Syngenta AG, a leading global agricultural company, helps farmers around the world restore soil fertility to protect crops and improve seed quality through sustainable practices
Leveraging AWS to Scale Data Analytics & Enhance Decision-Making for Syngenta
To effectively assess seed product outcomes and make more informed decisions on its product advancement, the company needed to refactor their data analytics workflow.
Key Challenges
Leveraging an on-premise, open source Hadoop environment for data analysis, Syngenta built several proof of concept (PoC) data analytics solutions. However, the data processes running on this infrastructure were not integrated, automated nor stable, and the data analytics tools had limited bandwidth. In addition, there was no dedicated processing power or storage for most of the system components and the processing power had to be started and stopped manually to avoid growing operating costs.
Additionally, while the system’s load was predictable most of the time, the PoC platform was unable to scale for data ingestion peaks during harvest periods. These harvest periods are the most demanding and important timeframes for the Syngenta team because the data scientists and applications require a steady supply of data to make business decisions without delay or interruption due to load issues.
Solution Highlights
EPAM developed, built, deployed, supported and maintained a data analysis solution based on AWS services. The solution aggregated data from multiple structured and unstructured sources and prepared the data for advanced analytics and machine learning (ML) procedures, tools and scientific application.
The new data workflow architected by EPAM provided Syngenta data scientists with access to useful, accurate and up-to-date data. The solution incorporated the following components:
- Platform Data Flow
- Data Processing
- Data Query Engines
- Data Sources & Acquisition
- Concurrency
- Storage
- Data Consumption
Once the approach was verified with a successful PoC, the team that had developed the solution deployed and supported it before transitioning to a dedicated support team from EPAM. This team provided an SLA-based managed service spanning performance monitoring, testing, remediation, release life cycle and change requests, as well as being responsible for the year-over-year (YoY) service improvement plan.
The Results
- With EPAM’s solution, key data sets are now accessible via the AWS Cloud, providing a stable, automated and integrated environment for the Syngenta team.
Syngenta data scientists are able to leverage a broader set of analytics tools and computing capabilities, leading to an easier and more manageable governance approach. - With greater infrastructure elasticity and scalability due to the cloud, Syngenta is able to better manage peaks during harvesting periods.
- Syngenta’s operational workload was reduced considerably compared to the on-premise environment.
- From a managed service perspective, the YoY service improvement plan meant that the number of tickets reduced to the agreed minimum level and the average SLA compliance rate met agreed objectives. With the solution stable and the operational efficiencies realized, Syngenta was able to manage it going forward with minimum workload impact.
Technologies used
- AWS
- Apache Airflow
- Grafana, Prometheus
- Terraform
- PySpark
- HEAR FROM THE CUSTOMER
Syngenta relied on EPAM’s AWS expertise and development speed to deliver this project successfully within our challenging time constraints.
William Burtle
DataOps Platform Manager, RDIT, Syngenta
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