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Enterprise Data Warehouse Modernization

So, you have made a major investment in your data warehouse. However, squeezing out marginal performance gains requires you to constantly invest in more hardware and services. There has to be a better, more cost-effective solution.

Increasingly, organizations are supplementing or replacing their enterprise data warehouse with Hadoop based platform for these reasons:


Save Millions of Dollars in License and Hardware Costs

With optimized data storage, you can store 10-30x more data per server than traditional data warehouses and save even more with a straightforward and affordable pricing model.

Improve Performance by Orders of Magnitude

Gain insights into your data in near-real time by running queries 50x-1,000x faster than legacy data warehouse solutions.

Skip the Costly and Cumbersome “Refrigerator” Upgrades

The Hadoop Platform is a software-only solution, so you have the freedom to choose the hardware that best suits your environment, and you can load and query data concurrently so you do not need to take your system offline for upgrades.

Increase Team Productivity and Organizational Value

Because operations that took days now take hours and hours now take seconds, your analytics team can be more productive and answer business-critical questions on the spot.

Be Prepared for Massive Scale

Infinitely and easily scale your solution by adding an unlimited number of industry-standard servers, preparing your organization for success in the face of an onslaught of Big Data.

Minimize Implementation and Administration Costs

With built-in simplicity and support for all industry standards, the Platform can be installed and set up in days (not weeks or months) with fewer costly administration resources, less retraining, and budget-friendly implementation engagement.

Hadoop Acceleration

Enterprises are struggling to derive business value from the onslaught of structured, semi-structured, and unstructured data. Legacy data warehousing and analytics platforms don’t deliver the speed and scale necessary in today’s big data world.

The Hadoop Distributed File System (HDFS), in turn, is an open-source distributed file system that can serve as an effective storage ground for large amounts of data. Hadoop is well suited for batch processing where immediate interactive analytics are not required. HP Vertica Analytics Platform consists of a massively parallel database and an extensible analytics framework optimized for real time analytics of data scaling from gigabytes to petabytes. HP Vertica and Hadoop are complementary analytics platforms purpose-built for big data. Both are modern, scalable, massively parallel processing (MPP) systems built for commodity hardware at considerably lower total cost of ownership.

Here are some real-world examples on how enterprises are using the HP Vertica Analytics Platform to help accelerate their Hadoop environment.


Processing social video events

A social video company uses Hadoop for batch processing of logs and HP Vertica Analytics Platform for ETL, ad hoc analytics, and interactive dashboards. In addition, the company uses a KV store for serving low-latency data needs.

Delivering digital consumer insights

A digital intelligence company uses HDFS to store raw input behavioral data and Hadoop to find conversions by determining what type of user clicked on a particular advertisement, and HP Vertica Analytics Platform to store and operationalize high-value business data. This helps the company achieve faster insights that are delivered more consistently with less administrative overhead and lower-cost, commodity hardware.

Accelerating drug discovery

A pharmaceutical company sought to analyze gene variants for improved drug targeting and discovery. It uses Hadoop to find the variants between a sample sequence and a reference genome, and uses the HP Vertica Analytics Platform to run analytics on very large sets of data to determine oncology targets.

Enabling privacy assurance

A company focused on web privacy uses HDFS to collect user privacy reporting requests, MapReduce to process and structure the data into HP Vertica Analytics Platform (ETL), and the platform to analyze statistics for every third-party tag on a website measuring site performance. Consumers benefit from a free browser plug-in that can tell them who is tracking them. Advertisers, in turn, can provide greater transparency to end users and better understand the impact of third-party tags on website performance.

Enterprise Data Hub

Organizations everywhere are grappling with how to manage their growing big data sets from ERP and e-commerce systems, log files, sensor data, social media and more. Apache Hadoop provides a cost-effective enterprise data hub (EDH) to store, transform, cleanse, filter, analyze and gain new value from all kinds of data.

Specific uses cases include:


Data Reservoir or “Data Lake”

Collecting raw data which was previously too expensive to store and process. Data is managed and governed here and can also act as an online archive for data infrequently accessed.

Data Refining

Optimize the process of integrating diverse data types from multiple sources to discover relationships. Parse, cleanse, transform, and integrate data.

Mainframe Optimization

Offload data and batch processing to Hadoop to free up expensive MIPS cycles and modernize the enterprise data architecture.

Big Data Exploration

Perform investigative analytics on large data volumes of unknown value. Apply a combination of SQL-on-Hadoop, machine learnings, statistics, and graph analysis techniques to unlock new insights and improve operational analytics such as anomaly detection and recommendations.

Data Warehouse Optimization

Capture, store, and refine incoming big data in an enterprise data hub (EDH) to free up valuable processing and storage space on the data warehouse for mission-critical reporting and analysis. Create online archive of infrequently queried data.

Operational Intelligence

Companies are continually looking for ways to maximize productivity and profitability. Even when operations have been analyzed and optimized, subtle changes in environments make room for further, significant improvement. Taking a wide variety of granular measurements from sensors – on vehicles, equipment, consumer products, smart meters, etc. – lets businesses track patterns in operations to find new optimization opportunities.

Specific use cases include:


Supply Chain and Logistics

Track the movement of vehicles and products to identify the “costs” of various transportation and process options. By analyzing large volumes of historical, time-stamped location data, businesses can calculate optimal delivery routes and enable dynamic rerouting to minimize the impact of arbitrary obstacles like traffic and weather. Businesses can also leverage the optimal delivery system as a revenue-generating basis for premium/expedited delivery services to consumers.

Assembly Line Quality Assurance

Take measurements of work-in-progress products to find manufacturing defects as early as possible, while also identifying any potential process or design flaws. Since defects are typically the result of many factors, analyzing long histories of assembly line sensor data can find subtle anomalies that signify product flaws.

Exploration and Production Optimization

Analyze data on existing operations and past exploration/production efforts to make decisions on ongoing and future operations. Tracking histories of successes and failures enable the development of models that provide guidance on where to pursue future projects, as well as when to halt existing projects due to reduced return on investment calculations.

Smart Meter Analysis

Get granular information from smart meters on energy utilization at a per-site basis to identify better pricing and utilization recommendations. Data from thousands or millions of remote sites give energy companies the complete picture of consumption, letting them better plan for energy purchases and allocation. Energy companies can also analyze usage patterns to identify inefficient appliances at customer homes to make new product recommendations.

Preventive Maintenance

Monitor equipment or product utilization in a live environment to identify patterns that indicate imminent failure. For revenue-generating operations equipment, downtime results in lost revenue as well as costly repairs. Ongoing analysis of an entire system lets businesses predict when failure might occur, so preventive maintenance can avoid the failure. For consumer products, failures or need for replacement will depend highly on usage patterns, and tracking those patterns help manufacturers to alert customers when their products need specific maintenance.

Customer Analytics

Customer analytics is vital for just about any industry or market segment. By understanding how your customers (or constituents) behave and interact with your organization, you are able to better serve, and profit, from the interactions.

The proliferation in the number of channels by which customers communicate has resulted in marketers drowning in data and struggling to better understand their customers. Marketers have to continually deliver above-market growth and show measurable results.

Specific uses cases include:


Clickstream Analysis

Analyze clickstream data to understand and optimize how consumers research and purchase products online and identify the next best action.

Advertising Optimization

Optimize ad campaigns by measuring effectiveness and adjusting campaign tactics in real time, incorporating data from all channels including search, ads, email and logs.

Recommendation Engine & Targeting

Generate consumer segments with similar behavior to target consumers in real time with relevant product offers and improve cross-sell based on usage patterns.

Social Media Analysis

Gain insights into consumer behavior, intent and social relationships by analyzing online behavior, social networks and transaction data.

360° Customer View

Improve customer satisfaction and cross-sell/up-sell opportunities by integrating all relevant customer data into one dashboard, accessible across company divisions. The key to effective customer analytics is first understanding customers, and then building products and services that best meet their needs. Some examples include:

Online & Mobile

Online and mobile businesses have a need to improve the effectiveness of their website. Analyzing clickstream data provides rich insight into which pages are effective and which pages site visitors ignore. When combined with sales and conversion data, clickstream analysis can help you discover the most effective series of steps needed to encourage conversions, sales, and add-on purchases.


Increased competition and shrinking margins are compelling retailers to increase the amount of data they collect to gain a competitive advantage. Loyalty programs, customer tracking solutions, and market research, when combined with sales and inventory data, provides rich insights that drive decisions around products, promotions, price, and distribution management. Data driven decisions enable sales based on actual purchase patterns, instead of guess work.

Financial Services

Banks, insurance companies, and other financial institutions are using customer analytics to understand the lifetime value of customers, increase cross-sales, and manage customer attrition, among other programs. The ability to understand how customers use different banking programs – credit and debit cards, mortgages and loans, and online banking tools – allows financial services companies to develop targeted campaigns and value added offers that increase customer satisfaction and profits.

Communication Service Providers (CSPs)

Call Detailed Records (CDRs) contain a wealth of information such as the length of a call, number called, weather the call was dropped or not, etc. CDRs create massive amounts of valuable data for CSPs. The ability to analyze these massive volumes of data allows CSPs to develop customer focused promotions that attract and retain customers, reducing churn and increasing profitability.

Sensor Analytics

In the coming years, data generated by sensors and machine-to-machine (M2M) communication, or the Internet of Things, will fill the storage systems of data centers across a wide range of industries. In fact, IDC forecasts that machine-generated data will increase to 42% of all data by 2020, up from 11% in 2005.

For years, people have been talking about the potential impacts of this data deluge from the Internet of Things. Today, the talk is shifting to the opportunity to use sensor data to drive new business models. Data-driven business models are already happening across a wide range of industries—from insurance and banking to manufacturing and medical products. The future will bring much more of this.

Across a wide range of industries, organizations are running analytics on sensor data to enable new value-added products, services, and business processes. All


Fleet management

Sensor data from delivery trucks is helping businesses schedule preventive maintenance before mechanical issues can disrupt fleet operations.

Healthcare sensing

Biosensors are now used to enable better and more efficient patient care across a wide range of healthcare operations, including telemedicine, telehealth, and mobile health.

Product monitoring

Manufacturers use sensor-data analytics to monitor the health and performance of their products and to work proactively to address service and maintenance issues before they lead to product downtime.

Predictive maintenance

Airlines use data from airplane sensors to proactively manage maintenance, improve reliability, reduce unplanned service work, and mitigate risk.

Safety compliance

Energy exploration companies analyze sensor data collected from oil drilling platforms to verify compliance with safety requirements and guide proactive steps to improve safety.

Smart appliances

Manufacturers use data from smart appliances to address a wide range of consumer needs—everything from replenishing groceries in a refrigerator to optimizing the use of washing machines and stoves.

Smart buildings

Facilities personnel use monitors to gather data on building systems, including heating, ventilation, and air conditioning (HVAC) equipment, to enable alarm monitoring and active notifications, proactive maintenance, and optimization of systems.

Smart Drilling

In the oil and gas industry, seismic exploration data is helping companies find profitable locations with less experimental drilling, lowering both operational cost and environmental impact.

Smart grids

Forward-looking cities and governments are upgrading electrical-grid infrastructure with smarter capabilities to enable smoother operation and tighter security.

Smart meters

Utilities are deploying digital, networked meters to systematically feed analytics tools and Web-based portals with consumption data.

Usage-based insurance

Insurance companies use data generated from sensors in automobiles to offer drivers rates based on the amount of driving they do, their driving habits, and even where they drive and park.

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