title: DAMA

why? to save the data in the database or information system, conceptual representation of data objects, associcated with other data object
benefits, helpful for visual representation, busisses rules, regulatory compliances, consistancy conventions

data moduling technologies

ERM, Entity Relation Model
UML, Unified Modeling Language

Data Type

Semi-Structured

self-describing structur json, XML, Email, Key-value-Store

Data Acquire

Harvesting from external into system storage
Ingesting from different storages to different step for processing

Database

Hierarchical database

hierarchical structured
1 to 1, 1 to n,
XML

Network database

Predecessor of relational database
more than one parent
allow n to n
uses a direct graph with direct link
can becomes a complex structure

Relational Database

not sufficient for fast growth of big data and its complexity
hard to scale horizontally
Normalization, reduce the disk space
DeNormalization, fast and optimised for query
Snowflake schema,
- less disk space
- normalized
- minimal redundency
- powerful for data analysis
- allow many to many relationship
Star Schema(denormalization)
- simple to understand and build
- fast querying, no join

ACID compliance

potential failures, server, power, OS
Atomicity
Consistency
Isolation
durablity IMB Statement

CAP Theorem

Consistency
Availability
Partition tolerance
sql(CAx), no-sql(xAP)

ETL Process

E, Extract: Read data from source
L, Load, store data in the final data store
T, Transform: modify data based on the requirement
mostly is ELT
but if the data source allow modification, TEL is also possible

Data Warehouse

integrated data from different source
resructure data (denormalization)
optimized for analysis
Building
- Data source Layer: integrate internal and external data
- Staging layer: conduct transformation
- Storage Layer: host a database

Dagtabase

real time data
optimized for modification and querying(very efficient)
normalized data

Data Marts

subject-oriented database
to meet the specific group of users
data access with higher performance
Data maintenance, different department can have their own controll
Easy setup, simple design, required less technical
Analysis, KPIs,
Easy input

Data Lake

data Warehouse Data Lake processed raw structured all possible and raw ready for analysis open business user data scientists costly easy more secure less sql no sql fast result slow resulte

Data Mesh

domain-oriented decentralized data owership and architecture
data as a product
self-serve data infrastructure as platform
federated computational governance
architecture for data governance

Data Fabric

conceptual
focus on highly automating and integrating
need more matedata

Meta Data

Meta data is must, a connect(descripation) data of data, necessary for control and efficiency while processing data, also important and required for access and discovery. problem, stored in different place also different form.

FAIR Principes

Findable
Accessable
interoperable
Reuseable
The principles emphasise machine-actionability, because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.

Type

direction

who created it, what it is, when, where, how, licence.

Meaning

Controlled Vocabular
- list of words, to tag the information
- predefined and authorized
- reduce ambiguities of natural language
- improve retrievablilty
- support interoperable
Texonomies
- with hierarchical structur
Thesaui
- control of terms
- Hierarchical, Equivalence, Association relationship within terms
- support consistent indexing
- serve interoperability
Ontologies, large Thesaui

Linked Data

URIs (Uniform Resource Identifiers) to name individual things
HTTP URIs to find things in web
linked data 2, all conceptual things should start with http

Signposting

An approach to make the scholarly web more friendly to machines.

PIDs

standard, invariant and long-term reference of a digital resource, regardless of status location or current owner

Actionable IDs

A Persistent Identifier (PID) policy for the European Open Science Cloud

Machine Actionable

means that a formal statement is syntactically and semantically specified enabling computing systems to carry out automatic processing.

URLS

subset of URIs
related to IP address

Sustainability by technology

reliable
robust
long term perspective

PID data type

Types are Metadata Elements

Data Governance

define

Data governance determines what the data represents and what it can be used for
Authentication and Authorization required

dimensions

Organization, roles, responsibilites(RACI model)
Process,
Technology
People, Trust, Ethics
Data

Role

Formed
Accountable
Consulted
Responsiable

Data Quality

Data quality describes how good it fits for its intended use: Operations, Decision making, Planning, Researching

Dimension

accessibility or availability
accuracy or correctness
comparability
completeness or comprehensiveness
consistency, coherence, or clarity
- refers to whether the same data kept at different places do or do not match.
- ACID ensure from one valid to another
- peer-2-peer may be violated
credibility, reliability, or reputation
flexibility
plausibility
relevance, pertinence, or usefulness
timeliness or latency
uniqueness
validity or reasonableness

Data Management(DAMA)

Dimension is defined as a measurable features of an object

Accuracy
Availability
Clarity
Completeness

Data Security

Definition, planning, development, and execution of security policies and procedures to provide proper authentication, authorization, access, and auditing of data and information assets.