Pariveda Featured on the Big Data Blog

This is a guest post by Stephen Verstraete, a manager at Pariveda Solutions. Pariveda Solutions is an AWS Advanced Consulting Partner.

Common patterns exist for batch processing and real-time processing of Big Data. However, we haven’t seen patterns that allow us to process batches of dependent data in real-time. Expedia’s marketing group needed to analyze interdependent data sets as soon as all of the data arrived to deliver operational direction to partners. The existing system ran on an on-premises Hadoop cluster, but the team was struggling to meet their internal SLAs. The information was also time-sensitive; getting the data faster meant giving better operational direction to partners.

The Pariveda team working at Expedia engaged with Solutions Architects at AWS to solve three distinct challenges: How to deliver analysis results as rapidly as the source data becomes available;  how to process data sets that are interdependent but are produced at different times; and how to manage dependencies between data sets that arrive at different times.

In this blog post, I describe how the Expedia, Pariveda, and AWS teams figured out a unique approach to real-time data processing using AWS Lambda, Amazon DynamoDB, Amazon EMR, and Amazon S3 as building blocks. You’ll learn how to implement a similar pipeline without managing any infrastructure.

Read the Full Post Here

Not All Ladders Are the Same


Career Ladders: Why Not All Ladders Are the Same

Like ladders in a home improvement store, career ladders within organizations vary by their intended use. Ladders can be described by the length of their rails and number of rungs, which determine the distance between rungs. Material composition varies as well, depending on strength requirements. Some ladders are short and easily moved, such as a lightweight kitchen stool. Others are long and strong, made of fiberglass with extension arms, to reach significant heights.
Career ladders can be evaluated within this context. Descriptions change from rungs to steps (titles, roles and pay grades), from rails to time in role, and path distance to (often scarcity of) the next step. Career ladders are defined by employers. Some have clarity. Most do not, leaving employees to struggle attempting to climb them.

Consider job candidates entering the workforce from college. Blurred career ladders cause candidates to zero in on what is visible — title and compensation at the first step on the ladder. Lacking visibility to other ladders, experienced candidate evaluations tend to be relative to the current step they are standing on (their current job) and exposed steps they can compare against (competing job offers). Unlike physical ladders, career ladders are geometric progressions. Steps are not distributed uniformly across the length of the ladder. The higher the step on the ladder, the greater the distance between it and the lower one. Candidates seek loftier titles and higher compensation. The higher the jump the better. Candidates compare steps, not differences between ladders. This is a mistake; a career trap significantly slowing advancement for high achievers.

Compare technology companies with professional service firms. Tech companies tend to offer higher first steps (more compensation) than professional services, but limit the number of reachable steps. Distance between steps is greater. Often tech company ladders are short with low inclines. Achievers must climb multiple short ladders at different places along the wall of business success which may include a corporate relocation for a few handpicked for greater leadership roles. Professional service firm ladders tend to have lower first steps, leading to more steps of somewhat shorter distance on the lower span of a single extension ladder, with a progressively steeper incline to mount. Instead of relocation, movement is from project to project, often requiring travel. Two different, yet equally difficult, ladders to scale for high achievers. Is there any real alternative to today’s “best practice” of jumping from role to role and company to company?

Pariveda Solutions is intentionally designed for high achievers to reach their fullest potential on our innovative career ladder. The wall of business achievement is absolute and enduring. It’s today’s ladders that are consistently geometric, even exponential in some cases. We don’t change the wall; we change the ladder. We apply a log function, our Cohort Scale and Expectations Framework, to discretely define all aspects of our career ladder from end to end with great clarity and transparency. Distances between steps are much shorter than other ladders. [We deliberately deconstruct traditional roles and add virtual ones to shorten distances between steps.] We provide strong rails to support our individuals’ climbs with enriched mentoring, coaching and on the job learning through a wide variety of challenging and interesting projects in fulfilling and exhilarating roles. The ladder leans on the wall of one of the fastest growing, best places to work with one of the world’s most engaged workforces.

Bruce Ballengee, CEO

VOL 7 Not All Ladders Are the Same PDF

Pariveda+AWS: Combating Big Data

By Mike Jurjovec, Vice President, Pariveda Solutions

It’s not always common that a startup has Big Data challenges from day one, but 3WON had a big idea on how to leverage Big Data.  3WON set out to be the one true source for consumers to find, locate and connect with physicians across the US.

To meet this goal, 3WON had to ingest, model, enhance and aggregate data from dozens disparate sources to provide a truly unique view of physician information.  By focusing on the credentialing aspect of physician data, 3WON was in a unique position to have such a breadth and depth of accurate and timely physician information.  As a startup, the initial processing power and storage needs were small, but 3WON knew they would grow quickly and need to scale to handle information for every physician in the United States.  Amazon Web Services (AWS) and Pariveda Solutions came together to provide the cost-effective platform and the solution designed for 3WON’s Big Data needs.

“We needed a fast, reliable and scalable solution that was cost-friendly to our startup needs,” said Marc Teller, COO for 3WON.  “Having Pariveda build an AWS solution allowed us to make sense of massive amounts of data.”

Want to learn how we can help you make your big ideas a reality with our Big Data solutions?
Contact us at
Mike.jurjovec at LinkedIn
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