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Leveraging AWS to Scale Data Analytics & Enhance Decision-Making for Syngenta

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

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

With the following components of the solution, EPAM architected a new data workflow so Syngenta data scientists were able to gain access to useful, accurate and up-to-date data:

  • Platform Data Flow
  • Data Processing
  • Data Query Engines
  • Data Sources & Acquisition
  • Concurrency
  • Storage
  • Data Consumption

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.
  • Finally, Syngenta’s operational workload was reduced considerably compared to the on-premise environment.

Technologies used

  • AWS
  • Apache Airflow
  • Grafana, Prometheus
  • Terraform
  • PySpark
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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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