Overview
KORE’s NBA data integration strives to synchronize data from NBA data warehouse into KORE’s data warehouse product (DWA) in an accurate, stable manner without affecting business operations.
The focus of the NBA league data integration is on all data related to customer/fan signup pages and secondary ticket buyers along with personal data collected from various sources like affiliates, partners, vendors, data brokers, and public sources.
This is a mono-directional integration in that KORE will only ingest data from the source.
KORE to ingest the data shared by NBA through weekly file drop in NBA hosted SFTP
Setup
- Client Team: Notify KORE of your intention to enable this integration.
- KORE Team: Send Client Team user guide.
- Client Team: Set up SFTP and provide access to KORE. Notify KORE when the files have been scheduled so KORE can monitor.
- KORE Team: Setup integration and confirm data is loaded correctly.
- KORE Team: Notify the Client Team the integration has been configured, and provide the user guide and a walk-through of the integration
ERD and data dictionary
(This information is provided on a best-effort basis without guarantees. For clarity, most column names are omitted from this ERD.)
Data Dictionary
Table Descriptions
All_Star_Voters
This table returns the list of All star voters. Each record represents a voter with his personal information. Key information includes marketing_consent, email_address, first_name, last_name, age, gender, postal code, favorite_nba_team, tickets_buyer_revenue_ever, retail_buyer_net_revenue_ever, nba_asg_voter_ever, fantasy_player, league_pass_revenue_ever, fico_score, year.
The data in these tables are updated fully once a year. You can check the warehouse status report on Tableau to view the exact time the update happens.
Top_Shot_Sign_Ups
This table returns the list of top shot sign up personels. Each record represents a person with his personal information. Key information includes marketing_consent, email_address, first_name, last_name, age, gender, postal code, favorite_nba_team, tickets_buyer_revenue_ever, retail_buyer_net_revenue_ever, nba_asg_voter_ever, fantasy_player, league_pass_revenue_ever, fico_score.
The data in these tables are updated fully once a week. You can check the warehouse status report on Tableau to view the exact time the update happens.
Sign_Ups
This table returns the list of new sign ups. Each record represents a person with his personal information. Key information includes marketing_consent, email_address, first_name, last_name, age, gender, postal code, favorite_nba_team, tickets_buyer_revenue_ever, retail_buyer_net_revenue_ever, nba_asg_voter_ever, fantasy_player, league_pass_revenue_ever, fico_score.
The data in these tables are updated fully once a week. You can check the warehouse status report on Tableau to view the exact time the update happens.
Tickets_Sign_Ups
This table returns the list of all customers who sign up for the tickets. Each record represents a customer with his personal information. Key information includes marketing_consent, email_address, first_name, last_name, age, gender, postal code, favorite_nba_team, tickets_buyer_revenue_ever, retail_buyer_net_revenue_ever, nba_asg_voter_ever, fantasy_player, league_pass_revenue_ever, fico_score.
The data in these tables are updated fully once a week. You can check the warehouse status report on Tableau to view the exact time the update happens.
Scored_Ticket_Buyers
This table returns the list of all customers who purchased the tickets. Each record represents a customer with his personal information. Key information includes marketing_consent, email_address, first_name, last_name, age, gender, postal code, favorite_nba_team, tickets_buyer_revenue_ever, retail_buyer_net_revenue_ever, nba_asg_voter_ever, fantasy_player, league_pass_revenue_ever, fico_score, total_spend_In_last_year, num_tickets, num_tickets_purchased_in_last_year, day_phone, evening_phone.
The data in these tables are updated fully once a week. You can check the warehouse status report on Tableau to view the exact time the update happens.
Example queries
to be decided
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