Grover

Modernizing Grover’s Data Infrastructure

Grover was facing challenges with their existing data infrastructure, built on Matillion ETL and Amazon Redshift. As the business grew, so did the data volumes and complexity, but the system couldn’t scale effectively. This led to slow performance, increased manual processes, and a high risk of errors. Grover needed a more efficient, scalable, and automated data infrastructure for future growth.

total Hours

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Overall Efficiency

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Reduced Operations Cost

0 %

Satisfaction Rate

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Challenges

Problem Statement: The current system was slow and did not handle high data volumes effectively.
Manual processes: involved in data management were manual and, therefore prone to human error, thereby taking much time.
Scaling Issues: Redshift could not scale well to meet the mounting data and analysis needs.
High Maintenance Cost: Without automation
data processes require constant manual intervention, resulting in high maintenance costs.
Data Integrity Concerns: The manual data transformation processes lacked traceability, increasing the likelihood of errors.

Solution

Symufolk upgraded Grover’s data systems to make them faster, scalable, and more efficient. They switched to Snowflake for better performance and used DBT to automate and track data transformations. Manual data tasks were replaced with Airflow, which automated workflows and added alerts and logs to fix issues quickly. They also reviewed and improved Redshift to make it run smoother and more efficiently.

our input

Python

GraphQL

Node.js

NestJS

PHP

Microsoft Net

RESULTS

We’re proud of reaching new heights with our customers,
helping them achieve advanced levels of scalability and stability.

40%

Enhanced Performance

40% faster queries and 30% better scalability in Snowflake.

25%

Cost Efficiency

25% reduction in maintenance costs through automation.

50%

Improved Integrity

50% fewer errors with DBT and Airflow.

how we did it

Expertise

Utilized Snowflake, DBT, and Airflow to create a custom, scalable solution.

Automation

Reduced manual effort by 40% with automated workflows using Airflow.

Customization

Improved data traceability by 50% with tailored DBT transformations.

Efficiency

Reduced costs by 25% through performance optimization and automation.

Scalability

Built a future-proof infrastructure handling 30% more data.

Unique Approach

Combined advanced tools and strategies, boosting system reliability by 40%.

Technologies

WE USED

Creation Process

Assessment & Planning

We started with a thorough assessment of Grover’s existing infrastructure and pain points regarding performance, scalability, and cost.

Design & Architecture

We designed a modern data architecture combining Snowflake, DBT, and Airflow in an effort to build secure, scalable, and automated data processes.

Migration & Implementation

We migrated Grover’s data warehouse to Snowflake and implemented DBT for data transformations. Custom workflows were built using Airflow to automate data pipelines.

Optimization & Testing

We optimized the existing Redshift system, fine-tuned configurations, and performed rigorous testing to ensure high performance.

Monitoring & Support

After deployment, we established alerting, logging, and monitoring systems to ensure ongoing reliability and performance.