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Apache Beam Corporate Training Program for Employees
Give your data engineering teams the skills to build unified batch and streaming data pipelines with Apache Beam, through instructor-led corporate training customized to your data stack and runners.
(Virtual / On-site / Off-site)
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What is Apache Beam? Apache Beam is an open-source, unified programming model for defining and running both batch and streaming data processing pipelines, with SDKs in Java, Python, and Go that execute on multiple engines such as Google Cloud Dataflow, Apache Flink, and Apache Spark. For data teams, it means writing a pipeline once and running it on the runner that fits the workload, without rewriting code for each engine.
As organizations process more real-time and large-scale data, this program helps your teams design portable, scalable pipelines confidently with Apache Beam. Empower your people with expert-led on-site, off-site, and virtual sessions delivered by Edstellar, a premier corporate training provider serving organizations worldwide. Built around your goals, the program turns Apache Beam skills into lasting capabilities that lift performance across data engineering, analytics, and platform teams.
Delivered instructor-led and fully customized to your data stack, the training is available worldwide in person and virtually across popular languages, and covers the Beam model end to end, including PCollections, transforms, windowing, watermarks, triggers, and I/O connectors across batch and streaming. Your organization gains pipelines that run consistently across runners, lower re-engineering costs, and engineers who can scale data processing as volumes grow. Request a tailored proposal to align the curriculum with your runners and use cases.
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- Build a pipeline once with the Beam model and run it on Dataflow, Flink, or Spark without rewriting code.
- Apply core abstractions, PCollections, PTransforms, ParDo, and DoFns, to model real data processing logic.
- Handle streaming data correctly using windowing, watermarks, triggers, and late-data handling.
- Connect pipelines to sources and sinks with Beam I/O connectors for files, messaging, and databases.
- Test, debug, and tune pipelines for performance, cost, and reliability across batch and streaming.
- Deploy and operate Beam pipelines in production on your chosen runner with monitoring and scaling.
- Apache Beam Foundations and the Unified Model
- The Beam model: batch and streaming in one programming model
- Pipelines, PCollections, and PTransforms explained
- SDKs and language portability: Java, Python, and Go
- Choosing a runner: Dataflow, Flink, Spark, and the Direct runner
- Building Pipelines with Core Transforms
- Reading and writing data with I/O connectors
- ParDo, DoFn, and element-wise processing
- Grouping, combining, and aggregations (GroupByKey, CoGroupByKey, Combine)
- Composite transforms and reusable pipeline components
- Streaming Data Processing with Beam
- Event time versus processing time
- Windowing strategies: fixed, sliding, session, and global windows
- Watermarks, triggers, and handling late data
- Stateful processing and timers
- Working with I/O Connectors and Data Sources
- File-based I/O and cloud object storage
- Messaging systems: Pub/Sub and Kafka
- Databases and data warehouses (BigQuery and JDBC)
- Building and configuring custom connectors
- Testing, Debugging, and Optimizing Pipelines
- Unit and integration testing with Beam test utilities
- Debugging pipeline logic and data correctness issues
- Performance tuning, fusion, and resource management
- Managing cost and throughput across runners
- Deploying and Operating Beam in Production
- Running pipelines on Google Cloud Dataflow
- Running pipelines on the Apache Flink and Spark runners
- Monitoring, logging, and pipeline observability
- Scaling, updates, and operational best practices
- Data Engineers
- Data Scientists
- ETL Developers
- Cloud Engineers
- Software Engineers
- Technical Managers
- Systems Architects
- Data Analysts
- Business Intelligence Developers
- Application Developers
- DevOps Engineers
- Workflow Coordinators
Participants should be comfortable with a programming language supported by Beam, typically Java or Python, and core data concepts such as databases and SQL. Familiarity with data pipelines or distributed processing is helpful but not required, as the program includes guided setup. Edstellar tailors the starting point to your team's experience, so both engineers new to Beam and those scaling existing pipelines can take part productively.
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64 hours of group training (includes VILT/In-person On-site)
Tailored for SMBs
160 hours of group training (includes VILT/In-person On-site)
Ideal for growing SMBs
Tailor-Made Trainee Licenses with Our Exclusive Training Packages!
400 hours of group training (includes VILT/In-person On-site)
Designed for large corporations
Tailor-Made Trainee Licenses with Our Exclusive Training Packages!
Unlimited duration
Designed for large corporations
Experienced Trainers
Our trainers are drawn from a vetted global network and bring years of industry expertise, keeping every session practical and impactful.
Proven Quality
With a strong global track record, Edstellar is known for quality and engaging delivery.
Industry-Relevant Curriculum
Our programs are built by experts to match the demands of today's industry.
Fully Customizable
Every program can be tailored to your organization's goals.
Comprehensive Support
We provide pre- and post-session support for a complete learning experience.
Global Multi-Location & Multilingual Training Delivery
We deliver in multiple languages to support diverse global teams.
Hear from Organizations We've Trained
Recognition That Motivates Your Team
Frequently Asked Questions
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