“To succeed with Big Data, start small.” – Bill Franks, HBR

The information age and related sources of data, gives businesses of all kinds access to Big Data that’s growing in volume, variety, velocity (the “3V’s”). With more data coming from more sources faster than ever, questions arise if you should adopt this technology. Is your company combining new and existing data sources to make better decisions about your business? How could new data sources including social, sensors, location and video help improve your business performance?

We really believe that Big Data projects should “start small”.
The budget constraints and the pressure of Return on Investment, based on working with fuzzy and unstructured data sets (apart of their relevance and analysis outcome value), forces new and innovative approaches.

Petapilot can help on your discovery process, easily setting up a Big Data environment.
On our platform, or installing a configurable pre-packaged appliance, we can ensure a cost effective and a quick-win implementation for your company.
With an experienced team of engineers and financial experts, PetaPilot will help you implement the technologies you need to manage and understand your data.
Our knowledge on ERP’s like SAP, Oracle and Navision can also help your company to extract the proper data from your information systems, combining it with the Big Data sets.

Whatever your Big Data challenges are, we will provide you the strategic and technological guidance you need to succeed.

The Apache Hadoop® software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

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The Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL.

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Apache Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark’s standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat.

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