Data Warehousing

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Data Warehousing

& Data Mining

Prof Dr. Wolf-Tilo Balke

Institut für Informationssysteme

Technische Universität Braunschweig

http://www.ifis.cs.tu-bs.de

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7. Building the DW

7.1 The DW Project

7.2 Data Extract/Transform/Load (ETL) 7.3 Metadata

7. Building the DW

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• Building a DW, is a complex IT project

– A middle size DW-project contains 500-1000 activities

• DW-Project organization

– Project roles and corresponding tasks, e.g.:

7.1 The DW Project

Roles Tasks

DW-PM Project Management

DW-Architect Methods, Concepts, Modeling DW-Miner Concepts, Analyze(non-standard) Domain Expert Domain Knowledge

DW-System Developer System- and metadata-management, ETL

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• Usual tasks in a DW-Project

– Communication, as process of information exchange between team members

– Conflict management

• The magical triangle, compromise between time, costs and quality

– Quality assurance

• Performance, reliability, scalability, robustness, etc.

– Documentation

7.1 The DW Project

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• Software choice

– Database system for the DW

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Usually the choice is to use the same technology provider as for the operational data

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MDB vs. RDB

– ETL tools

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Differentiate by the data cleansing needs

– Analysis tools

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Varying from data mining to OLAP products, with a focus on reporting functionality

– Repository

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Not very oft used

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Helpful for metadata management

7.1 The DW Project

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• Hardware choice

– Data storage

• RAID systems, SAN’s, NAS’s

– Processing

• Multi-CPU systems, SMP, Clusters

– Failure tolerance

• Data replication, mirroring RAID, backup strategies

– Other factors

• Data access times, transfer rates, memory bandwidth, network throughput and latency

7.1 The DW Project

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• Project Timeline, depends on the development methodology, but usually

– Phase I – Proof of Concept

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Establish Technical Infrastructure

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Prototype Data Extraction/Transformation/Load

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Prototype Analysis & Reporting

– Phase II – Controlled Release

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Iterative Process of Building Subject Oriented Data Marts

– Phase III – General Availability

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On going operations, support and training, maintenance and growth

• The most important part of the DW building project is defining the ETL process

7.1 The DW Project

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• What is ETL?

– Short for extract, transform, and load

– Three database functions that are combined into one tool to pull data out of productive databases and place it into the DW

• Migrate data from one database to another, to form data marts and data warehouses

• Convert databases from one format or type to another

7.2 ETL

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• When should we ETL?

– Periodically (e.g., every night, every week) or after significant events

– Refresh policy set by administrator based on user needs and traffic

– Possibly different policies for different sources – Rarely, on every update (real-time DW)

• Not warranted unless warehouse data require current data (up to the minute stock quotes)

7.2 ETL

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• ETL is used to integrate heterogeneous systems

– With different DBMS, operating system, hardware, communication protocols

• ETL challenges

– Getting the data from the source to target as fast as possible

– Allow recovery from failure without restarting the whole process

• This leads to balance between writing data to staging tables or keeping it in memory

7.2 ETL

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• Staging area, basic rules

– Data in the staging area is owned by the ETL team

• Users are not allowed in the staging area at any time

– Reports cannot access data from the staging area

– Only ETL processes can write to and read from the staging area

7.2 ETL

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• Staging area structures for holding data

– Flat files

– XML data sets – Relational tables

7.2 Staging Area

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• Flat files

– ETL tools based on scripts, such as Perl, VBScript or JavaScript

– Advantages

• No overhead of maintaining metadata as DBMS does

• Sorting, merging, deleting, replacing and other data-migration functions are much faster outside the DBMS

– Disadvantages

• No concept of updating

• Queries and random access lookups are not well supported by the operating system

• Flat files cannot be indexed for fast lookups

7.2 Data Structures

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• When should flat files be used?

– Staging source data for safekeeping and recovery

• Best approach to restart a failed process is by having data dumped in a flat file

– Sorting data

• Sorting data in a file system may be more efficient as performing it in a DBMS with Order By clause

• Sorting is important: a huge portion of the ETL processing cycles goes to sorting

7.2 Data Structures

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• When should flat files be used?

– Filtering

• Using grep-like functionality

– Replacing text strings

• Sequential file processing is much faster at system-level than using a database

7.2 Data Structures

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• XML Data sets

– Used as common format for both input and output from the ETL

system

– Generally, not used for persistent staging

– Useful mechanisms

• XML schema (successor of DTD)

• XQuery, XPath

• XSLT

7.2 Data Structures

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• Relational tables

– Using tables is most appropriate especially when there are no dedicated ETL tools

– Advantages

• Apparent metadata: column names data types and lengths, cardinality, etc.

• Relational abilities: data integrity as well as normalized staging

• Open repository/SQL interface: easy to access by any SQL compliant tool

– Disadvantages

• Sometimes slower than the operating file system

7.2 Data Structures

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• How is the staging area designed?

– Staging database, file system, and directory structures are set up by the DB and OS administrators based on ETL architect estimations e.g., tables volumetric

worksheet

7.2 Staging Area - Storage

Table Name

Update strategy

Load frequency

ETL Job Initial row count

Avg row length

Grows with

Expected rows/mo

Expected bytes/mo

Initial table size

Table Size 6 mo. (MB) S_ACC Truncate/

Reload

Daily SAcc 39,933 27 New

account

9,983 269,548 1,078,191 2.57

S_ASSETS Insert/

Delete

Daily SAssets 771,500 75 New

assets

192,875 15,044,250 60,177,000 143.47

S_DEFECT Truncate/

Reload

On demand

SDefect 84 27 New

defect

21 567 2,268 0.01

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• ETL

– Data extraction

– Data transformation – Data loading

7.2 ETL

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• Data Extraction

– Data needs to be taken from a data source so that it can be put into the DW

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Internal scripts/tools at the data source, which export the data to be used

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External programs, which extract the data from the source

– If the data is exported, it is typically exported into a text file that can then be brought into an intermediary

database

– If the data is extracted from the source, it is typically transferred directly into an intermediary database

7.2 Data Extraction

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• Steps in data extraction

– Initial extraction

• Preparing the logical map

• First time data extraction

– Ongoing extraction

• Just new data

• Changed data

• Or even deleted data

7.2 Data Extraction

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• Logical map connects the original source data to the final data

• Building the logical map: first identify the data sources

– Data discovery phase

• Collecting and documenting source systems: databases, tables, relations, cardinality, keys, data types, etc.

– Anomaly detection phase

• NULL values can destroy any ETL process, e.g., if a foreign key is NULL, joining tables on a NULL column results in data loss, because in RDB NULL ≠ NULL

7.2 Data Extraction

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• Example of solving NULL anomalies

– If NULL on foreign key then use outer joins

– If NULL on other columns then create a business rule to replace NULLs while loading data in DW

• Ongoing Extraction

– Data needs to be maintained in the DW also after the initial load

• Extraction is performed on a regular basis

• Only changes are extracted after the first time

• How do we detect changes?

7.2 Data Extraction

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• Detecting changes (new/changed data)

– Using audit columns

– Database log scraping or sniffing – Process of elimination

• Using audit columns

– Store the date and time a record has been added or modified at

– Detect changes based on date stamps higher than the last extraction date

7.2 Ongoing Extraction

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• Log scraping

– Takes a snapshot of the database redo log at a

certain time (e.g., midnight) and finds the transactions affecting the tables ETL is interested in

– It can be problematic when the redo log gets full and is emptied by the DBA

• Log sniffing

– Pooling the redo log at small time granularity, capturing the transactions on the fly

– The better choice: suitable also for real-time ETL

7.2 Detecting Changes

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• Process of elimination

– Preserves exactly one copy of each previous extraction

– During next run, it compares the entire source tables against the extraction copy

– Only differences are sent to the DW – Advantages

• Because the process makes row by row comparisons, it is impossible to miss data

• It can also detect deleted rows

7.2 Detecting Changes

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• Data transformation

– Uses rules, lookup tables, or combinations with other data, to convert source data to the desired state

• 2 major steps

– Data Cleaning

• May involve manual work

• Assisted by artificial intelligence algorithms and pattern recognition

– Data Integration

• May also involve manual work

7.2 Data Transformation

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• Extracted data can be dirty. How does clean data look like?

• Data Quality characteristics:

– Correct: values and descriptions in data represent their associated objects truthfully

• E.g., if the city in which store A is located is Braunschweig, then the address should not report Paris

– Unambiguous: the values and descriptions in data can be taken to have only one meaning

7.2 Data Cleaning

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– Consistent: values and descriptions in data use one constant notational convention

• E.g., Braunschweig can be expressed as BS or Brunswick, by our employees in USA. Consistency means using just BS in all our data

– Complete

• Individual values and descriptors in data have a value (not null)

7.2 Data Cleaning

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• The data cleaning engine produces 3 main deliverables:

– Data-profiling results:

• Meta-data repository describing schema definitions, business objects, domains, data sources, table definitions, data rules, value rules, etc.

• Represents a quantitative assessment of original data sources

7.2 Data Cleaning

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– Error event table

• Structured as a dimensional star schema

• Each data quality error identified by the cleaning subsystem is inserted as a

row in the error event fact table

7.2 Cleaning Deliverables

Data Quality Screen:

- Status report on data quality

- Gateway which lets only clean data go through

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– Audit dimension

• Describes the data-quality context of a fact table record being loaded into the DW

• Attached to each fact record

• Aggregates the information from the error event table on a per record basis

7.2 Cleaning Deliverables

Audit key (PK)

Completeness category (text) Completeness score (integer)

Number screens failed Max severity score

Extract timestamp Clean timestamp

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• Overall Process Flow

• Is data really dirty?

7.2 Data Cleaning

Data

Transformation

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• Sometimes data is just garbage

– We shouldn’t load garbage in the DW

• Cleaning data manually takes just…forever!!!

7.2 Data Quality

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• Use tools to clean data semi-automatically

– Commercial software

• SAP Business Objects

• IBM InfoSphere Data Stage

• Oracle Data Quality and Oracle Data Profiling

– Open source tools

• E.g., Eobjects DataCleaner, Talend Open Profiler

7.2 Data Quality

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• Data cleaning process: use of thesauris, regular expressions, geographical information, etc.

7.2 Data Quality

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• Regular expressions for date/time data

7.2 Data Quality

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• Core of the data cleaning engine: anomaly detection phase

– Data anomaly is a piece of data which doesn’t fit into the domain of the rest of the data it is stored with

• E.g., Male named Mary???

7.2 Cleaning Engine

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• Anomaly detection

– Count the rows in a table while grouping on the column in question e.g.,

• SELECT city, count(*) FROM order_detail GROUP BY city

7.2 Anomaly Detection

City Count(*)

Bremen 2

Berlin 3

WOB 4,500

BS 12,000

HAN 46,000

…

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• What if our table has 100 million rows with 250,000 distinct values?

– Use data sampling e.g.,

• Divide the whole data into 1000 pieces, and choose 1 record from each

• Add a random number column to the data, sort it an take the first 1000 records

• Etc.

– Common mistake is to select a range of dates

• Most anomalies happen temporarily

7.2 Anomaly Detection

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• Data profiling

– Pay closer look to strange values – Observe data distribution pattern

• Gaussian distribution

7.2 Data Quality

SELECT AVERAGE(sales_value) – 3 * STDDEV(sales_value), AVERAGE(sales_value) + 3 * STDDEV(sales_value) INTO Min_resonable, Max_resonable FROM …

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• Data distribution

– Flat distribution

• Identifiers distributions (keys)

– Zipfian distribution

• Some values appear more often than others

– In sales, more cheap goods are sold than expensive ones

7.2 Data Quality

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• Data distribution

– Pareto, Poisson, S distribution

• Distribution discovery

– Statistical software: SPSS, StatSoft, R, etc.

7.2 Data Quality

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• Data integration

– Several conceptual schemas need to be combined into a unified global schema

– All differences in perspective and terminology have to be resolved – All redundancy has to be

removed

7.2 Data Integration

schema 2

schema 4 schema 3

schema 1

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• There are four basic steps needed for conceptual schema integration

1. Preintegration analysis 2. Comparison of schemas 3. Conformation of schemas 4. Merging and restructuring

of schemas

• The integration process needs continual refinement and

reevaluation

7.2 Schema Integration

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7.2 Schema Integration

schemas with conflicts

modified schemas list of conflicts

integrated

identify conflicts

resolve conflicts

integrate schemas

Preintegration analysis Comparison of schemas Conformation of schemas

Merging and restructuring

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• Preintegration analysis needs a close look on the individual conceptual schemas to decide for an adequate integration strategy

– The larger the number of constructs, the more important is modularization

– Is it really sensible/possible to integrate all schemas?

7.2 Schema Integration

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7.2 Schema Integration

Integration strategy

Binary approach n-ary approach

Sequential integration

Balanced integration

One-shot integration

Iterative integration

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• Schema conflicts

– Table/Table conflicts

• Table naming e.g., synonyms, homonyms

• Structure conflicts e.g., missing attributes

• Conflicting integrity conditions

– Attribute/Attribute conflicts

• Naming e.g., synonyms, homonyms

• Default value conflicts

• Conflicting integrity conditions e.g., different data types or boundary limitations

– Table/Attribute conflicts

7.2 Schema Integration

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• The basic goal is to make schemas compatible for integration

• Conformation usually needs manual interaction

– Conflicts need to be resolved semantically – Rename entities/attributes

– Convert differing types, e.g., convert an entity to an attribute or a relationship

– Align cardinalities/functionalities – Align different datatypes

7.2 Schema Integration

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• Schema integration is a semantic process

– This usually means a lot of manual work

– Computers can support the process by matching some (parts of) schemas

• There have been some approaches towards (semi-)automatic matching of schemas

– Matching is a complex process and usually only

focuses on simple constructs like ‘Are two entities semantically equivalent?’

– The result is still rather error-prone…

7.2 Schema Integration

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• Schema Matching

– Label-based matching

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For each label in one schema consider all labels of the other schema and every time gauge their semantic similarity (Price vs Cost)

– Instance-based matching

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E.g., find correlations between attributes: ‘Are there duplicate tuples?’ or ‘Are the data distributions in their respective domains similar?’

– Structure-based matching

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Abstracting from the actual labels, only the structure of the schema is evaluated, e.g., regarding element types, depths in hierarchies, number and type of relationships, etc.

7.2 Schema Integration

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• If integration is query-driven only Schema Mapping is needed

– Mapping from one or more source schemas to a target schema

7.2 Schema Integration

Data Source schema S

Correspondence

Target schema T

Correspondence

Mapping compiler

Low-level mapping High-level mapping

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• Schema Mapping

– Abstracting from the actual labels, regarding element types, depths in hierarchies, number and type of

relationships, etc.

7.2 Schema Integration

Product

ProdID: Decimal Product: VARCHAR(50)

Group: VARCHAR(50) Categ: VARCHAR(50) Product

ID: Decimal Product: VARCHAR(50)

GroupID:Decimal

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• Schema integration in pratice

– BEA AquaLogic Data Services

• Special Feature: easy-to-use modeling: “Mappings and transformations can be designed in an easy-to-use GUI tool using a library of over 200 functions. For

complex mappings and

transformations, architects and developers can bypass the GUI tool and use an Xquery source code editor to define or edit services. “

7.2 Schema Integration

www.bea.com

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• What tools are actually given to support integration?

– Data Translation Tool

• Transforms binary data into XML

• Transforms XML to binary data

– Data Transformation Tool

• Transforms an XML to another XML

– Base Idea

• Transform data to application specific XML → Transform to XML specific to other application / general schema →

Transform back to binary

7.2 Schema Integration

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• “I can’t afford expensive BEA consultants and the AquaLogic Integration Suite, what now??”

– Do it completely yourself

• Most used technologies can be found as Open Source projects (data mappers, XSL engines, XSL editors, etc)

– Do it yourself with specialized tools

• Many companies and open source projects are specialized in developing data integration and transformation tools

– CloverETL

– Altova MapForce

– BusinessObjects Data Integrator – Informatica Powerhouse, etc…

7.2 Schema Integration

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• Altova MapForce

– Same idea as the BEA Integrator

• Also based on XSLT and a data description language

– Editors for binary/DB to XML mapping

– Editor for XSL transformation

– Automatic generation of data sources, web- services, and

transformation modules in Java, C#, C++

7.2 Schema Integration

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• OpenRefine (Google Refine)

7.2 Schema Integration

http://openrefine.org/

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• The loading process can be broken down into 2 different types:

– Initial load

– Continuous load (loading over time)

7.2 Loading

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• Issues

– Huge volumes of data to be loaded

– Small time window available when warehouse can be taken off line (usually nights)

– When to build index and aggregated tables

– Allow system administrators to monitor, cancel, resume, change load rates

– Recover gracefully - restart after failure from where you were and without loss of data integrity

7.2 Loading

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• Initial Load

– Deliver dimensions tables

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Create and assign surrogate keys, each time a new cleaned and conformed dimension record has to be loaded

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Write dimensions to disk as physical tables, in the proper

dimensional format

– Deliver fact tables

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Utilize bulk-load utilities

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Load in parallel

– Tools

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DTS – Data Transformation Services (set of tools)

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bcp utility – batch copy

7.2 Loading

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• Continuous load (loading over time)

– Must be scheduled and processed in a specific order to maintain integrity, completeness, and a satisfactory level of trust (if done once a year… the data is

obsolete)

– Should be the most carefully planned step in data warehousing or can lead to:

• Error duplication

• Exaggeration of inconsistencies in data

7.2 Loading

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• Continuous load of facts

– Separate updates from inserts

– Drop any indexes not required to support updates – Load updates

– Drop all remaining indexes

– Load inserts through bulk loaders – Rebuild indexes

7.2 Loading

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• Metadata - data about data

– In DW, metadata describe the contents of a data warehouse and how to use it

• What information exists in a data warehouse, what the information means, how it was derived, from what source systems it comes, when it was created, what pre-built

reports and analyses exist for manipulating the information, etc.

7.3 Metadata

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• Types of metadata in DW

– Source system metadata – Data staging metadata – DBMS metadata

7.3 Metadata

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• Source system metadata

– Source specifications

• E.g., repositories, and source logical schemas

– Source descriptive information

• E.g., ownership descriptions, update frequencies and access methods

– Process information

• E.g., job schedules and extraction code

7.3 Metadata

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