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    Data Scientists
    Hadoop Experts
    Problem Solvers

Our Services

Insight Development

We transform raw data into actionable insights using analytics & Data Science

Data Exploration

We help companies understand patterns, trends and relationships in their data that can produce real ROI

Hadoop Strategy

We help companies develop a strategy for implementing Hadoop & building analytics with Data Science


Legacy Migration

We migrate existing application environments from traditional data warehousing/BI platforms to the Hadoop Ecosystem

Hadoop Jumpstart

We enrich the knowledge canvas of company resources and render them efficient in the Hadoop Ecosystem

Hadoop Implementation

We help companies implement production Hadoop environments complete with security, high-availability and multi-tenancy

Latest From Our Blog

Written by Spry

Spry was recently given the opportunity to be a guest author for the Hortonworks blog. The post is available in its entirety here. A sneak peek of the blog is given below!

In early 2014, Spry developed a solution that heavily utilized Hive for data transformations. When the project was complete, three distinct data sources were integrated through a series of HiveQL queries using Hive 0.11 on HDP 2.0. While the project was ultimately successful, the workflow itself took an astounding two full days to execute, with one query taking 11 hours.

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Written by Spry

What is Kafka?

In terms of the distributed environment...

Apache Kafka is a distributive commit log service. It leverages a language independent TCP protocol to provide functionality as a messaging system over partitioned and replicated feeds called "topics". The partitioned logs are the object of distribution, as each active node constitutes a Kafka server and remains responsible for processing data and requests for a section of the partitions.

This post will provide an overview of these concepts and give you more insight into how Kafka functions.

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Written by Spry

In many of our use cases, the data we work with does not come ready to be fed into an analytics workflow. It must first be ingested and prepared. This includes renaming and/or reordering fields, changing data types, filtering out invalid values, and combining different parts of the same data source. In this post, we will be covering how to perform these steps using a Data Pipeline tool called Alteryx. We will walk through a workflow used for one of our clients.

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About Spry

We are a high standards Data Science & Hadoop firm solving complex problems for Fortune 500 companies

Keep In Touch

  •   info (@) spryinc.com
  •   +443.212.5072
  •   53 Loveton Circle
    Suite 114
    Sparks, MD 21152