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Andreas Buckenhofer
Data Warehouse (Datenbanken II)
Daimler TSS GmbH
Overview of the lecture
Data Warehouse / DHBW / Fall 2016 / Page 2
1. Introduction to DWH, DWH Architectures - 20.10.2016
2. Data Modeling, OLAP 1 - 27.10.2016
3. OLAP 2, ETL - 03.11.2016
4. Metadata, DWH Projects, Advanced Topics - 10.11.2016
Daimler TSS GmbH
What you will learn today
Data Warehouse / DHBW / Fall 2016 / Page 3
• After the end of this lecture you will be able to
• Understand the necessity for metadata
• Understand lifecycle of DWH projects
• Advanced topics like Operational BI, DWH Appliances, Cloud BI
Daimler TSS GmbH
Metadata
Data Warehouse / DHBW / Fall 2016 / Page 4
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What is metadata?
Data Warehouse / DHBW / Fall 2016 / Page 5
Data
about
other data
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Types of metadata
Data Warehouse / DHBW / Fall 2016 / Page 6
• Business Metadata
• Definition of business vocabulary and relationships
• Definition of the value range
• Linkage to physical representation
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Types of metadata
Data Warehouse / DHBW / Fall 2016 / Page 7
• Report metadata
• Report definitions
• Data sources
• Column definitions
• Computations
• Logical and physical metadata of data model
• Table structure
• Definition of columns
• Relationships between tables and columns
• Dimension hierarchy
Daimler TSS GmbH
Types of metadata
Data Warehouse / DHBW / Fall 2016 / Page 8
• ETL metadata
• Job design
• Input-/output tables
• computations
• Mappings / transformations
• Operational meta data of ETL jobs
• Start time and duration
• Return code
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The Areas of Metadata
Data Warehouse / DHBW / Fall 2016 / Page 9
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The Areas of Metadata Connected
Data Warehouse / DHBW / Fall 2016 / Page 10
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Why a common metadata repository?
Data Warehouse / DHBW / Fall 2016 / Page 11
• Components of a data warehouse system are interconnected
• BI report user has to know
• the meaning, definitions of the shown measures, „KPIs“ (key performance
indicators)
• BI report designer has to know
• the table definitions
• the meaning of the column values
• ETL job designer has to know
• the table definitions
• the exact definition of the measures
• Database administrator has to know
• Which tables are used by ETL jobs, reports
Daimler TSS GmbH
Why a common metadata repository?
Data Warehouse / DHBW / Fall 2016 / Page 12
• Metadata driven ETL development
• Generate parts of ETL code
• increasing interest for Data Vault development projects
• Tools e.g. MID Innovator, Quipu, AnalytiX DS, Talend, Pentaho, Wherescape, and
others
• Common metadata repository ensures consistency across all components
• Many tools involved (DB, ETL, Frontend, …)
• Enables cross component metadata analysis
• Data Lineage
• Impact Analysis
Daimler TSS GmbH
Where does a field of data in this report come from?
Data Warehouse / DHBW / Fall 2016 / Page 13
• “Data lineage”
• Import & Browse Full BI Report Metadata
• Navigate through report attributes
• Visually navigate through data lineage across tools
• Combines
operational &
design viewpoint
Daimler TSS GmbH
What happens if I change this column?
Data Warehouse / DHBW / Fall 2016 / Page 14
• “Impact Analysis”
• Show complete change impact in graphical or list form
• Includes impact on reports in BI tools
• Visually navigate through impacted objects across tools
• Allows impact analysis on any object type
Daimler TSS GmbH
What does this field mean?
Data Warehouse / DHBW / Fall 2016 / Page 15
• Show relationships between business terms, data model entities, and technical and
report fields
• Requires cross-tool mapping of business terms
• Allows field meaning to be understood
• Allows business term relationships to be understood
Daimler TSS GmbH
What objects does this user own?
Data Warehouse / DHBW / Fall 2016 / Page 16
• Shows objects that user manages
• Shows stewardship relationships on business terms
• Shows user group associations
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What happened on the last job run?
Data Warehouse / DHBW / Fall 2016 / Page 17
• Navigation through complete job details
• Navigation of complete operational metadata
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Data Warehousing Projects
Data Warehouse / DHBW / Fall 2016 / Page 18
Daimler TSS GmbH
Data Warehouse
FrontendBackend
External data
sources
Internal data
sources
Top-Down vs Bottom-Up Approach
Data Warehouse / DHBW / Fall 2016 / Page 19
Staging Layer
(Input Layer)
Core Warehouse
Layer
(Storage Layer)
Reporting Layer
(Output Layer)
(Mart Layer)
Top Down (Inmon)
Bottom Up (Kimball)
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Top-Down vs Bottom-Up Approach
Data Warehouse / DHBW / Fall 2016 / Page 20
• Top-Down (Inmon)
• Design Core Warehouse Layer = integrated data model first
• Design data marts afterwards
• Bottom-Up (Kimball)
• Design data marts first
• Combine data Marts together
• DWH Bus architecture
• conformed dimensions to integrate different data marts / fact tables
Daimler TSS GmbH
Think big, start local
Data Warehouse / DHBW / Fall 2016 / Page 21
• Both approaches have their down-sides
• Top-Down takes enormous initial effort to build data model for Core Warehouse
Layer
• Bottom-Up is risky as central / integrated focus is lost
�Think big, start local
• Small iterations
• Waterfall approach taking 8-12 months or longer often fails or does not deliver in
time
• Always think about how to achieve flexible data integration in Core Warehouse Layer
• Data Marts can be dropped and reloaded from Data in the Core Warehouse Layer
• Dropping the Core Warehouse Layer not possible. Data loss (history)
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Why do DWH projects fail?
Data Warehouse / DHBW / Fall 2016 / Page 22
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Critical success factors for building a data warehouse
Data Warehouse / DHBW / Fall 2016 / Page 23
• Answer most important questions of participating business units
• Provide high-quality data
• Introduction in time
• Usage of modern technology
• Business orientation
• Easy to use
• Executive sponsor
• Patience – user acceptance evolves over time
• “Quick wins”
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DWH project phases
Data Warehouse / DHBW / Fall 2016 / Page 24
Daimler TSS GmbH
1. Project start
Data Warehouse / DHBW / Fall 2016 / Page 25
• Describe future situations and scenarios
• No technical details
• Develop multiple solutions and discuss their advantages and disadvantages
• Maybe start with a Proof of Concept (PoC)
• Estimate expected amount of data
Daimler TSS GmbH
2. Analysis/Technical Concept
Data Warehouse / DHBW / Fall 2016 / Page 26
• Information requirements analysis
• Logical modeling of data / information
• Transform knowledge from interviews into logical data schemas (represented by
Multidimensional or Star Schemas)
• Define transformation and unification rules (from data in operative systems to the
data warehouse)
• Identify Frontend requirements
• Define dimensions and measures
• Define reports (layout, prompts, output fields, filter, etc)
• Analyze operative data sources
• Very important task to get an understanding of source data, structures of the data,
data quality
Daimler TSS GmbH
2. Analysis/Technical Concept
Data Warehouse / DHBW / Fall 2016 / Page 27
• Data and Architectural Concept
• Important: Scalability
• Top-down design
• Transform abstract data model into the world of hardware (e.g. separate servers for
DB, ETL, Frontend), software, scalability, return times, etc.
• Ensure that data warehouse works together with other IT systems
• Tool Selection / Evaluation
• Choose tools: ETL tool, database, Frontend tools
• Has to know own tool-requirements very detailed
• Aspects: performance, availability and uniformness (interfaces, query languages,
etc.)
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3. System Design
Data Warehouse / DHBW / Fall 2016 / Page 28
• Transition from business view to technical view
• Transform requirements into actual solutions
• Describe how to implement the system
• Create catalog of actual technical and other requirements
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Possible DWH Analysis and Design work products
Data Warehouse / DHBW / Fall 2016 / Page 29
• How to document / identify requirements?
• Must be easy to understand from non-technical users during Analysis/Technical
concept phase
• Must provide sufficient information for System Design phase
• The following slides provide some example work products that are produced during
Analysis/Technical concept phase and may be refine during System Design phase
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Possible DWH Analysis and Design work products
Data Warehouse / DHBW / Fall 2016 / Page 30
Source: Lawrence Corr: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
BEAM = Business Event
Analysis and Modeling
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Possible DWH Analysis and Design work products
Data Warehouse / DHBW / Fall 2016 / Page 31
Source: Lawrence Corr: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
Daimler TSS GmbH
Possible DWH Analysis and Design work products
Data Warehouse / DHBW / Fall 2016 / Page 32
Source: Lawrence Corr: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
Daimler TSS GmbH
Possible DWH Analysis and Design work products
Data Warehouse / DHBW / Fall 2016 / Page 33
Source: Lawrence Corr: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
Daimler TSS GmbH
Possible DWH Analysis and Design work products
Data Warehouse / DHBW / Fall 2016 / Page 34
Source: Lawrence Corr: Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema
SK = Surrogate Key
BK = Business Key
CV = Current Value (SCD1)
GD = Granular Dimension
NA = Nonadditive fact
FA = Fully Additive fact
SA = Semiadditive fact
PS = Periodic Snapshot
RP = Role-playing
HV = One Historic value (SCD2)
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4. Implementation/Realization
Data Warehouse / DHBW / Fall 2016 / Page 35
• Data storage
• Install and configure database system
• Create physical data schema for all DWH layers
• Usage of database design tools
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4. Implementation/Realization
Data Warehouse / DHBW / Fall 2016 / Page 36
• Data Integration, ETL
• Transfer data from company-internal and -external sources into the data warehouse
• Connect data sources
• Eliminate mistakes / inconsistencies in data / possible error origins
• Transform data to unique coding
• Aggregate data
• Frontend
• Set up front ends, OLAP tools
• Connect to Data Mart Layer
• Create reports or other visualizations
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5. Test & Rollout
Data Warehouse / DHBW / Fall 2016 / Page 37
• Authorization concept
• Access control
• Not static
• Enable administration
• Production concept
• Concept for initial load and incremental/delta loads
• Concepts to keep the system running, even if amount of data and users increases
exponentially
• Define responsibilities
• Educate users
• Classes for different types of users
Daimler TSS GmbH
BICC: BI Center of Excellence
Data Warehouse / DHBW / Fall 2016 / Page 38
• Organizational teams that coordinate and standardize DWH activities within an (end
user) organization
• Define standards and create BI portfolio (e.g. which tools/products to use)
• Create DWH architecture and govern BI activities
• Establish processes for business and IT interaction DWH application development
• Monitor DWH/BI market for new trends
• Determine skills and experience of Business users
Daimler TSS GmbH
Exercises
Data Warehouse / DHBW / Fall 2016 / Page 39
• List 3 reasons why common metadata is important in the context of warehousing
• Define 3-5 criteria for the evaluation of an ETL tool
• How does a relational DBMS (like Oracle, DB2, MS SQL Server) meet these
requirements?
Daimler TSS GmbH
Exercises
Data Warehouse / DHBW / Fall 2016 / Page 40
• List 3 reasons why common metadata is important in the context of warehousing
• Components of a data warehouse system are interconnected (high complexity!)
• Metadata driven ETL development
• Common metadata repository ensures consistency across all components
• Enables cross component metadata analysis
Daimler TSS GmbH
Exercises
Data Warehouse / DHBW / Fall 2016 / Page 41
• Define 5 criteria for the evaluation of an ETL tool
• Supplier profile
• Support
• HW/SW requirements
• Costs
• Usability
• Reliability
• Performance and scalability
• Multi-tenant
• Interfaces
• Scheduling
Daimler TSS GmbH
Exercises
Data Warehouse / DHBW / Fall 2016 / Page 42
• How does a relational DBMS meet these requirements?
• RDBMS provide many of the functionalities but additional programming required
• RDBMS are often used for ETL/ELT by programming with SQL, PL/SQL, SQLT, etc
ETL Tool Manual ETL
Informatica, Talend, Oracle ODI, etc. SQL, PL/SQL, SQLT, etc.
Separate license No additional license
Workflow, error handling, and restart/recovery
functionality included
Workflow, error handling, and restart/recovery
functionality must be implemented manually
Impact analysis and where-used (lineage)
functionality available
Impact analysis and where-used (lineage)
functionality difficult
Faster development, easier maintenance Slower development, more difficult maintenance
Additional (Tool-) Know How required Know How often available
Daimler TSS GmbH
Frontend
Data Warehouse / DHBW / Fall 2016 / Page 43
Daimler TSS GmbH
Interface to the end user
Data Warehouse / DHBW / Fall 2016 / Page 44
• Reporting (Standard, ad-hoc)
• OLAP
• Dashboards, Scorecards
• Advanced Analytics / Data Mining / Text Mining
• Search & Discovery
Daimler TSS GmbH
Reporting (Standard, ad-hoc)
Data Warehouse / DHBW / Fall 2016 / Page 45
• Standard Reports
• Prepared static reports that can be executed at request by end users
• Are executed at the end of an ETL process and e.g. send by email to end users
• Normally based on fact tables and its dimensions
• Reports are often lists similar to Excel-Sheets but can also contain graphics (e.g. line
charts)
• Ad-hoc Reports
• End users create their own reports („Self service“)
Daimler TSS GmbH
OLAP
Data Warehouse / DHBW / Fall 2016 / Page 46
• ROLAP / MOLAP Client Frontend
• Prepared cubes (multidimensional or relational fact tables)
• User can perform interactive analysis of data
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Dashboards, Scorecards
Data Warehouse / DHBW / Fall 2016 / Page 47
• „Progress reports“
• Provide an overall view of KPIs (Key Performance Indicators)
• Combination of several elements from Reporting and/or OLAP (e.g. line charts) into an
overall view (like a „cockpit“)
• Dashboard is more focused on operational goals
• High-level overview what is happening
• Scorecard is more focused on strategic goals
• Plan a strategy and identify why something happens
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Advanced Analytics / Data Mining / Text Mining
Data Warehouse / DHBW / Fall 2016 / Page 48
• See Mr. Bollinger‘s lecture
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Search & Discovery
Data Warehouse / DHBW / Fall 2016 / Page 49
• Not just numerical data
• Analysis of new data types gets more and more important
• Text
• GPS coordinates
• Pictures
• Videos
• Data can be available in RDBMS (e.g. text modules/indexes available), Hadoop or SQL
DBs
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Many graphical elements to use in reports
Data Warehouse / DHBW / Fall 2016 / Page 50
Daimler TSS GmbH
Many graphical elements to use in reports
Data Warehouse / DHBW / Fall 2016 / Page 51
Source: https://github.com/d3/d3/wiki/Gallery
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Many graphical elements … chamber of horror
Data Warehouse / DHBW / Fall 2016 / Page 52
Source: Hichert / Faisst, http://www.backup-page.hichert.com/
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Information Design
Data Warehouse / DHBW / Fall 2016 / Page 53
• Information design is the practice of presenting information in a way that fosters
efficient and effective understanding of it.
(source: Wikipedia, https://en.wikipedia.org/wiki/Information_design )
• Some authors are well known for their criticism of many graphical representations -
they provide rules for good information design
• Edward Tufte
• Stephen Few
• Rolf Hichert
• Define standards, e.g.
• use always the same colors and with care (red = negative, green = positive)
• pie charts are rarely useful and should be avoided (better use bar chart or line chart)
• No 3D elements as these elements don’t enhance information but introduce clutter
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Table with integrated bar charts
Data Warehouse / DHBW / Fall 2016 / Page 54
Source: Hichert, http://www.hichert.com/de/resource/table-template-02/
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BI end user roles
Data Warehouse / DHBW / Fall 2016 / Page 55
• Consumers / BI Users
• use reports and dashboards to obtain information
• Power Users
• Use reports and dashboards to obtain information
• Create new reports and dashboards
• Data Scientists
• Statistical / mathematical geeks
• Analyze / explore data
• Need to analyze raw (non-cleansed, non-transformed) data
Daimler TSS GmbH
Visual data discovery and automatic data analysis
Data Warehouse / DHBW / Fall 2016 / Page 56
Source: Kohlhammer, J., Proff, D.U., Wiener, A.: Visual Business Analytics – Effektiver Zugang zu Daten und Informationen. dpunkt Verlag GmbH, Heidelberg (2013b)
Daimler TSS GmbH
Newer / Advanced Topics
Data Warehouse / DHBW / Fall 2016 / Page 57
Daimler TSS GmbH
Newer / advanced Topics
Data Warehouse / DHBW / Fall 2016 / Page 58
1. Operational Data Warehousing
2. Data Warehouse Appliances
3. Cloud BI
Daimler TSS GmbH
Operational Data Warehousing
Data Warehouse / DHBW / Fall 2016 / Page 59
• Classical“ Data Warehouses
• Information in the warehouse used to support strategic business decisions
• Kept separate from operational systems
• Load of new data only in larger intervals (mostly weekly or monthly)
• Shorter intervals not required by users
• Huge system resources of the ETL process made it necessary to run it in low
usage periods of the warehouse (like night or weekend)
• Near/Real Time Operational Data Warehousing
• Information in the warehouse used for tactical business decisions as well
• Low latency of information in data warehouse therefore needed
• Not only mathematical aggregations
Daimler TSS GmbH
Why operational Data Warehousing?
Data Warehouse / DHBW / Fall 2016 / Page 60
• With classical data warehouses users have to access two types of systems to get a
complete image of a customer (for instance for CRM applications or in call centers)
• the data warehouse to see what happened in the past
• the OLTP systems to get the most current information
• With an operational data warehouse
• all this information is in one system
• tighter integration with operational systems is easier
• for instance personalized offers � „closing the loop“
Daimler TSS GmbH
Examples of Operational Data Warehousing
Data Warehouse / DHBW / Fall 2016 / Page 61
New applications and data sources
Increase demand for an
Operational DWH, e.g.
• Industry 4.0 / Smart Factory
• Internet Of Things
• Internet of medical things
• Connected Cars
Source: Gluchowski: Analytische Informationssysteme, 5.Aufl., p. 277
Replace pen
& paper with
electronic
workflows
Decision support for
each end user and not
only management
Increasing demand to
publish same content
on different devices
Daimler TSS GmbH
SmartFactory Service Platform
Data Warehouse / DHBW / Fall 2016 / Page 62
Source: Gluchowski: Analytische Informationssysteme, 5.Aufl., p. 279
Workers
getting
alarms
Containing
and
displaying
complex
manuals,
e.g. during
repair
New data
source
sending lots
of data with
high speed
Real-Time
data
required for
automated
actions
Daimler TSS GmbH
Challenges for Operational Data Warehousing
Data Warehouse / DHBW / Fall 2016 / Page 63
• Real time ETL
• Triggered by business transactions in the operational systems
• Executed asynchronously
• Incremental real-time load
• Tighter integration of operational and data warehouse systems
• DWHs become „mission critical“
• Higher requirements on availability and performance
• Higher „transactional“ system load on data warehouse system
• Data warehouse DB has to deal with typical DWH system load and transactional
load
• Not just aggregations on high amount of data rows
Daimler TSS GmbH
Comparison classical DWH – Operational DWH
Data Warehouse / DHBW / Fall 2016 / Page 64
Classical DWH Operational DWH
Strategic
• Passive
• Historical trends
Tactical
• Execution of strategy
• Prediction
Batch
• E.g. daily batch
Real-Time
• Up-to-data view
Availability
• System can be down for maintenance and
longer response times for some reports are
accepted
Availability
• System becomes critical and must fulfill high
availability and performance requirements
Daimler TSS GmbH
Data Warehouse Appliances
Data Warehouse / DHBW / Fall 2016 / Page 65
• Setting up and configuring a data warehouse system is a complex task
• Hardware
• Servers
• Storage
• Network
• Connectivity to source systems
• Software
• Database management system
• ETL software
• Reporting and analytics software
• ...
• An optimal performance of the whole system is difficult to achieve
Daimler TSS GmbH
Data Warehouse Appliances
Data Warehouse / DHBW / Fall 2016 / Page 66
• Data Warehouse Appliances are
• Pre-configured and pre-tested hard- and software configurations developed for
running a data warehouse
• Optimized for data warehousing workload
• They are ready to be used after they are delivered to the customer
• Only suited for running OLAP
• In contrast RDBMS: one size fits all: RDBMS are suited for OLTP, OLAP and mixed
workloads
• Products, e.g. Teradata, IBM Netezza (IBM PureData System for Analytics), HP Vertica,
Exasol, Oracle Exadata, MS Analytic Platform System
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Simplicity (e.g. Netezza)
Data Warehouse / DHBW / Fall 2016 / Page 67
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Typical enhancements
Data Warehouse / DHBW / Fall 2016 / Page 68
• Move as many operations as possible to storage cell instead of moving data to the DB
server
• E.g. filter data already at storage cell and not at DB server
• Avoid transferring unnecessary data
• Column-oriented In-memory storage with high compression
• Many appliances are based on shared nothing architecture
• Each node is independent
• Each node has its own storage or memory
• Parallel processing simpler and faster as no overhead due to contention
Daimler TSS GmbH
Cloud BI
Data Warehouse / DHBW / Fall 2016 / Page 69
• BI applications (database, ETL tools, Frontend) are hosted in a public cloud, e.g.
• AWS (Amazon Web Services)
• Microsoft Azure
• …
• Many tools nowadays are available in the cloud first
• Vendors try to force customers to use clouds
• Or even available in the cloud only
• E.g. Microsoft Power BI
• Security concerns for sensitive data
• But new data source coming from Internet. Storing the data in a (public) cloud can
make sense, e.g.
• Connected Cars, IOT in general
Daimler TSS GmbH
Cloud BI architecture
Data Warehouse / DHBW / Fall 2016 / Page 70
Source: Lang: Business Intelligence erfolgreich umsetzen, 5.Aufl., p. 185
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Cloud BI architecture
Data Warehouse / DHBW / Fall 2016 / Page 71
• Analytics as a service
• Provide complete BI (Analytics) SW stack including
• data storage
• data integration (ETL)
• data visualization and/or data modeling (Frontend)
• Meta data management
• Data as a service
• Provide quality data for further usage
• Data marketplace
Daimler TSS GmbH
Cloud BI – Data Warehousing services
Data Warehouse / DHBW / Fall 2016 / Page 72
Source: http://db-engines.com/en/system/Amazon+Redshift%3BSnowflake
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Snowflake Architecture
Data Warehouse / DHBW / Fall 2016 / Page 73
Don‘t confuse
Snowflake product
with Snowflake
dimensional model
from session 2
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Snowflake Architecture
Data Warehouse / DHBW / Fall 2016 / Page 74
• Snowflake Storage
• Snowflake loads data into its internal optimized, compressed, columnar format
• Snowflake itself uses (!) Amazon Web Service’s S3 (Simple Storage Service) cloud
storage
• Query Processing
• Each virtual warehouse is an MPP (Multi Parallel Processing) compute cluster
composed of multiple compute nodes allocated by Snowflake from Amazon EC2
• Each virtual warehouse is an independent compute cluster that does not share
compute resources with other virtual warehouses
Daimler TSS GmbH
Snowflake Architecture
Data Warehouse / DHBW / Fall 2016 / Page 75
• Cloud Services
• Authentication and access control
• Infrastructure management
• Metadata management
• Query parsing and optimization
• Security
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Exercise
Data Warehouse / DHBW / Fall 2016 / Page 76
• For one of the following companies
• Bank
• Telecommunication company
• Online book store (like Amazon.com)
• Discount furniture store (like IKEA)
• Airline
• Car manufacturer
sketch an application based on a classical and
another based on a (near) real time operational data warehouse
Daimler TSS GmbH
Exercise
Data Warehouse / DHBW / Fall 2016 / Page 77
• Compare lecture 1. Possible solutions
• Standard Data Warehouse Architecture
• Data Vault 2.0 Architecture (Dan Linstedt) including log-based discovery (CDC) or
replication for Data extraction
Thank you!
Daimler TSS GmbH
Wilhelm-Runge-Straße 11, 89081 Ulm, Germany / Phone +49 731 505-06 / Fax +49 731 505-65 99
[email protected] / Internet: www.daimler-tss.com / Intranet portal code: @TSS
Domicile and Court of Registry: Ulm / Commercial Register No.: 3844 / Management: Christoph Röger (Vorsitzender), Steffen Bäuerle