HBaseCon 2012 | Getting Real about Interactive Big Data Management with Lily & HBase - ngdata
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The document discusses Lily, an HBase-backed data repository designed for real-time recommendations, showcasing its capabilities in data modeling, indexing, and API functionality. It emphasizes the use of collaborative filtering for personalized recommendations across various industries, including finance and retail. Additionally, it highlights upcoming features such as secondary indexes and integration with MapReduce and SolrCloud.
![Lily Core 2’ recap
• HBase-backed data repository,
with batteries included
• Data model:
• high-level data model on top of HBase’s
client app
byte[]’s
• schema
• versioning (schema and data) Lily
• links, variants
RowLog
• Java & REST API's
• Indexing: HBase Solr et al.
• through configuration, not implementation
• incremental and batch index maintenance
• RowLog: distributed, durable queue for sec.
actions
• Open Source: www.lilyproject.org (Apache
License)
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HBaseCon 2012 | Getting Real about Interactive Big Data Management with Lily & HBase - ngdata
- 1. Making Sense of Data Lily goes shopping – real-time recommendations with HBase HBaseCon, May 2012 Steven Noels – VP Product – @stevenn WWW.NGDATA.COM
- 2. Lily Core 2’ recap • HBase-backed data repository, with batteries included • Data model: • high-level data model on top of HBase’s client app byte[]’s • schema • versioning (schema and data) Lily • links, variants RowLog • Java & REST API's • Indexing: HBase Solr et al. • through configuration, not implementation • incremental and batch index maintenance • RowLog: distributed, durable queue for sec. actions • Open Source: www.lilyproject.org (Apache License) WWW.NGDATA.COM
- 3. Why HBase? • BigTable model • sparseness • atomic row updates aka concistency • auto-partitioning • Apache license • A great community led by a Saint J WWW.NGDATA.COM
- 4. Portfolio Overview Real-time AI Recommendations Industry algorithms and rules commercial availability Trend Analytics Pattern Detection Profile Development Context and Activity Tracking open source Social Stream Ingestion Schema and Data Management Total Data Aggregation Real-time Index and Retrieval Security and Enterprise Connectors WWW.NGDATA.COM
- 5. Lily (=HBase) In Use Some of the larger Lily deployments • media • aggregation, database publishing and online archives • finance • real-time identity fraud detection • retail banking • contextualized (time+loc+person) mobile coupons • retail • e-commerce platform: product catalog, consumer data store, real-time indexing WWW.NGDATA.COM
- 6. Collaborative Filtering? Recommend items similar to a user’s highly-preferred items WWW.NGDATA.COM
- 7. Collaborative Filtering is … Matrixes Sean likes “Scarface” a lot (123,654,5.0)! Robin likes “Scarface” somewhat (789,654,3.0)! Grant likes “The Notebook” not at all (345,876,1.0)! … …! (Magic) Grant may like “Scarface” quite a bit (345,654,4.5)! … …! WWW.NGDATA.COM
- 8. Contextualized recommendations Personalized offers shops & merchants Profile Acitvity Item product families offers/coupons creditcard statements WWW.NGDATA.COM
- 9. Fitting Recommendations into the Lily Architecture LILY CRUD API Lily/HBase Secondary Indexes read/write demultiplexer co-occurence lookup matrix rowlog activity store Steven Noels stevenn@ngdata.com www.ngdata.com telephone: +32 9 33 engine LILY recommender 88 220 data profile data, activity, profile scoring indexes store store Gent (Belgium) propensity custom ... k-means ALS Makers of Lily Core Repository algorithm support WWW.NGDATA.COM
- 10. Preferencing aka Feeding the Matrix • Transaction-based preferencing • Pluggable preference strategies, using Lily-based data (HBase&Solr) for decision making • e.g. credit card statement = transactions between users and product families • Preference weighting • Ingest: REST API, bulk support • Real-time updating of the recommendation model • Profile Store • Profile activities can be preferenced • Support for Profile behavior analysis WWW.NGDATA.COM
- 11. Making recommendations • Recommender • Pluggable recommender strategies, using Lily-based data (HBase&Solr) for decision making • Multi-model support: user-item & item-user recommendations • Estimation of both preferenced and non-preferenced items • Geolocation-based recommendations • Re-scoring • REST API • (Planned) • Support for Classifications (scenario - Recommend me all (possible) coffee drinkers) • Matrix / recommendation indexing WWW.NGDATA.COM
- 12. Other upcoming Lily Features • Secondary indexes (= Lily Core!) • indexes are defined through configuration • single or multi-field indexes • range queries and prefix queries • asc or desc sorted results • can read huge, sorted lists • synchronously updated: index updates are applied by rowlog secondary actions • online building of new indexes (no table locks) • MapReduce integration • SolrCloud integration • Index shards and configuration managed through ZooKeeper WWW.NGDATA.COM
- 13. Making Sense of Data Questions? Thank you! WWW.NGDATA.COM