SlideShare a Scribd company logo
1 of 50
Download to read offline
@fdouetteau#lambdataiku
Lambda Architecture
@fdouetteau
Dataiku, www.dataiku.com
Florian Douetteau, CEO Dataiku
@fdouetteau#lambdataiku
Topics For Today
•WHAT is a lambda architecture
•Examples - Principle
•Motivation – Hard Points
•HOW to you build a lambda architecture ?
•Components per component
@fdouetteau#lambdataiku
Lambda
EVENTS PROCES
S
STATE
SER
VE
@fdouetteau#lambdataiku
ƛ : SOME USE CASES
• Online Advertising
• Keep track of number of displays / clicks per
positions / campaigns
• Recommender Systems
• Keep track of production displays / views / click /
buy
• Statistical Time Line
• Keep Track of number of tweets per hashtag /
hour
@fdouetteau#lambdataiku
SQL WAY
EVENTS PROCES
S
STATE
SER
VE
USER1 ITEM1 VIEW
USER1 ITEM2 BUY
INSERT OR UPDATE VIEWS
SET pageviews = pageviews + 1
WHERE user=USER1 …
RDBMSSQL
@fdouetteau#lambdataiku
Functional Programming
Append Only
EVENTS PROCES
S
STATE
(APPEND ONLY)
SER
VE
newstate = Fagg (oldstate, Fstore(events))
result= F (state, lastevents, scope)
@fdouetteau#lambdataiku
E.g. counting twitter hashtags
EVENTS PROCES
S
STATE
SER
VE
Fmap ( ) = { (#tag, time) -> count }
FReduce( hashmap, hashmap ) = fuse count in
maps
FDisplay( hashmap, events ) = Freduce(hashmap,
Fmap(events))
TWEET COUNTS
(2014-02-31 13, #foo) -> 3
(2014-02-31 13, #foo) -> 3
(2014-02-31 13, #foo) -> 3
(2014-02-31 13, #foo) -> 3
NEW TWEETS TABLE
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
@fdouetteau#lambdataiku
E.g. counting twitter hashtags in “SQL”
SER
VE
TWEET COUNTS TABLE
(2014-02-31 13, #foo) -> 8
(2014-02-31 13, #foo2) -> 3
(2014-02-31 13, #foo3) -> 3
(2014-02-31 13, #foo4) -> 1
NEW TWEETS TABLE
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
2014-02-31 13:14 #foo bar
PARTIAL TWEET COUNT TABLE
(2014-02-31 13, #foo) -> 1
(2014-02-31 14, #foo) -> 3
(2014-02-31 14, #foo) -> 3
(2014-02-31 14, #foo) ->
NEW TWEET COUNT TABLE
(2014-02-31 13, #foo) -> 9
(2014-02-31 13, #foo) -> 3
(2014-02-31 13, #foo) -> 3
(2014-02-31 13, #foo) -> 3
CREATE … AS SELECT time, tag, COUNT(*) GROUP BY TIME, TAG
CREATE AS
SELEC time, tag, SUM(counts)
FROM ( oldtable … UNION
partialtable)
GROUP BY TIME, TAG
SELECT, time, tag, SUM(c) FROM (
SELECT time, tag, c FROM
oldtable WHERE tag = …
UNION
SELECT time, tag, c FROM partialtable
WHERE tag=…
)
INSERT VALUES …
RENAME TABLE …
EXECUTE EACH 5 MINUTES
EXECUTE
EACH HOUR
@fdouetteau#lambdataiku
ƛ : PRINCIPLE
EVENTS
BATCH
VIEW
REAL-TIME
RESULT
BATCH
PROC
REAL-
TIME
PROC
FEDER
ATION
@fdouetteau#lambdataiku
Backtype Story
Capture events and logs from twitter
25TB binary data
100 Billlios records
400 QPS Average
Scale 1 -> 150 on peak
Take off with a team of 3 engineers with seed funding in 2008
Christopher Golda
Michael Montano
Nathan Marz
Acquired by Twitter ( power twitter trends …) in 2011
Cascalog
Storm
ElephantDB
@fdouetteau#lambdataiku
TWITTER HASHTAGS
2014-02-31 13:14
#foo bar
BATCH
VIEW
REAL-TIME
RESULT
BATCH
PROC
REAL-
TIME
PROC
FEDER
ATION
2014-02-31 13:14
#foo bar
2014-02-31 13:14
#foo bar
(2014-02-31 13, #foo) -> 3
(2014-02-31 13, #foo) -> 3
COMPUTE EVERY 5 MINUTES
HASHTAG COUNTS FOR
THE LAST 5 MINUTES
(IN MEMORY)
COMPUTE
EVERY HOUR HASHTAG
COUNT FOR THE LAST HOUR
(ON DISK)
@fdouetteau#lambdataiku
RECOMMENDER SYSTEM
BATCH
VIEW
REAL-TIME
RESULT
BATCH
PROC
REAL-
TIME
PROC
FEDER
ATION
USER1 ITEM1 VIEW
USER1 ITEM2 BUY
USER1 ITEM1 VIEW
USER1 ITEM1 VIEW
ITEM-ITEM SIMILARITY
MATRIX
USER -> [ ITEM1, … ITEMn]
RECOMMENDATION
@fdouetteau#lambdataiku
THREE KEY
DRIVERS FOR
LAMBDA ARCH
@fdouetteau#lambdataiku
DRIVER 1: Support Smooth Evolution
2014-02-31 13:14
#foo bar
BATCH
VIEW
REAL-TIME
RESULT
BATCH
PROC
REAL-
TIME
PROC
FEDER
ATION
2014-02-31 13:14
#foo bar
2014-02-31 13:14
#foo bar
(2014-02-31 13:14,, #foo) -> 3
(2014-02-31 13:14, #foo) -> 3
(1) RECOMPUTE NEW
VERSION
ON BATCH WHILE KEEPING
THE
OLD ONE (2014-02-31 13, #foo) -> 3
(2) THEN UPDATE THE ONLINE
VERSION
@fdouetteau#lambdataiku
DRIVER 2: Real-Time System Offline
2014-02-31 13:14
#foo bar
BATCH
VIEW
REAL-TIME
RESULT
BATCH
PROC
REAL-
TIME
PROC
FEDER
ATION
2014-02-31 13:14
#foo bar
2014-02-31 13:14
#foo bar
(2014-02-31 13, #foo) -> 3
(2014-02-31 13, #foo) -> 3
COMPUTE
EVERY HOUR HASHTAG
COUNT FOR THE LAST HOUR
(ON DISK)
FALLBACK TO
PARTIAL RESULT
WHEN REAL-TIME
GRID IS OFFLINE
@fdouetteau#lambdataiku
DRIVER 3 : CAN‟T RECOMPUTE
BATCH
VIEW
REAL-TIME
RESULT
BATCH
PROC
REAL-
TIME
PROC
FEDER
ATION
USER1 ITEM1 VIEW
USER1 ITEM2 BUY
USER1 ITEM1 VIEW
USER1 ITEM1 VIEW
ITEM-ITEM SIMILARITY
MATRIX
USER -> [ ITEM1, … ITEMn]
RECOMMENDATION
@fdouetteau#lambdataiku
PAIN POINTS
@fdouetteau#lambdataiku
PAINT POINT 1 : EXACTLY ONCE
2014-02-31 13:14 #foo bar
2014-02-31 13:15 toto
2014-02-31 13:15 tutu
2014-02-31 13:16 #two
…
…
Retry
@fdouetteau#lambdataiku
PAINT POINT 2 : DYNAMIC SCALE
START AT 100 events per second
HOW TO GROW TO 10k events
per second without rebuilding everything ?
@fdouetteau#lambdataiku
PAINT POINT 3 : SCHEMA CHANGE
BATCH
VIEW
REAL-TIME
RESULT
BATCH
PROC
REAL-
TIME
PROC
FEDER
ATION
EVENTS V1
EVENTS V2
MIX OF VERSION 1
AND VERSION 2 !!!!
@fdouetteau#lambdataiku
TOOLS
AND
FRAMEWOR
K
@fdouetteau#lambdataiku
Lambda Architecture Building Blocks
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
@fdouetteau#lambdataiku
Components
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
STORM
HDFS MapRed HBASE
MEMCACHE MONGODB
WEBAPPRABBITMQ
FLUME
@fdouetteau#lambdataiku
Components
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
@fdouetteau#lambdataiku
Message Queues
Kestrel
(Single Node)
Kafka
(Linkedin, Distributed)
RabbitMQ
ActiveMQ
Micro-Batch, State in Processor
Persitent
Event, State in Queue, Rich Routing
@fdouetteau#lambdataiku
TOPOLOGY : SINGLE PIPE
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
STORM
STORM
@fdouetteau#lambdataiku
Storm
Developped in 2008-2009 at BackType
First open source release in 2011
BOLT
TUPLE
TUPLE
TUPLE
SPOUT
TUPLE
@fdouetteau#lambdataiku
Topologies
SPOUT
SPOUT
BOLT
BOLT
BOLT
BOLT
This one
likely to write in
a State
This one tooo
@fdouetteau#lambdataiku
public class HashTagParseBolt extends BaseRichBolt {
OutputCollector _collector
public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
_collector = collector;
}
public void execute(Tuple tweet) {
for(String hashtag : tweet.getString(„hashtags‟)) {
_collector.emit(new Values(tweet.time, hashtag));
}
}
public void deplaceOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields(„time‟, „hashtag‟));
}
}
Parse Tweet Bolt
@fdouetteau#lambdataiku
Topologies
Tweet
Spout
Parse
Tweet
Bolt
Count
HashT
ags
Bolt
Store
in Flat
File
Tweet
@fdouetteau#lambdataiku
BALANCING
CLUSTER
NODE
PROCESS
EXECUTOR
TASK
TASK
ONE PER
TOPOLOGY
PER SPOUT OR
BOLT
EXECUTOR
TASK
NODE
PROCESS
REBALANCE
@fdouetteau#lambdataiku
(Optional) RELIABILITY
• When emitting a tuple from
an existing tuple, trace origin
• “Ack” or “Fail” each tuple
• If a tuple or dependent tuples not
fully “acked” REPLAY
@YourTwitterHandle#YourSessionHashtag
public class HashTagParseBolt extends BaseRichBolt {
OutputCollector _collector
public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
_collector = collector;
}
public void execute(Tuple tweet) {
for(String hashtag : tweet.getString(„hashtags‟)) {
_collector.emit(tweet, new Values(tweet.time, hashtag));
}
_collector.ack(tweet);
}
public void deplaceOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields(„time‟, „hashtag‟));
}
}
Reliable Parse Tweet
@fdouetteau#lambdataiku
TOPOLOGY 2 : SHARE RT
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
TRIDENT
TRIDENT
TRIDENT
@fdouetteau#lambdataiku
TRIDENT
• Higher Level Operations
• Use Storm as an RPC Framework
• State “Management”
@fdouetteau#lambdataiku
From Schema To Storm Topology
@fdouetteau#lambdataiku
How is exactly-once implemented?
{user=paul, item=car, event=imp}
{user=pierre, item=car, event=imp}
{user=1, item=car, event=imp}
{user=paul, item=car, event=imp}
{user=pierre, item=car, event=imp}
{user=pierre, item=car, event=imp}
…
txid=1
txid=3
txid=2
@fdouetteau#lambdataiku
Exactly-Once in state
paul -> { car: 2, txid=2 }
pierre -> {car : 5, txid=3 }
paul -> { car: 3, txid=3 }
pierre -> {car : 5, txid=3 }
{user=paul, item=car, event=imp}
{user=pierre, item=car, event=imp}
{user=pierre, item=car, event=imp}
txid=3
Keep Track of
last transaction in
state
Transaction
does not apply
to newer state parts
@fdouetteau#lambdataiku
TOPOLOGY 1 : SHARE STATE
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
USE A SINGLE NOSQL
SERVICE FOR ALL USE
CASES
@fdouetteau#lambdataiku
REDIS VARIANT
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
REDIS
REDIS REDIS
REDIS
ALSO USE THE NOSQL
AS A MESSAGE QUEUE
@fdouetteau#lambdataiku
TOPOLOGY 3 : SHARED PROCESSING
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
@fdouetteau#lambdataiku
SummingBird
Single Scala specification than can run in
“Batch” on “Real-Time” Mode
Single Scala
Code
Run on
Storm Topology
Run on
Cascading
(Batch)
@fdouetteau#lambdataiku
object TweetHashTagCount {
implicit val timeOf: TimeExtractor[Status] = TimeExtractor(_.getCreatedAt.getTime)
implicit val batcher = Batcher.ofHours(1)
….
def hashTagCount[P <: Platform[P]](
source: Producer[P, Status],
store: P#Store[String, Long]) =
source
.filter(_.getText != null)
.flatMap { tweet: Status => tweet.getHashTags.map(_ -> 1L) }
.sumByKey(store)
}
Tweet SummingBird
@fdouetteau#lambdataiku
Putting this together
SUMMING BIRD
CASCADING
MAP REDUCE
TRIDENT STORM
RT STORES
(NoSQL .. etc..
BATCH STORES
(HDFS …)
Distributed
Batch Computation
SQL Level
Abstraction
Distributed
RT Computation
COMMON
ABSTRACTION
STATE
RPC
@fdouetteau#lambdataiku
WEB-SCALE VARIANT
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
Insert in
Mongo
Insert in
Mongo
Mongo
MapReduc
e
Mongo
Collection
Mongo Mongo
Aggregation
@fdouetteau#lambdataiku
HADOOPY VARIANT
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
INSERT
IN HBASE
HIVE
/MAP
REDUCE HBASE
HBASE HBASE
Queries
@fdouetteau#lambdataiku
Integrated Publish
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
@fdouetteau#lambdataiku
SploutSQL
@fdouetteau#lambdataiku
SPARK VARIANT
Message
Queue
Batch State
Batch
Pump
Real-Time
State
Real-Time
Views
Service
Federated
View
Batch
Views
Service
Batch
Processin
g
Real-Time
Processing
SPARK
STREAMING
HDFS SPARK
MEMORY
@fdouetteau#lambdataiku
QUESTIONS
QUESTION
QUEUE
florian.douetteau@
dataiku.com
MAIL
MY
MEMORY ANSWER
AUDIENCE
HAPPY
ANSWER
TO
MAIL
Batch
Processin
g
Real-Time
Processing

More Related Content

What's hot

Hadoop Graph Processing with Apache Giraph
Hadoop Graph Processing with Apache GiraphHadoop Graph Processing with Apache Giraph
Hadoop Graph Processing with Apache GiraphDataWorks Summit
 
Developing high frequency indicators using real time tick data on apache supe...
Developing high frequency indicators using real time tick data on apache supe...Developing high frequency indicators using real time tick data on apache supe...
Developing high frequency indicators using real time tick data on apache supe...Zekeriya Besiroglu
 
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in ProductionTugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in ProductionCodemotion
 
Hw09 Hadoop Applications At Yahoo!
Hw09   Hadoop Applications At Yahoo!Hw09   Hadoop Applications At Yahoo!
Hw09 Hadoop Applications At Yahoo!Cloudera, Inc.
 
Graph Processing with Apache TinkerPop
Graph Processing with Apache TinkerPopGraph Processing with Apache TinkerPop
Graph Processing with Apache TinkerPopJason Plurad
 
Serverless Data Architecture at scale on Google Cloud Platform
Serverless Data Architecture at scale on Google Cloud PlatformServerless Data Architecture at scale on Google Cloud Platform
Serverless Data Architecture at scale on Google Cloud PlatformMeetupDataScienceRoma
 
The Netflix data platform: Now and in the future by Kurt Brown
The Netflix data platform: Now and in the future by Kurt BrownThe Netflix data platform: Now and in the future by Kurt Brown
The Netflix data platform: Now and in the future by Kurt BrownData Con LA
 
Community-Driven Graphs with JanusGraph
Community-Driven Graphs with JanusGraphCommunity-Driven Graphs with JanusGraph
Community-Driven Graphs with JanusGraphJason Plurad
 
Big Data Pipeline and Analytics Platform
Big Data Pipeline and Analytics PlatformBig Data Pipeline and Analytics Platform
Big Data Pipeline and Analytics PlatformSudhir Tonse
 
Pulsar: Real-time Analytics at Scale with Kafka, Kylin and Druid
Pulsar: Real-time Analytics at Scale with Kafka, Kylin and DruidPulsar: Real-time Analytics at Scale with Kafka, Kylin and Druid
Pulsar: Real-time Analytics at Scale with Kafka, Kylin and DruidTony Ng
 
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Amazon Web Services
 
Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...
Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...
Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...Data Con LA
 
Realtime streaming architecture in INFINARIO
Realtime streaming architecture in INFINARIORealtime streaming architecture in INFINARIO
Realtime streaming architecture in INFINARIOJozo Kovac
 
An Architect's guide to real time big data systems
An Architect's guide to real time big data systemsAn Architect's guide to real time big data systems
An Architect's guide to real time big data systemsRaja SP
 
IBM Open by Design: Graph Technology
IBM Open by Design: Graph TechnologyIBM Open by Design: Graph Technology
IBM Open by Design: Graph TechnologyJason Plurad
 
JanusGraph, Jupyter Meetup NYC
JanusGraph, Jupyter Meetup NYCJanusGraph, Jupyter Meetup NYC
JanusGraph, Jupyter Meetup NYCJason Plurad
 
Graph Computing with JanusGraph
Graph Computing with JanusGraphGraph Computing with JanusGraph
Graph Computing with JanusGraphJason Plurad
 
Atlanta hadoop users group july 2013
Atlanta hadoop users group july 2013Atlanta hadoop users group july 2013
Atlanta hadoop users group july 2013Christopher Curtin
 

What's hot (20)

Hadoop Graph Processing with Apache Giraph
Hadoop Graph Processing with Apache GiraphHadoop Graph Processing with Apache Giraph
Hadoop Graph Processing with Apache Giraph
 
Developing high frequency indicators using real time tick data on apache supe...
Developing high frequency indicators using real time tick data on apache supe...Developing high frequency indicators using real time tick data on apache supe...
Developing high frequency indicators using real time tick data on apache supe...
 
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in ProductionTugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production
 
Hw09 Hadoop Applications At Yahoo!
Hw09   Hadoop Applications At Yahoo!Hw09   Hadoop Applications At Yahoo!
Hw09 Hadoop Applications At Yahoo!
 
Graph Processing with Apache TinkerPop
Graph Processing with Apache TinkerPopGraph Processing with Apache TinkerPop
Graph Processing with Apache TinkerPop
 
Serverless Data Architecture at scale on Google Cloud Platform
Serverless Data Architecture at scale on Google Cloud PlatformServerless Data Architecture at scale on Google Cloud Platform
Serverless Data Architecture at scale on Google Cloud Platform
 
Data Science on Google Cloud Platform
Data Science on Google Cloud PlatformData Science on Google Cloud Platform
Data Science on Google Cloud Platform
 
The Netflix data platform: Now and in the future by Kurt Brown
The Netflix data platform: Now and in the future by Kurt BrownThe Netflix data platform: Now and in the future by Kurt Brown
The Netflix data platform: Now and in the future by Kurt Brown
 
Community-Driven Graphs with JanusGraph
Community-Driven Graphs with JanusGraphCommunity-Driven Graphs with JanusGraph
Community-Driven Graphs with JanusGraph
 
Big Data Pipeline and Analytics Platform
Big Data Pipeline and Analytics PlatformBig Data Pipeline and Analytics Platform
Big Data Pipeline and Analytics Platform
 
Pulsar: Real-time Analytics at Scale with Kafka, Kylin and Druid
Pulsar: Real-time Analytics at Scale with Kafka, Kylin and DruidPulsar: Real-time Analytics at Scale with Kafka, Kylin and Druid
Pulsar: Real-time Analytics at Scale with Kafka, Kylin and Druid
 
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
Running Fast, Interactive Queries on Petabyte Datasets using Presto - AWS Jul...
 
Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...
Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...
Big Data Day LA 2015 - Applications of the Apriori Algorithm on Open Data by ...
 
Realtime streaming architecture in INFINARIO
Realtime streaming architecture in INFINARIORealtime streaming architecture in INFINARIO
Realtime streaming architecture in INFINARIO
 
OSCON 2015
OSCON 2015OSCON 2015
OSCON 2015
 
An Architect's guide to real time big data systems
An Architect's guide to real time big data systemsAn Architect's guide to real time big data systems
An Architect's guide to real time big data systems
 
IBM Open by Design: Graph Technology
IBM Open by Design: Graph TechnologyIBM Open by Design: Graph Technology
IBM Open by Design: Graph Technology
 
JanusGraph, Jupyter Meetup NYC
JanusGraph, Jupyter Meetup NYCJanusGraph, Jupyter Meetup NYC
JanusGraph, Jupyter Meetup NYC
 
Graph Computing with JanusGraph
Graph Computing with JanusGraphGraph Computing with JanusGraph
Graph Computing with JanusGraph
 
Atlanta hadoop users group july 2013
Atlanta hadoop users group july 2013Atlanta hadoop users group july 2013
Atlanta hadoop users group july 2013
 

Viewers also liked

Development of Twitter Application #8 - Streaming API
Development of Twitter Application #8 - Streaming APIDevelopment of Twitter Application #8 - Streaming API
Development of Twitter Application #8 - Streaming APIMyungjin Lee
 
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...Dataiku
 
The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016 The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016 Dataiku
 
Batch Indexing & Near Real Time, keeping things fast
Batch Indexing & Near Real Time, keeping things fastBatch Indexing & Near Real Time, keeping things fast
Batch Indexing & Near Real Time, keeping things fastMarc Sturlese
 
Big data: analyzing large data sets
Big data: analyzing large data setsBig data: analyzing large data sets
Big data: analyzing large data setsR A Akerkar
 
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku
 
Development of Twitter Application #1 - Overview
Development of Twitter Application #1 - OverviewDevelopment of Twitter Application #1 - Overview
Development of Twitter Application #1 - OverviewMyungjin Lee
 
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...Dataiku
 
Dataiku productive application to production - pap is may 2015
Dataiku    productive application to production - pap is may 2015 Dataiku    productive application to production - pap is may 2015
Dataiku productive application to production - pap is may 2015 Dataiku
 
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) Dataiku
 
How to Build Successful Data Team - Dataiku ?
How to Build Successful Data Team -  Dataiku ? How to Build Successful Data Team -  Dataiku ?
How to Build Successful Data Team - Dataiku ? Dataiku
 
The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
 
The paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECHThe paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECHDataiku
 
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team Dataiku
 
OWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - DataikuOWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - DataikuDataiku
 
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...sparktc
 
The US Healthcare Industry
The US Healthcare IndustryThe US Healthcare Industry
The US Healthcare IndustryDataiku
 
Data Flow Diagram and Sequence Diagram
Data Flow Diagram and Sequence DiagramData Flow Diagram and Sequence Diagram
Data Flow Diagram and Sequence DiagramHamna Shahzad
 

Viewers also liked (20)

Development of Twitter Application #8 - Streaming API
Development of Twitter Application #8 - Streaming APIDevelopment of Twitter Application #8 - Streaming API
Development of Twitter Application #8 - Streaming API
 
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...Dataiku  -  data driven nyc  - april  2016 - the  solitude of the data team m...
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
 
The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016 The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016
 
Batch Indexing & Near Real Time, keeping things fast
Batch Indexing & Near Real Time, keeping things fastBatch Indexing & Near Real Time, keeping things fast
Batch Indexing & Near Real Time, keeping things fast
 
Twitter and Medical Education: Information and Inspiration
Twitter and Medical Education: Information and InspirationTwitter and Medical Education: Information and Inspiration
Twitter and Medical Education: Information and Inspiration
 
Big data: analyzing large data sets
Big data: analyzing large data setsBig data: analyzing large data sets
Big data: analyzing large data sets
 
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex ChallengeDataiku at SF DataMining Meetup - Kaggle Yandex Challenge
Dataiku at SF DataMining Meetup - Kaggle Yandex Challenge
 
Development of Twitter Application #1 - Overview
Development of Twitter Application #1 - OverviewDevelopment of Twitter Application #1 - Overview
Development of Twitter Application #1 - Overview
 
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...Dataiku   hadoop summit - semi-supervised learning with hadoop for understand...
Dataiku hadoop summit - semi-supervised learning with hadoop for understand...
 
Dataiku productive application to production - pap is may 2015
Dataiku    productive application to production - pap is may 2015 Dataiku    productive application to production - pap is may 2015
Dataiku productive application to production - pap is may 2015
 
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku) How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
 
How to Build Successful Data Team - Dataiku ?
How to Build Successful Data Team -  Dataiku ? How to Build Successful Data Team -  Dataiku ?
How to Build Successful Data Team - Dataiku ?
 
The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products
 
The paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECHThe paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECH
 
Sequence Diagram
Sequence DiagramSequence Diagram
Sequence Diagram
 
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team Dataiku -  Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
 
OWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - DataikuOWF 2014 - Take back control of your Web tracking - Dataiku
OWF 2014 - Take back control of your Web tracking - Dataiku
 
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...
 
The US Healthcare Industry
The US Healthcare IndustryThe US Healthcare Industry
The US Healthcare Industry
 
Data Flow Diagram and Sequence Diagram
Data Flow Diagram and Sequence DiagramData Flow Diagram and Sequence Diagram
Data Flow Diagram and Sequence Diagram
 

Similar to Lambda Architecture Overview

Apache StreamPipes – Flexible Industrial IoT Management
Apache StreamPipes – Flexible Industrial IoT ManagementApache StreamPipes – Flexible Industrial IoT Management
Apache StreamPipes – Flexible Industrial IoT ManagementApache StreamPipes
 
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...Soroosh Khodami
 
Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...
Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...
Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...Karthik Ramasamy
 
PGConf APAC 2018 - Monitoring PostgreSQL at Scale
PGConf APAC 2018 - Monitoring PostgreSQL at ScalePGConf APAC 2018 - Monitoring PostgreSQL at Scale
PGConf APAC 2018 - Monitoring PostgreSQL at ScalePGConf APAC
 
KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...
KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...
KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...Chris Fregly
 
Azure tales: a real world CQRS and ES Deep Dive - Andrea Saltarello
Azure tales: a real world CQRS and ES Deep Dive - Andrea SaltarelloAzure tales: a real world CQRS and ES Deep Dive - Andrea Saltarello
Azure tales: a real world CQRS and ES Deep Dive - Andrea SaltarelloITCamp
 
Integrate Solr with real-time stream processing applications
Integrate Solr with real-time stream processing applicationsIntegrate Solr with real-time stream processing applications
Integrate Solr with real-time stream processing applicationsthelabdude
 
Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...
Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...
Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...StreamNative
 
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureMLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureData Science Milan
 
Introduction to Stream Processing
Introduction to Stream ProcessingIntroduction to Stream Processing
Introduction to Stream ProcessingGuido Schmutz
 
Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...
Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...
Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...Landon Robinson
 
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...Big Data Spain
 
Terraform AWS modules and some best practices - September 2019
Terraform AWS modules and some best practices - September 2019Terraform AWS modules and some best practices - September 2019
Terraform AWS modules and some best practices - September 2019Anton Babenko
 
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDKBigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDKnagachika t
 
Apache Spark Listeners: A Crash Course in Fast, Easy Monitoring
Apache Spark Listeners: A Crash Course in Fast, Easy MonitoringApache Spark Listeners: A Crash Course in Fast, Easy Monitoring
Apache Spark Listeners: A Crash Course in Fast, Easy MonitoringDatabricks
 
O'Reilly Media Webcast: Building Real-Time Data Pipelines
O'Reilly Media Webcast: Building Real-Time Data PipelinesO'Reilly Media Webcast: Building Real-Time Data Pipelines
O'Reilly Media Webcast: Building Real-Time Data PipelinesSingleStore
 
SplunkLive! Presentation - Data Onboarding with Splunk
SplunkLive! Presentation - Data Onboarding with SplunkSplunkLive! Presentation - Data Onboarding with Splunk
SplunkLive! Presentation - Data Onboarding with SplunkSplunk
 
Intro To Spring Python
Intro To Spring PythonIntro To Spring Python
Intro To Spring Pythongturnquist
 
Architecting next generation big data platform
Architecting next generation big data platformArchitecting next generation big data platform
Architecting next generation big data platformhadooparchbook
 

Similar to Lambda Architecture Overview (20)

Apache StreamPipes – Flexible Industrial IoT Management
Apache StreamPipes – Flexible Industrial IoT ManagementApache StreamPipes – Flexible Industrial IoT Management
Apache StreamPipes – Flexible Industrial IoT Management
 
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...
Why And When Should We Consider Stream Processing In Our Solutions Teqnation ...
 
Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...
Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...
Twitter's Real Time Stack - Processing Billions of Events Using Distributed L...
 
PGConf APAC 2018 - Monitoring PostgreSQL at Scale
PGConf APAC 2018 - Monitoring PostgreSQL at ScalePGConf APAC 2018 - Monitoring PostgreSQL at Scale
PGConf APAC 2018 - Monitoring PostgreSQL at Scale
 
KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...
KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...
KubeFlow + GPU + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTo...
 
Azure tales: a real world CQRS and ES Deep Dive - Andrea Saltarello
Azure tales: a real world CQRS and ES Deep Dive - Andrea SaltarelloAzure tales: a real world CQRS and ES Deep Dive - Andrea Saltarello
Azure tales: a real world CQRS and ES Deep Dive - Andrea Saltarello
 
Integrate Solr with real-time stream processing applications
Integrate Solr with real-time stream processing applicationsIntegrate Solr with real-time stream processing applications
Integrate Solr with real-time stream processing applications
 
Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...
Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...
Cross the Streams! Creating Streaming Data Pipelines with Apache Flink + Apac...
 
WebRTC 101
WebRTC 101WebRTC 101
WebRTC 101
 
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureMLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
 
Introduction to Stream Processing
Introduction to Stream ProcessingIntroduction to Stream Processing
Introduction to Stream Processing
 
Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...
Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...
Spark + AI Summit 2019: Apache Spark Listeners: A Crash Course in Fast, Easy ...
 
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...
Shortening the Feedback Loop: How Spotify’s Big Data Ecosystem has evolved to...
 
Terraform AWS modules and some best practices - September 2019
Terraform AWS modules and some best practices - September 2019Terraform AWS modules and some best practices - September 2019
Terraform AWS modules and some best practices - September 2019
 
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDKBigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
BigQuery case study in Groovenauts & Dive into the DataflowJavaSDK
 
Apache Spark Listeners: A Crash Course in Fast, Easy Monitoring
Apache Spark Listeners: A Crash Course in Fast, Easy MonitoringApache Spark Listeners: A Crash Course in Fast, Easy Monitoring
Apache Spark Listeners: A Crash Course in Fast, Easy Monitoring
 
O'Reilly Media Webcast: Building Real-Time Data Pipelines
O'Reilly Media Webcast: Building Real-Time Data PipelinesO'Reilly Media Webcast: Building Real-Time Data Pipelines
O'Reilly Media Webcast: Building Real-Time Data Pipelines
 
SplunkLive! Presentation - Data Onboarding with Splunk
SplunkLive! Presentation - Data Onboarding with SplunkSplunkLive! Presentation - Data Onboarding with Splunk
SplunkLive! Presentation - Data Onboarding with Splunk
 
Intro To Spring Python
Intro To Spring PythonIntro To Spring Python
Intro To Spring Python
 
Architecting next generation big data platform
Architecting next generation big data platformArchitecting next generation big data platform
Architecting next generation big data platform
 

More from Dataiku

Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...Dataiku
 
Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...Dataiku
 
Applied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelApplied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelDataiku
 
Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Dataiku
 
04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku 04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku Dataiku
 
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015Dataiku
 
Dataiku big data paris - the rise of the hadoop ecosystem
Dataiku   big data paris - the rise of the hadoop ecosystemDataiku   big data paris - the rise of the hadoop ecosystem
Dataiku big data paris - the rise of the hadoop ecosystemDataiku
 
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014Dataiku
 
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages JaunesBreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages JaunesDataiku
 
Dataiku - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku  - for Data Geek Paris@Criteo - Close the Data CircleDataiku  - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku - for Data Geek Paris@Criteo - Close the Data CircleDataiku
 
Dataiku - google cloud platform roadshow - october 2013
Dataiku  - google cloud platform roadshow - october 2013Dataiku  - google cloud platform roadshow - october 2013
Dataiku - google cloud platform roadshow - october 2013Dataiku
 
Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013
Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013
Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013Dataiku
 
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16thDataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16thDataiku
 
Data Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from thData Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from thDataiku
 
Dataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine LearningDataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine LearningDataiku
 
Online Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for FunOnline Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for FunDataiku
 
Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch Dataiku
 

More from Dataiku (17)

Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
Applied Data Science Part 3: Getting dirty; data preparation and feature crea...
 
Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...Applied Data Science Course Part 2: the data science workflow and basic model...
Applied Data Science Course Part 2: the data science workflow and basic model...
 
Applied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML modelApplied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML model
 
Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem Before Kaggle : from a business goal to a Machine Learning problem
Before Kaggle : from a business goal to a Machine Learning problem
 
04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku 04Juin2015_Symposium_Présentation_Coyote_Dataiku
04Juin2015_Symposium_Présentation_Coyote_Dataiku
 
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
Coyote & Dataiku - Séminaire Dixit GFII du 13 04-2015
 
Dataiku big data paris - the rise of the hadoop ecosystem
Dataiku   big data paris - the rise of the hadoop ecosystemDataiku   big data paris - the rise of the hadoop ecosystem
Dataiku big data paris - the rise of the hadoop ecosystem
 
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014Dataiku  - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
 
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages JaunesBreizhJUG - Janvier 2014 - Big Data -  Dataiku - Pages Jaunes
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
 
Dataiku - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku  - for Data Geek Paris@Criteo - Close the Data CircleDataiku  - for Data Geek Paris@Criteo - Close the Data Circle
Dataiku - for Data Geek Paris@Criteo - Close the Data Circle
 
Dataiku - google cloud platform roadshow - october 2013
Dataiku  - google cloud platform roadshow - october 2013Dataiku  - google cloud platform roadshow - october 2013
Dataiku - google cloud platform roadshow - october 2013
 
Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013
Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013
Dataiku, Pitch at Data-Driven NYC, New York City, September 17th 2013
 
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16thDataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
 
Data Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from thData Disruption for Insurance - Perspective from th
Data Disruption for Insurance - Perspective from th
 
Dataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine LearningDataiku - From Big Data To Machine Learning
Dataiku - From Big Data To Machine Learning
 
Online Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for FunOnline Games Analytics - Data Science for Fun
Online Games Analytics - Data Science for Fun
 
Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch Dataiku - Paris JUG 2013 - Hadoop is a batch
Dataiku - Paris JUG 2013 - Hadoop is a batch
 

Recently uploaded

Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
 
Digital Tools & AI in Career Development
Digital Tools & AI in Career DevelopmentDigital Tools & AI in Career Development
Digital Tools & AI in Career DevelopmentMahmoud Rabie
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkPixlogix Infotech
 
Dublin_mulesoft_meetup_API_specifications.pptx
Dublin_mulesoft_meetup_API_specifications.pptxDublin_mulesoft_meetup_API_specifications.pptx
Dublin_mulesoft_meetup_API_specifications.pptxKunal Gupta
 
Transport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MITransport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MIRomil Mishra
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesBernd Ruecker
 
WomenInAutomation2024: AI and Automation for eveyone
WomenInAutomation2024: AI and Automation for eveyoneWomenInAutomation2024: AI and Automation for eveyone
WomenInAutomation2024: AI and Automation for eveyoneUiPathCommunity
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observabilityitnewsafrica
 
Deliver Latency Free Customer Experience
Deliver Latency Free Customer ExperienceDeliver Latency Free Customer Experience
Deliver Latency Free Customer ExperienceOpsTree solutions
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Kaya Weers
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024TopCSSGallery
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFMichael Gough
 
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...BookNet Canada
 
Arti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdfArti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdfwill854175
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesManik S Magar
 
Software Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey HightowerSoftware Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey HightowerAnchore
 

Recently uploaded (20)

Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
 
Digital Tools & AI in Career Development
Digital Tools & AI in Career DevelopmentDigital Tools & AI in Career Development
Digital Tools & AI in Career Development
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App FrameworkReact Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App Framework
 
Dublin_mulesoft_meetup_API_specifications.pptx
Dublin_mulesoft_meetup_API_specifications.pptxDublin_mulesoft_meetup_API_specifications.pptx
Dublin_mulesoft_meetup_API_specifications.pptx
 
Transport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MITransport in Open Pits______SM_MI10415MI
Transport in Open Pits______SM_MI10415MI
 
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
 
WomenInAutomation2024: AI and Automation for eveyone
WomenInAutomation2024: AI and Automation for eveyoneWomenInAutomation2024: AI and Automation for eveyone
WomenInAutomation2024: AI and Automation for eveyone
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS:  6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS:  6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
 
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security ObservabilityGlenn Lazarus- Why Your Observability Strategy Needs Security Observability
Glenn Lazarus- Why Your Observability Strategy Needs Security Observability
 
Deliver Latency Free Customer Experience
Deliver Latency Free Customer ExperienceDeliver Latency Free Customer Experience
Deliver Latency Free Customer Experience
 
Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)Design pattern talk by Kaya Weers - 2024 (v2)
Design pattern talk by Kaya Weers - 2024 (v2)
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024
 
All These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDFAll These Sophisticated Attacks, Can We Really Detect Them - PDF
All These Sophisticated Attacks, Can We Really Detect Them - PDF
 
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
Transcript: Green paths: Learning from publishers’ sustainability journeys - ...
 
Arti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdfArti Languages Pre Seed Pitchdeck 2024.pdf
Arti Languages Pre Seed Pitchdeck 2024.pdf
 
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotesMuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
 
Software Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey HightowerSoftware Security in the Real World w/Kelsey Hightower
Software Security in the Real World w/Kelsey Hightower
 

Lambda Architecture Overview