Boll and Branch Builds Enterprise Data Warehouse on Google BigQuery

Boll and Branch, New Jersey, USA,, is a premium bedding and sheet e-commerce retailer with a fast-growing online business had the need for a better data-driven business decision-making approach.  Existing BigQuery based data mart had limited scope and capabilities. The need for a comprehensive Enterprise Data Warehousing solution has been identified.

Myers-Holum Inc. led the Enterprise Data Warehousing implementation for Boll and Branch using Google Cloud Platform serverless technologies.  Data was landed daily directly into BigQuery from multiple SaaS operational systems such as Netsuite, Shopify, Zendesk, Iterable using serverless platform, as well as directly from Segment audience tracking system. Monthly data files from 3rd party providers were being landed onto Google Cloud Storage. Cloud Scheduler, Cloud Functions, and BigQuery SQL were used to serverlessly process landed data into staging, consolidated, prepared layers. Data Studio dashboards were implemented to show analytical reports.  Dimensional data modeling techniques were applied to build the consolidated layer with multiple fact, type 1 and type 2 slowly changing dimension tables.

With the new Enterprise Data Warehouse in place Boll and Branch was able to get additional and deeper insights into business performance and plan for continued growth.

PepkorIT Migrates Oracle Enterprise Data Warehouse to Google BigQuery

Steinhoff International Holdings, Cape Town, South Africa,, is a global retailer with stores across multiple regions. Steinhoff’s IT division PepkorIT was responsible for maintaining an existing on-prem multi-tenant Oracle-based Enterprise Data Warehouse (EDW) with custom SQL script ETL pipelines, daily store transaction activity batch loads, and various monthly consumer information data feeds, and analytical BI dashboards.

Legacy Oracle EDW was running out of capacity and needed to improve time to insights from days to hours and minutes.

The decision was made to migrate EDW to Google Cloud Platform and needed planning, design, implementation assistance. Myers-Holum Inc. (MHI) led the project to define the future Google Cloud-Based solution architecture and implement a data ingestion framework using Data Flow that reused the same pipeline for both batch ingestion from Google Cloud Storage, and real-time ingestion from OLTP database binary logs streaming through Google Cloud PubSub into BigQuery.

The MHI solution centered around self-healing Data Flow pipelines that allowed for schema changes over time with minimum operational intervention and automatic data reprocessing. The solution included sensitive data masking, balance and control system tables, full data lineage for data landed into GCP, data quality rules implementation, BigQuery schema design based on Myers-Holum industry best practices, downstream data processing for BI and Analytics use cases, and job monitoring using Stackdriver and Datastudio dashboards.

Google Cloud BigQuery-based EDW allowed Steinhoff to reduce time to insights from days to minutes. Google Cloud serverless technologies such as Data Flow provided scalable infrastructure to ingest batch and real-time data quickly and reliably while reducing CapEx and Opex costs.