Navigating the Storm When You’re Drowning in Data

The rise in demand for accurate, verifiable, and up-to-the-second data, has put a massive amount of pressure on companies’ existing data warehouse architecture. Many companies are struggling with large sets of data, what to do with it, and how to make sense of it all. These companies built out a large, robust data lake, but now they don’t know what to do with it, with large data sets being too overwhelming for IT teams to handle. One could even say, they’re drowning in data!

Luckily, our team here at Strive, while partnering with Snowflake, the data cloud platform, is capable of being your port in the storm. Bringing a much-needed life preserver to organizations through our expertise to help right-size data elements and simplify data challenges. Interested? Let’s walk through a recent example of doing just that.

This year, a Client partnered with Strive to help assess their current data warehouse initiative. Our Client had multiple source systems (15+) with desires to have a single source of truth for reporting. Drowning in manual, highly cumbersome tasks, and long hours for their employees, the Client needed a more efficient process regarding data extraction, transformation, loading, and sharing.

Enter Strive.

Our approach was simple – assist with the building of a strategic vision of future business processes, while implementing a proactive, rather than reactive, approach to technology adoption. Strive introduced the Client to our trusted partner Snowflake as the data platform. Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings. Additionally, the sharing functionality makes it easy for organizations to quickly share governed and secure data in real time. 

Knowing that executives and senior leadership wanted “better” reporting accuracy, but development staff wanted a more efficient way to accomplish these tasks, Strive had their work cut out for them. Our Data & Analytics team turned to the implementation of Strive’s proprietary ELT Accelerator to help build a dedicated reporting environment using Snowflake. We analyzed the Client’s current processes, conducted technical interview meetings, and identified opportunities to achieve the desired future state and enable a modern data integration platform capable of high-volume data transfer.

Using the ELT Accelerator, Strive was able to:

  • Set up an Azure Database for ETL Accelerator source tables and stored procedures
  • Customize Azure Data Factory Settings to support new environments
  • Create Scheduled pipeline for recurring data extraction to the Snowflake data lake

The Extract, Load, and Transform (ELT) process leverages built-in distributed query processing capabilities and eliminated resources needed for data transformation prior to loading, saving the Client headache and unnecessary spending on compute resources. Once our team fully implemented our ELT tool, we were able to help our Client load millions of rows of data, drive additional insights, and ultimately, enable the business to make better decisions.

So again, we ask you – is your organization drowning in data? By partnering with our team of trusted advisors, Strive can help you navigate the rough waters of any large data lake and calm the storm.

Interested in Strive’s ELT Accelerator?

Strive Consulting is a business and technology consulting firm, and proud partner of Snowflake, having direct experience helping our clients understand and monopolize the benefits the Snowflake Data Platform presents. Our team of experts can work hand-in-hand with you to determine if leveraging Snowflake is right for your organization. Check out Strive’s additional Snowflake thought leadership here.

About Snowflake

Snowflake delivers the Data Cloud – a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Join the Data Cloud at