Melissa and Doug Customer Success Story

Melissa and Doug, Wilton, Connecticut,, is a privately held American manufacturer of children’s toys with about 1,000 employees worldwide. Melissa and Doug has experienced steady growth since the 1980’s and was operating the business with a homegrown ERP and Warehouse Management system with ad-hoc reporting capabilities using Tableau. 2020 brought changing business requirements and MnD experienced a difficult time accommodating them due to an inflexible Data Management architecture.

Myers-Holum, Inc (MHI) provided the strategic recommendation to implement an improved Data Management platform using Google Cloud with Looker for their data modernization efforts. We worked with the business to document future state KPI requirements, facilitated a data governance program, and defined their future state BigQuery EDW architecture using Cloud Composer, Cloud Storage, BigQuery SQL.

New requirements came along at the end of the implementation project to report across newly created channel hierarchies, sales hierarchies, and region hierarchies. The newly built Data Management solution allowed for a quick implementation of the new requirements to satisfy business needs to respond to the changing business landscape of 2020.

“The newly built data management solution  allowed for a quick implementation of new requirements that satisfied the business need to respond to the fast-changing landscape in 2020”  Chris Conway, CTO, Melissa and Doug

Aristocrat/VGT Customer Success Story

Aristocrat/VGT, Franklin, Tennessee,, is a leading developer, manufacturer, and distributor of Class II casino games in North America. Aristocrat handles an exorbitant amount of data and had an existing 5-year-old Hadoop Big Data platform that was unable to scale to their 10s of TBs of data volumes. They experienced functional requirement changes, had concerns around stability, high availability, and uptime, and maintained only a single environment with no separation for their dev, test, and prod environments. Furthermore, there were serious capacity concerns that required a separate Vertica-based BI layer which meant increased costs, maintenance, and more data pipelines to manage.

Myers-Holum, Inc (MHI) engaged with Aristocrat to provide expertise on their migration to Google BigQuery. We leveraged a Data Transfer Appliance to move their 10s of TB of data off Hadoop and to Google BigQuery and rebuilt the existing data processing pipelines using Google Cloud Composer and BigQuery SQL capabilities. MHI also completed the data modeling and best practices for Data Warehousing on BigQuery. Finally, we enabled analytics using Tableau.

The results of this initiative were more precise and faster data insights which left Aristocrat positioned and enabled for future data growth.

“We successfully addressed serious capacity concerns that previously required a separate Hadoop and Vertica instances with a single unified GCP Data Management platform which reduced costs, reduced complexity, and increased capacity to onboard new data sources” – Rick Watts, Sr. Architect

Myers-Holum, Inc. Achieves the Data Analytics Partner Specialization in the Google Cloud Partner Program

Google Cloud Recognizes Myers-Holum, Inc.’s Technical Proficiency and Proven Success In Data Analytics

New York, April 17, 2019 — Myers-Holum, Inc. today announced that it has achieved the Data Analytics Partner Specialization in the Google Cloud Partner Program. By earning the Partner Specialization, Myers-Holum, Inc. has proven their expertise and success in building customer solutions in the data analytics field using Google Cloud Platform technology.


Specializations in the Google Cloud Partner Program are designed to provide Google Cloud customers with qualified partners that have demonstrated technical proficiency and proven success in specialized solution and service areas.


Partners with this specialization have proven success from ingestion to data preparation, storage, and analysis.


“We are thrilled at the culmination of our experience and partnership with Google Cloud to come together and offer us this opportunity to be a specialized partner and we look forward to growing the Google Cloud Platform footprint through Google BigQuery, DataFlow, Dataproc, DataFusion, DataPrep, and other Google Analytics products utilization,” said Darius Kemeklis, EVP of Cloud Practice, Myers Holum.


Myers-Holum, Inc. (MHI) is a privately held enterprise systems and data integration consulting firm founded in 1981 in New York, New York. Having consulted for more than 800 companies ranging from the Fortune 500 to the lower Mid-Market, our staff represents a diverse and ambitious group of consulting and development professionals with in-depth industry, systems, and data management expertise. Today, MHI has regional presence in core markets across North America.


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.