Albertsons: Using Apache Beam for Unified Analytics Ingestion

Context

Albertsons Companies is one of the largest retail grocery organizations in North America, operating over 2,200 stores and serving millions of customers across physical and digital channels.

Apache Beam is the foundation of the internal Unified Data Ingestion framework, a standardized enterprise ELT platform that delivers both streaming and batch data into modern cloud analytics systems. The framework uses both Java and Python Beam SDKs, Dataflow Flex Templates, enabling flexibility across workloads. When a capability is not yet supported in the Python SDK but is available in the Java SDK, we can seamlessly leverage Java-based implementations to deliver the required functionality.

This unified architecture reduces duplicated logic, standardizes governance, and accelerates data enablement across business domains.

Challenges and Use Cases

Before Apache Beam, ingestion patterns were fragmented across streaming and batch pipelines. This led to longer development cycles, inconsistent data quality, and increased operational overhead.

The framework’s architecture emphasizes object-oriented principles including single responsibility, modularity, and separation of concerns. This enables reusable Beam transforms, configurable IO connectors, and clean abstractions between orchestration and execution layers.

Beam enabled:

• Unified development for real-time and scheduled ingestion
• Standardized connectivity to enterprise systems
• Reliable governance and observability baked into pipelines

The framework supports:

• Real-time streaming analytics from operational and digital signals
• Batch ingestion from mission-critical enterprise systems
• File-based ingestion for vendor and financial datasets
• Legacy MQ ingestion using JMSIO-based connectors

To scale efficiently, the framework features Apache Airflow dynamic DAG creation.

Metadata-driven ingestion jobs generate DAGs automatically at runtime, and BashOperator is used to submit Dataflow jobs for consistent execution, security, and monitoring.

Common Beam transforms include Impulse, windowing, grouping, and batching optimizations.

In Albertsons we utilized Apache Beam to write an in-house framework that enabled our data engineering teams to create robust data pipelines through a consistent - single interface. The framework helped reduce the overall development cycle since we templatized the various data integration patterns. Having a custom framework gave us flexibility to prioritize and configure multiple technologies/integration points like Kafka, Files, Managed Queues, Databases (Oracle, DB2, Azure SQL etc.) and Data Warehouses like BigQuery and Snowflake. Moreover this helped the production support teams to manage and debug 2500+ jobs with ease since the implementations were consistent across 17+ data engineering teams

Mohammed Jawed Khan

Principal Data Engineer @ Albertsons

Technical Data

Apache Beam pipelines operate at enterprise scale:

• Hundreds of production pipelines
• Terabytes of data processed weekly, including thousands of streaming events per second.

All ingestion paths adhere to internal security controls and support tokenization for PII and sensitive data protection using Protegrity.

Results

Apache Beam has significantly improved the reliability, reusability, and speed of Albertsons’ data platforms:

Business Impact

Beam enabled one unified ingestion framework that supports both streaming and batch workloads - eliminating fragmentation and delivering trusted signals to analytics.

Integrating Apache Beam into our in-house ELT platform has reduced engineering effort and operational overhead, while improving efficiency at scale. Teams can now focus more on delivering business outcomes instead of managing infrastructure.

Vinay Desai

Director Engineering @ Albertsons

By leveraging Apache Beam into the ACI platform, we achieved a significant reduction in downtime. The adoption of reusable features further minimized the risk of production issues.

Ankur Raj

Director , Data Engineering Operations @ Albertsons

Infrastructure

Deployment is portable across Dev, QA, and Prod environments.

Beam Community & Evolution

Beam community resources supported the framework’s growth through:

• Slack & developer channels
• Documentation
• Beam Summit participation