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Working with Serverless Data Infrastructure and FME

Tandra Geraedts
Tandra Geraedts
  • Updated

Introduction

As organizations move data operations to the cloud, managing data across serverless storage, managed databases, and cloud data warehouses has become a core part of modern infrastructure. For teams using FME, this shift doesn't require replacing existing workflows; it means extending them.

This article introduces how FME connects to and works with serverless data infrastructure in practical, operational terms. It is intended for both practitioners building FME workflows and decision-makers evaluating whether FME fits their cloud data strategy.

By the end of this article, you will understand how to:

  • Connect FME to cloud object storage and managed databases
  • Read, write, and transform data across cloud platforms
  • Automate event-driven workflows using FME Flow
  • Route and validate data within serverless pipelines
  • Apply FME to cloud data infrastructure without replacing existing systems

Understanding Serverless Data Infrastructure in FME Terms

Serverless data infrastructure refers to cloud services where storage, compute, and database capacity are managed by a provider, with no servers for your team to provision or maintain. The following concepts appear frequently in this context and map directly to FME capabilities.

Serverless Infrastructure: Cloud services where the provider manages all underlying hardware and scaling. FME connects to these services as sources and destinations without requiring changes to how the infrastructure is managed.

Object Storage: A scalable way to store files, documents, and datasets in the cloud (for example, Amazon S3, Azure Blob Storage, Google Cloud Storage). FME can upload, download, list, and delete objects in cloud storage buckets using connector transformers such as the S3Connector, AzureBlobStorageConnector, and GoogleCloudStorageConnector.

Managed NoSQL Database: A fully managed, schema-flexible database service (for example, Amazon DynamoDB, Azure Cosmos DB). FME can read and write large volumes of structured or semi-structured data to these services, making them accessible to downstream systems or analysis.

Cloud Data Warehouse: A managed analytical database optimized for large-scale queries (for example, Snowflake, Google BigQuery). FME can load, transform, and extract data from cloud data warehouses, supporting both bulk ingestion and incremental updates.

Event-Driven Workflow: A process that runs automatically in response to a trigger, such as a file arriving in cloud storage. FME Flow Automations can monitor cloud storage buckets and databases for new or changed data, then run workspaces automatically in response.

Cloud-Native Format: A data format designed for efficient access directly from cloud storage (for example, GeoParquet, Cloud Optimized GeoTIFF, STAC). FME Form supports several cloud-native formats, enabling direct reads from object storage without first downloading full datasets.

Data Pipeline: A sequence of automated steps that moves and transforms data between systems. FME workspaces and FME Flow Automations together form repeatable, auditable pipelines across cloud services.

FME and Serverless Infrastructure at a Glance

Cloud Service Type Examples How FME Connects
Object Storage Amazon S3, Azure Blob, Google Cloud Storage Connector transformers (upload, download, list, delete)
Managed NoSQL Database Amazon DynamoDB, Azure Cosmos DB Reader/writer formats
Cloud Data Warehouse Snowflake, Google BigQuery Reader/writer formats with dynamic schema support
Event-Driven Triggers S3 bucket events, webhooks FME Flow Automations
Cloud-Native Formats GeoParquet, COG, STAC, FlatGeobuf Native format support in FME Form

FME Flow can be hosted entirely within cloud environments, including AWS, Azure, and Google Cloud, allowing FME workflows to run where your data already lives.

The Role of FME in Serverless Data Infrastructure

Connecting to Cloud Object Storage

Object storage is one of the most common components in serverless architectures — used for raw data lakes, file staging, backups, and data delivery. FME connects to the major cloud providers via dedicated connector transformers, enabling files to be read from or written to cloud buckets within any workspace.

FME can:

  • Upload and download files to and from Amazon S3, Azure Blob Storage, and Google Cloud Storage
  • List, filter, and manage objects in storage buckets
  • Use cloud storage as a staging layer between systems
  • Trigger FME Flow Automations when new files arrive in a bucket

Learn More:

Customer Story: Avineon-Tensing used FME to modernize the processing of over one million historical service records. FME orchestrated the full workflow. Cataloging scanned records stored in Azure Blob Storage, queuing them for parallel processing, and writing the extracted data to an Azure SQL Database. This cloud-native approach enabled high-speed processing of 20 records per second while keeping data accessible and structured for downstream use. 

Reading and Writing Cloud Databases

Managed cloud databases offer scalability and availability without requiring database administration. FME supports reading from and writing to both NoSQL databases, such as Amazon DynamoDB, and cloud data warehouses, such as Snowflake and Google BigQuery.

FME can:

  • Read and write structured and semi-structured data to Amazon DynamoDB
  • Load data into Google BigQuery from files, databases, or other cloud sources
  • Connect to Snowflake for both spatial and non-spatial data operations
  • Perform merge, insert, update, and delete operations against cloud database tables
  • Use dynamic schema definitions to load data from variable sources into cloud warehouses

Learn More:

Customer Story: IHS Markit needed to migrate terabytes of business intelligence data into Snowflake as quickly as possible without compromising quality. Using FME with parallel processing across multiple engines, the team migrated 1.5 billion rows in just five hours — consolidating data from multiple legacy systems into a single cloud data warehouse and providing business intelligence users with a reliable, centralized source of truth. 

Automating Event-Driven Workflows with FME Flow

One of the key advantages of serverless infrastructure is the ability to trigger processes automatically in response to data changes. FME Flow Automations are designed specifically for this pattern — workspaces run when something happens, rather than on a fixed schedule.

FME Flow can:

  • Monitor S3 buckets for new or modified files and trigger workspaces automatically
  • Respond to webhook events from cloud platforms and external systems
  • Chain multiple workspaces together so output from one step triggers the next
  • Run FME workflows entirely within AWS, Azure, or Google Cloud environments

Learn More:

Customer Story: The Arkansas GIS Office replaced on-premise infrastructure with an AWS and FME solution, eliminating hardware costs and building a scalable, fault-tolerant system. By integrating FME with cloud storage and automated workflows, the agency saved over $200,000 over three years while maintaining secure, reliable public access to spatial data, without requiring internal infrastructure.

Transforming and Routing Data Between Cloud Services

Serverless pipelines often involve moving data between multiple cloud services — from storage to database, from one format to another, or from a cloud platform to a downstream application. FME workspaces act as the transformation and routing layer between these services.

FME can:

  • Convert between formats as data moves between cloud services
  • Enrich, filter, and restructure data in transit
  • Route data to multiple destinations simultaneously
  • Support cloud-native formats like GeoParquet, Cloud Optimized GeoTIFF (COG), STAC, and FlatGeobuf for direct cloud access

Learn More:

Customer Story: Plains Midstream uses FME to route real-time trucking data into Azure Data Lake, transforming and publishing it for visualization through Esri's Experience Builder. FME serves as the central layer, connecting data sources to cloud storage and downstream applications, providing operations teams with timely insights into truck locations and availability without requiring multiple workbenches. 

Validating and Governing Data in Serverless Pipelines

Moving data to the cloud doesn't remove the need for quality checks and governance — it makes them more important. FME can embed validation and governance steps directly into cloud pipelines, ensuring data is checked before it reaches its destination.

FME can:

  • Validate attributes and schema structure before writing to cloud databases
  • Flag or reject non-conforming records automatically
  • Generate QA reports and logs for audit purposes
  • Apply the same governance rules consistently across multiple cloud destinations

Learn More:

Conclusion

FME doesn't require your organization to choose between existing workflows and modern cloud infrastructure. Whether your data lives in Amazon S3, DynamoDB, Snowflake, Google BigQuery, or Azure Blob Storage, FME connects to it, transforms it, and moves it — automatically and reliably.

For users, this means no-code access to cloud-native services through familiar FME Form interfaces and connectors. For decision-makers, it means the organization can adopt serverless infrastructure incrementally, without replacing the data integration layer that already works.


 

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