SlideShare a Scribd company logo
1 of 23
Download to read offline
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
DMITRIY	
  SETRAKYAN	
  
Founder,	
  PPMC	
  
Apache	
  IgniteTM	
  (Incubating)	
  -­‐	
  In-­‐Memory	
  Data	
  Fabric	
  
Fast	
  Data	
  Meets	
  Open	
  Source
http://www.ignite.incubator.apache.org @apacheignite @dsetrakyan
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
Agenda
• About	
  In-­‐Memory	
  Computing	
  
• Apache	
  Ignite
(tm)
	
  In-­‐Memory	
  Data	
  Fabric	
  
• Advanced	
  Clustering	
  
• Data	
  Grid	
  
• Compute	
  Grid	
  
• Service	
  Grid	
  
• Ignite	
  For	
  Analytics	
  
• Streaming	
  &	
  CEP	
  
• Share	
  State	
  Across	
  Spark	
  Jobs	
  
• In-­‐Memory	
  MapReduce	
  
• Interactive	
  SQL	
  
• DevOps:	
  Yarn	
  and	
  Mesos	
  
• Q	
  &	
  A
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
Apache	
  IgniteTM
	
  In-­‐Memory	
  Data	
  Fabric:	
  	
  
Strategic	
  Approach	
  to	
  IMC
• Supports Applications of
various types and
languages
• Open Source – Apache 2.0
• Simple Java APIs
• 1 JAR Dependency
• High Performance & Scale
• Automatic Fault Tolerance
• Management/Monitoring
• Runs on Commodity Hardware
• Supports existing & 

new data sources
• No need to rip & replace
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
In-­‐Memory	
  Data	
  Fabric:	
  More	
  Than	
  Data	
  Grid
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• Automatic	
  Discovery	
  
– Simple	
  Configuration	
  
– AWS/EC2/S3	
  
– Google	
  Compute	
  Engine	
  (NEW)	
  
– Other	
  Clouds	
  with	
  JClouds	
  (NEW)	
  
• Docker	
  Support	
  
– Automatically	
  Build	
  and	
  Deploy
Apache	
  Ignite:	
  Better	
  Cloud	
  Support
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• JCache	
  (JSR	
  107)	
  
– Basic	
  Cache	
  Operations	
  
– ConcurrentMap	
  APIs	
  
– Collocated	
  Processing	
  (EntryProcessor)	
  
– Events	
  and	
  Metrics	
  
– Pluggable	
  Persistence	
  
• Ignite	
  Data	
  Grid	
  
– ACID	
  Transactions	
  
– SQL	
  Queries	
  (ANSI	
  99)	
  
– In-­‐Memory	
  Indexes	
  
– Automatic	
  RDBMS	
  Integration
Data	
  Grid:	
  JCache	
  (JSR	
  107)
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
Data	
  Grid:	
  Partitioned	
  Cache
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
Data	
  Grid:	
  Replicated	
  Cache
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• Unlimited	
  Vertical	
  Scale	
  
• Avoid	
  Java	
  Garbage	
  Collection	
  Pauses	
  
• Small	
  On-­‐Heap	
  Footprint	
  
• Large	
  Off-­‐Heap	
  Footprint	
  
• Off-­‐Heap	
  Indexes	
  
• Full	
  RAM	
  Utilization	
  
• Simple	
  Configuration
Data	
  Grid:	
  Off-­‐Heap	
  Memory
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• ANSI-­‐99	
  SQL	
  
• Always	
  Consistent	
  
• Fault	
  Tolerant	
  
• In-­‐Memory	
  Indexes	
  (On-­‐Heap	
  and	
  Off-­‐Heap)	
  
• Automatic	
  Group	
  By,	
  Aggregations,	
  Sorting	
  
• Cross-­‐Cache	
  Joins,	
  Unions,	
  etc.	
  
• Ad-­‐Hoc	
  SQL	
  Support
Data	
  Grid:	
  Ad-­‐Hoc	
  SQL	
  (ANSI	
  99)
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
SQL	
  Cross-­‐Cache	
  JOIN	
  Example
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
SQL	
  Cross-­‐Cache	
  GROUP	
  BY	
  Example
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• Direct	
  API	
  for	
  MapReduce	
  
• Direct	
  API	
  for	
  ForkJoin	
  
• Zero	
  Deployment	
  
• Cron-­‐like	
  Task	
  Scheduling	
  
• State	
  Checkpoints	
  
• Load	
  Balancing	
  
• Automatic	
  Failover	
  
• Full	
  Cluster	
  Management	
  
• Pluggable	
  SPI	
  Design
In-­‐Memory	
  Compute	
  Grid
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• Streaming	
  Data	
  Never	
  Ends	
  
• Branching	
  Pipelines	
  
• Pluggable	
  Routing	
  
• Sliding	
  Windows	
  for	
  

CEP/Continuous	
  Query	
  
• SQL	
  Queries	
  (ANSI	
  99)	
  
• Query	
  Across	
  Sliding	
  Windows	
  
• Real	
  Time	
  Analysis
In-­‐Memory	
  Streaming	
  and	
  CEP
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• Singletons	
  on	
  the	
  Cluster	
  
– Cluster	
  Singleton	
  
– Node	
  Singleton	
  
– Key	
  Singleton	
  
• Distribute	
  any	
  Data	
  Structure	
  
– Available	
  Anywhere	
  on	
  the	
  Grid	
  
– Access	
  Anywhere	
  via	
  Proxies	
  
• Guaranteed	
  Availability	
  
– Auto	
  Redeployment	
  in	
  Case	
  of	
  Failures
In-­‐Memory	
  Service	
  Grid
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
Apache	
  Ignite	
  for	
  BI	
  and	
  Analytics
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• Automatic	
  Resource	
  Management	
  
• Easy	
  Data	
  Center	
  Installation	
  
• Easy	
  Data	
  Center	
  Configuration	
  
• On-­‐Demand	
  Elasticity
DevOps:	
  Integration	
  with	
  Yarn	
  and	
  Mesos
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• IgniteRDD	
  	
  
– Share	
  RDD	
  across	
  jobs	
  on	
  the	
  host	
  
– Share	
  RDD	
  across	
  jobs	
  in	
  the	
  application	
  
– Share	
  RDD	
  globally	
  
• Faster	
  SQL	
  
– In-­‐Memory	
  Indexes	
  
– SQL	
  on	
  top	
  of	
  Shared	
  RDD
Share	
  RDDs	
  Across	
  Spark	
  Jobs	
  
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
• Ignite	
  In-­‐Memory	
  File	
  System	
  (IGFS)	
  
– Hadoop-­‐compliant	
  
– Easy	
  to	
  Install	
  
– On-­‐Heap	
  and	
  Off-­‐Heap	
  
– Caching	
  Layer	
  for	
  HDFS	
  
– Write-­‐through	
  and	
  Read-­‐through	
  HDFS	
  
– Performance	
  Boost
Ignite	
  In-­‐Memory	
  File	
  System
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
Ignite	
  In-­‐Memory	
  Map	
  Reduce
• In-­‐Memory	
  Native	
  
Performance	
  
• Zero	
  Code	
  Change	
  
• Use	
  existing	
  MR	
  code	
  
• Use	
  existing	
  Hive	
  queries	
  
• No	
  Name	
  Node	
  
• No	
  Network	
  Noise	
  
• In-­‐Process	
  Data	
  Colocation	
  
• Eager	
  Push	
  Scheduling
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
Interactive	
  SQL	
  with	
  Apache	
  Zeppelin
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
GridGain	
  Enterprise	
  &	
  Apache	
  Ignite	
  Comparison	
  Chart
GridGain	
  Enterprise	
  Subscriptions	
  include	
  the	
  
following	
  during	
  the	
  term	
  of	
  the	
  subscription:	
  
> Right	
  to	
  use	
  GridGain	
  Enterprise	
  Edition	
  
> Bug	
  fixes,	
  patches,	
  updates	
  and	
  upgrades	
  
> 9x5	
  or	
  24x7	
  Support	
  	
  
> Ability	
  to	
  procure	
  Training	
  and	
  Consulting	
  
Services	
  from	
  GridGain	
  
> Confidence	
  and	
  protection,	
  not	
  provided	
  
under	
  Open	
  Source	
  licensing,	
  that	
  only	
  a	
  
commercial	
  vendor	
  can	
  provide,	
  such	
  as	
  
indemnification
Features Apache Ignite
Enterprise
Edition
In-Memory Data Grid ✓
CHECK
✓
In-Memory Compute Grid ✓ ✓
Real-Time Streaming & CEP ✓ ✓
Hadoop Acceleration ✓ ✓
Management & Monitoring GUI ✓
Portable Objects ✓
.Net and C++ APIs ✓
Enterprise-grade Security ✓
Network Segmentation Protection ✓
Local Restartable Store ✓
Rolling Production Updates ✓
Datacenter Replication ✓
9x5 and 24x7 Support ✓
Long Term Support & Patches ✓
Apache®,	
  Apache	
  Ignite,	
  Ignite®,	
  and	
  the	
  Apache	
  Ignite	
  logo	
  are	
  either	
  registered	
  trademarks	
  or	
  trademarks	
  of	
  the	
  Apache	
  Software	
  Foundation	
  in	
  the	
  United	
  States	
  and/or	
  other	
  countries.
ANY	
  QUESTIONS?
Thank	
  you	
  for	
  joining	
  us.	
  Follow	
  the	
  conversation.
http://www.ignite.incubator.apache.org
@apacheignite @dsetrakyan

More Related Content

What's hot

Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...
Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...
Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...Data Con LA
 
Getting Spark ready for real-time, operational analytics
Getting Spark ready for real-time, operational analyticsGetting Spark ready for real-time, operational analytics
Getting Spark ready for real-time, operational analyticsairisData
 
Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...
Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...
Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...Databricks
 
Data Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache HadoopData Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache HadoopCloudera, Inc.
 
Building Effective Near-Real-Time Analytics with Spark Streaming and Kudu
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduBuilding Effective Near-Real-Time Analytics with Spark Streaming and Kudu
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard
 
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan VolzArchiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan VolzDatabricks
 
SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...
SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...
SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...Databricks
 
Apache Spark At Apple with Sam Maclennan and Vishwanath Lakkundi
Apache Spark At Apple with Sam Maclennan and Vishwanath LakkundiApache Spark At Apple with Sam Maclennan and Vishwanath Lakkundi
Apache Spark At Apple with Sam Maclennan and Vishwanath LakkundiDatabricks
 
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...Spark Summit
 
Apache Ignite - Distributed Database Orchestration
Apache Ignite - Distributed Database OrchestrationApache Ignite - Distributed Database Orchestration
Apache Ignite - Distributed Database OrchestrationAriel Jatib
 
Exponea - Kafka and Hadoop as components of architecture
Exponea  - Kafka and Hadoop as components of architectureExponea  - Kafka and Hadoop as components of architecture
Exponea - Kafka and Hadoop as components of architectureMartinStrycek
 
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...Data Con LA
 
Low latency high throughput streaming using Apache Apex and Apache Kudu
Low latency high throughput streaming using Apache Apex and Apache KuduLow latency high throughput streaming using Apache Apex and Apache Kudu
Low latency high throughput streaming using Apache Apex and Apache KuduDataWorks Summit
 
Improving Python and Spark Performance and Interoperability with Apache Arrow...
Improving Python and Spark Performance and Interoperability with Apache Arrow...Improving Python and Spark Performance and Interoperability with Apache Arrow...
Improving Python and Spark Performance and Interoperability with Apache Arrow...Databricks
 
Spark introduction and architecture
Spark introduction and architectureSpark introduction and architecture
Spark introduction and architectureSohil Jain
 
Apache Spark: Lightning Fast Cluster Computing
Apache Spark: Lightning Fast Cluster ComputingApache Spark: Lightning Fast Cluster Computing
Apache Spark: Lightning Fast Cluster ComputingAll Things Open
 
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
 Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng ShiDatabricks
 
Stream All Things—Patterns of Modern Data Integration with Gwen Shapira
Stream All Things—Patterns of Modern Data Integration with Gwen ShapiraStream All Things—Patterns of Modern Data Integration with Gwen Shapira
Stream All Things—Patterns of Modern Data Integration with Gwen ShapiraDatabricks
 
Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...
Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...
Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...Spark Summit
 

What's hot (20)

Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...
Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...
Big Data Day LA 2016/ NoSQL track - Apache Kudu: Fast Analytics on Fast Data,...
 
Getting Spark ready for real-time, operational analytics
Getting Spark ready for real-time, operational analyticsGetting Spark ready for real-time, operational analytics
Getting Spark ready for real-time, operational analytics
 
Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...
Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...
Real-Time Machine Learning with Redis, Apache Spark, Tensor Flow, and more wi...
 
Data Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache HadoopData Science at Scale Using Apache Spark and Apache Hadoop
Data Science at Scale Using Apache Spark and Apache Hadoop
 
Building Effective Near-Real-Time Analytics with Spark Streaming and Kudu
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduBuilding Effective Near-Real-Time Analytics with Spark Streaming and Kudu
Building Effective Near-Real-Time Analytics with Spark Streaming and Kudu
 
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan VolzArchiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz
Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz
 
SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...
SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...
SparkOscope: Enabling Apache Spark Optimization through Cross Stack Monitorin...
 
Apache Spark At Apple with Sam Maclennan and Vishwanath Lakkundi
Apache Spark At Apple with Sam Maclennan and Vishwanath LakkundiApache Spark At Apple with Sam Maclennan and Vishwanath Lakkundi
Apache Spark At Apple with Sam Maclennan and Vishwanath Lakkundi
 
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...
Teaching Apache Spark Clusters to Manage Their Workers Elastically: Spark Sum...
 
Apache Ignite - Distributed Database Orchestration
Apache Ignite - Distributed Database OrchestrationApache Ignite - Distributed Database Orchestration
Apache Ignite - Distributed Database Orchestration
 
Exponea - Kafka and Hadoop as components of architecture
Exponea  - Kafka and Hadoop as components of architectureExponea  - Kafka and Hadoop as components of architecture
Exponea - Kafka and Hadoop as components of architecture
 
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...
Big Data Day LA 2016/ Hadoop/ Spark/ Kafka track - Introduction to Kafka - Je...
 
Low latency high throughput streaming using Apache Apex and Apache Kudu
Low latency high throughput streaming using Apache Apex and Apache KuduLow latency high throughput streaming using Apache Apex and Apache Kudu
Low latency high throughput streaming using Apache Apex and Apache Kudu
 
Improving Python and Spark Performance and Interoperability with Apache Arrow...
Improving Python and Spark Performance and Interoperability with Apache Arrow...Improving Python and Spark Performance and Interoperability with Apache Arrow...
Improving Python and Spark Performance and Interoperability with Apache Arrow...
 
Spark introduction and architecture
Spark introduction and architectureSpark introduction and architecture
Spark introduction and architecture
 
Data science lifecycle with Apache Zeppelin
Data science lifecycle with Apache ZeppelinData science lifecycle with Apache Zeppelin
Data science lifecycle with Apache Zeppelin
 
Apache Spark: Lightning Fast Cluster Computing
Apache Spark: Lightning Fast Cluster ComputingApache Spark: Lightning Fast Cluster Computing
Apache Spark: Lightning Fast Cluster Computing
 
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
 Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
 
Stream All Things—Patterns of Modern Data Integration with Gwen Shapira
Stream All Things—Patterns of Modern Data Integration with Gwen ShapiraStream All Things—Patterns of Modern Data Integration with Gwen Shapira
Stream All Things—Patterns of Modern Data Integration with Gwen Shapira
 
Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...
Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...
Solving Real Problems with Apache Spark: Archiving, E-Discovery, and Supervis...
 

Viewers also liked

August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...Yahoo Developer Network
 
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...In-Memory Computing Summit
 
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and Ignite
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and IgniteJCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and Ignite
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and IgniteJoseph Kuo
 
IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing Hub
IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing HubIMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing Hub
IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing HubIn-Memory Computing Summit
 
Apache Cassandra Ignite Presentation
Apache Cassandra Ignite PresentationApache Cassandra Ignite Presentation
Apache Cassandra Ignite PresentationJared Winick
 
Is An Agile Standard Possible For Java?
Is An Agile Standard Possible For Java?Is An Agile Standard Possible For Java?
Is An Agile Standard Possible For Java?Simon Ritter
 
IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...
IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...
IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...In-Memory Computing Summit
 
Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...
Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...
Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...DataStax
 
55 New Features in JDK 9
55 New Features in JDK 955 New Features in JDK 9
55 New Features in JDK 9Simon Ritter
 

Viewers also liked (9)

August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
August 2016 HUG: Better together: Fast Data with Apache Spark™ and Apache Ign...
 
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
IMCSummite 2016 Breakout - Nikita Ivanov - Apache Ignite 2.0 Towards a Conver...
 
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and Ignite
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and IgniteJCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and Ignite
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and Ignite
 
IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing Hub
IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing HubIMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing Hub
IMC Summit 2016 Breakout - Roman Shtykh - Apache Ignite as a Data Processing Hub
 
Apache Cassandra Ignite Presentation
Apache Cassandra Ignite PresentationApache Cassandra Ignite Presentation
Apache Cassandra Ignite Presentation
 
Is An Agile Standard Possible For Java?
Is An Agile Standard Possible For Java?Is An Agile Standard Possible For Java?
Is An Agile Standard Possible For Java?
 
IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...
IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...
IMC Summit 2016 Breakout - Matt Coventon - Test Driving Streaming and CEP on ...
 
Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...
Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...
Fast, In-Memory SQL on Apache Cassandra with Apache Ignite (Rachel Pedreschi,...
 
55 New Features in JDK 9
55 New Features in JDK 955 New Features in JDK 9
55 New Features in JDK 9
 

Similar to Apache Ignite In-Memory Data Fabric for Fast Data and Analytics

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis MagdaApache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis MagdaDatabricks
 
Microservices Architectures With Apache Ignite
Microservices Architectures With Apache IgniteMicroservices Architectures With Apache Ignite
Microservices Architectures With Apache IgniteDenis Magda
 
What is Apache spark
What is Apache sparkWhat is Apache spark
What is Apache sparkmanisha1110
 
Streaming Solutions for Real time problems
Streaming Solutions for Real time problemsStreaming Solutions for Real time problems
Streaming Solutions for Real time problemsAbhishek Gupta
 
Detailed guide to the Apache Spark Framework
Detailed guide to the Apache Spark FrameworkDetailed guide to the Apache Spark Framework
Detailed guide to the Apache Spark FrameworkAegis Software Canada
 
Apache Spark in Scientific Applications
Apache Spark in Scientific ApplicationsApache Spark in Scientific Applications
Apache Spark in Scientific ApplicationsDr. Mirko Kämpf
 
Apache Spark in Scientific Applciations
Apache Spark in Scientific ApplciationsApache Spark in Scientific Applciations
Apache Spark in Scientific ApplciationsDr. Mirko Kämpf
 
Spark introduction and architecture
Spark introduction and architectureSpark introduction and architecture
Spark introduction and architectureSohil Jain
 
Simplify IT: Oracle SuperCluster
Simplify IT: Oracle SuperCluster Simplify IT: Oracle SuperCluster
Simplify IT: Oracle SuperCluster Fran Navarro
 
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09Chris Purrington
 
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)Spark Summit
 
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Databricks
 
Unit II Real Time Data Processing tools.pptx
Unit II Real Time Data Processing tools.pptxUnit II Real Time Data Processing tools.pptx
Unit II Real Time Data Processing tools.pptxRahul Borate
 
Konsolidace Oracle DB na systémech s procesory M7
Konsolidace Oracle DB na systémech s procesory M7Konsolidace Oracle DB na systémech s procesory M7
Konsolidace Oracle DB na systémech s procesory M7MarketingArrowECS_CZ
 
Performance tuning your Hadoop/Spark clusters to use cloud storage
Performance tuning your Hadoop/Spark clusters to use cloud storagePerformance tuning your Hadoop/Spark clusters to use cloud storage
Performance tuning your Hadoop/Spark clusters to use cloud storageDataWorks Summit
 
Exadata x4 for_sap
Exadata x4 for_sapExadata x4 for_sap
Exadata x4 for_sapFran Navarro
 

Similar to Apache Ignite In-Memory Data Fabric for Fast Data and Analytics (20)

Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis MagdaApache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
Apache Spark and Apache Ignite: Where Fast Data Meets the IoT with Denis Magda
 
Microservices Architectures With Apache Ignite
Microservices Architectures With Apache IgniteMicroservices Architectures With Apache Ignite
Microservices Architectures With Apache Ignite
 
What is Apache spark
What is Apache sparkWhat is Apache spark
What is Apache spark
 
Streaming Solutions for Real time problems
Streaming Solutions for Real time problemsStreaming Solutions for Real time problems
Streaming Solutions for Real time problems
 
Detailed guide to the Apache Spark Framework
Detailed guide to the Apache Spark FrameworkDetailed guide to the Apache Spark Framework
Detailed guide to the Apache Spark Framework
 
Apache Spark in Scientific Applications
Apache Spark in Scientific ApplicationsApache Spark in Scientific Applications
Apache Spark in Scientific Applications
 
Apache Spark in Scientific Applciations
Apache Spark in Scientific ApplciationsApache Spark in Scientific Applciations
Apache Spark in Scientific Applciations
 
Spark introduction and architecture
Spark introduction and architectureSpark introduction and architecture
Spark introduction and architecture
 
Using Data Lakes
Using Data LakesUsing Data Lakes
Using Data Lakes
 
Why_Oracle_Hardware.ppt
Why_Oracle_Hardware.pptWhy_Oracle_Hardware.ppt
Why_Oracle_Hardware.ppt
 
Simplify IT: Oracle SuperCluster
Simplify IT: Oracle SuperCluster Simplify IT: Oracle SuperCluster
Simplify IT: Oracle SuperCluster
 
AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09AWS (Hadoop) Meetup 30.04.09
AWS (Hadoop) Meetup 30.04.09
 
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
 
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...
 
Unit II Real Time Data Processing tools.pptx
Unit II Real Time Data Processing tools.pptxUnit II Real Time Data Processing tools.pptx
Unit II Real Time Data Processing tools.pptx
 
Konsolidace Oracle DB na systémech s procesory M7
Konsolidace Oracle DB na systémech s procesory M7Konsolidace Oracle DB na systémech s procesory M7
Konsolidace Oracle DB na systémech s procesory M7
 
Oracle Cloud
Oracle CloudOracle Cloud
Oracle Cloud
 
Using Data Lakes
Using Data LakesUsing Data Lakes
Using Data Lakes
 
Performance tuning your Hadoop/Spark clusters to use cloud storage
Performance tuning your Hadoop/Spark clusters to use cloud storagePerformance tuning your Hadoop/Spark clusters to use cloud storage
Performance tuning your Hadoop/Spark clusters to use cloud storage
 
Exadata x4 for_sap
Exadata x4 for_sapExadata x4 for_sap
Exadata x4 for_sap
 

More from In-Memory Computing Summit

IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...
IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...
IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...
IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...
IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...
IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...
IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...In-Memory Computing Summit
 
IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...
IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...
IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...In-Memory Computing Summit
 
IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...
IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...
IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...In-Memory Computing Summit
 
IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...
IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...
IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...In-Memory Computing Summit
 
IMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X Platform
IMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X PlatformIMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X Platform
IMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X PlatformIn-Memory Computing Summit
 
IMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage Tier
IMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage TierIMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage Tier
IMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage TierIn-Memory Computing Summit
 
IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...
IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...
IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...
IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...
IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...
IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...
IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent Memory
IMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent MemoryIMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent Memory
IMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent MemoryIn-Memory Computing Summit
 
IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...
IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...
IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise Grade
IMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise GradeIMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise Grade
IMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise GradeIn-Memory Computing Summit
 
IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...
IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...
IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of Statelessness
IMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of StatelessnessIMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of Statelessness
IMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of StatelessnessIn-Memory Computing Summit
 
IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...
IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...
IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...
IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...
IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...
IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...
IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...In-Memory Computing Summit
 
IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...
IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...
IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...In-Memory Computing Summit
 

More from In-Memory Computing Summit (20)

IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...
IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...
IMC Summit 2016 Breakout - Per Minoborg - Work with Multiple Hot Terabytes in...
 
IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...
IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...
IMC Summit 2016 Breakout - Henning Andersen - Using Lock-free and Wait-free I...
 
IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...
IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...
IMC Summit 2016 Breakout - Nikita Shamgunov - Propelling IoT Innovation with ...
 
IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...
IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...
IMC Summit 2016 Innovation - Derek Nelson - PipelineDB: The Streaming-SQL Dat...
 
IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...
IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...
IMC Summit 2016 Innovation - Dennis Duckworth - Lambda-B-Gone: The In-memory ...
 
IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...
IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...
IMC Summit 2016 Innovation - Steve Wilkes - Tap Into Your Enterprise – Why Da...
 
IMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X Platform
IMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X PlatformIMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X Platform
IMC Summit 2016 Innovation - Girish Mutreja - Unveiling the X Platform
 
IMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage Tier
IMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage TierIMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage Tier
IMC Summit 2016 Breakout - Ken Gibson - The In-Place Working Storage Tier
 
IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...
IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...
IMC Summit 2016 Breakout - Brian Bulkowski - NVMe, Storage Class Memory and O...
 
IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...
IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...
IMC Summit 2016 Breakout - Yanping Wang - Non-volatile Generic Object Program...
 
IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...
IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...
IMC Summit 2016 Breakout - Andy Pavlo - What Non-Volatile Memory Means for th...
 
IMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent Memory
IMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent MemoryIMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent Memory
IMC Summit 2016 Breakout - Gordon Patrick - Developments in Persistent Memory
 
IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...
IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...
IMC Summit 2016 Breakout - Girish Kathalagiri - Decision Making with MLLIB, S...
 
IMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise Grade
IMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise GradeIMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise Grade
IMC Summit 2016 Breakout - Steve Wikes - Making IMC Enterprise Grade
 
IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...
IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...
IMC Summit 2016 Breakout - Noah Arliss - The Truth: How to Test Your Distribu...
 
IMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of Statelessness
IMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of StatelessnessIMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of Statelessness
IMC Summit 2016 Breakout - Aleksandar Seovic - The Illusion of Statelessness
 
IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...
IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...
IMC Summit 2016 Breakout - Girish Mutreja - Extreme Transaction Processing in...
 
IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...
IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...
IMC Summit 2016 Breakout - Greg Luck - How to Speed Up Your Application Using...
 
IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...
IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...
IMC Summit 2016 Breakout - Pandurang Naik - Demystifying In-Memory Data Grid,...
 
IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...
IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...
IMC Summit 2016 Breakout - William Bain - Implementing Extensible Data Struct...
 

Recently uploaded

SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphNeo4j
 
Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024BookNet Canada
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxnull - The Open Security Community
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsAndrey Dotsenko
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentationphoebematthew05
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptxLBM Solutions
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfjimielynbastida
 
Bluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfBluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfngoud9212
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...shyamraj55
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024BookNet Canada
 
APIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April  Automation LPDGAPIForce Zurich 5 April  Automation LPDG
APIForce Zurich 5 April Automation LPDGMarianaLemus7
 
Artificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning eraArtificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning eraDeakin University
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationRidwan Fadjar
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024Scott Keck-Warren
 

Recently uploaded (20)

SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge GraphSIEMENS: RAPUNZEL – A Tale About Knowledge Graph
SIEMENS: RAPUNZEL – A Tale About Knowledge Graph
 
Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
 
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptxMaking_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
Making_way_through_DLL_hollowing_inspite_of_CFG_by_Debjeet Banerjee.pptx
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
costume and set research powerpoint presentation
costume and set research powerpoint presentationcostume and set research powerpoint presentation
costume and set research powerpoint presentation
 
Key Features Of Token Development (1).pptx
Key  Features Of Token  Development (1).pptxKey  Features Of Token  Development (1).pptx
Key Features Of Token Development (1).pptx
 
Science&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdfScience&tech:THE INFORMATION AGE STS.pdf
Science&tech:THE INFORMATION AGE STS.pdf
 
Bluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdfBluetooth Controlled Car with Arduino.pdf
Bluetooth Controlled Car with Arduino.pdf
 
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
Automating Business Process via MuleSoft Composer | Bangalore MuleSoft Meetup...
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
New from BookNet Canada for 2024: BNC BiblioShare - Tech Forum 2024
 
APIForce Zurich 5 April Automation LPDG
APIForce Zurich 5 April  Automation LPDGAPIForce Zurich 5 April  Automation LPDG
APIForce Zurich 5 April Automation LPDG
 
Artificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning eraArtificial intelligence in the post-deep learning era
Artificial intelligence in the post-deep learning era
 
My Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 PresentationMy Hashitalk Indonesia April 2024 Presentation
My Hashitalk Indonesia April 2024 Presentation
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024SQL Database Design For Developers at php[tek] 2024
SQL Database Design For Developers at php[tek] 2024
 
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptxVulnerability_Management_GRC_by Sohang Sengupta.pptx
Vulnerability_Management_GRC_by Sohang Sengupta.pptx
 

Apache Ignite In-Memory Data Fabric for Fast Data and Analytics

  • 1. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. DMITRIY  SETRAKYAN   Founder,  PPMC   Apache  IgniteTM  (Incubating)  -­‐  In-­‐Memory  Data  Fabric   Fast  Data  Meets  Open  Source http://www.ignite.incubator.apache.org @apacheignite @dsetrakyan
  • 2. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. Agenda • About  In-­‐Memory  Computing   • Apache  Ignite (tm)  In-­‐Memory  Data  Fabric   • Advanced  Clustering   • Data  Grid   • Compute  Grid   • Service  Grid   • Ignite  For  Analytics   • Streaming  &  CEP   • Share  State  Across  Spark  Jobs   • In-­‐Memory  MapReduce   • Interactive  SQL   • DevOps:  Yarn  and  Mesos   • Q  &  A
  • 3. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. Apache  IgniteTM  In-­‐Memory  Data  Fabric:     Strategic  Approach  to  IMC • Supports Applications of various types and languages • Open Source – Apache 2.0 • Simple Java APIs • 1 JAR Dependency • High Performance & Scale • Automatic Fault Tolerance • Management/Monitoring • Runs on Commodity Hardware • Supports existing & 
 new data sources • No need to rip & replace
  • 4. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. In-­‐Memory  Data  Fabric:  More  Than  Data  Grid
  • 5. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • Automatic  Discovery   – Simple  Configuration   – AWS/EC2/S3   – Google  Compute  Engine  (NEW)   – Other  Clouds  with  JClouds  (NEW)   • Docker  Support   – Automatically  Build  and  Deploy Apache  Ignite:  Better  Cloud  Support
  • 6. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • JCache  (JSR  107)   – Basic  Cache  Operations   – ConcurrentMap  APIs   – Collocated  Processing  (EntryProcessor)   – Events  and  Metrics   – Pluggable  Persistence   • Ignite  Data  Grid   – ACID  Transactions   – SQL  Queries  (ANSI  99)   – In-­‐Memory  Indexes   – Automatic  RDBMS  Integration Data  Grid:  JCache  (JSR  107)
  • 7. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. Data  Grid:  Partitioned  Cache
  • 8. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. Data  Grid:  Replicated  Cache
  • 9. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • Unlimited  Vertical  Scale   • Avoid  Java  Garbage  Collection  Pauses   • Small  On-­‐Heap  Footprint   • Large  Off-­‐Heap  Footprint   • Off-­‐Heap  Indexes   • Full  RAM  Utilization   • Simple  Configuration Data  Grid:  Off-­‐Heap  Memory
  • 10. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • ANSI-­‐99  SQL   • Always  Consistent   • Fault  Tolerant   • In-­‐Memory  Indexes  (On-­‐Heap  and  Off-­‐Heap)   • Automatic  Group  By,  Aggregations,  Sorting   • Cross-­‐Cache  Joins,  Unions,  etc.   • Ad-­‐Hoc  SQL  Support Data  Grid:  Ad-­‐Hoc  SQL  (ANSI  99)
  • 11. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. SQL  Cross-­‐Cache  JOIN  Example
  • 12. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. SQL  Cross-­‐Cache  GROUP  BY  Example
  • 13. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • Direct  API  for  MapReduce   • Direct  API  for  ForkJoin   • Zero  Deployment   • Cron-­‐like  Task  Scheduling   • State  Checkpoints   • Load  Balancing   • Automatic  Failover   • Full  Cluster  Management   • Pluggable  SPI  Design In-­‐Memory  Compute  Grid
  • 14. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • Streaming  Data  Never  Ends   • Branching  Pipelines   • Pluggable  Routing   • Sliding  Windows  for  
 CEP/Continuous  Query   • SQL  Queries  (ANSI  99)   • Query  Across  Sliding  Windows   • Real  Time  Analysis In-­‐Memory  Streaming  and  CEP
  • 15. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • Singletons  on  the  Cluster   – Cluster  Singleton   – Node  Singleton   – Key  Singleton   • Distribute  any  Data  Structure   – Available  Anywhere  on  the  Grid   – Access  Anywhere  via  Proxies   • Guaranteed  Availability   – Auto  Redeployment  in  Case  of  Failures In-­‐Memory  Service  Grid
  • 16. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. Apache  Ignite  for  BI  and  Analytics
  • 17. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • Automatic  Resource  Management   • Easy  Data  Center  Installation   • Easy  Data  Center  Configuration   • On-­‐Demand  Elasticity DevOps:  Integration  with  Yarn  and  Mesos
  • 18. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • IgniteRDD     – Share  RDD  across  jobs  on  the  host   – Share  RDD  across  jobs  in  the  application   – Share  RDD  globally   • Faster  SQL   – In-­‐Memory  Indexes   – SQL  on  top  of  Shared  RDD Share  RDDs  Across  Spark  Jobs  
  • 19. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. • Ignite  In-­‐Memory  File  System  (IGFS)   – Hadoop-­‐compliant   – Easy  to  Install   – On-­‐Heap  and  Off-­‐Heap   – Caching  Layer  for  HDFS   – Write-­‐through  and  Read-­‐through  HDFS   – Performance  Boost Ignite  In-­‐Memory  File  System
  • 20. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. Ignite  In-­‐Memory  Map  Reduce • In-­‐Memory  Native   Performance   • Zero  Code  Change   • Use  existing  MR  code   • Use  existing  Hive  queries   • No  Name  Node   • No  Network  Noise   • In-­‐Process  Data  Colocation   • Eager  Push  Scheduling
  • 21. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. Interactive  SQL  with  Apache  Zeppelin
  • 22. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. GridGain  Enterprise  &  Apache  Ignite  Comparison  Chart GridGain  Enterprise  Subscriptions  include  the   following  during  the  term  of  the  subscription:   > Right  to  use  GridGain  Enterprise  Edition   > Bug  fixes,  patches,  updates  and  upgrades   > 9x5  or  24x7  Support     > Ability  to  procure  Training  and  Consulting   Services  from  GridGain   > Confidence  and  protection,  not  provided   under  Open  Source  licensing,  that  only  a   commercial  vendor  can  provide,  such  as   indemnification Features Apache Ignite Enterprise Edition In-Memory Data Grid ✓ CHECK ✓ In-Memory Compute Grid ✓ ✓ Real-Time Streaming & CEP ✓ ✓ Hadoop Acceleration ✓ ✓ Management & Monitoring GUI ✓ Portable Objects ✓ .Net and C++ APIs ✓ Enterprise-grade Security ✓ Network Segmentation Protection ✓ Local Restartable Store ✓ Rolling Production Updates ✓ Datacenter Replication ✓ 9x5 and 24x7 Support ✓ Long Term Support & Patches ✓
  • 23. Apache®,  Apache  Ignite,  Ignite®,  and  the  Apache  Ignite  logo  are  either  registered  trademarks  or  trademarks  of  the  Apache  Software  Foundation  in  the  United  States  and/or  other  countries. ANY  QUESTIONS? Thank  you  for  joining  us.  Follow  the  conversation. http://www.ignite.incubator.apache.org @apacheignite @dsetrakyan