The European Parliament elections for 2019 will begin in May and this election is particularly important due to recent Brexit and Yellow Vests like populist movement unfolding across Europe where established political parties are facing
challenges due to negligence in acknowledging common people’s voice. One of the main arguments of Brexiter was the policies which were irrelevant to the people of UK made by EU and the flaws in the immigration system this also seems to be now major driver for Yellow Vests movement while it challenges local government but both events relates to a common point which is policymakers failure to address common people’s problems and aspirations. In this study we will be using the Future of Europe survey carried out in the 28 EU member states, the human freedom and happiness index and country’s debt to GDP data to build data warehouse and analyze how EU nationals feel about being heard in the union as compared to individual nation’s personal freedom rank and what is the difference in Ideal Future expectations between different EU nations and how it relates to global happiness index. We will also study what are the main risks/threats people feel that the EU is facing and we will analyze how the choice of each nation differs on the probable solution on the flaws of immigration system.
2. SLIDE /
“Analysis of the EU Citizen's Response on the
Challenges and Aspirations of EU”
2
Overview
1. Context & Sources
2. ETL Integration with SSIS/SSAS &
Automation
3. Tableau Dashboards
3. SLIDE / 3
Data warehouse used for this project
is built on mainly five sources and
one generated Geocoding for
countries geographical location.
1. EU Open Data Portal
2. The European Parliament
3. Wikipedia
4. Kaggle
5. Statista
6. OpenCage Data
Context & Sources
4. SLIDE / 4
Source Extraction &
Cleansing Container. Takes
care of calling 6 Execute
Process Task of SSIS. Each
task calls 6 different
extraction and cleaning
python scripts per source.
ContainerContainerContainerContainer
Staging Area Load
Container, It consists of 1
SQL task and 6 Data Flow
Tasks. SQL Task is used to
first truncate staging tables
every time there is new load
and dataflow task to load
tables.
Dimensions Load Container,
5 Execute SQL Tasks are
used for loading the
dimension tables while 1 SQL
task is for truncating
dimension before it gets
loaded with newly refreshed
staging data.
Fact & Cube Container, it is
comprised of one SQL task,
one Data flow task and two
tasks are Analysis Services
Processing Task to automate
cube processing and fact
loading.
01 02 03 04
ETL INTEGRATION
WITH SSIS/SSAS &
AUTOMATION
Entire SSIS (ETL) and SSAS process
is automated by grouping in SSIS
based on execution relevance in
four sequence containers.
5. SLIDE /
Tableau Dashboard (BI Queries)
5
Dashboard 1: Differences in ideal future
expectations in relation with the global
happiness index.
Dashboard 2: Overall comparison of
priorities on tackling immigration of highly
indebted nations with other nations.
Dashboard 3: Feeling about being
heard in the Union as compared to
the national personal freedom rank and
MEPs.
