VF Department Store Co., Ltd.

About VF Department Store Co., Ltd.

VF Corporation is an American worldwide apparel and footwear company founded in 1899 and headquartered in Greensboro, North Carolina. The company’s more than 30 brands are organized into four categories: Outdoor, Active, Work and Jeans. The company controls 55% of the U.S. backpack market with the Jansport, Eastpak, Timberland, and North Face brands. The headquarters is moving to Denver as VF changes its focus to outdoor wear, and the jeans business is being spun off into Kontoor Brands.

VF has a diverse, worldwide portfolio of iconic brands. Keeping them at the top of their game requires innovation and experiences that build and maintain strong equity with consumers. And it means delivering all of this whenever and wherever consumers shop. Powerful brands are at the core of VF’s success. Those brands including Wrangler, Lee, Rusters, Rock & Republic, Dickies, Red Kap, Horace Small, Bulwark Protective Apparel, Altra, Eagle Creek, Eastpak, Icebreaker, JanSport, Kipling Europe, Lucy, The North Face, Napapijri, Reef, SmartWool, Timberland, Vans and so on.

Business Needs and Challenges

As a leading global company, VF needs to accumulate and analyze historical transaction data for management to get the full picture of the corporate and individual brand performances. The existing on-premise data warehouse could no longer cater the massive data volume growth with a large dataset and fast-growing business users’ unexpected analytic requests. Long waiting time for standard report generation, limitations to ad-hoc analytics, rigidity in traditional data warehouse are all the pain points to customer in unleashing its own data analytical capability. On the other hand, as a global company, how to create an easy managing cross-regional data platform is another important issue.

Solutions Provided by eCloudvalley

To receive order/sales/inventory transaction to provide ad-hoc analysis on sales/inventory for an apparel and footwear business, VF want to implement a scalable data lake integrates with a unified analytics platform. VF system collect different data every day such as merchandising, inventory, sales and orders, consumer info or some master data. Those data will be stored into S3 data lake for extract, transform, load and related analytics. Overviewing the AWS practice, we using S3 as staging storage, EMR as ETL and Redshift as Data Warehouse which we have Tableau model to query data against.

This is a simple end-to-end data flow in VF case. The data is collected from the MuleSoft then moving to AWS. We store the data into S3 bucket and process the data (ETL job) with EMR clusters. After extract, transform and load process, the data loaded into Redshift (data warehouse) and consume by Tableau server for visualization.

Serve as Data Lake in S3 for unlimited storage to both structured and unstructured data.

With well integration with other AWS services, Amazon EMR provides several examples of this integration: like EC2, S3, Data Pipeline.

Utilize AWS Glue, EMR and Redshift, leveraging various analytical services for data cleansing, data transformation, and data modeling depending in different use cases and scenarios.

ECV provide an end-to-end data solution that leverage different AWS services that including ingestion, validation & mapping, data lake, modeling, metadata management, data storage, data warehouse, and data visualization. The whole procedure as a data pipeline build with full AWS services.

In data ingestion, initially we utilize AWS Database Migration Service(DMS) to replicate data with high availability and migrate to Amazon Kinesis, then we can do data validation and Mapping with AWS Lambda and AWS Glue, preprocessing the data and store into a data lake as intermediate storage as Amazon S3. Next, processing the ETL job with Amazon EMR and store into the Amazon Redshift as data warehouse then was consumed by Tableua to display the data visualization chart.


  • Modern data lake & data warehouse, as a unified data platform
  • Handing of real-time streaming data
  • Consolidation of data from a different system
  • Enterprise data warehouse for flexible data analytics
  • Reduce 90% of the time with 10X performance gain of data processing and analytics
  • Better allocation of the stocks into different stores in different regions
  • Target marketing to specific group users by knowing their customer interest and shopping behavior