Vertica

Archive for January, 2013

Recapping the HP Vertica Boston Meet-Up

This week, some of our Boston-area HP Vertica users joined our team at the HP Vertica office in Cambridge, MA. Over some drinks and great food, we had the honor of hearing from HP Vertica power users Michal Klos followed by Andrew Rollins of Localytics. Both Michal and Andrew offered some valuable insight into how their businesses use the HP Vertica Analytics Platform on Amazon Web Service (AWS).

Michal uses the HP Vertica installation in the cloud, hosted on AWS. The highlight of Michal’s presentation was a live demonstration of a Python script using Fabric (a Python library and command-line tool) and Boto (Python interface to AWS) that executed code to quickly set up and deploy a Vertica cluster in AWS. Launching nodes on the HP Vertica Analytics Platform in AWS eliminates the need to acquire hardware and allows for an extremely speedy deployment. Michal was very complimentary of the recent enhancements to our AWS capabilities in the recently-released version 6.1 of the HP Vertica software.

Michael Klos Demonstration

Following Michal’s demonstration, Andrew took the floor to talk about how Localytics uses the HP Vertica Analytics Platform to  analyze user behavior in mobile and tablet apps.  With HP Vertica, Localytics gives their customers access to granular detail in real-time. Localytics caters to their clients by launching a dedicated node in the cloud for each customer. With the HP Vertica Analytics Platform powering their data in AWS, their customers can start gathering insightful data almost immediately.

Our engineers then took the stage to serve as a panel for questions from the floor. It’s not often that our engineers get the opportunity to answer questions from customers and interested BI professionals in an open forum discussion. Everyone took full advantage of the occasion, asking a number of questions about upcoming features and current use cases.  In addition, our engineers were able to highlight a number of new features from the 6.1 release that the users in attendance may not have been taking advantage of yet.

Meet-ups serve as a fantastic catalyst for users and future users to interact with each other, share best practices and have a valuable conversation with different members of the HP Vertica team. We reiterate our thanks to Michal and Andrew, and to all those that joined us at our offices — thank you for an excellent meet- up!

Don’t miss another valuable opportunity to hear from fellow HP Vertica user Chris Wegrzyn of the Democratic National Committee on our January 24th webinar at 1PM EST. We will discuss how the HP Vertica Analytics Platform revolutionized the way a presidential campaign is run. Register now!

Top 4 Considerations When Evaluating a Data Analytics Platform

From fraud detection to clickstream analytics to simply building better products or delivering a more optimal customer experience, Big Data use cases are abounding with analytics at the core.

With a solid business or use case in place, the next step that organizations typically take is to investigate and evaluate the appropriate set of analytics technology from which to accomplish their analysis, often starting with a data analytics platform. But what are the requirements from which to base your evaluation?

The Winter Corporation, the large-scale data experts, just finalized an in-depth white paper (The HP Vertica Analytics Platform: Large Scale Use and Advanced Analytics) that reflects the results and findings through evaluation, independent research, customer and employee interviews, and documentation review.

Intended for a more technical audience, this white paper focuses on key evaluation criteria that your organization can use as a guide as you conduct your own evaluation.

 

 

Winter Corporation identified these key feature areas as critical for any data analytics platform:

1. Architecture
• Column store architecture
• Shared nothing parallelism
• Cluster size and elasticity
• Smart K-Safety based availability
• Hybrid storage model
• Multiple database isolation modes
• Both bulk load and trickle feed

2. Performance
• Extensive data compression and data encoding
• Read-optimized storage
• Highly parallel operation
• Storage of multiple projections
• Automatic physical database design

3. General Useful and Noteworthy Features for Large-Scale Use
• Export-import
• Backup/restore
• Workload analyzer
• Workload management
• Role-based security

4. Extensions for Advanced Analytics
• SQL extensions
• Built-in functions
• User-defined extensions
• Flexibility in accessing and analyzing all data (structured, semistructured, or unstructured)

Finally, once you have evaluated and confirmed that the data analytics platform meets your feature and technology requirements, you want to hear from other organizations that have deployed large-scale analytics’ initiatives in real-world environments.

The white paper concludes with a write-up on how Zynga, a social game services company with more than 240 million users of its online games, stores the actions of every player in every game — about 6 TB per day of data — in near-real time in the HP Vertica Analytics Platform. No matter where in the world a game event occurs, the data can be retrieved via a report or query from the central HP Vertica database no more than five minutes later.

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