What is Stratio? This is a question that we can really only answer now, three years after our foundation by a team of seasoned engineers in 2013. Why has it taken us so long? Because we have been busy pulling together the most transformational and disruptive tool ever to exist.
Nowadays, there are a lot of Big Data query engines available. Some companies struggle to choose which one to use. Benchmarks exist, but results can be contradictory and thus difficult to trust.
This two-article series explains how to design and implement a hybrid recommender system that works just like the ones used by Amazon or Ebay.
Conway’s Game of Life: You could hardly imagine a simpler set of rules to code on your computer and you wouldn’t expect any interesting result at all, but… behold the wonders of its hidden might!
In this first issue, we will follow how pipelines are being used at Stratio Big Data to achieve full lifecycle traceability, from the development team to a final productive environment.
Implicit parameters and conversions are powerful tools in Scala increasingly used to develop concise, versatile tools such as DSLs, APIs, libraries… When used correctly, they reduce the verbosity of Scala programs thus providing easy to read code.
When surfing the internet, it is quite easy to find sites comparing the most popular Machine learning toolkits. These sites give you a lot of information about the strengths and weaknesses of the libraries, how they work and some examples to compare how easy it is to use these types of tools.
In this post we will show how to use the different SQL contexts for data query on Spark. We will begin with Spark SQL and follow up with HiveContext. In addition to this, we will conduct queries on various NoSQL databases and analyze the advantages / disadvantages of using them.
When working with Big Data, it’s frequent to have the need to aggregate data in real-time, whether it comes from a specific service, such as social networks (Twitter, Facebook…) or even from more diverse sources, like a weather station.
When working with Big Data, sometimes it’s useful to remember that powerful products wouldn’t work properly without the tools that build them.
