Enterprise Data Warehouse (EDW) is an organization’s central data repository that is built to support business decisions. EDW contains data related to areas that the company wants to analyze. For a manufacturer, it might be customer, product or bill of material data. EDW is built by extracting data from a number of operational systems. As the data is fed into EDW it is converted, reformatted and summarized to present a single corporate view. Data is added into the data warehouse over time in the form of snapshots and normally an enterprise data warehouse contains data spanning 5 to 10 years. A Hadoop data warehouse architecture enables deeper analytics and advanced reporting from these diverse sets of data.
Problemas com um EDW típico
The Enterprise Data Warehouse has become a standard component of the corporate data architectures. However, the complexity and volume of data has posed some interesting challenges to the efficiency of existing EDW solutions.
Realizing the transformative potential of Big Data depends on the corporations’ ability to manage complexity while leveraging data sources of all types such as social, web, IoT and more. The integration of new data sources into the existing EDW system will empower corporations more and deeper analytics and insights. More importantly, EDW optimization using Hadoop provides a highly cost-efficient environment with optimal performance, scalability and flexibility.
Elementos da solução
Hortonworks Data Platform
Powerful open Hadoop data warehouse architecture with capabilities for data governance and integration, data management, data access, security and operations—designed for deep integration with your existing data center technology. Learn More
EDW offload to Hadoop - High-performance ETL software to access and easily onboard traditional enterprise data to HDP. Learn More
Suporte e orientação especializada para comprovar rapidamente o valor da sua nova arquitetura e maximizar o valor da solução de otimização de arquitetura de dados, totalmente testada e validada, da Hortonworks. Saiba Mais
EDW optimization with Apache Hadoop ®
Data can be loaded in HDP without having a data model in place
O modelo de dados pode ser aplicado tendo como base perguntas feitas sobre os dados (schema-on-read)
A HDP foi projetada para responder a perguntas conforme elas ocorrem para o usuário
100% dos dados estão disponíveis no nível granular para análise
A HDP pode armazenar e analisar dados estruturados e não estruturados
Os dados podem ser analisados de diferentes formas para apoiar diversos casos de uso
HDP (Hortonworks Data Platform) é 100% aberta – não há taxa de licenciamento para software
A HDP é executada em hardware de commodity
Novos dados podem ser desembarcados na HDP e utilizados em dias ou até mesmo horas
Casos de uso sobre otimização de EDW
CASO DE USO 1
Fast BI no Hadoop
Os sistemas EDW patenteados foram adotados para fast BI e análises extremamente detalhistas, mas os preços do EDW são insustentavelmente elevados e esses sistemas não se adaptaram a grandes desafios de dados modernos, como dados não estruturados e análise em grande escala.
A Hortonworks torna a fast BI no Hadoop uma realidade, com a combinação de um rápido mecanismo SQL in-memory para criar data marts com um mecanismo de cubos OLAP, que permite consultar enormes conjuntos de dados em segundos. Dessa forma, você tem a opção de consultar dados pré-agregados para ter máximo desempenho ou uma forma de fidelização total quando os detalhes são necessários, permitindo acesso de qualquer ferramenta de BI compatível com ODBC, JDBC ou MDX.
A typical EDW spends between 45 to 65 percent of its CPU cycles on ETL processing.These lower-value ETL jobs compete for resources with more business-critical workloads and can cause SLA misses. Hadoop can EDW offload these ETL jobs with minimal porting effort and at substantially lower cost, saving money and freeing up capacity on your EDW for higher-value analytical workloads. Hortonworks makes it easy by providing high-performance ETL tools, a powerful SQL engine and integration with all major BI vendors.
Os crescentes volumes de dados e as pressões de custo forçam muitas empresas a arquivar dados antigos em fita, onde já não podem ser analisados ou precisam ser recuperados por um alto custo.
A Hadoop data warehouse architecture offers cost per terabyte on par with tape backup solutions. Because of the appealing cost, you can store years of data rather than months. All of your enterprise data remains available for retrieval, query and deep analytics with the same tools you use on existing EDW systems.
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