Cube Dev Case Studies ShopBack's Journey to Efficient Embedded Analytics with Cube

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	ShopBack's Journey to Efficient Embedded Analytics with Cube Cube Dev

ShopBack's Journey to Efficient Embedded Analytics with Cube

Cube Dev

Technology Category	Application Infrastructure & Middleware - Event-Driven Application Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries	E-Commerce Retail
Applicable Functions	Procurement Sales & Marketing
Use Cases	Retail Store Automation Time Sensitive Networking
Challenge	ShopBack, a leading shopping rewards and discovery platform in Asia-Pacific, faced a significant challenge in analyzing transactions on its sites. The company needed to assess various transactional aspects, including purchase value and sales volume. In January 2020, ShopBack embarked on a new project that required extensive dashboard reporting of aggregated data for both internal and external users. One of the options considered was building an in-house application and storing the data in a graph database. However, the data was highly relational and needed to be pre-aggregated into OLAP cubes for analytics. The company also faced performance issues, with p95 query loading times taking as long as 50 seconds, leading to a poor user experience. Read More
About Customer	ShopBack is the largest shopping rewards and discovery platform in the Asia-Pacific region, with a presence in nine countries including Australia, Indonesia, South Korea, the Philippines, Singapore, Malaysia, Taiwan, Thailand, and Vietnam. The company started with a small team of six in 2014 and has since grown to over six hundred employees across the region. ShopBack's platform enables users to make better purchase decisions while delivering performance-based marketing with high and measurable return on investment to merchants. In 2020 alone, the company powered close to US$3 billion in sales for over 5,000 merchant partners across the Asia-Pacific and awarded over US$200 million of cashback to its users. Read More
Solution	ShopBack decided to use Cube, a platform that was a better fit for their use case. Cube allowed the company to add new schemas and measures as their data continued to grow, while also improving the performance of their queries. To address performance concerns, ShopBack implemented pre-aggregations in early 2021, which drastically improved the performance of their Cube queries. The company also optimized their pre-aggregations with indexes and modified their approach to pre-aggregations for unique queries. ShopBack's architecture uses pre-aggregations stored in the same Postgres database and a non-clustered instance of Redis for storing fetched data and tracking the latest refreshed pre-aggregations for a multi-tenant application setup. The company also learned to pay attention to details to ensure they hit their pre-aggregations database and indexed their pre-aggregation tables to improve the performance of their queries. Read More Log in to view content
Contents

Technology Category

Application Infrastructure & Middleware - Event-Driven Application

Infrastructure as a Service (IaaS) - Cloud Databases

Applicable Industries

E-Commerce

Retail

Applicable Functions

Procurement

Sales & Marketing

Use Cases

Retail Store Automation

Time Sensitive Networking

Challenge

ShopBack, a leading shopping rewards and discovery platform in Asia-Pacific, faced a significant challenge in analyzing transactions on its sites. The company needed to assess various transactional aspects, including purchase value and sales volume. In January 2020, ShopBack embarked on a new project that required extensive dashboard reporting of aggregated data for both internal and external users. One of the options considered was building an in-house application and storing the data in a graph database. However, the data was highly relational and needed to be pre-aggregated into OLAP cubes for analytics. The company also faced performance issues, with p95 query loading times taking as long as 50 seconds, leading to a poor user experience.

About Customer

ShopBack is the largest shopping rewards and discovery platform in the Asia-Pacific region, with a presence in nine countries including Australia, Indonesia, South Korea, the Philippines, Singapore, Malaysia, Taiwan, Thailand, and Vietnam. The company started with a small team of six in 2014 and has since grown to over six hundred employees across the region. ShopBack's platform enables users to make better purchase decisions while delivering performance-based marketing with high and measurable return on investment to merchants. In 2020 alone, the company powered close to US$3 billion in sales for over 5,000 merchant partners across the Asia-Pacific and awarded over US$200 million of cashback to its users.

Solution

ShopBack decided to use Cube, a platform that was a better fit for their use case. Cube allowed the company to add new schemas and measures as their data continued to grow, while also improving the performance of their queries. To address performance concerns, ShopBack implemented pre-aggregations in early 2021, which drastically improved the performance of their Cube queries. The company also optimized their pre-aggregations with indexes and modified their approach to pre-aggregations for unique queries. ShopBack's architecture uses pre-aggregations stored in the same Postgres database and a non-clustered instance of Redis for storing fetched data and tracking the latest refreshed pre-aggregations for a multi-tenant application setup. The company also learned to pay attention to details to ensure they hit their pre-aggregations database and indexed their pre-aggregation tables to improve the performance of their queries.

Impact #1	The implementation of Cube at ShopBack has led to significant operational improvements. The company has been able to add new Cube schemas and measures as their data continues to grow, while also improving the performance of their queries. The use of pre-aggregations has drastically improved the performance of their Cube queries, reducing p95 query loading times to below 5 seconds. The company has also learned to pay attention to details to ensure they hit their pre-aggregations database and indexed their pre-aggregation tables to improve the performance of their queries. Furthermore, the Cube community has been a valuable resource for ShopBack, providing answers to questions and offering collaborative support.

Impact #1

The implementation of Cube at ShopBack has led to significant operational improvements. The company has been able to add new Cube schemas and measures as their data continues to grow, while also improving the performance of their queries. The use of pre-aggregations has drastically improved the performance of their Cube queries, reducing p95 query loading times to below 5 seconds. The company has also learned to pay attention to details to ensure they hit their pre-aggregations database and indexed their pre-aggregation tables to improve the performance of their queries. Furthermore, the Cube community has been a valuable resource for ShopBack, providing answers to questions and offering collaborative support.

Benefit #1	Reduced p95 query loading time from ~50 seconds to below 5 seconds
Benefit #2	Powered close to US$3 billion in sales for over 5,000 merchant partners in 2020
Benefit #3	Awarded over US$200 million of cashback to users

Benefit #1

Reduced p95 query loading time from ~50 seconds to below 5 seconds

Benefit #2

Powered close to US$3 billion in sales for over 5,000 merchant partners in 2020

Benefit #3

Awarded over US$200 million of cashback to users

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Overview

ShopBack's Journey to Efficient Embedded Analytics with Cube

Operational Impact

Quantitative Benefit