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2014 © Trivadis
BASEL BERN BRUGG LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN
2014 © Trivadis
Big Data und Fast Data - Lambda Architektur und deren Umsetzung
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
1
Guido Schmutz
DOAG Konferenz 2014
19.11.2014 – 16:00 Raum Oslo
2014 © Trivadis
Guido Schmutz
• Working for Trivadis for more than 17 years
• Oracle ACE Director for Fusion Middleware and SOA • Co-Author of different books • Consultant, Trainer Software Architect for Java, Oracle, SOA and
Big Data / Fast Data • Member of Trivadis Architecture Board • Technology Manager @ Trivadis
• More than 25 years of software development experience
• Contact: guido.schmutz@trivadis.com • Blog: http://guidoschmutz.wordpress.com • Twitter: gschmutz
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
Trivadis is a market leader in IT consulting, system integration, solution engineering and the provision of IT services focusing on and technologies in Switzerland, Germany and Austria.
We offer our services in the following strategic business fields: Trivadis Services takes over the interacting operation of your IT systems.
Our company
O P E R A T I O N
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
AGENDA
1. Big Data and Fast Data, what is it?
2. Architecting (Big) Data Systems
3. The Lambda Architecture
4. Use Case and the Implementation
5. Summary and Outlook
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
4
2014 © Trivadis
Big Data Definition (4 Vs)
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
+ Time to action ? – Big Data + Event Processing = Fast Data
Characteristics of Big Data: Its Volume, Velocity and Variety in combination
5
2014 © Trivadis
The world is changing …
The model of Generating/Consuming Data has changed ….
Old Model: few companies are generating data, all others are consuming data
New Model: all of us are generating data, and all of us are consuming data
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
Internet Of Things – Sensors are/will be everywhere
There are more devices tapping into the internet than people on earth
How do we prepare our systems/architecture for the future?
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Source: Cisco Source: The Economist 8
2014 © Trivadis
The world is changing … new data stores
Problem of traditional (R)DBMS approach: § Complex object graph § Schema evolution § Semi-structured data § Scaling
Polyglot persistence § Using multiple data storage technologies (RDMBS + NoSQL + NewSQL + In-
Memory)
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
9
ORDER
ADDRESS
CUSTOMER
ORDER_LINES
OrderID: 1001Order Date: 15.9.2012
Line Items
Customer
First Name: PeterLast Name: Sample
Billing AddressStreet: Somestreet 10City: SomewherePostal Code: 55901
Name
Ipod Touch
Monster Beat
Apple Mouse
Quantity
1
2
1
Price
220.95
190.00
69.90
2014 © Trivadis
The world is changing … New platforms evolving (i.e. Hadoop Ecosystem)
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
Data as an Asset – Store everything?
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Data is just too valuable to delete! We must store anything!
Nonsense! Just store the data
you know you need today!
It depends … Big Data technologies allow to store the raw information from new and existing data sources so that you can later use it to create new data-driven products, which you haven’t thought about today!
11
2014 © Trivadis
AGENDA
1. Big Data and Fast Data, what is it?
2. Architecting (Big) Data Systems
3. The Lambda Architecture
4. Use Case and the Implementation
5. Summary and Outlook
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
What is a data system?
• A (data) system that manages the storage and querying of data with a lifetime measured in years encompassing every version of the application to ever exist, every hardware failure and every human mistake ever made.
• A data system answers questions based on information that was acquired in the past
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
How do we build (data) systems today – Today’s Architectures
Source of Truth is mutable!
• CRUD pattern
What is the problem with this?
• Lack of Human Fault Tolerance
• Potential loss of information/data
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Mutable Database
Application (Query)
RDBMS NoSQL
NewSQL
Mobile Web RIA
Rich Client
Source of Truth
Source of Truth
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2014 © Trivadis
Lack of Human Fault Tolerance
Bugs will be deployed to production over the lifetime of a data system
Operational mistakes will be made
Humans are part of the overall system • Just like hard disks, CPUs, memory, software • design for human error like you design for any other fault
Examples of human error • Deploy a bug that increments counters by two instead of by one • Accidentally delete data from database • Accidental DOS on important internal service
Worst two consequences: data loss or data corruption
As long as an error doesn‘t lose or corrupt good data, you can fix what went wrong
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
Lack of Human Fault Tolerance – Immutability vs. Mutability
The U and D in CRUD
A mutable system updates the current state of the world
Mutable systems inherently lack human fault-tolerance
Easy to corrupt or lose data
An immutable system captures historical records of events
Each event happens at a particular time and is always true
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Immutability restricts the range of errors causing data loss/data corruption
Vastly more human fault-tolerant
Conclusion: Your source of truth should always be immutable
16
2014 © Trivadis
A different kind of architecture with immutable source of truth
Instead of using our traditional approach … why not building data systems like this
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
HDFS NoSQL
NewSQL RDBMS
View on Data
Mobile Web RIA
Rich Client
Source of Truth
Immutable data
View on Data
Application (Query)
Source of Truth
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2014 © Trivadis
How to create the views on the Immutable data?
On the fly ?
Materialized, i.e. Pre-computed ?
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Immutable data View
Immutable data
Pre- Computed
Views
Query
Query
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2014 © Trivadis
(Big) Data Processing
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Immutable data
Pre-Computed
Views Query ? ? Incoming
Data
How to compute the materialized views ?
How to compute queries from the views ?
19
2014 © Trivadis
Today Big Data Processing means Batch Processing …
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
HDFS
Data Store optimized for appending large
results
Queries
Stream 1
Stream 2
Event
Hadoop cluster (Map/Reduce)
Hadoop Distributed File System
20
2014 © Trivadis
Big Data Processing - Batch
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
1.2.13 Add iPAD 64GB 10.3.13 Add Sony RX-100 11..3.13 Add Canon GX-10 11.3.13 Remove Sony RX-100 12.3.13 Add Nikon S-100 14.4.13 Add BoseQC-15 15.4.13 Add MacBook Pro 15 20.4.13 Remove Canon GX10
iPAD 64GB Nikon S-100 BoseQC-15 MacBook Pro 15
4 derive derive
Favorite Product List Changes Current Favorite
Product List Current Product Count
Raw information => data Information => derived
21
2014 © Trivadis
Big Data Processing – Batch
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
§ Using only batch processing, leaves you always with a portion of non-processed data.
Fully processed data Last full batch period
Time forbatch job
time
now non-processed data
time
now
batch-processed data
But we are not done yet …
22
2014 © Trivadis
Big Data Processing - Adding Real-Time
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Immutable data
Batch Views
Query
? Data Stream
Realtime Views
Incoming Data
How to compute queries from the views ? How to compute real-time views
23
2014 © Trivadis
Big Data Processing - Adding Real-Time
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
1.2.13 Add iPAD 64GB 10.3.13 Add Sony RX-100 11..3.13 Add Canon GX-10 11.3.13 Remove Sony RX-100 12.3.13 Add Nikon S-100 14.4.13 Add BoseQC-15 15.4.13 Add MacBook Pro 15 20.4.13 Remove Canon GX10 Now Add Canon Scanner
iPAD 64GB Nikon S-100 BoseQC-15 MacBook Pro 15
5
compute
Favorite Product List Changes Current Favorite Product List
Current Product Count
Now Canon Scanner compute Add Canon Scanner
Stream of Favorite Product List Changes
Immutable data
Views
Data Stream
Query
incoming
24
2014 © Trivadis
Big Data Processing - Batch & Real Time
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
time
Fully processed data Last full batch period
now
Time forbatch job
batch processingworked fine here
(e.g. Hadoop)
real time processingworks here
blended view for end user
Adapted from Ted Dunning (March 2012): http://www.youtube.com/watch?v=7PcmbI5aC20
25
2014 © Trivadis
AGENDA
1. Big Data and Fast Data, what is it?
2. Architecting (Big) Data Systems
3. The Lambda Architecture
4. The Use Case and the Implementation
5. Summary and Outlook
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
Lambda Architecture
Lambda => Query = function(all data)
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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Immutable data
Batch View
Query
Data Stream
Realtime View
Incoming Data
Serving Layer
Speed Layer
Batch Layer
A
B C D
E F
G
2014 © Trivadis
Lambda Architecture
A. All data is sent to both the batch and speed layer
B. Master data set is an immutable, append-only set of data
C. Batch layer pre-computes query functions from scratch, result is called Batch Views. Batch layer constantly re-computes the batch views.
D. Batch views are indexed and stored in a scalable database to get particular values very quickly. Swaps in new batch views when they are available
E. Speed layer compensates for the high latency of updates to the Batch Views
F. Uses fast incremental algorithms and read/write databases to produce real-time views
G. Queries are resolved by getting results from both batch and real-time views
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
Lambda Architecture
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Stores the immutable constantly growing dataset Computes arbitrary views from this dataset using BigData technologies (can take hours) Can be always recreated Computes the views from the constant stream of data it receives Needed to compensate for the high latency of the batch layer Incremental model and views are transient Responsible for indexing and exposing the pre-computed batch views so that they can be queried Exposes the incremented real-time views Merges the batch and the real-time views into a consistent result
Serving Layer
Batch Layer
Speed Layer
29
2014 © Trivadis
Lambda Architecture
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Adapted from: Marz, N. & Warren, J. (2013) Big Data. Manning.
30
Distribution Layer
Speed Layer
Precompute Views
Vis
ualiz
atio
n
Batch Layer
Precomputed information All data
Incremented information Process stream
Batch recompute
Realtime increment
Serving Layer
batch view
batch view
real time view
real time view
Dat
a Se
rvic
e (M
erge
)
Sensor Layer
Incoming Data
social
mobile
IoT
…
2014 © Trivadis
AGENDA
1. Big Data and Fast Data, what is it?
2. Architecting (Big) Data Systems
3. The Lambda Architecture
4. Use Case and the Implementation
5. Summary and Outlook
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
31
2014 © Trivadis
Project Definition
• Build a platform for analyzing Twitter communications in retrospective and in real-time
• Scalability and ability for future data fusion with other information is a must
• Provide a Web-based access to the analytical information
• Invest into new, innovative and not widely-proven technology • PoC environment, a pre-invest for future systems
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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2014 © Trivadis
"profile_banner_url":"https:\/\/pbs.twimg.com\/profile_banners\/15032594\/1371570460", "profile_link_color":"2FC2EF", "profile_sidebar_border_color":"FFFFFF", "profile_sidebar_fill_color":"252429", "profile_text_color":"666666", "profile_use_background_image":true, "default_profile":false, "default_profile_image":false, "following":null, "follow_request_sent":null, "notifications":null}, "geo":{ "type":"Point","coordinates":[43.28261499,-2.96464655]}, "coordinates":{"type":"Point","coordinates":[-2.96464655,43.28261499]}, "place":{"id":"cd43ea85d651af92", "url":"https:\/\/api.twitter.com\/1.1\/geo\/id\/cd43ea85d651af92.json", "place_type":"city", "name":"Bilbao", "full_name":"Bilbao, Vizcaya", "country_code":"ES", "country":"Espa\u00f1a", "bounding_box":{"type":"Polygon","coordinates":[[[-2.9860102,43.2136542], [-2.9860102,43.2901452],[-2.8803248,43.2901452],[-2.8803248,43.2136542]]]}, "attributes":{}}, "contributors": null, "retweet_count":0, "favorite_count":0, "entities":{"hashtags":[{"text":"quelosepash","indices":[58,70]}], "symbols":[], "urls":[], "user_mentions":[]}, "favorited":false, "retweeted":false, "filter_level":"medium", "lang":"es“ }
Anatomy of a tweet
33
{ "created_at":"Sun Aug 18 14:29:11 +0000 2013", "id":369103686938546176, "id_str":"369103686938546176", "text":"Baloncesto preparaci\u00f3n Eslovenia, Rajoy derrota a Merkel. #quelosepash", "source":"\u003ca href=\"http:\/\/twitter.com\/download\/iphone\" rel=\"nofollow\” \u003eTwitter for iPhone\u003c\/a\u003e", "truncated":false, "in_reply_to_status_id":null, "in_reply_to_status_id_str":null, "in_reply_to_user_id":null, "in_reply_to_user_id_str":null, "in_reply_to_screen_name":null, "user":{ "id":15032594, "id_str":"15032594", "name":"Juan Carlos Romo\u2122", "screen_name":"jcsromo", "location":"Sopuerta, Vizcaya", "url":null, "description":"Portugalujo, saturado de todo, de baloncesto no. Twitter personal.", "protected":false, "followers_count":1331, "friends_count":1326, "listed_count":31, "created_at":"Fri Jun 06 21:21:22 +0000 2008", "favourites_count":255, "utc_offset":7200, "time_zone":"Madrid", "geo_enabled":true, "verified":false, "statuses_count":22787, "lang":"es", "contributors_enabled":false, "is_translator":false, … "profile_image_url_https":"https:\/\/si0.twimg.com\/profile_images\/2649762203\ be4973d9eb457a45077897879c47c8b7_normal.jpeg",
Time Space Content Social Technic
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
2014 © Trivadis
Views on Tweets in four dimensions
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
34
when ⇐ where+what+who • Time series • Timelines
where ⇐ when+what+who • Geo maps • Density plots
what ⇐ when+where+who • Word clouds • Topic trends
who ⇐ when+where+what • Social network graphs • Activity graphs
Time
Space
Social
Content
Time
Space
Social
Content
Time
Space
Social
Content
Time
Space Social
Content
2014 © Trivadis
Accessing Twitter
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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Quelle Limitierungen Zugang Twitter’s Search API 3200 / user
5000 / keyword 180 Anfragen / 15 Minuten
gratis
Twitter’s Streaming API 1%-40% des Volumens gratis
DataSift keine 0.15 -0.20$ /
unit Gnip keine Auf Anfrage
2014 © Trivadis
Lambda Architecture
Open Source Frameworks for implementing a Lambda Architecture
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
36
Distribution Layer
Speed Layer
Precompute Views
Vis
ualiz
atio
n
Batch Layer
Precomputed information All data
Incremented information Process stream
Batch recompute
Realtime increment
Serving Layer
batch view
batch view
real time view
real time view
Dat
a Se
rvic
e (M
erge
)
Sensor Layer
Incoming Data
social
mobile
IoT
…
2014 © Trivadis
Lambda Architecture in Action
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
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Cloudera Distribution
• Distribution of Apache Hadoop: HDFS, MapReduce, Hive, Flume, Pig, Impala
Cloudera Impala
• distributed query execution engine that runs against data stored in HDFS and HBase
Apache Zookeeper
• Distributed, highly available coordination service. Provides primitives such as distributed locks
Apache Storm & Trident
• distributed, fault-tolerant realtime computation system
Apache Cassandra
• distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure
Twitter Horsebird Client (hbc)
• Twitter Java API over Streaming API
Spring Framework
• Popular Java Framework used to modularize part of the logic (sensor and serving layer)
Apache Kafka
• Simple messaging framework based on file system to distribute information to both batch and speed layer
Apache Avro
• Serialization system for efficient cross-language RPC and persistent data storage
JSON
• open standard format that uses human-readable text to transmit data objects consisting of attribute–value pairs.
2014 © Trivadis
Facts & Figures
Currently in total
• 2.7 TB Raw Data
• 1.1 TB Pre-Processed data in Impala
• 1 TB Solr indices for full text search
Cloudera 4.7.0 with Hadoop, Pig, Hive, Impala and Solr
Kafka 0.7, Storm 0.9, DataStax Enterprise Edition
14 active twitter feeds
• ~ 14 million tweets/day ( > 5 billion tweets/year)
• ~ 8 GB/day raw data, compressed (2 DVDs)
• 66 GB storage capacity / day (replication & views/results included)
Cluster of 10 nodes
• ~100 processors
• ~40 TB HD capacity in total; 46% used
• >500 GB RAM
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
38
2014 © Trivadis
Lambda Architecture with Oracle Product Stack
Possible implementation with Oracle Product stack
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
39
Distribution Layer
Speed Layer
Precompute Views
Vis
ualiz
atio
n
Batch Layer
Precomputed information All data
Incremented information Process stream
Batch recompute
Realtime increment
Serving Layer
batch view
batch view
real time view
real time view
Dat
a Se
rvic
e (M
erge
)
Sensor Layer
Incoming Data
social
mobile
IoT
…
Oracle NoSQL
Oracle RDBMS Oracle Coherence
Oracle BigData Appliance
Oracle NoSQL Oracle Coherence
Oracle Event Processing Oracle GoldenGate
Oracle Data Integrator
Oracle GoldenGate
Oracle Event Processing For Embedded Oracle Service Bus
Ora
cle
Web
Log
ic S
erve
r
OBI
EE
Ora
cle
Ende
ca
Ora
cle
Big
Dat
a C
onne
ctor
s
Oracle Coherence
WebLogic JMS
Ora
cle
BAM
2014 © Trivadis
AGENDA
1. Big Data and Fast Data, what is it?
2. Architecting (Big) Data Systems
3. The Lambda Architecture
4. Use Case and the Implementation
5. Summary and Outlook
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
40
2014 © Trivadis
Summary – The lambda architecture
• Can discard batch views and real-time views and recreate everything from scratch
• Mistakes corrected via re-computation
• Scalability through platform and distribution
• Data storage layer optimized independently from query resolution layer
• Still in a early stage …. But a very interesting idea! • Today a zoo of technologies are needed => Infrastructure group might not like
it • Better with so-called Hadoop distributions and Hadoop V2 (YARN)
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
41
2014 © Trivadis
Alternative Approaches – Motivation
Data Sharing in Map Reduce …
23/06/14 Obsidian
42
iter. 1 iter. 2 . . .
Input
HDFS"read
HDFS"write
HDFS"read
HDFS"write
Input
query 1
query 2
query 3
result 1
result 2
result 3
. . .
HDFS"read
2014 © Trivadis
iter. 1 iter. 2 . . .
Input
Alternative Approaches – Motivation
What we would like …
23/06/14 Obsidian
43
Distributed"memory
Input
query 1
query 2
query 3
. . .
one-time"processing
2014 © Trivadis
Alternatives – Apache Spark
23/06/14 Obsidian
44
Spark
Spark Streaming"
real-time
Spark SQL structured
GraphX graph
MLlib machine learning
…
YARN HDFS
HDFS Cassandra
2014 © Trivadis
Alternative Technologies – Apache Spark
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
45
Distribution Layer
Speed Layer
Precompute Views
Vis
ualiz
atio
n
Batch Layer
Precomputed information All data
Incremented information Process stream
Batch recompute
Realtime increment
Serving Layer
batch view
batch view
real time view
real time view
Dat
a Se
rvic
e (M
erge
)
Sensor Layer
Incoming Data
social
mobile
IoT
…
2014 © Trivadis
“Kappa Architecture”
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
Adapted from: Marz, N. & Warren, J. (2013) Big Data. Manning.
46
Distribution Layer
Speed Layer
Vis
ualiz
atio
n
Batch Layer
All data
Incremented information Process stream
Realtime increment
Serving Layer
real time view
real time view Dat
a Se
rvic
e
Sensor Layer
Incoming Data
social
mobile
IoT
…
Precomputed analytics analytic view
Dat
a Se
rvic
e
Batch Analytical analysis
Replay
2014 © Trivadis
Unified Log Processing Architecture
Stream processing allows for computing feeds off of other feeds
Derived feeds are no different than original feeds they are computed off
Single deployment of “Unified Log” but logically different feeds
August 2014 Einheitlicher Umgang mit Ereignisströmen - Unified Log Processing Architecture
47
Meter Readings Collector
Enrich / Transform
Aggregate by Minute
Raw MeterReadings
Meter with Customer
Meter by Customer by Minute
Customer Aggregate by Minute
Meter by Minute
Persist
Meter by Minute
Persist
Raw Meter Readings
2014 © Trivadis
Weitere Informationen...
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
48
INFOBOX – Lesen und Löschen • Folie wenn auf weitere Informationen
verwiesen werden soll, also z.B. Bücher, Websiten, etc.
2014 © Trivadis
BASEL BERN BRUGG LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN
Fragen und Antworten...
2013 © Trivadis
Guido Schmutz
Technology Manager
guido.schmutz@trivadis.com
19.11.2014 DOAG 2014 | Big Data und Fast Data - Lambda Architektur und deren Umsetzung
INFOBOX – Lesen und Löschen • Die Schlussfolie steht in zwei Varianten
zur Verfügung, einmal für die Kontaktdaten eines Referenten, einmal in der Variante für zwei oder mehr Referenten
• Name, Titel und Location jeweils untereinander in eine Zeile (Shift+Return)
• Die Idee ist das diese Folie als letzte Folie (auch für Fragen und Antworten) am Ende der Präsentation lange stehen bleibt, somit haben die Zuhörer die Möglichkeit die Kontaktdaten aufzuschreiben J
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