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SwiftIQ leverages Google Cloud Connectors to unleash Hadoop ecosystem
Thursday, April 3, 2014
Our guest post today comes from Massimo Ilario, co-founder and principal engineer of
SwiftIQ
, a cloud-based API infrastructure to facilitate data accessibility and adaptive machine learning predictions.
At SwiftIQ, we unify and analyze vast amounts of disparate data and apply scalable algorithms to extract insights for our customers, enabling smarter, real-time decisions. For instance, we help lots of supermarkets collect and analyze customer transaction data to predict in-store shopper engagement, which helps retailers plan floor layouts, optimize promotions and recommend product upsells to their customers. Historically, most supermarkets could not even store detailed in-store basket information because of the size of this data.
Swift Predictions
is our machine learning environment that makes predictions based on vast amounts of data. As we thought about scaling Swift Predictions, we knew we needed substantial infrastructure with reliable file storage, long-running processes and the ability to handle unpredictable scale. Google Cloud Platform met all our requirements for fast, powerful, cost-efficient cloud infrastructure. Starting with
Google App Engine
for development, we also use
Google Compute Engine
,
Google Cloud Storage
,
Google BigQuery
and
Google Cloud Datastore
.
One of the more data intensive algorithms, frequent pattern mining (FPM), is intended to analyze order combinations and return the top occurrences. For a supermarket with 50,000 items typically available in store, the FPM algorithm commonly creates a million rows of unique order combinations. Once processed, these results span tens of millions of records and are stored in BigQuery, which allows for rapid retrieval. Our infrastructure can then scale these models across dozens or hundreds of stores using Cloud Platform.
Swift Predictions runs on Apache Hadoop, where we were writing and tuning MapReduce jobs to shuffle data between App Engine and Compute Engine. Google’s recent introduction of
Google Cloud Storage Connector for Hadoop
has removed this obstacle. We can now freely move data between our Web-facing project and our long-running, backend processes. As developers, this higher level of interoperability means we have the flexibility to pick the best tool to house our results from Hadoop: Cloud Storage or BigQuery.
One of our product features, a data mining algorithm for analyzing shopper buying patterns, creates a considerable amount of input data in a sparse matrix representation. Our input data can only be represented as a split of large files into the Hadoop Distributed File System (HDFS), and our output data is best represented as results within BigQuery. "Cloud Storage Connector for Hadoop has proven to make this workflow a more manageable process since our Hadoop workflow communicates with Cloud Storage and BigQuery as if these were HDFS and any other native Hadoop InputFormat and OutputFormat."
App Engine modules are ideal for packaging and isolating functionality in our system. We benefit by dedicating our default module to serve the web application and then develop secondary modules to handle specific tasks that may be longer running or more intensive in certain areas. It has allowed us to decouple much of our source code from a main web application and simplify the maintenance for the long term. Coding changes to isolated modules need not require a more comprehensive QA cycle for a deployment.
Thanks to Cloud Platform, our development team spends a minimal amount of time managing the performance tuning and reliability of our entire machine learning piece, which allows us to focus on unlocking and delivering new insights for our customers.
-Contributed by Massimo Ilario, co-founder and Principal Engineer, SwiftIQ.
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