Introduction, Business
Intelligence, Data
Warehouse
PA220: Database systems for data analytics
Overview of data warehousing
• motivation
• business intelligence / data analytics
• data warehouse
• architecture
• oltp vs. olap
• big data
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Motivation
• Data production
• Information systems
• Monitoring services
• Sensors, GPS tracking
• Social networks
• Data processing
• Storage & archiving
• Summarization
• Reporting
• Visualization
• Insights
• Predictions
3
Business Intelligence pyramid
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Business Intelligence
• a process of analyzing data and presenting results to business managers
• to make informed decisions
• tools and applications
• to collect data
• to prepare it for storage and analysis
• to develop and run queries
• to create reports and dashboards
• to visualize data
• evolved from decision support systems
• business analytics / (advanced) data analytics
• prescriptive analytics
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Business Intelligence vs. Artificial Intelligence
• BI is the “opposite” of Artificial Intelligence (AI)
• AI systems make decisions for the users
• BI systems help users make the right decisions, based on the available data
• Many BI techniques have roots in AI, though.
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Business Intelligence: Key Problems
1. Complex and unusable models
• Many DB models are difficult to understand
• DB models do not focus on a single clear business purpose
2. Same data found in many different systems
• Example: customer data in many different systems
• The same concept is defined differently
3. Data is suited for operational systems
• Accounting, billing, etc.
• Do not support analyses across business functions
4. Data quality is bad
• Missing data, imprecise data, different use of systems
5. Data are ”volatile”
• Data deleted in operational systems (6 months)
• Data change over time – no historical information
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Business Intelligence Architecture
Data Warehouse
Reporting
Visualization
Analysis
Data Mart
Sales
Marketing
Transaction data
Stock
Management
E-shop
ERP
Services
CRM
Transforming
Raw data
Meta data
Data
dimensions
Summary
data
Cleaning
Integrating
Web
Logs
Files
Data Staging
Staging data
Aggregate Precalc.
Storage
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Data Warehouse
• Typically:
• One large repository for entire company
• Dedicated hard- and software
• Enterprise-grade DBMS
• Often: database appliances (e.g., Teradata, Oracle Exadata, IBM Netezza, …)
• Goal:
• Single source of truth for analysis and reporting
• Requires data cleansing and conflict resolution
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Data Warehouse Users
• Business analysts:
• Explore data to discover information
• Use for decision making -> “Decision Support System (DSS)”
• Consequences:
• Workloads and access patterns not known in advance
• For exploration, data representation must be easy to understand (even by
business analysts)
• Design and usage driven by data, not applications
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Definition of Data Warehouse
• Barry Devlin, IBM Consultant
• A data warehouse is simply a single, complete, and consistent store of data
obtained from a variety of sources and made available to end users in a way
they can understand and use it in a business context.
• Ralph Kimball, The Data Warehouse Toolkit
• A copy of transaction data specifically structured for query and analysis.
• W. H. Inmon, Building the Data Warehouse
• A data warehouse is a subject-oriented, integrated, time-varying, nonvolatile
collection of data that is used primarily in organizational decision
making.
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Definition of Data Warehouse (1)
• Subject-oriented
• The data in the DW is organized in such a way that all the data elements
relating to the same real-world event or object are linked together
• Typical subject areas in DWs are Customer, Product, Order, Claim, Account,…
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Base tables hold original data
and can be different in
schema.
Customer activity represents
summary (derived)
information.
Definition of Data Warehouse (2)
• Integrated
• The DW contains data from most or all the organization's operational systems
and this data is made consistent
• E.g., gender, measurement, conflicting keys, consistency,…
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Definition of Data Warehouse (3)
• Non-volatile
• Data in the DW is never over-written or deleted-once committed, the data is
static, read-only, and retained for future reporting
• Data is loaded, but not updated
• When subsequent changes occur, a new version or snapshot record is written
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Definition of Data Warehouse (4)
• Time-varying
• The changes to the data in the DW are tracked and recorded so that reports
show changes over time
• Different environments have different time horizons associated
• While for operational systems a 60-to-90 daytime horizon is normal, DWs have a 5-to-10year
horizon
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General DW Definition
• A large repository of some organization’s electronically stored data
• Specifically designed to facilitate reporting and analysis
• Requirements:
• information easily accessible
• consistent information
• present information timely
• protect the information
• adapt to change
• accepted by users
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Example: Oracle Exadata X8-2
• Up to 912 CPU cores and 28.5TB memory per rack for database processing
• Up to 576 CPU cores per rack dedicated to SQL processing in storage
• From 2 to 19 Database Servers per rack; From 3 to 18 Storage Servers per rack
• Up to 3.0 PB of disk capacity (raw) per rack
• Up to 920 TB of flash capacity (raw) per rack
• Ability to perform up to 4.8M 8K database read I/O operations,
• or 4.3M 8K flash write I/O operations per second per full rack
• Hybrid Columnar Compression (10-15x compression ratio)
• 40 Gb/second (QDR) InfiniBand Network
• Uncompressed I/O bandwidth of up to 560 GB/second per full rack from SQL
• Complete redundancy for high availability
• Scale by connecting by InfiniBand up to 18 racks
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(Enterprise) Data Warehouse
• integrates data from more sources
• subject-oriented
• stores current and historical data
• answers multidimensional queries
• create aggregated data reports
• is it a relational DBMS?
• on-line transaction processing (OLTP)
• on-line analytical processing (OLAP)
• MOLAP (Multidimensional OLAP)
• ROLAP (Relational OLAP)
OLTP DW
few Indexes many
many Joins some
normalized Schema denormalized
rare
Derived data
and aggregates
common
update/select Workload select
predefined
Typical
operations
ad hoc
few days Historical data years
indexing /
hashing
Data access
method
full scans
GB Data volume TB
Thousands Users Hundreds2216.09.2021 PA220 DB for Analytics
OLTP (Online Transaction Processing)
• Day-to-day business operations
• Mix of insert, update, delete, and read operations
• e.g., enter orders, maintain customer data, etc.
• System sometimes called operational data store (ODS)
• Up-to-date state of the data
• From a database perspective:
• Short-running operations
• Most queries known in advance
• Often point access, usually through indexes
• write access⇝ ACID principles
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OLAP (Online Analytical Processing)
• Provide data for reporting and decision making
• Mostly read-only access
• e.g., resource planning, marketing initiatives
• Need archive data; (slightly) outdated information might be okay
• Report changes over time
• Can use separate data store (non-ODS)
• From a database perspective:
• Long-running operations, mostly read-only
• Queries not known in advance, often complex (⇝indexing?)
• Might need to scan through large amounts of data
• Data is (almost) append-only.
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Transactional vs. Analytical Workloads
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OLTP/OLAP and DW
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OLTP/OLAP and DW (2)
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Architecture Alternatives
• Variants of the full data warehouse architecture:
1. Independent data marts (no central warehouse)
• Populate data marts directly from sources
• Like several “mini warehouses”
• Redundancy, no “single source of truth”
2. Logical data marts (no explicit, physical data marts)
• Data mart just a logical view on full warehouse
• Easier to provide integrated, consistent view across the
• enterprise
• Data marts (and warehouse) might also reside at different geographic
locations.
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Building DW: Top-down Approach
• Analyze global business needs, plan how to develop a data warehouse,
design it, and implement it as a whole
• This procedure is promising: it is based on a global picture of the goal to achieve, and
in principle it ensures consistent, well integrated data warehouses.
• High-cost estimates with long-term implementations discourage company managers
from embarking on these kind of projects.
• Analyzing and integrating all relevant sources at the same time is a very difficult task,
even because it is not very likely that they are all available and stable at the same
time.
• It is extremely difficult to forecast the specific needs of every department involved in
a project, which can result in the analysis process coming to a standstill.
• Since no working system is going to be delivered in the short term, users cannot
check for this project to be useful, so they lose trust and interest in it.
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Building DW: Bottom-up Approach
• DWs are incrementally built, and several data marts are iteratively
created. Each data mart is based on a set of facts that are linked to a
specific department and that can be interesting for a user group.
• Leads to concrete results in a short time
• Does not require huge investments
• Enables designers to investigate one area at a time
• Gives managers a quick feedback about the actual benefits of the system
being built
• Keeps the interest for the project constantly high
• May determine a partial vision of the business domain
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Building DW: Comparison of Approaches
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Data Warehouse Preparation
• Data is periodically brought from the ODS to the data warehouse.
• This is also referred to as ETL Process.
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Data Warehouse Preparation - Transformation
• Query-Driven Data Integration
• Data is integrated on demand (lazy)
• PROS
• Access to most up-to-date data (all source data directly available)
• No duplication of data
• CONS
• Delay in query processing
• Slow (or currently unavailable) information sources
• Complex filtering and integration
• Inefficient and expensive for frequent queries
• Competes with local processing at sources
• Data loss at the sources (e.g., historical data) cannot be recovered
• Has not caught on in industry
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Data Warehouse Preparation - Transformation
• Warehouse-Driven Data Integration
• Data is integrated in advance (eager)
• Data is stored in DW for querying and analysis
• PROS
• High query performance
• Does not interfere with local processing at sources
• Assumes that data warehouse update is possible during downtime of local processing
• Complex queries are run at the data warehouse
• OLTP queries are run at the source systems
• CONS
• Duplication of data
• The most current source data is not available
• Has caught on in industry
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Data Warehouse Lifecycle
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Data Warehouse – Lifecycle (1)
• gather system goals, borders, and
size & prioritize them
• estimate costs and benefits
• analyze risks and expectations
• examine the skills of the working
team
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Data Warehouse – Lifecycle (2)
• analyze and compare the
possible architectural solutions
• assess the available technologies
and tools
• select an approach for design
and implementation
• create a preliminary plan of the
whole system
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Data Warehouse – Lifecycle (3)
• Every iteration causes a new data
mart and new applications to be
created and progressively added
to the DW system.
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First Data Mart
• is the one playing the most
strategic role for the
enterprise
• should be a backbone for
the whole DW
• should lean on available
and consistent data
sources
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Modelling and Data Warehouse
• Operational stores – normalized database
• E.g., e-shop
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Modelling and Data Warehouse
• Data Warehouse is subject-oriented -> sales are important
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Data Warehouse – Data Model
• star schema – fact table and dimension tables
• one purpose-focus analytics
• not very good for complex analytics
• fact table
• data measurements/metrics
• numeric values and foreign keys to dimensions
• atomic level of detail
• dimension table
• description of fact
• many attributes
Fact_sales
id_time
id_store
id_product
quantity
price
Dim_store
id (PK)
id_store
name
street
city
state
country
region
manager
Dim_product
id (PK)
id_product
name
brand
category
Dim_time
id
date
day
month
year
day_of_week4316.09.2021 PA220 DB for Analytics
Nature of Current Data and Processing
• Volume
• the amount of data increases tenfold every five years
• Velocity
• continuous data flow from sensors, social networks, …
• Variety
• data structure can change, text, multimedia, …
• Veracity
• with different data sources, it is getting more difficult to maintain data certainty
• Real-time processing
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Distributed Data Warehouse
• new platforms
• columnar DBMSs (e.g., C-Store)
• HDFS & MapReduce (e.g., Hadoop)
• NoSQL platforms (e.g., HBase)
• in-memory DBMSs (e.g., VoltDB)
• horizontal scaling instead of vertical scaling
• ETL in Hadoop
• Storing raw data in HDFS
• SQL-based analytics in columnar DBMSs
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Distributed Data Warehouse
46
Distributed Data Warehouse
Reporting /
Visualization
Analytics
R / Apache
Mahout
Hive & Pig
Data
Warehouse
Hadoop
Sources
Batch
ETL
Spark
Hive
Impala
Kylin
Stream
Staging
Hadoop
Strom / Spark &
Impala / VoltDB
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Hadoop Platform
• SW library for distributed processing of large data sets
• across clusters of computers
• High-availability achieved on application layer by replication
• tasks run / data stored on unreliable HW
• MapReduce – programming model for large scale data processing
• Map() – filtering and sorting, outputs “key,value” pairs
• Reduce() – summarizing Map() results by their keys
• HDFS – distributed high-throughput file system
• designed for mostly immutable files
• concurrent write not supported
• cooperation with MapReduce – data & computation locality
Map(k1,v1) → list(k2,v2)
Reduce(k2, list (v2)) → list(v3)
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Distributed Data Warehouse
• Apache Hive – data warehouse for large datasets
• projects structure onto the data in HDFS
• manages and queries data using HiveQL
• converts them to Map-Reduce jobs
• supports indexing
• DML operations
• UPDATE & DELETE at row level (new since end of 2014)
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Apache Hive
CREATE TABLE dim_product ( id INT,
id_product INT,
name STRING,
brand STRING,
category STRING );
LOAD DATA LOCAL INPATH ‘…/file.txt' OVERWRITE INTO TABLE dim_product PARTITION (ds='2014-12-15');
INSERT OVERWRITE TABLE dim_product
SELECT TRANSFORM (id, id_product, name, brand, category) USING 'python cleaning_mapper.py'
AS (id, id_product, name, brand, category)
FROM tmp_product;
SELECT p.name, SUM(s.quantity * price) AS income
FROM dim_product p LEFT JOIN fact_sales s ON (p.id = s.id_product)
GROUP BY p.name;
Fact_sales
id_time
id_store
id_product
quantity
price
Dim_product
id (PK)
id_product
name
brand
category
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Apache Impala
• similar to Apache Hive
• native analytical database for Hadoop
• low-latency queries
• no translation to Map-Reduce jobs
• in-memory data in Parquet format
• preference to numeric values than strings
• joins done in memory
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Apache Kylin
• framework for OLAP in Hadoop
• uses Hive
• precalculates aggregations
• query engine translation
• exploit prepared aggregations
• integrate with your favorite BI tools like Tableau and Power BI
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Summary
• BI is well-recognized and is a combination of a number of techniques to
support decision making.
• DW is at the core of BI that
• provides a complete, consistent, subject-oriented and time-varying collection of the
data;
• allows to separate OLTP from OLAP.
• Applications that use the DW include OLAP, data mining, visualization
• BI can provide many advantages to an organization
• Creates added value by transforming data into information
• Provides comprehensive knowledge about your business
• A good DW is a prerequisite for BI
• But a DW is a means rather than a goal … it is only a success if it is heavily used
• Following a clear design methodology is important.
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What next?
• Modeling for Data Warehouse
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