Project Name
How Ksolves Built a Unified Snowflake Data Platform for a Global E-Commerce Retailer
For a global retail business processing millions of transactions every day across multiple countries, fragmented data was slowing everything down. Customer insights were sitting in disconnected S3 buckets, CSV files, and databases. Reports took hours to run. Real-time decisions were impossible. And with petabytes of data growing every day, the problem was getting worse.
The client is a retail company operating across North America, Europe, and Asia-Pacific, serving millions of online and in-store customers. Their data came from transactions, product reviews, supply chain systems, and inventory platforms, all in different formats and locations. Marketing, analytics, and data science teams all needed access to the same data but were working off different, often outdated copies.
Ksolves stepped in to design and build a unified, scalable Snowflake data platform that brought all of this data together in one place, in real time, with the security and AI capabilities the business needed to act on it.
The client came to Ksolves, an AI-first company, with seven data problems that were holding back their business:
- Data Scattered Everywhere: Customer, transaction, and inventory data lived across S3 buckets, multiple databases, and CSV files. Pulling it all together for any analysis required manual effort every time.
- Too Much Data to Handle at Scale: The client generated petabytes of structured and unstructured data every day. Their existing tools could not store, manage, or process it at the speed the business needed.
- Real-Time and Historical Data Needed at the Same Time: Some teams needed live transaction data while others needed months of historical records. There was no single system that could handle both well.
- Hard to Understand Customer Behavior: The volume and variety of data made it difficult to build customer segments, run personalization, or analyze product performance in a consistent way.
- Sharing Data with Remote Teams Was Complicated: Data science teams working remotely needed access to large datasets. The current process involved duplicating data, which was slow, expensive, and created version control problems.
- No Way to Run AI on Customer Reviews:The client had millions of product reviews but no infrastructure to run sentiment analysis at scale. The insights were there but could not be accessed.
- Performance Slowing Down as Data Grew: Query times were getting longer as data volumes increased. The architecture was not built to scale, and performance bottlenecks were starting to affect business decisions.
Ksolves designed and built the Snowflake data platform using an AI-first delivery approach, applying AI-assisted data modeling and pipeline design to accelerate architecture decisions before any development began.
- Unified Data Ingestion: Raw data from S3 was loaded automatically and continuously into Snowflake using Snowpipe. Real-time transaction and event data was streamed in through Kafka Connect. Apache NiFi handled ETL from databases, transforming and loading data into Snowflake in a clean, structured format. External tables allowed querying S3 data directly without moving it, keeping storage costs down.
- Centralized Storage in Snowflake: All data, both real-time streams and batch records, is stored in Snowflake's multi-cluster shared architecture. Automatic compression and micro-partitioning manage structured and semi-structured formats including JSON, Avro, and Parquet. Time Travel and Fail-Safe features mean historical data can always be recovered if needed.
- Data Processing with Snowpark and SQL: Transformations and data cleaning run directly inside Snowflake using Snowpark and SQL, removing the need for external tools. Stored procedures and Snowflake Tasks automate pipeline workflows so data is always ready for analytics, segmentation, and machine learning.
- Role-Based Access Control: Fine-grained permissions are assigned by role including Admin, Analyst, Data Engineer, and Data Scientist. Remote data science teams access shared data through Reader Accounts without needing full Snowflake licences or duplicating datasets. Column-level security, row access policies, and dynamic data masking protect sensitive customer information.
- Secure Data Sharing and AI Insights: Snowflake Private Sharing gives remote teams live access to datasets without copying data. Snowflake Cortex AI runs sentiment analysis on customer reviews, extracting insights from millions of unstructured text records. Snowpark ML and Snowflake Arctic power AI and machine learning workloads directly on the platform.
- Real-Time Dashboards with Power BI: Business intelligence teams connect Power BI directly to Snowflake for live dashboards. Query acceleration, result caching, and materialized views keep dashboards fast even on large datasets.
- Cost-Efficient Scaling: Storage and compute scale independently. Auto-scaling warehouses adjust to demand and shut down during quiet periods so the business only pays for what it uses. Dedicated warehouses for different workload types keep costs predictable and performance consistent.
Technology Stack
| Component | Details |
|---|---|
| Data Platform | Snowflake (multi-cluster shared architecture) |
| Cloud Storage | Amazon S3 |
| Data Streaming | Apache Kafka, Kafka Connect |
| ETL Pipelines | Apache NiFi |
| Processing | Snowpark, SQL, Snowflake Tasks |
| AI and ML | Snowflake Cortex AI, Snowpark ML, Snowflake Arctic |
| Security | RBAC, Reader Accounts, dynamic data masking |
| Data Sharing | Snowflake Private Sharing |
| Visualization | Microsoft Power BI |
| AI Tooling | AI-assisted data modeling and pipeline design |
The Snowflake platform delivered measurable improvements across query speed, data access, cost, and AI capability:
- 60% Faster Query Times: Snowflake's auto-clustering, materialized views, and result caching cut average dashboard load times by 60%. Reports that used to take hours now run in seconds.
- Real-Time Insights Across All Data Sources: All transaction, inventory, and customer data now updates in real time through Snowpipe and Kafka Connect. Marketing and analytics teams make decisions on the same data, the same day it was generated.
- 40% Reduction in Storage and Compute Costs: Independent scaling of storage and compute, combined with auto-scaling warehouses and data compression, reduced total infrastructure costs by 40% compared to the previous setup.
- Remote Teams Get Secure Data Access in Under 24 Hours: Reader Accounts replaced a weeks-long provisioning process. Data science teams can now access live datasets securely within a day, without duplicating data or compromising access controls.
- AI-Powered Sentiment Analysis Now Running at Scale: Snowflake Cortex AI processes millions of customer reviews and returns sentiment insights within hours of ingestion. The client's brand teams can now act on customer feedback in near real time.
- One Platform for Every Team: Marketing, data science, analytics, and operations all work from the same Snowflake environment. Data silos are gone and every team works from a single, consistent source of truth.
“Before Ksolves, our analytics team waited hours for reports and our data science team spent more time moving data than using it. Now everything runs in one place. Our dashboards are live, our ML models run on fresh data, and our remote teams got access in a day. It changed how fast we can act as a business.”
— Head of Data Engineering, Global Retail Organization
Before this engagement, the client’s data was fragmented across dozens of disconnected systems, making real-time analytics impossible and leaving petabytes of customer intelligence underused. Today, Ksolves, with its AI-first delivery approach, has built a unified Snowflake platform that processes petabyte-scale data in real time, serves every team from a single source of truth, and runs AI-powered sentiment analysis on millions of customer reviews.
Query times are down 60%, storage costs are down 40%, and the business can now act on customer insights the same day they are generated. As data volumes grow, the platform scales with them.
For e-commerce businesses and retailers dealing with fragmented data, slow reporting, or rising infrastructure costs, explore Ksolves Snowflake Consulting Services and find out what a unified data platform looks like for your business.
Is Your E-Commerce Data Slowing Down Your Business Decisions?
