Build Your Own Recommendation Engine

This technical tutorial is designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. The session starts with a quick introduction to Redis and the capabilities Redis provides. It will cover the basic data types provided by Redis and the module system. Using an ad serving use case, Griffith will look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. You will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark and then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will also be discussed, focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these features. By the end of the session, you should feel confident building a prototype/proof-of-concept application using Redis and Spark. You’ll understand how Redis complements Spark, and how to use Redis to serve complex, ML-models with high performance.

Cascalog is a Clojure-based query language for Hadoop that provides a powerful and easy-to-use tool for data analysis. It allows users to write queries as regular Clojure code, offering features like joins, aggregators, functions, and sorting. Cascalog is unique in that it offers the full power of Clojure at all times by integrating queries directly into the programming language. BackType uses Cascalog for tasks like identifying influencers on social media, determining exposure to URLs, and studying engagement over time.

Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems. As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark. In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience. Finally, we will share the future challenges for 2018 and beyond.

Big data tools such as Hadoop and Spark allow you to process data at unprecedented scale, but keeping your processing engine fed can be a challenge. Metadata in upstream sources can ‘drift’ due to infrastructure, OS and application changes, causing ETL tools and hand-coded solutions to fail. StreamSets Data Collector (SDC) is an Apache 2.0 licensed open source platform for building big data ingest pipelines that allows you to design, execute and monitor robust data flows. In this session we’ll look at how SDC’s “intent-driven” approach keeps the data flowing, with a particular focus on clustered deployment with Spark and other exciting Spark integrations in the works.

This document discusses scalable ensemble learning using the H2O platform. It provides an overview of ensemble methods like bagging, boosting, and stacking. The stacking or Super Learner algorithm trains a "metalearner" to optimally combine the predictions from multiple "base learners". The H2O platform and its Ensemble package implement Super Learner and other ensemble methods for tasks like regression and classification. An R code demo is presented on training ensembles with H2O.

This document discusses patterns for modern data integration using streaming data. It outlines an evolution from data warehouses to data lakes to streaming data. It then describes four key patterns: 1) Stream all things (data) in one place, 2) Keep schemas compatible and process data on, 3) Enable ridiculously parallel single message transformations, and 4) Perform streaming data enrichment to add additional context to events. Examples are provided of using Apache Kafka and Kafka Connect to implement these patterns for a large hotel chain integrating various data sources and performing real-time analytics on customer events.

About our experience with realtime analyses on never-ending stream of user events. Discuss Lambda architecture, Kappa, Apache Kafka and our own approach.

This document discusses using Hadoop for archiving, e-discovery, and supervision. It outlines the key components of each task and highlights traditional shortcomings. Hadoop provides strengths like speed, ease of use, and security. An architectural overview shows how Hadoop can be used for ingestion, processing, analysis, and machine learning. Examples demonstrate surveillance use cases. While some obstacles remain, partners can help address areas like user interfaces and compliance storage.

Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.