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Exam Details

• Exam Name: Big Data Certified Associate (BDCA)
• Exam Format: Multiple choice, online with remote supervision (proctoring)
• Exam Delivery: Safe Exam Browser
• Number of Questions: 50 questions: 60% scenarios / 40% conceptual
• Exam Duration: 75 minutes
• Passing Score: 70% (35 out of 50 correct answers)
• Available Languages: Spanish / English
• Attempts: Maximum 2 attempts (minimum 15 days between attempts)
• Validity: 3 years · 15 CPD credits per year

Main Subjects

• Big Data fundamentals and the modern ecosystem (14%): What Big Data is: volume, velocity, variety, veracity and value; Evolution of the ecosystem, from Hadoop to the Lakehouse; Data Warehouse, Data Lake and Lakehouse; Data Mesh and Data Fabric, and how they complement each other; Representative use cases by industry.
• Data processing and architecture (20%): Batch versus streaming processing; Apache Spark and SparkSQL; Apache Kafka and Apache Flink; Data workflow orchestration: Airflow, Prefect, Dagster; Transactional storage: Delta Lake, Apache Iceberg, Apache Hudi; Leading platforms: Databricks, Snowflake, BigQuery, Redshift.
• Data science and AI applied to Big Data (16%): From data mining to modern data science; Machine learning and deep learning on large data volumes; MLOps and automated pipelines (MLflow, Kubeflow); AutoML and current frameworks; LLMs and generative AI applied to Big Data; Ethics, bias and responsibility in data projects.
• Practical data engineering (18%): ETL versus ELT in modern pipelines; Data quality and observability (Great Expectations, Monte Carlo); Real-time data integration (Kafka Connect, Debezium); End-to-end pipeline: processing with Spark, storage with Delta Lake and a dashboard in Power BI or Looker.
• Data governance, security and compliance (18%): Policies, metadata and data catalogs; Encryption, masking and zero trust; GDPR, CCPA, ISO/IEC 27001 and ISO/IEC 27701; FinOps and cloud cost optimization; Data lineage and observability.
• Trends and the future of Big Data (14%): Real-time AI and streaming analytics; Edge computing and IoT data; DataOps and automated data management; Sustainability and responsible AI (Green AI); Case studies: fintech, digital health and global e-commerce.

Who is this certification for?

• Technology professionals moving into data roles: Who seek a solid, recognized foundation in the Big Data ecosystem.
• Data and business intelligence analysts: Who need to broaden their understanding toward larger-scale architectures and tools.
• Students and recent graduates: In technology-related fields, seeking a recognized entry-level Big Data certification.
• Leaders of non-technical areas: Who work with data teams and need to understand the core terminology and concepts.
• Consultants: Who are starting to specialize in large-scale data projects and want to broaden their skill portfolio.

Prerequisites

No mandatory prerequisites: no prior professional experience, specific academic training or other certification is required. Registration is direct with CertJoin or through an authorized partner. Recommended (not mandatory): general IT concepts and basic use of spreadsheets or data analysis tools. Next levels: Big Data Certified Expert (BDCE) and Big Data Certified Specialist (BDCS).