Optimizing Data Processing with Snowflake

This case study highlights how a multinational tech company optimized its data processing by migrating from an on-premise infrastructure to a cloud-based solution using Snowflake and GCP. By addressing challenges such as performance bottlenecks, diverse data sources, and high operational costs, EasyStepIn implemented a scalable architecture that enabled real-time analytics and improved data integration.

Client Background & Overview

A global leader in networking hardware, software, and telecommunications, the client operates across multiple regions, managing large-scale data infrastructure for marketing, sales, and customer insights. As their business grew, so did the complexity of their on-premise data platform, making it increasingly difficult to scale, manage, and optimize costs.

Business Challenges

On-Premise Infrastructure Limitations

The client’s Marketing Data Platform was hosted entirely on-premise, leading to performance bottlenecks as data volumes grew.

Diverse & Expanding Data Sources

The platform pulled data from various sources, including marketing databases and HDFS, making integration increasingly inefficient.

Schema Changes During Migration

Moving from Hadoop/HBase to Snowflake required careful schema restructuring to ensure data consistency and integrity.

Scalability & Performance Issues

Processing large volumes of data was becoming a challenge, leading to delays in analytics and slower time-to-market for marketing insights.

Client Goals

Faster Processing
Cost Optimization
Data Consistency
Cloud Migration

Optimized query performance for large datasets

Handling large-scale data workloads required faster query execution and optimized data retrieval mechanisms.
The client sought to improve Spark-based processing speeds, minimize latency in data pipelines, and enhance real-time analytics capabilities by utilizing Snowflake’s optimized query engine.

Lower operational costs with cloud efficiencies

Maintaining on-premise servers was becoming costly and inefficient. By transitioning to a cloud-native architecture, the client aimed to reduce infrastructure costs, improve resource utilization, and eliminate overhead expenses.

Ensuring structured, accurate, and reliable data

Migrating from Hadoop/HBase to Snowflake required careful schema restructuring and validation to prevent data loss, ensure accuracy, and maintain governance standards.
The objective was to automate schema transformations, maintain referential integrity, and optimize data models for cloud-based storage while ensuring business continuity.

Seamless transition to a scalable environment

The client aimed to migrate from on-premise infrastructure to a cloud-based solution to enhance scalability, availability, and operational efficiency.
By leveraging Snowflake and GCP, the goal was to ensure high-performance data access, reduce hardware dependencies, and improve infrastructure flexibility for future growth.

EasyStepIn's Solution Approach

EasyStepIn designed and implemented a modernized cloud-based data platform leveraging Snowflake and GCP, ensuring scalability, efficiency, and cost optimization.

Cloud-Native Security

Enhanced security protocols were implemented in the cloud to protect sensitive marketing data, ensuring compliance with industry standards.

Continuous Monitoring & Reporting

Automated real-time monitoring tools were established to track the data platform’s health, enabling proactive issue resolution and optimization.

Data Platform Migration

Conducted a technical deep-dive with client architects to explore various migration strategies. Designed a scalable, cloud-native architecture using Snowflake on GCP.

Seamless Data Migration

Developed Hadoop-to-Snowflake connectors in Spark to automate data migration. Built custom connectors to read and write data between GCP Dataproc and Snowflake.

Performance Optimization

Optimized data pipelines to process 2M+ records per day efficiently. Leveraged GCP’s resource optimization tools to reduce infrastructure costs by 20%.

Scalable Data Processing

Implemented automated schema restructuring for seamless transition from Hadoop/HBase to Snowflake. Enabled real-time data processing and analytics.

Business Outcomes

50% Faster Time to Market

By reducing data processing time, the client was able to generate insights faster. This accelerated their customer outreach campaigns, enabling them to make quicker data-driven decisions.

20% Cost Savings

Optimizing GCP resource utilization led to a reduction in operational costs. By leveraging GCP’s cloud-native efficiencies, the client enjoyed significant cost savings while scaling their platform.

2M+ Records Processed Daily

The newly designed scalable pipelines enabled the client to process over 2 million records daily. This ensured a smooth and efficient data ingestion process, even with increasing data volumes.

Enhanced Real-Time Analytics

With improved data processing capabilities, the client was able to access real-time analytics. This resulted in more timely and accurate marketing insights, empowering better decision-making.

Improved Data Accuracy & Consistency

The migration to Snowflake ensured better data consistency across multiple sources. This enhancement led to more accurate analytics, reducing discrepancies in reporting and improving the trust in data-driven decisions.

Optimized Query Performance

The cloud-based platform optimizations significantly boosted query performance. This not only improved analytics efficiency but also enhanced the overall user experience by reducing query response times by 50%.

Client Feedback

The transition to Snowflake and GCP has been a game-changer for our marketing data operations. EasyStepIn’s expertise played a pivotal role in making the migration process seamless and efficient. We’ve noticed a significant improvement in both data processing and analysis.

The transition not only enhanced our performance but also reduced operational costs. Their team’s thorough understanding of the technologies helped us achieve our goals with minimal disruption. Overall, the migration has empowered us to make more informed, data-driven decisions.