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Enterprise Data Warehouse Optimization

Reduce costs by moving data and processing to Hadoop®

cloud Learn how you can modernize your data warehouse with Hadoop


What is an EDW?

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. 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 EDW contains data spanning 5 to 10 years.

EDW Optimization

Problems with a typical EDW

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 the existing EDW solution.

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, it is becoming increasingly challenging for existing EDW technologies to provide a highly cost-efficient environment with optimal performance, scalability and flexibility.

Solution Elements

Hortonworks Data Platform


Powerful open Hadoop capabilities for data governance and integration, data management, data access, security and operations— architected for deep integration with your existing data center technology. Learn More



High-performance ETL software to access and easily onboard traditional enterprise data to HDP. Learn More



The business interface for fast business intelligence (BI) on Hadoop to bridge the gap between business users and their data. Learn More

Professional Services


Expert guidance and support to quickly prove the value of your new architecture and maximize the value of the full tested and validated Hortonworks data architecture optimization solution. Learn More

Optimizing EDW with Apache Hadoop ®



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


Data model can be applied based on the questions being asked of data (schema-on-read


HDP is designed to answer questions as they occur to the user



100% of the data is available at granular level for analysis


HDP can store and analyze both structured and unstructured data


Data can be analyzed in different ways to support diverse use cases

Cost Effective


HDP (Hortonworks Data Platform) is 100% open - there is no licensing fee for software


HDP runs on commodity hardware


New data can be landed in HDP and used in days or even hours

Use-Cases on EDW Optimization

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Fast BI on Hadoop

Proprietary EDW systems were adopted for fast BI and deep slice-and-dice analytics, but EDW prices are unsustainably high and these systems have not adapted to modern big data challenges like unstructured data and large-scale analytics.

Hortonworks makes fast BI on Hadoop a reality, with the combination of a fast in-memory SQL engine to create data marts with an OLAP cubing engine that lets you query huge datasets in seconds. This gives you the choice of querying pre-aggregated data for maximum performance or in full-fidelity form when the nest grains of detail are needed, allowing access from any major BI tool that supports ODBC, JDBC or MDX.

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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 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.

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Increasing data volumes and cost pressures force many companies to archive old data to tape where it can’t be analyzed or must be retrieved at great expense.

Hadoop 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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