Data Management , Data Bases

Database Technologies: Databases

collections of data sets or records stored in a systemic way

• the data in databases are extremely—> volatile

Band-width is measured in

BITS

Storage is measured in

BITES

Database Management Systems (DBMS)

a software program designed to organize and administer a database 

1. Data Filtering and Profiling

   1. process and store data efficiently and inspect data for errors

2. Data Integrity and Maintenance

   1. correct, standardize, and verify the consistency and integrity of the data

3. Data Synchronization

   1. integrate, match, or link data from disparate source

4. Data Security

   1. check and control data integrity over time

5. Data Access

   1. provide authorized access to data in both planned acceptable time

Centralized Database Architecture CDA

• better control of data quality 

• better IT security 

Distributed Database Architecture

• allow both local and remote access

• easier and faster

Structured Query Language SQL

simplifies data access by requiring that users only specify what data they want to access without defining how they will be achieved

• SELECT column_name(s)

• FROM table_name

• WHERE condition

Online Analytical Processing (OLAP)

• included in many business intelligence software application and is used for a variety of data discovery activities including report creation and analysis

  • OLAP software allows users to perform multidimensional analysis of a wide range of business data, complex calculation, and trend analysis 

Extract, Transform and Load (ETL)

matching the structure of the other environment

Data Analyitics

• a technique of qualitatively or quantitatively analyzing a data set to reveal patterns, trends, and associations that often relate to human behavior and interaction, to enhance productivity and business gain

Big data

• an extremely large data set that is too large or complex to be analyzed using traditional data processing techniques

4 V’s Big Data and Data Analytics

1. Variety: the analytic environment has expanded from pulling data from enterprise systems to include big data and unstructured sources

2. Volume: large volumes of structured and unstructured data are analyzed 

3. Velocity: speed of access to reports that are drawn from data defines the differences between effective and ineffective analytics

4. Veracity: validating data and extracting insight that manager and workers can trust are key factors of successful analytics