Thanks to Jeff Palmucci, Director of Machine Learning at TripAdvisor, for permission to republish the following (originally appeared in TripAdvisor’s Engineering/Operations blog).
Here at TripAdvisor we have a lot of reviews, several hundred million according to the last announcement. I work with machine learning, and one thing we love in machine learning is putting lots of data to use.
I’ve been working on an interesting problem lately and I’d like to tell you about it.
Cloudera Director 1.5 introduces a new plugin architecture to enable support for additional cloud providers. If you want to implement a plugin to add integration with a cloud provider that is not supported out-of-the-box, or to extend one of the existing plugins, these details will get you started.
As discussed in our previous blog post, the Cloudera Director Service Provider Interface (Cloudera Director SPI) defines a Java interface and packaging standards for Cloudera Director plugins.
Cloudera Director 1.5 is now available; this post describes what’s inside, including a new open source plugin interface.
Cloudera Director is the manifestation of Cloudera’s commitment to providing a simple and reliable way to deploy, scale, and manage Apache Hadoop in the cloud of your choice. With Cloudera Director 1.5, we continue the story of enabling production-ready clusters and big data applications by focusing on the following themes.
Thanks to Barclays employees Sam Savage, VP Data Science, and Harry Powell, Head of Advanced Analytics, for the guest post below about the Barclays use case for Apache Spark and its Scala API.
At Barclays, our team recently built an application called Insights Engine to execute an arbitrary number N of near-arbitrary SQL-like queries and execute them in a way that can scale with increasing N.
Learn about the near real-time data ingest architecture for transforming and enriching data streams using Apache Flume, Apache Kafka, and RocksDB at Santander UK.
Cloudera Professional Services has been working with Santander UK to build a near real-time (NRT) transactional analytics system on Apache Hadoop. The objective is to capture, transform, enrich, count, and store a transaction within a few seconds of a card purchase taking place. The system receives the bank’s retail customer card transactions and calculates the associated trend information aggregated by account holder and over a number of dimensions and taxonomies.