Elastic Meetup with Dun & Bradstreet

Elasticsearch journey at D&B

We'll cover how we use elasticsearch for information retrieval of data about 300 million companies worldwide. Finally we'll touch on how we plan to use Graph Plugin to power our recommendation engine.

Dun & Bradstreet is a provider of commercial data to businesses on credit history, business-to-business sales and marketing, counterparty risk exposure, supply chain management, lead scoring and social identity matching. Dun & Bradstreet maintains a database of over 240 million companies globally and over 100 million professional contact names using a variety of sources including public records, trade references, telco providers, telephone interviews, print, digital and trade publications, among others.

Our solution uses Elasticsearch and other supporting tools & technologies to support information retrieval on data curated from our 30,000 different data sources.

Pradeep Bhattiprolu, Senior Architect (Search) at D&B's Platform development team. At D&B, I lead the efforts in developing global information retrieval as a core platform component, to be leveraged by all products. I have more than a decade of experience in developing high-performance data management software for large enterprises.Prior to D&B I have worked at companies (Informatica , Oracle etc) building software products for Data management.

http://www.meetup.com/Austin-Elastic-Fantastics/events/234268181/

Transcript

1. Austin Elastic User Group Meetup Pradeep Bhattiprolu, Sr. Architect (Search) 11th Oct 2016 Elasticsearch at Dun & Bradstreet
2. 2 Agenda • About  us • Search  at  Dun  &  Bradstreet • Recommender  System  using  Elastic  Graph  API
3. About Us
4. 4 175 years of growing relationships through data • Founded  in  1841 • Collect  data  from  over  30000  data   sources • Patented  DUNSRighttm process  is   used  in  collecting,  cleansing,   matching  and  enriching  data. • Data  about  over  250  million   companies  powering  solutions   across  multiple  business  verticals   like  Trade  Risk,  Sales  and   Marketing  ,  Supply  chain   management  and  compliance
5. Search @ Dun & Bradstreet
6. 6 Challenges • Achieving  common  search  experience. • Achieving  capability  parity  in  different  systems • Searching  multiple  sources. • Agility  to  cater  to  new  use  cases
7. 7 Information Retrieval 2.0 Integration   API’s Entity   Matching search suggest Other   applications Log  &   Audit IR  Cluster Data  Ingestion   Pipeline
8. 8 Data Ingestion Pipeline Message  broker   1 Message  broker   2 Message  broker   n CSV  File  Producer RDBMS  Producer Custom  Producer Producer’s  Group Parsing   rules Elastic  search   Writer Consumer’s  Group Custom  Consumer Error  processor Parsing   rules Data   Sources Horizontal  Scale Horizontal  Scale Horizontal  Scale Horizontal  Scale
9. Recommender System
10. 10 Use Case
11. 11 Graph API Search  for  the   original  company Use  Elasticsearch Graphing  API Use  the  results  from   the  graph  to  create   recommendations Return  to  the  client
12. Demo
13. Thank You http://www.dnb.com/about-us/careers-and-people.html