasore Software company Odisha
Balasore Best It Company|| Top 10 IT Company || Balasore Software company Odishasmiwainfosol
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsChristian Birchler
 
Post Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on IdentityPost Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on Identityteam-WIBU
 
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...OnePlan Solutions
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceBrainSell Technologies
 
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...Matt Ray
 
A healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfA healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfMarharyta Nedzelska
 
Large Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and RepairLarge Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and RepairLionel Briand
 
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Angel Borroy López
 
Patterns for automating API delivery. API conference
Patterns for automating API delivery. API conferencePatterns for automating API delivery. API conference
Patterns for automating API delivery. API conferencessuser9e7c64
 

Recently uploaded (20)

Unveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML DiagramsUnveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML Diagrams
 
Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...Simplifying Microservices & Apps - The art of effortless development - Meetup...
Simplifying Microservices & Apps - The art of effortless development - Meetup...
 
Cyber security and its impact on E commerce
Cyber security and its impact on E commerceCyber security and its impact on E commerce
Cyber security and its impact on E commerce
 
What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...What is Advanced Excel and what are some best practices for designing and cre...
What is Advanced Excel and what are some best practices for designing and cre...
 
Salesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZSalesforce Implementation Services PPT By ABSYZ
Salesforce Implementation Services PPT By ABSYZ
 
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdfExploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
Exploring Selenium_Appium Frameworks for Seamless Integration with HeadSpin.pdf
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion Application
 
Real-time Tracking and Monitoring with Cargo Cloud Solutions.pptx
Real-time Tracking and Monitoring with Cargo Cloud Solutions.pptxReal-time Tracking and Monitoring with Cargo Cloud Solutions.pptx
Real-time Tracking and Monitoring with Cargo Cloud Solutions.pptx
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their Engineering
 
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
Tech Tuesday - Mastering Time Management Unlock the Power of OnePlan's Timesh...
 
Balasore Best It Company|| Top 10 IT Company || Balasore Software company Odisha
Balasore Best It Company|| Top 10 IT Company || Balasore Software company OdishaBalasore Best It Company|| Top 10 IT Company || Balasore Software company Odisha
Balasore Best It Company|| Top 10 IT Company || Balasore Software company Odisha
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
 
Post Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on IdentityPost Quantum Cryptography – The Impact on Identity
Post Quantum Cryptography – The Impact on Identity
 
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
Maximizing Efficiency and Profitability with OnePlan’s Professional Service A...
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. Salesforce
 
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
Open Source Summit NA 2024: Open Source Cloud Costs - OpenCost's Impact on En...
 
A healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfA healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdf
 
Large Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and RepairLarge Language Models for Test Case Evolution and Repair
Large Language Models for Test Case Evolution and Repair
 
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
 
Patterns for automating API delivery. API conference
Patterns for automating API delivery. API conferencePatterns for automating API delivery. API conference
Patterns for automating API delivery. API conference
 

5 levels of high availability from multi instance to hybrid cloud