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Technical Considerations: DBMS (1)T

Technical Considerations: DBMS (1)
The two important challenges of data warehouses:
The very large size of the databases
The need to process complex ad hoc queries in a relatively short time
Therefore, the most important requirements for the data warehouse DBMS are:
Performance
Throughput
Scalability

Technical Considerations: DBMS (2)
The majority of Relational DBMS vendors has implemented parallelism in their products:
DB2
Oracle
Sybase
In addition to the “traditional” Relational DBMSs, there are databases that have been optimized specifically for data warehousing, such as Red Brick Warehouse from Red Brick Systems

Technical Considerations: Communication Infrastructure
Communication Infrastructure is often neglected during the planning of data warehouse
The cost and efforts associated with bringing access to corporate data directly to the desktop could be significant, since many large organizations do not have a large user population with direct electronic access to information
Communications networks have to be expanded, new hardware and software may have to be purchased

Implementation Considerations
Steps to build a data warehouse
Access Tools
Data extraction, cleanup, transformation, and migration
Data placement strategies
Metadata
User sophistication levels

Steps to build a data warehouse
Collect and analyze business requirements
Create a data model and a physical design for the data warehouse
Define the data sources
Choose the database technology and platform for the warehouse
Extract the data from the operational databases, transform it, clean it up, and load it into the database
Choose database access and reporting tools
Choose database connectivity software
Choose data analysis and presentation software
Update the data warehouse

Access Tools
Currently, no single tool on the market can handle all possible data warehouse access needs
Most implementations rely on a suite of tools
The best way to choose this suite includes the definition of different types of access to the data and selecting the best tool for that kind of access

Example of access types
Simple tabular form reporting
Ranking
Multivariable and/or Time series analysis
Data visualization, graphing, charting, and pivoting
Complex textual search
Statistical analysis
AI techniques for testing of hypothesis, trends discovery, definition, and validation of data clusters
Information mapping (I.e., mapping of spatial data in geographic information systems)
Ad hoc user-specified queries
Predefined repeatable queries
Interactive drill-down reporting and analysis
Complex queries with multi-table joins, multi-level sub-queries, and sophisticated search criteria

Data extraction, cleanup, transformation, and migration
Selection criteria affecting the transformation process:
The capability to merge data from multiple data stores
The specification interface to indicate the data to be extracted and the conversion criteria
The ability to read information from data dictionaries or import information from repository products
The code generated by the tool should be completely maintainable
Selective data extraction of both data elements and records enables users to extract only the required data
A field level data examination for the transformation of data into information
The ability to perform data-type and character-set translation is a requirement when moving data between incompatible systems
The capability to create summarization, aggregation, and derivation records and fields is very important
The data warehouse DBMS should be able to load directly from the tool
Vendor stability and support for the product must be evaluated

Data extraction, cleanup, transformation, and migration
Vendor solutions:
Prism Warehouse Manager: model based approach
Information Builder: gateway approach
SAS Institute: all the warehouse functions, including extraction

Prism Warehouse Manager
Maps source data to a target DBMS to be used as a warehouse
Warehouse Manager generates code to extract and integrate data, create and manage metadata, and build a subject-oriented, historical base
The standard conversions, key changes, structural changes, and condensations needed to transform operational data into data warehouse information are automatically created
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Technical Considerations: DBMS (1)The two important challenges of data warehouses:The very large size of the databasesThe need to process complex ad hoc queries in a relatively short timeTherefore, the most important requirements for the data warehouse DBMS are:PerformanceThroughputScalabilityTechnical Considerations: DBMS (2)The majority of Relational DBMS vendors has implemented parallelism in their products:DB2OracleSybaseIn addition to the “traditional” Relational DBMSs, there are databases that have been optimized specifically for data warehousing, such as Red Brick Warehouse from Red Brick SystemsTechnical Considerations: Communication InfrastructureCommunication Infrastructure is often neglected during the planning of data warehouseThe cost and efforts associated with bringing access to corporate data directly to the desktop could be significant, since many large organizations do not have a large user population with direct electronic access to informationCommunications networks have to be expanded, new hardware and software may have to be purchasedImplementation ConsiderationsSteps to build a data warehouseAccess ToolsData extraction, cleanup, transformation, and migrationData placement strategiesMetadataUser sophistication levelsSteps to build a data warehouseCollect and analyze business requirementsCreate a data model and a physical design for the data warehouseDefine the data sourcesChoose the database technology and platform for the warehouseExtract the data from the operational databases, transform it, clean it up, and load it into the databaseChoose database access and reporting toolsChoose database connectivity softwareChoose data analysis and presentation softwareUpdate the data warehouseAccess ToolsCurrently, no single tool on the market can handle all possible data warehouse access needsMost implementations rely on a suite of toolsThe best way to choose this suite includes the definition of different types of access to the data and selecting the best tool for that kind of accessExample of access typesSimple tabular form reportingRankingMultivariable and/or Time series analysisData visualization, graphing, charting, and pivotingComplex textual searchStatistical analysisAI techniques for testing of hypothesis, trends discovery, definition, and validation of data clustersInformation mapping (I.e., mapping of spatial data in geographic information systems)Ad hoc user-specified queriesPredefined repeatable queriesInteractive drill-down reporting and analysisComplex queries with multi-table joins, multi-level sub-queries, and sophisticated search criteriaData extraction, cleanup, transformation, and migrationSelection criteria affecting the transformation process:The capability to merge data from multiple data storesThe specification interface to indicate the data to be extracted and the conversion criteriaThe ability to read information from data dictionaries or import information from repository productsThe code generated by the tool should be completely maintainableSelective data extraction of both data elements and records enables users to extract only the required dataA field level data examination for the transformation of data into informationThe ability to perform data-type and character-set translation is a requirement when moving data between incompatible systemsThe capability to create summarization, aggregation, and derivation records and fields is very importantThe data warehouse DBMS should be able to load directly from the toolVendor stability and support for the product must be evaluatedData extraction, cleanup, transformation, and migrationVendor solutions:Prism Warehouse Manager: model based approachInformation Builder: gateway approachSAS Institute: all the warehouse functions, including extractionPrism Warehouse ManagerMaps source data to a target DBMS to be used as a warehouseWarehouse Manager generates code to extract and integrate data, create and manage metadata, and build a subject-oriented, historical baseThe standard conversions, key changes, structural changes, and condensations needed to transform operational data into data warehouse information are automatically created
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Pertimbangan Teknis: DBMS (1)
Dua tantangan penting dari data warehouse:
Ukuran yang sangat besar database
Kebutuhan untuk memproses query yang kompleks ad hoc dalam waktu yang relatif singkat
Oleh karena itu, persyaratan yang paling penting bagi DBMS data warehouse adalah:
Kinerja
throughput
Skalabilitas Pertimbangan Teknis: DBMS (2) Mayoritas vendor Relational DBMS telah menerapkan paralelisme dalam produk mereka: DB2 Oracle Sybase Selain "tradisional" Relational DBMS, ada database yang telah dioptimalkan secara khusus untuk data warehousing, seperti Red Brick Gudang dari Red Brick Sistem Pertimbangan Teknis Komunikasi Infrastruktur Infrastruktur Komunikasi sering diabaikan selama perencanaan data warehouse Upaya terkait dengan membawa akses ke data perusahaan secara langsung ke desktop bisa menjadi signifikan, karena banyak organisasi besar biaya dan tidak memiliki pengguna yang besar populasi dengan akses elektronik langsung ke informasi jaringan Komunikasi telah diperluas, perangkat keras dan perangkat lunak baru mungkin harus dibeli Pelaksanaan Pertimbangan Langkah-langkah untuk membangun data warehouse Access Alat data ekstraksi, pembersihan, transformasi, dan migrasi strategi penempatan data Metadata tingkat kecanggihan Pengguna Langkah untuk membangun sebuah gudang data Mengumpulkan dan menganalisis kebutuhan bisnis Buat model data dan desain fisik untuk data warehouse Tentukan sumber data Pilih teknologi database dan platform untuk gudang Ekstrak data dari database operasional, mengubahnya, bersih itu, dan beban ke dalam database Pilih akses database dan alat pelaporan Pilih Database konektivitas perangkat lunak Pilih analisis data dan perangkat lunak presentasi Update data warehouse Alat Access Saat ini, tidak ada alat tunggal di pasar dapat menangani semua data warehouse mungkin kebutuhan akses Kebanyakan implementasi mengandalkan suite alat Cara terbaik untuk memilih suite ini meliputi definisi dari berbagai jenis akses ke data dan memilih alat terbaik untuk jenis akses Contoh jenis akses bentuk tabel sederhana melaporkan Ranking analisis multivariabel seri dan / atau waktu visualisasi data, grafik , charting, dan berputar pencarian tekstual Kompleks analisis statistik teknik AI untuk pengujian hipotesis, tren penemuan, definisi, dan validasi cluster Data pemetaan Informasi (Ie, pemetaan data spasial dalam sistem informasi geografis) Ad hoc query pengguna ditentukan Predefined pertanyaan berulang interaktif drill-down pelaporan dan analisis query Kompleks dengan multi-tabel bergabung, multi-level sub-query, dan kriteria pencarian canggih ekstraksi data, pembersihan, transformasi, dan migrasi Kriteria seleksi yang mempengaruhi proses transformasi: Kemampuan untuk menggabungkan data dari beberapa data yang toko Spesifikasi antarmuka untuk menunjukkan data yang akan diekstrak dan kriteria konversi Kemampuan untuk membaca informasi dari kamus data atau mengimpor informasi dari produk repositori Kode yang dihasilkan oleh alat harus benar-benar dipertahankan Selektif ekstraksi data dari kedua elemen data dan catatan memungkinkan pengguna untuk mengekstrak hanya data yang dibutuhkan Pemeriksaan Data tingkat lapangan untuk transformasi data menjadi informasi Kemampuan untuk melakukan data-jenis dan terjemahan karakter-set merupakan kebutuhan ketika memindahkan data antara sistem yang tidak kompatibel Kemampuan untuk membuat catatan summarization, agregasi, dan derivasi dan bidang sangat penting DBMS data warehouse harus dapat memuat langsung dari alat stabilitas vendor dan dukungan untuk produk harus dievaluasi data ekstraksi, pembersihan, transformasi, dan migrasi solusi Vendor: Prism Gudang Manager: Pendekatan berbasis model Informasi Builder: Pendekatan gerbang SAS Institute: semua fungsi gudang, termasuk ekstraksi Prism Gudang Manajer Maps sumber data ke DBMS target yang akan digunakan sebagai gudang Gudang manajer menghasilkan kode untuk mengekstrak dan mengintegrasikan data, membuat dan mengelola metadata, dan membangun subjek berorientasi, dasar sejarah The konversi standar, perubahan kunci, perubahan struktural, dan kondensasi diperlukan untuk mengubah data menjadi informasi operasional data warehouse secara otomatis dibuat










































































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