Senior Big Data Engineer

About the SBDE™ Exam

The two handbooks on Big Data Engineering included in the official DASCA exam preparation kit are the recommended and essential readings for the online SBDE™ certification examination. These handbooks have been developed based on the DASCA Essential Knowledge Framework (DASCA-EKF™) to ensure effective preparation for the SBDE™ certification exam and, at the same time, help Big Data Engineers and application developers build a longer–lasting base of knowledge and a more in-depth perspective of the most advanced concepts in data science that underlie data analytics technologies. In addition to the handbooks, SBDE™ candidates are provided with a candidate guide and practice tests on their myDASCA dashboard.

The content in the two official DASCA handbooks is clustered into nine sections covering all prescribed SBDE™ exam areas and aims to cultivate in learners a strong generic and platform-based understanding of the concepts, tools, and technologies of handling storage, processing, management, and security infrastructure required in managing large volumes of streaming data.

Examination Coverage Information

• Data Ingestion & Transformation

  15%

  Description & Practical Applications

  Collecting raw data from various sources and converting it into usable formats through extraction, transformation, and loading processes using tools such as Talend Open Studio and dbt. Includes implementing ETL/ELT pipelines, data cleansing and normalization, handling batch and real-time acquisition, managing schema evolution, and establishing connectivity with diverse source systems like databases, APIs, and files.

  Why It Matters

  The first critical step in any data pipeline - without reliable data coming in, nothing else can happen. Ensures data quality at entry and standardizes formats across sources, creating a foundation of trust for all downstream analytics.

• Data Storage & Management

  15%

  Description & Practical Applications

  Designing and implementing systems to store data efficiently with considerations for volume, access patterns, and cost. Involves creating data warehouse/lake architectures using tools like Apache Hive, implementing storage optimization strategies, setting up partitioning and indexing, managing data lifecycle and retention policies, designing hybrid storage solutions, and performance tuning for query optimization.

  Why It Matters

  The cornerstone of data engineering - ensures data is persistently and scalably stored for analysis while balancing cost and performance. Provides the foundation for all data consumers, from analysts to applications, ensuring they can access the right data at the right time.

• Data Pipeline Orchestration & Monitoring

  20%

  Description & Practical Applications

  Automating, scheduling, and overseeing end-to-end data workflows with proper dependency management and observability using tools like Apache Airflow. Encompasses workflow scheduling and triggering, complex dependency management, comprehensive monitoring and alerting, implementing error handling and recovery mechanisms, optimizing resource utilization, and managing SLAs for data delivery.

  Why It Matters

  Keeps data moving without manual effort - ensures reliable data flow and quick issue resolution. Creates maintainable, observable pipelines that can evolve with business needs and scale with data volume, providing predictable data refresh cycles for business operations.

• Data Security & Governance

  10%

  Description & Practical Applications

  Protecting data throughout its lifecycle while ensuring compliance with regulations and organizational policies. Involves implementing access control and authentication systems, configuring data encryption, ensuring regulatory compliance, establishing data quality validation frameworks, managing metadata and data catalogs, and tracking data lineage across transformations using tools like Great Expectations.

  Why It Matters

  Builds trust and safety into data pipelines - prevents data breaches and ensures compliance. Maintains data integrity while providing appropriate access and transparency about data origins and transformations, enabling confident business decisions based on trusted information.

• Data Modeling & Architecture

  20%

  Description & Practical Applications

  Designing optimal data structures and system architectures based on business requirements and technical constraints using tools DBeaver, MySQL Workbench. Includes creating dimensional models, developing logical and physical data models, designing overall system architecture, making technology selection decisions, implementing modern paradigms like data mesh/fabric, and managing schema evolution strategies.

  Why It Matters

  The backbone of data-driven organizations - creates efficient, scalable systems that serve business needs. Ensures data structures align with query patterns and analysis requirements while remaining adaptable to changing needs and growing data volumes. Directly impacts analytical capabilities and query performance.

• Streaming Data Processing

  10%

  Description & Practical Applications

  Building systems that process and analyze data in real-time as it's generated, rather than in batches using tools like Apache Kafka. Focuses on practical implementation of streaming pipelines that capture, process, and deliver continuously flowing data with minimal latency.. Emphasis on hands-on experience with stream processing frameworks and message brokers to build resilient real-time data flows.

  Why It Matters

  Enables time-sensitive insights and actions - critical for applications requiring immediate data analysis. Supports real-time dashboards, anomaly detection, and responsive business processes in a world expecting instant updates and immediate action on incoming data.

• AI/ML Integration Overview

  5%

  Description & Practical Applications

  Building specialized data infrastructure to support machine learning workflows from training to inference using tools like MLflow. Includes implementing feature stores, integrating with ML pipelines, creating data infrastructure for model deployment, formatting data for AI-specific needs, optimizing data for training and inference, and establishing data feeds for model monitoring.

  Why It Matters

  Bridges traditional data engineering with modern AI needs. Ensures ML models have appropriate data infrastructure for both training and production deployment, enabling reliable, scalable AI applications that can deliver consistent value to the business.

• Generative AI & LLM Operations Overview

  5%

  Description & Practical Applications

  Designing specialized data systems for generative AI applications, including vector databases, embedding pipelines, and LLM operations. Encompasses implementing vector database solutions, creating embedding generation pipelines, building RAG (Retrieval Augmented Generation) systems, preparing data for LLM fine-tuning, developing prompt management infrastructure, optimizing semantic search capabilities, and managing token usage and costs.

  Why It Matters

  Supports the rapidly growing field of generative AI applications. Enables organizations to build and deploy LLM-powered solutions with proper data context, retrieval capabilities, and optimization for both quality and cost. Creates the foundation for next-generation AI assistants and knowledge systems.