7023t - tp3 - w7 - s8 - r1 - answer

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LZT4 – 1701497885 7023T – Advanced Database System

7023T – Advanced Database System of 13

Due Date : 01 November 2015

Tugas Personal ke – 3

Week 7 - Session 8

Answer these questions below and submit it before 3rd

personal assignment deadline.

1. What is BI (Business Intelligent) and is there any correlation with Data Warehouse?

Answer :

Bisnis inteligensi ( BI ) mengacu pada teknologi, aplikasi dan praktek untuk

pengumpulan, integrasi, analisis, dan penyajian informasi bisnis dan kadang-kadang ke

informasi itu sendiri. tujuan intelijen bisnis adalah untuk mendukung keputusan bisnis

yang lebih baik pembuatan. Bisnis inteligensi juga menggambarkan sebagai sistem

pendukung keputusan. Sistem BI memberikan sejarah, saat ini, dan prediksi dilihat dari

operasi bisnis, yang paling sering menggunakan data yang telah dikumpulkan ke dalam

datawarehouse atau data mart dan kadang-kadang bekerja dari data operasional.

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2. What is EIS (Executive Information System) and is there any correlation with Data

Warehouse?

Answer :

Executive Information System (EIS) sebagai sistem informasi manajemen umumnya

dirancang untuk ditekankan dengan tampilan grafis dan sangat mudah digunakan dan

menarik antarmuka karena hal ini diasumsikan akan digunakan untuk mendukung dan

memfasilitasi informasi dan pengambilan keputusan kebutuhan eksekutif senior. EIS

menawarkan kuat ad-hoc query, analisis, pelaporan dan drill-down kemampuan tanpa

harus khawatir tentang kompleksitas algoritma yang terlibat dalam sistem. Karena orang-

orang di tingkat atas dari sebuah organisasi yang dikenal sebagai eksekutif, seperti sistem

kemudian disebut Sistem Informasi Eksekutif (EIS). Sebuah gudang data adalah sangat

baik dasar untuk EIS. Data warehouse dibuat khusus untuk kebutuhan analis EIS. Sekali

data warehouse telah dibangun, tugas dari EIS adalah jauh lebih mudah dari sebelumnya.

Dengan penuh penduduknya gudang data di tempat, analis dapat berada dalam sikap

proaktif, bukan selamanya sikap reaktif, berkaitan dengan memenuhi kebutuhan

manajemen.

3. What is BigData and is there any correlation with Data Warehouse?

Answer :

Big Data adalah istilah yang digunakan untuk set data yang ukurannya luar kemampuan

umum digunakan alat untuk menangkap, mengelola dan mengolah data dalam waktu

yang telah berlalu ditoleransi. Ada yang berbeda interpretasi dari apa yang dimaksud

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dengan data yang besar, dan ada interpretasi yang berbeda dari apa yang dimaksud

dengan data warehousing. Pada prinsipnya, ada pendekatan Kimball untuk data

warehousing, dan ada pendekatan Inmon untuk data warehousing. Untuk tujuan pasal ini,

Inmon pendekatan untuk data warehousing akan dibahas. Pendekatan Inmon untuk data

warehousing berpusat di sekitar definisi data gudang, yang diberikan bertahun-tahun

yang lalu. Sebuah gudang data adalah subjek berorientasi, nonvolatile, koleksi varian

terintegrasi, saat data yang dibuat untuk tujuan manajemen pengambilan keputusan. Cara

lain untuk mengatakan hal yang sama adalah bahwa data warehouse menyediakan "Versi

tunggal kebenaran" untuk pengambilan keputusan di perusahaan. Dengan data warehouse

ada adalah terintegrasi, granular, sejarah titik acuan untuk data didalam perusahaan. Jadi,

mengapa orang ingin solusi big data? Orang ingin solusi data besar karena dalam banyak

perusahaan ada banyak data. Dan pada mereka perusahaan yang Data - jika terkunci

benar - dapat berisi banyak informasi berharga yang dapat menyebabkan keputusan yang

lebih baik, yang pada gilirannya, dapat menyebabkan lebih banyak pendapatan,

profitabilitas lebih dan lebih banyak pelanggan. Dan itulah yang paling diinginkan

perusahaan.

Dan mengapa orang membutuhkan data warehouse? Orang membutuhkan data

warehouse untuk membuat keputusan. Untuk benar-benar tahu apa yang sedang terjadi di

perusahaan Anda, Anda perlu data yang handal, dipercaya dan dapat diakses oleh semua

orang.

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4. What is OLAP (Online Analytical Processing) and OLTP (Online Transactional

Processing), and what the differences and correlation with Data Warehouse?

Answer :

OLTP (Online Transactional Processing) :

Biasanya ditandai dengan beberapa transaksi online (insert, update, delete). Penekatan

utama pada OLTP pada pemprosesan query lebih cepat, menjaga integritas data dalam

lingkungan multi akses dan efektivitas diukur dengan jumlah transaksi per detik. Dalam

database OLTP ada data rinci dan saat ini, dan skema yang digunakan untuk menyimpan

database transaksional adalah model entitas (biasanya hingga 3NF).

OLAP (Online Analytical Processing) :

Biasanya ditndai denngan rendahnya transaksi, query lebih sering kompleks dan

melibatkan agregasi. Aplikasi OLAP banyak digunakan oleh teknik Data Mining, dalam

OLAP Database ada dikumpulkan, data historis, disimpan dalam skema multidimensi.

Biasanya menggunakan starschema.

OLAP OLTP

Source Data Consolidation data; OLAP

data comes from the various

OLTP Databases

Operational data; OLTPs are the

original source of the data.

Purpose of Data To help with planning,

problem solving, and decision

support

To control and run fundamental

business tasks

What a data Multi-dimensional views of

various kinds of business

activities

Reveals a snapshot of ongoing

business processes

Insert and Update Periodic long-running batch

jobs refresh the data

Short and fast inserts and

updates initiated by end users

Query Often complex queries

involving aggregations

Relatively standardized and

simple queries Returning

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relatively few records

Proccesing speed Depends on the amount of

data involved; batch data

refreshes and complex

queries may take many hours;

query speed can be improved

by creating indexes

Fast

Space Requirements Larger due to the existence of

aggregation structures and

history data; requires more

indexes than OLTP

Can be relatively small if

historical data is archived

Database Design Typically de-normalized with

fewer tables; use of star

and/or snowflake schemas

Highly normalized with many

tables

Backup and Recovery Instead of regular backups,

some environments may

consider simply reloading the

OLTP data as a recovery

method

Backup religiously; operational

data is critical to run the

business, data loss is likely to

entail significant monetary loss

and legal liability

5. What is Data Mining and what the correlation with Data Warehouse?

Answer :

Data mining merupakan metode untuk membandingkan data dalam jumlah besar untuk

tujuan menemukan pola. Data mining biasanya digunakan untuk model dan peramalan.

Data mining adalah proses korelasi, pola dengan menggeser melalui repositori data besar

menggunakan pengenalan pola teknik. Data dapat ditambang apakah itu disimpan dalam

flat file, spreadsheet, tabel database, atau beberapa format penyimpanan lainnya. Kriteria

penting untuk data yang tidak format penyimpanan, namun penerapan untuk masalah

yang akan dipecahkan.

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Data warehouse dapat memfasilitasi kegiatan ini. Namun, data warehouse akan ada

gunanya jika tidak mengandung data yang Anda butuhkan untuk memecahkan masalah

Anda .

6. Why data warehouse as foundation to improve decision making?

Answer :

There are four tasks that can be done with the data warehouse :

1. Making Reports

Making the report is one of the uses of the most common data warehouse is done. By

using: simple query reports obtained daily, monthly, annually or whenever desired

time period.

2. OLAP

With the data warehouse, all the information both detail and summary results needed

in the analysis of easily obtained. Utilizing OLAP multi-dimensional concept of data

and allows users to analyze the data to detail, without typing any SQL commands.

This is possible because the concept of multi- dimensional, then the data in the form

of the same facts can be seen by using different functions. Other facilities that exist in

the OLAP software is the facility rool-up and drill- down. Drill-down is the ability to

see the detail of the information and the roll-up is the opposite.

3. Data Mining

Data mining is the process to gain knowledge and new information from a large

number of data in the data warehouse, using artificial intelligence (Artificial

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Intelligence), statistics and mathematics. Data mining is a technology that is expected

to facilitate communication between the data and user

4. EIS

The data warehouse can make a summary of important information with the purpose

of making business decisions, without having to explore the entire data. By using a

data warehouse of all reports have been summarized and can also find out all the

details are complete, thus simplifying the decision-making process. The information

and data in the report becomes the target data warehouse informative for the user.

Data warehouse is required for management decision makers of an organization /

company. With the data warehouse, will facilitate the making of applications DSS and

EIS because the usefulness of the data warehouse is specialized to create a database that

can be used to support the process of analysis for decision makers.

7. Can we do update on a record in Data Warehouse? Explain your answering, please!

Answer :

Data warehouse dapat diupdate dengan tercatat record baru, dan tidak dapat ditimpa atau

diperbaharui pada file yang sama. Kenapa tidak dapat ditimpa dikerenakan bersifat

Nonvolatile.

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8. What are centralized Data Warehouse and its opponent? Which one better and

why? Explain your answering!

Answer :

1. Centeralized Data Warehouse (CDW)

Centralized Data Warehouses are great for small and mid-size data warehouses (less

than 15-40Tb). There are great benefits in terms of the ease to mange upgrades,

support packs, enforcing development standards, transport control, master data

management and the overall total cost of ownership To make CDW successful, there

needs to be:

o Adequate funding of hardware, application servers, database servers

o Serious consideration should be made to move BI and reporting to BWA

o Focus on using the database capacity on storage and data loads-- not queries

o No direct reporting from DSOs (takes too much system resources)

o Broadcasting , caching and performance tuning is a dedicated support effort

o A plan for data partitioning and archiving needs to be in-place as soon as the

system exceeds 5-8 TB. If the data is centralized it is faster to develop new

solutions for the business and merging from different data sources are easier.

2. De-centeralized Data Warehouse (DDW)

A Decentralized Data Warehouses makes sense if there are logical division between

business units, geographies and little shared reporting I.e. in a conglomerate

organization with diverse business units. The benefits of DDWs include the flexibility

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of the FDW with the technology standardization and lower cost of ownership of the

CDW. To make DDWs successful, there needs to be:

o A formal Masterdata Management (MDM) strategy with clearly defined

standards

o A rule based data cleaning and data integration plan for centralized reporting

o A shared hardware location to keep costs lower

o Tight integration with upgrades, support packs and interface standards With

DDWs there is a risk of creating st ove-pipe data marts that cannot be

integrated at the corporate level without very high costs.

9. What the disadvantages of Data Warehouse?

Answer :

1. Extra Reporting Work

Depending on the size of the organization, a data warehouse runs the risk of extra

work on departments. Each type of data that's needed in the warehouse typically has

to be generated by the IT teams in each division of the business. This can be as simple

as duplicating data from an existing database, but at other times, it involves gathering

data from customers or employees that wasn't gathered before.

2. Cost/Benefit Ratio

A commonly cited disadvantage of data warehousing is the cost/benefit analysis. A

data warehouse is a big IT project, and like many big IT projects, it can suck a lot of

IT man hours and budgetary money to generate a tool that doesn't get used often

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enough to justify the implementation expense. This is completely sidestepping the

issue of the expense of maintaining the data warehouse and updating it as the business

grows and adapts to the market.

3. Data Ownership Concerns

Data warehouses are often, but not always, Software as a Service implementations, or

cloud services applications. Your data security in this environment is only as good as

your cloud vendor. Even if implemented locally, there are concerns about data access

throughout the company. Make sure that the people doing the analysis are individuals

that your organization trusts, especially with customers' personal data. A data

warehouse that leaks customer data is a privacy and public relations nightmare.

4. Data Flexibility

Data warehouses tend to have static data sets with minimal ability to "drill down" to

specific solutions. The data is imported and filtered through a schema, and it is often

days or weeks old by the time it's actually used. In addition, data warehouses are

usually subject to ad hoc queries and are thus notoriously difficult to tune for

processing speed and query speed. While the queries are often ad hoc, the queries are

limited by what data relations were set when the aggregation was assembled.

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10. What is top-down approach in data warehouse development? And what is the

opponent? What are the differences? Which one better? Explain your answering,

please!

Answer :

When you consider methodological approaches, their top-down structures or bottom-up

structures play a basic role in creating a data warehouse. Both structures deeply affect the

datawarehouse lifecycle. If you use a top-down approach, you will have to analyze global

business needs, plan how to develop a data warehouse, design it, and implement it as a

whole. This procedure is promising: it will achieve excellent results because it is based

on a global picture of the goal to achieve, and in principle it ensures consistent, well

integrated data warehouses. However, a long story of failure with top-down approaches

teaches that:

high-cost estimates with long-term implementations discourage company managers

from embarking on these kind of projects;

analyzing and bringing together all relevant sources is a very difficult task, also

because it is not very likely that they are all available and stable at the same time;

it is extremely difficult to forecast the specific needs of every department involved in

a project, which can result in the analysis process coming to a standstill;

since no prototype is going to be delivered in the short term, users cannot check for

this project to be useful, so they lose trust and interest in it. In a bottom-up approach,

data warehouses are incrementally built and several data marts are iteratively created.

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Each data mart is based on a set of facts that are linked to a specific company

department and that can be interesting for a user subgroup (for example, data marts

for inventories, marketing, and soon).

If this approach is coupled with quick prototyping, the time and cost needed for

implementation can be reduced so remarkably that company managers will notice how

useful the project being carried out is. In this way, that project will still be of great

interest. The bottom-up approach turns out to be more cautious than the top-down one

and it is almost universally accepted. Naturally the bottom-up approach is not risk-free,

because it gets a partial picture of the whole field of application. We need to pay attention

to the first data mart to be used as prototype to get the best results: this should play a very

strategic role in a company. In fact, its role is so crucial that this data mart should be a

reference point for the whole data warehouse. In this way, the following data marts can

be easily added to the original one. Moreover, it is highly advisable that the selected data

mart exploit consistent data already made available.

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Referensi :

Connolly, Thomas M. and Carolyn E.Begg. (2005). Database system A Practical Approach,

Implementasi and Management. Fourth Edition. Addison – Wesley Publishing Company,

United States of America

Kimbal, Raphl and Margy Ross. (2007). The Data Warehouse Toolkit. Third Edition. John Wiley

& sons Inc, United States of America

http://smallbusiness.chron.com/disadvantages-data-warehouse-73584.html

http://smallbusiness.chron.com/disadvantages-data-warehouse-73584.html

7023t - tp3 - w7 - s8 - r1 - answer

Documents

eis menawarkan kuat

sistem bi memberikan

baik dasar untuk eis

digunakan untuk set

bisnis inteligensi bi

penuh penduduknya gudang

memenuhi kebutuhan manajemen

untuk tujuan pasal