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Business intelligence & business analytics (BIA) (BiBa) SUMMARY
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summary of all lectures (11) including examples
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Business intelligence & business analyticsLecture 1: introduction to business intelligence/business analytics, databases and relational
database
Business intelligence: Transforming data into meaningful information, information into knowledge
and knowledge into plans that drive profitable business action. It enables business decision-making.
The business intelligence process transforms the data and information into the BI product. This
contains information and knowledge that enables decision-making. The BI transforming process is
supported by the BI solution (tools, application and techniques).
Business intelligence architecture (corporate information factory): on the left side, you see the data
factory, here all the data is produced by business processes. The events of the processes are stored
as data. On the right side, the decision makers use techniques to understand and consume the data.
ETL staging area: extract, transform and load. This is a system that extracts the data and loads it into
the Datawarehouse. This is the starting point for the analytics process.
OLAP (online analytical processing): It presents data in cubes. This can be explored by analyst to roll
down a cube.
ERP is the backbone of business information systems. It supports decision making at all levels of the
organization. A central database is the heart of an ERP. Information is shared across the organization
via a single system. The DBMS is the software that controls the data.
Inside the database:
- Primary key: Uniquely identifies each record in a table. Mostly defined in the columns of the
tables. In textual representations of the database tables the primary key attributes are
underlined.
- Keys: consists of 1 or more attributes that determine other attributes. The keys in the same
row functionally depend on the primary key.
- Composite primary key: combination of keys. This is indicated by a +.
- Superkey: Any key that uniquely identifies each row
- Candidate key: Superkey without unnecessary attributes
- Foreign key: Attributes whose values match the primary key values in the related parent
table.
The way tables are related is called cardinality. There are three types:
- One-to-one (or zero-to-one)
- One-to-many (infinity). Most common form.
, - Many-to-many. Not supported directly in relational environments.
Lecture 2: SQL and data warehousing
Trends in the database world
1. From on-premise databases to cloud databases (MS azure, google cloud): It becomes an
operating expense instead of a capital expense. A database on premise gives more
opportunities for security. With the cloud you often rely on SLA’s.
For all the applications in the data warehouse you have the option to have it on premise or in
the cloud. This choice is often based on the requirements.
2. No (not only) SQL databases: In SQL the data is very rigid. NoSQL are a class of databases that
can be used when the data is different and not entirely relational.
o Key-values: Option to store array data. Small in terms of disc space. Blockchain
applications often relate to key-value applications.
o Document: XML documents. Files that do not obtain columns.
o Graph: Database to store graphs.
o Wide-column: Closely related to SQL. The only difference is that the columns are not
fixed and can differ in size.
3. Alternative data representations: XML vs. Relational database. This can be used for storing
document-oriented files with hierarchies; use XQuery.
4. From disk-based to in-memory databases: Classic disc-based are replaced. An example is SAP
HANA. The data is stored on the hard disk, this was relative slow. When the entire database
can be provides in-memory, this is quicker and gives a lot more opportunities.
SQL (structured query language):
Architectures have a dominant communication language. Sometimes languages are combined.
, Static vs. dynamic typing
- Static language: type checking at compile-time. Type checking means seeing if it is an integer
or float etc. Examples are Java, SQL
- Dynamic language: type checking at run-time (Python). The variables can change
dynamically.
Compiler vs. Interpreter
- Compiler: Program that transforms source code in a lower-level (machine) language. Code is
executed directly by processor and the entire program is used as input (fast execution). The
entire program as input.
o Java, python
- Interpreter: Program that directly executes instructions written in a programming language.
It read line by line and generates machine code. A single instruction as input (slower
execution)
o SQL
Join is (generally) composed of an equality comparison between foreign key and primary key of
related tables.
Data warehouse: A database that is maintained separately from the organization’s operational
databases for the purpose of managerial decision making.
A database warehouse needs to be separate because:
- It contain historic data which DBs do not typically maintain.
- It is about data consolidation. The data warehouse requires aggregation & summarization of
data from heterogenous sources.
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