Category Archives: Agile

In-Memory Logical Data Warehouse for accelerating Machine Learning Pipelines on top of Spark and Alluxio

Abstract: Legacy enterprise architectures still rely on relational data warehouse and require moving and syncing with the so-called “Data Lake” where raw data is stored and periodically ingested into a distributed file system such as HDFS. Moreover, there are a … Continue reading

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The Barclays Data Science Hackathon: Building Retail Recommender Systems based on Customer Shopping Behaviour

In the depths of the last cold, wet British winter, the Advanced Data Analytics team from Barclays escaped to a villa on Lanzarote, Canary Islands, for a one week hackathon where they collaboratively developed a recommendation system on top of Apache Spark. The contest consisted on using Bristol customer shopping behaviour data to make personalised recommendations in a sort of Kaggle-like competition where each team’s goal was to build an MVP and then repeatedly iterate on it using common interfaces defined by a specifically built framework. Continue reading

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Surfing and Coding in Lanzarote, the Barclays Data Science hackathon

This post has been published on the Cloudera blog and summurises the results and takeaways of a week-long hackathon happened in Lanzarote in December 2015. The goal was to prototype a recommender systems for retail customers of shops in Bristol in Bristol, UK. The article shows how the stack composed by Scala and Spark was great for quickly writing some prototyping code to run locally in a single laptop and at the same time scalable for larger dataset to process in the cluster. Continue reading

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Lessons learnt from building data-driven production systems at Barclays

In the last years at Barclays we learnt and tried a lot of stuff that made the Advanced Analytics team very successful inside a large organization where, as such, being a productive data scientist is a tough challenge.

Our team works on a mix of descriptive, predictive and prescriptive projects that make use of machine learning and big data technologies, mainly on top of Apache Spark. Even though we deliver per-request insights coming from manual analysis, we primarily build automated and scalable systems to be periodically used either internally for a better decision-making or customer-facing in the form of analytics services (e.g. via the web portal).
In this post series I want to share some of the best practices, tools, methodologies and workflows that we experimented and the lessons learnt from them. Continue reading

Posted in Agile, Big Data, Machine Learning | Tagged , , , , | 4 Comments

The balance of exploratory analysis and development

Exploratory analysis should precede and follow any task from the modelling, design and development to the benchmarking. Major problem is how do you share, track and monitor your findings? How do you make your analysis repeatable and scrutinizable from the outside? This is still an open problem. Continue reading

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Coding practices for data products development

Code should be developed in a proper IDE and make use of advanced tools for re-factoring, auto-completion, syntax highlighting and auto-formatters; at least.

Notebooks should use routine libraries from the main codebase. As soon as some code is developed in a notebook and is reusable, it should be moved into a codebase. Continue reading

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The ScrumBan Jira board

Let’s start with one of the core tool of the agile workflow. We use a Jira board for tracking and organizing all of our projects. We developed a custom board which uses the sprints concept of Scrum but in a more flexible way as in Kanban. Continue reading

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Logical Data Warehouse for Data Science: map raw data directly from source to Spark in-memory with Tachyon

Common problems for large organizations dealing with Big Data and Data Science applications are: Data stored in non scalable infrastructure for analysis and processing Data governance and security policies 1. Data often resides into central data warehouse and RDBMS of which many legacy applications … Continue reading

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6 points to compare Python and Scala for Data Science using Apache Spark

Apache Spark is a distributed computation framework that simplifies and speeds-up the data crunching and analytics workflow for data scientists and engineers working over large datasets. It offers an unified interface for prototyping as well as building production quality application which makes it particularly suitable for an agile approach. I personally believe that Spark will inevitably become the de-facto Big Data framework for Machine Learning and Data Science.

Despite of the different opinions about Spark, let’s assume that a data science team wants to start adopting it as main technology. The choice of programming language is often a dilemma. Shall we build our models in Python or in Scala? Shall we run the exploratory analysis using the iPython notebook or iScala? Continue reading

Posted in Agile, Machine Learning, Python, Scala, Spark | Tagged , , | 13 Comments

WordPress Blog Posts Recommender in Spark, Scala and the SparkNotebook

—At the Advanced Data Analytics team at Barclays we solved the Kaggle competition as proof-of-concept of how to use Spark, Scala and the Spark Notebook to solve a typical machine learning problem end-to-end.
—The case study is recommending a sequence of WordPress blog posts that the users may like based on their historical likes and blog/post/author characteristics. Continue reading

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