Columnar Db Para Pequenas
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Apresentação revisada da tecnologia dos bancos colunares(em particular do SADAS) aplicável a pequenas e médias empresas.
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Columnar Db Para Pequenas
- 1. SADAS – UMA VIS ÃO PRÁTICA DOS BANCOS DE DADOS COLUNARES PARA PEQUENAS E MÉDIAS EMPRESAS Marco Aurelio Ribeiro Khydra Serviços de Bancos de Dados www.khydra.com
- 6. New Students Indicators Graduation Income to Date 14/Sep 21/Sep 28/Sep 05/Oct 12/Oct 19/Oct 26/Oct 02/Nov 09/Nov 16/Nov 23/Nov Entries to Date 0 0 0 0 23 143 1,019 4,146 4,744 6,027 6,719 Partial History(Acumulated) 1 22 83 201 549 1,020 1,724 10,405 11,156 13,021 14,535 Proportional Goal 1 28 106 256 699 1,299 2,196 13,252 14,209 16,584 18,513 % Proportional Goal 0.0% 0.0% 0.0% 0.0% 3.3% 11.0% 46.4% 31.3% 33.4% 36.3% 36.3% Final Goal 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 % Final Gaol 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 1.7% 7.0% 8.0% 10.2% 11.3% Unit Dados Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Grand Total Bauru Average Ticket 295 385 401 305 186 302 212 325 406 305 284 306 309 Average ticket - Budget 316 330 329 331 329 326 301 311 305 321 321 324 320 Average Ticket x Budget 93% 117% 122% 92% 57% 93% 70% 105% 133% 95% 88% 94% 97% Scholarships 286 400 564 505 300 336 438 380 604 394 430 469 5,106 Scholarships - Budget 409 409 410 410 410 409 412 412 413 413 413 413 4,932 Scholarships x Budget 70% 98% 138% 123% 73% 82% 106% 92% 146% 96% 104% 114% 104% Scholarship/Monthly Payments 24.3% 26.7% 32.1% 31.8% 24.1% 24.2% 26.7% 24.1% 47.3% 36.9% 34.1% 34.6% 30.3% Scholarship/Monthly Payments- Budget 30.3% 30.3% 29.9% 29.9% 30.0% 30.4% 31.5% 31.5% 31.3% 31.6% 31.6% 31.7% 30.8% Scholarship/Monthly Payments x Budget 80% 88% 107% 107% 80% 80% 85% 77% 151% 117% 108% 109% 98% Cancellations 0.5% 0.9% 5.3% 10.4% 27.0% 12.0% 34.5% 16.7% -35.8% -19.0% 0.8% 1.2% 5.8% Average Payments 453 574 642 549 344 466 416 516 683 495 489 525 513 Average Payments - Budget 522 522 516 517 517 516 509 509 512 513 513 513 515 Average Payments x Budget 87% 110% 125% 106% 67% 90% 82% 101% 133% 96% 95% 102% 100% Taxes and Services Monthly Payments 0.9% 0.9% 0.4% 0.4% 0.6% -0.2% 1.2% 0.7% 0.3% 0.2% 0.2% 0.1% 0.5% Taxes and Services Monthly Payments - Budget 0.6% 0.6% 0.6% 0.7% 0.6% 0.6% 0.8% 0.4% 0.3% 0.8% 0.5% 0.8% 0.6% Taxes and Services Monthly Payments x Budget(%) 147% 150% 57% 62% 100% -32% 151% 157% 116% 21% 40% 13% 80%
- 10. A Hierarquia de informação Ideal Empresa Areas Processos de Negócio Fontes de Dados Indicador de Desempenho (KPI) Relatório KPIs New Students Indicators Graduation Income to Date 14/Sep 21/Sep 28/Sep 05/Oct 12/Oct 19/Oct 26/Oct 02/Nov 09/Nov 16/Nov 23/Nov Entries to Date 0 0 0 0 23 143 1,019 4,146 4,744 6,027 6,719 Partial History(Acumulated) 1 22 83 201 549 1,020 1,724 10,405 11,156 13,021 14,535 Proportional Goal 1 28 106 256 699 1,299 2,196 13,252 14,209 16,584 18,513 % Proportional Goal 0.0% 0.0% 0.0% 0.0% 3.3% 11.0% 46.4% 31.3% 33.4% 36.3% 36.3% Final Goal 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 59,262 % Final Gaol 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% 1.7% 7.0% 8.0% 10.2% 11.3%
- 11. A Ferramenta
- 12. SADAS ( System for static data analysis ) É um banco relacional especificamente projetado para endereçar problemas de desempenho em aplicações de data warehouse. SADAS utiliza estruturas e algoritmos baseados na natureza de “ apenas leitura” dos dados e também adota técnicas que não se aplicam a um ambiente OLTP (On Line Transaction Processing) tradicional, visando sempre o máximo desempenho. OLTP DBMS OLAP DBMS Usuários milhares (Usuários, Pessoal de TI) Centenas (knowledge workers) Acess0 Read /write (em centenas de registros) Leitura complexa (em milhões de registros) Atualizações Contínuas Periódicas Uso / Dados 90% repetitivas / correntes, detalhados relacional 90% ad-hoc / historico summarizado, multidimensional Orientação Transações Análise
- 13. SADAS - Principais características
- 14. Clientes
- 15. Como utilizar o SADAS
- 16. Feeds (Rewrite,append, update) Sql ODBC JDBC SCI RelatóriosAnaliticos Criamos um fluxo simples para gerar um repositório centralizado de dados históricos e extrair relatórios. Fonte de Dados Única Data Mining Carga de Dados Streams Apps OLTP EDW Files Sistemas-Fonte
- 17. Vantagens
- 18. Utilizando o enfoque colunar obtemos:
- 19. Menos Hardware Estimativa de Máquina MILHÕES DE REGISTROS PLATAFORMA NUCLEOS DO PROCESSADOR RAM(GB) 50 32 2 2 100 64 2 4 500 64 4 8 1.000 64 8 16 2.500 64 8 32
- 20. Exemplo Prático
- 23. Rumo à Excelência Tempo de Projeto Estimado (18 meses) Projetos Desenvolvidos Identificar Fontes de Informação Implementar Data Mining Direcionar Demandas de relatórios
- 28. KHYDRA SERVI ÇOS RUA ORLANDO MARRONI,11 13272-101 -VALINHOS – SP TEL:(19)3869-3823 [email_address] A Khydra Serviços de Bancos de Dados é uma empresa situada em SP, na cidade de Valinhos especializada em serviços e projetos para aplicações de bancos de dados em geral, ERPs, Business Intelligence e Gestão de conteúdo com parceiros internacionais nestes segmentos e capaz de atender a clientes com produtos dos principais forncedores do mercado Nossas Tecnologias
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