7023T - TP3 - W7 - S8 - R1 - ANSWER

GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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 orangorang 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 7023T – Advanced Database System

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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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.

Source Data

Purpose of Data

What a data

Insert and Update Query

7023T – Advanced Database System

OLAP Consolidation data; OLAP data comes from the various OLTP Databases To help with planning, problem solving, and decision support Multi-dimensional views of various kinds of business activities Periodic long-running batch jobs refresh the data Often complex queries involving aggregations

OLTP Operational data; OLTPs are the original source of the data. To control and run fundamental business tasks Reveals a snapshot of ongoing business processes Short and fast inserts and updates initiated by end users Relatively standardized and simple queries Returning Page 4 of 13

GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

Proccesing speed

Space Requirements

Database Design

Backup and Recovery

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 Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP Typically de-normalized with fewer tables; use of star and/or snowflake schemas Instead of regular backups, some environments may consider simply reloading the OLTP data as a recovery method

relatively few records Fast

Can be relatively small historical data is archived

if

Highly normalized with many tables 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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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 7023T – Advanced Database System

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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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 7023T – Advanced Database System

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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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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GHEMA NUSA PERSADA LZT4 – 1701497885 7023T – Advanced Database System

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

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