Accelerating data analytics in the cloud with Snowflake | HCLTech

Accelerating data analytics in the cloud with snowflake

HCLTech achieved 5x growth in digital data modernization with enhanced capabilities
5 min read
5 min read

Our client is the world's largest Swiss multinational food and health/nutrition consumer packed goods (CPG) industry leader. HCLTech collaborated with to modernize their data analytics platform as per their digital and data analytics transformation roadmap. We recommended a modern microservice-based architecture to perform data engineering operations and data analysis with increased efficiency of the analytical and data integration platform (ADI). This helped our client in performing data consolidation tasks with improved harmonization of data and process across systems. Their growing demands could be addressed with Snowflake as the primary cloud data platform. Overall, Snowflake’s scalability and built-in capabilities improved data extraction, transformation and loading performance, empowering our client in their data modernization journey and unlocking the power of Snowflake to enhance quality of life for everyone.

The Challenge

Delivering value with data-intensive applications using Snowflake

Our client wanted to improve high availability, addressing data quality issues and forecasting the Sales and Profit and Loss (P/L) across markets using Snowflake. They were experiencing data movement failures which impacted data availability that was crucial for performing analyses of sales cum marketing data. Additionally, they wanted to democratize the data for reporting, data science activities, efficiently handling external partner workloads and citizen analytics and managing their enterprise-scale Global Data Hub. Moreover, additional challenges they wanted to address:

  • Challenges with un-streamlined data extraction and reconciliation process, hindering end-to-end data-ops visibility
  • Data silos and lack of real-time data caused delays in marketing campaigns
  • Lack of data governance setup which resulted in data security leading to unauthorized access of data within the organization
  • Struggles with data access, interoperability, security, data quality and limited data sharing within the organization which resulted in high costs related to compute and storage
  • Increased CapEx and OpEx costs impacted our client’s ability to respond to business needs
  • Manual scaling of computational power led to un-reliable and delayed data delivery, affecting business operations
  • Data management and ingestion from various sources were time consuming processes
  • Their on-prem infrastructure couldn’t handle exponential data growth and leverage AI for operational efficiencies
  • Productionizing processes for existing technologies were complex and time-consuming for them
The Challenge

The Objective

Embracing innovation and growth with the data champions

HCLTech was tasked to deliver and manage our client’s enterprise-scale Global Data Hub as they were experiencing failures in data movement which impacted data availability crucial for performing analysis of sales and marketing data. Our client wanted to leverage data modernization services to create cloud-based data warehouses and data lakes, reducing operational overhead. They wanted to improve high availability of data, address data quality issues and forecast the Sales and P/L across markets using Snowflake as a next-gen cloud data warehouse.

We recommended Snowflake to the client for performing centralized data analysis for improved planning in various areas such as supply chain, marketing, finance and technical operations. The Snowflake Data Platform ensured a unified source of truth across global business units and markets, along with “Design to Operate” services for a cohesive cloud and Snowflake platform for data and analytics. In collaboration with Snowflake and our client, we integrated and migrated data from silos and combined into a Snowflake Global Data Hub strategized to enable data democratization for reporting, data science, external partner workloads and citizen analytics.

The Objective

The Solution

Accelerating SAP to Snowflake migration: Improving operational efficiency and agility with Snowflake's data cloud

We implemented a metadata-driven data migration framework aimed at migrating SAP objects to Snowflake seamlessly with automation. Our client noticed immediate benefits with the established centralized global data hub for real-time access by business users with the framework created through DBT (Data Build Tool).

  • By leveraging Snowflake’s data sharing capabilities, we reduced data transit time securely. The benefits were evident based on accurate data consumption, improved integration and visualization workflows.
  • The transition from Azure Blob storage to Snowflake meant that we could have direct data consumption by downstream applications without manual coding. This improved data standardization across various business streams, aiding in seamless data analysis.
  • Additionally, we brought in an in-house tool – Gatekeeper (HCLTech accelerator for data testing and reconciliation) to drive data reconciliation and reduce incidents while increasing trust in data.
  • Snowflake’s scalability and capabilities boosted data modernization efforts for stakeholders. We created an operational dashboard for comprehensive data operations visibility.
The Solution

The Impact

Harnessing the power of analytics

Our data engineering and data science teams leveraged Snowflake to implement cloud analytics, at scale for our client. The success of the initial implementation has led to plans for expanding our client’s current 200TB Snowflake data warehouse to multi‐petabytes. The noticeable benefits achieved include:

  • 99.9% maintenance uptime for market data analysis, reducing data ingestion and data transformation failures.
  • 50% improvement in the performance of the data transformation and data ingestion process.
  • 50% faster audit logging compared to the previous Azure blob storage solution.
  • 10 times faster acceleration in migrating new tables using metadata-driven Snowflake Migration Framework
  • A 10% enhancement in query performance using Snowflake compared to previous SAP solutions
  • Achieving a 10% growth in data reconciliation, resulting in reduced man-hours and cost savings by avoiding data re-loading efforts.
The Impact