Data engineering courses

Data Engineering Training Programs

Comprehensive courses covering distributed processing, data warehousing, and real-time analytics systems

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Technical Training Methodology

Our training programs focus on implementation skills required for production data infrastructure. Each course combines theoretical foundations with extensive hands-on work using distributed computing systems, cloud platforms, and professional development tools. Participants build complete data processing systems rather than working through isolated exercises, gaining experience with integration challenges and operational considerations that affect real deployments.

The curriculum emphasizes performance optimization and system scalability. Engineers learn to profile processing workflows, identify bottlenecks, and implement solutions that improve throughput while managing resource costs. This includes partition strategy optimization, query tuning techniques, and architectural patterns that maintain efficiency as data volumes increase.

Course content reflects practices used at technology companies across various sectors. We cover version control workflows for data pipelines, testing strategies for ETL systems, monitoring approaches, and documentation practices that support team collaboration. The training environment includes distributed computing clusters configured similarly to enterprise deployments, providing realistic experience with resource constraints and system behavior at scale.

Available Training Programs

Apache Spark training

Data Engineering with Apache Spark

¥61,000

Build scalable data processing pipelines using Apache Spark for big data analytics. This comprehensive program covers Spark architecture, RDDs, DataFrames, and Spark SQL with focus on production optimization techniques.

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Course Coverage

Core Concepts
  • Spark architecture and execution model
  • RDD operations and transformations
  • DataFrame and Dataset APIs
  • Spark SQL query optimization
Advanced Topics
  • Structured Streaming implementation
  • Performance tuning and memory management
  • Cluster deployment strategies
  • Integration with data lakes
Practical Projects
  • Real-time analytics pipelines
  • Data quality frameworks
  • Feature engineering workflows
  • ETL optimization case studies
Expected Outcomes
  • Configure Spark clusters effectively
  • Optimize shuffle operations
  • Implement streaming applications
  • Troubleshoot performance issues
Data warehouse training

Modern Data Warehouse Architecture

¥57,000

Design and implement cloud-native data warehouses for enterprise analytics needs. Learn dimensional modeling, data vault methodology, and optimization techniques for Snowflake, BigQuery, and Redshift.

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Course Coverage

Architecture Design
  • Dimensional modeling techniques
  • Slowly changing dimensions
  • Data vault methodology
  • Star and snowflake schemas
Platform Implementation
  • Snowflake architecture and features
  • BigQuery optimization strategies
  • Redshift cluster configuration
  • Cross-platform comparison
Data Lakehouse
  • Delta Lake implementation
  • Hybrid architecture patterns
  • Data governance frameworks
  • Metadata management
Hands-On Labs
  • Legacy warehouse migration
  • Real-time warehousing setup
  • Self-service analytics platforms
  • Cost optimization techniques
Stream processing training

Stream Processing Systems

¥53,000

Master real-time data processing using Apache Kafka and Flink. Learn event-driven architectures, complex event processing, and streaming SQL for building production streaming applications.

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Course Coverage

Kafka Fundamentals
  • Kafka architecture overview
  • Producer and consumer patterns
  • Topic partitioning strategies
  • Kafka Streams applications
Flink Processing
  • Complex event processing
  • Window operations
  • Stateful computations
  • Exactly-once semantics
Design Patterns
  • Event sourcing implementation
  • CQRS patterns
  • Backpressure handling
  • Streaming SQL queries
Applied Projects
  • Fraud detection systems
  • IoT data pipelines
  • Real-time recommendation engines
  • Analytics dashboards

Course Selection Guide

Feature Apache Spark Data Warehouse Stream Processing
Processing Type Batch & Streaming Batch Analytics Real-Time Streaming
Primary Use Case Large-scale ETL pipelines Enterprise analytics Event-driven systems
Technical Focus Distributed computing Data modeling Low-latency processing
Skill Level Intermediate to Advanced Intermediate Advanced
Prerequisites Python or Scala, SQL SQL, basic ETL concepts Java or Scala, distributed systems
Duration 8 weeks 6 weeks 7 weeks
Investment ¥61,000 ¥57,000 ¥53,000

Selection Recommendations

Choose Apache Spark if: You work with large datasets requiring distributed processing, need to implement ETL pipelines at scale, or want comprehensive coverage of both batch and streaming workflows.

Choose Data Warehouse if: Your focus is business intelligence and analytics infrastructure, you're migrating to cloud platforms, or you need to design dimensional models for reporting systems.

Choose Stream Processing if: You're building real-time applications, implementing event-driven architectures, or need to process continuous data streams with low latency requirements.

Technical Standards & Protocols

Development Practices

All courses incorporate version control with Git, code review processes, unit testing for data pipelines, and continuous integration workflows that support team collaboration in production environments.

Performance Standards

Training emphasizes optimization techniques including query profiling, execution plan analysis, partition strategy tuning, and resource allocation approaches that maintain efficiency at scale.

Data Quality

Participants implement validation frameworks, data quality metrics, monitoring solutions, and error handling patterns that ensure accuracy throughout processing workflows.

Documentation Requirements

Courses cover documentation approaches for data pipelines, schema definitions, transformation logic, and operational procedures that facilitate knowledge transfer within engineering teams.

Training Infrastructure

Our training facility provides access to distributed computing clusters configured with multiple processing frameworks. Participants work with Apache Spark clusters, Kafka broker deployments, and Flink job managers in environments that replicate production system characteristics. This infrastructure allows for realistic implementation exercises where engineers encounter actual resource constraints and performance considerations.

Cloud platform access includes Snowflake, BigQuery, and Redshift environments for data warehouse training. Students implement complete architectures including data ingestion pipelines, transformation workflows, and query optimization techniques on platforms used in enterprise settings. The setup supports experimentation with different configurations and optimization approaches.

Development environments include professional tools such as Jupyter notebooks for interactive development, DataGrip for database management, version control systems, and monitoring solutions like Prometheus and Grafana. This tooling mirrors what engineers use in professional data infrastructure roles, providing familiarity with the complete development ecosystem.

Training resources include sample datasets at various scales, from smaller sets suitable for algorithm development to larger datasets that require distributed processing. This progression helps engineers understand how techniques and architectures scale from development to production environments, building intuition about system behavior under different data volumes.

Training Packages

Complete Data Engineering Path

¥155,000

All three courses with integrated curriculum covering the full spectrum of modern data infrastructure. Includes Apache Spark, Data Warehouse Architecture, and Stream Processing with unified projects connecting concepts across courses.

  • Save ¥16,000 compared to individual courses
  • Comprehensive end-to-end data infrastructure knowledge
  • Capstone project integrating all technologies

Processing Systems Bundle

¥104,000

Apache Spark and Stream Processing courses focused on building data processing pipelines for both batch and real-time workflows. Covers distributed computing fundamentals through advanced streaming applications.

  • Save ¥10,000 compared to individual courses
  • Focus on data processing pipeline development
  • Integrated batch and streaming projects

Custom Training Solutions

We offer customized training programs for engineering teams with specific technical requirements. Contact us to discuss curriculum tailored to your organization's data infrastructure needs, technology stack, and skill development goals.

Frequently Asked Questions

What are the technical prerequisites for these courses?

Apache Spark course requires proficiency in Python or Scala plus SQL experience. Data Warehouse course needs solid SQL knowledge and familiarity with basic ETL concepts. Stream Processing course expects experience with Java or Scala and understanding of distributed systems fundamentals. All courses assume comfort working in command-line environments and basic programming competency.

How are the courses structured and delivered?

Courses combine instructor-led sessions with hands-on lab work. Classes meet twice weekly in the evening to accommodate working professionals, with each session lasting three hours. Additional lab access is available for independent practice. The format balances conceptual instruction with substantial implementation time on actual systems.

Do I receive any certification upon completion?

Participants receive DataPipe completion certificates documenting the technical topics covered and projects completed. These certificates are not vendor certifications but demonstrate practical training in specific technologies. Many employers recognize DataPipe training when evaluating data engineering candidates.

What equipment or software do I need to provide?

You need a laptop capable of running development tools like code editors and terminal applications. All specialized infrastructure including distributed computing clusters, cloud platform access, and professional software licenses are provided as part of the training. Internet connectivity is required for accessing course resources and systems.

Can I take courses individually or must I enroll in a package?

All courses are available individually. Package options offer cost reduction for participants planning to complete multiple programs, but there is no requirement to purchase packages. You can start with one course and add others later based on your learning needs and schedule.

What is the class size and instructor availability?

Classes are limited to 12-15 participants to ensure adequate instructor interaction and lab resource availability. Instructors are accessible during class sessions and lab hours for technical questions. We maintain discussion forums for each cohort where participants can ask questions and share insights between sessions.

How current is the course content with industry practices?

Course material is updated regularly to reflect current versions of frameworks and platforms. Our instructors work actively in data engineering and incorporate recent developments in the field. While specific tool versions evolve, we focus on fundamental concepts and patterns that remain relevant across version changes.

What happens if I need to miss a class session?

Session recordings are available for participants who miss classes due to scheduling conflicts. Lab exercises remain accessible so you can complete work independently. However, in-person participation is recommended as substantial learning occurs through instructor interaction and peer collaboration during sessions.

Do you offer job placement assistance?

We do not provide job placement services. However, many technology companies in Tokyo are familiar with DataPipe training and actively recruit from our alumni network. We maintain a community forum where participants share job opportunities and career information relevant to data engineering roles.

Can my company sponsor multiple team members?

Yes, we work with companies sending multiple engineers for training. Group enrollments of three or more participants from the same organization receive consideration for customized scheduling and content emphasis areas. Contact us to discuss corporate training arrangements that address your team's specific technical development needs.

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