Grover

Modernizing Grover’s Data Infrastructure

Grover, a fast-growing company, faced major hurdles with its data infrastructure. Built on Matillion ETL and Amazon Redshift, the system couldn’t keep pace with the company’s rapid expansion. Slow performance, manual workflows, and scalability challenges were causing inefficiencies and driving up operational costs. Symufolk stepped in to modernize Grover’s data systems, transforming them into a scalable, efficient, and automated solution that empowers seamless growth and operational excellence.

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Overall Efficiency

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Reduced Operations Cost

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Zufriedenheitsrate

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Herausforderungen

Performance Issues: Slow query speeds made accessing and analyzing data frustrating and inefficient.
Manual Workflows: Many processes were still manual, leading to errors and consuming valuable time.
Scalability Problems: Redshift struggled to handle the increasing complexity of data pipelines and analytics needs.
High Maintenance Costs: Frequent manual interventions drove up costs and diverted resources.
Data Integrity Concerns: Manual data transformation processes lacked traceability, increasing the risk of errors and inconsistencies.

Lösung

SymuFolk addressed Grover’s challenges by migrating their data infrastructure to Snowflake, a scalable cloud-based data warehouse that improved performance and efficiency. To enhance data accuracy and eliminate human error, DBT (Data Build Tool) was implemented, automating and streamlining data transformations.

Manual workflows were replaced with Apache Airflow, automating data pipelines, providing real-time logging, and introducing alerts for quick issue resolution. Additionally, SymuFolk optimized Grover’s existing Redshift environment, ensuring legacy systems operated smoothly during the transition.

unser Beitrag

Python

GraphQL

Node.js

NestJS

PHP

Microsoft Net

ERGEBNISSE

SymuFolk modernized Grover’s data infrastructure, enhancing scalability, automation, and processing speed, while reducing costs and manual inefficiencies, ensuring seamless data management.

40%

Faster
Queries

Data processing became quicker and more efficient with Snowflake.

25%

Kosteneffizienz

utomating workflows significantly reduced operational expenses.

50%

Fewer Errors

Automation through DBT and Airflow drastically reduced human errors.

wie wir es gemacht haben

Assessment & Planning

We started by analyzing Grover’s existing setup, identifying bottlenecks, and defining clear objectives.

Design & Architecture

We designed a new infrastructure that integrated Snowflake, DBT, and Airflow, ensuring scalability and automation.

Migration & Implementation

Data was migrated to Snowflake, workflows were automated with Airflow, and data transformations were streamlined using DBT.

Optimization & Testing

We optimized Redshift configurations and rigorously tested the new system to ensure seamless performance.

Monitoring & Support

Real-time monitoring systems were put in place, complete with alerts and logs for ongoing reliability.

Streamlined Workflows

Apache Airflow eliminated manual processes, ensuring smoother operations.

Technologien

WIR VERWENDET

Schöpfungsprozess

Erste Einschätzung

We reviewed Grover’s data ecosystem to identify pain points and opportunities for improvement.

Solution Design

A tailored architecture was developed using the best tools to meet Grover’s specific needs.

Migration & Automation

Legacy systems were migrated to Snowflake, while workflows were automated with Airflow and DBT.

Testing & Optimization

We ensured all systems worked seamlessly through thorough testing and fine-tuning.

Ongoing Monitoring

Robust monitoring solutions were implemented to keep everything running smoothly long-term.