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Apache Hadoop Data Warehouse Architecture for EDW Optimization

Reduce Costs by Moving Data and Processing to Hadoop®

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EDW とは?

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.

EDW の最適化

典型的な EDW の問題

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.


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



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EDW optimization with Apache Hadoop ®



Data can be loaded in HDP without having a data model in place




HDP はユーザーが考えた質問に答えるよう設計されている



粒度の細かい分析で、データを 100% 利用可能


HDP は構造化、非構造化の両タイプのデータを格納、分析可能





HDP(Hortonworks Data Platform)は 100% オープン - ソフトウェアのライセンス料は不要


HDP はコモディティハードウェア上で動作


HDP に新しいデータを入れて数日内、あるいは数時間内に利用可能

EDW の最適化に関するユースケース

ユースケース 1

Hadoop で迅速な BI を実現

迅速な BI と深く細かい分析のために専用の EDW システムが採用されましたが、EDW は価格が極端に高い上に、これらのシステムはモダンビッグデータが抱える非構造化データや大規模スケール分析などの課題に適応していません。

Hortonworks は、データマートを構築する高速 SQL インメモリエンジンと、大規模データセットのクエリを数秒で行える OLAP キューブエンジンを採用し、Hadoop 上で迅速な BI を実現しています。これにより、あらかじめ集積されたデータに対するクエリを最大限のパフォーマンスのために実行するのか、あるいは詳細を必要とする場合に高忠実度を維持して実行するのかを選択することができ、ODBC、JDBC、MDX をサポートするすべての主要な BI ツールからアクセスが可能です。


ユースケース 2

ETL 処理を Hadoop に移植

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.


ユースケース 3

Hadoop でデータをアーカイブ


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.