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Databricks Certified Data Engineer Associate certification is widely recognized in the industry and is highly valued by employers. Earning this certification demonstrates to employers that a candidate has the skills and knowledge required to build and maintain data solutions on the Databricks platform. Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate日本語版) certification can help professionals advance their careers and increase their earning potential.
Reference: https://www.databricks.com/learn/certification/data-engineer-associate
Databricks Certified Data Engineer Associate Exam covers a range of topics related to data engineering, including data ingestion, ETL (extract, transform, load) processes, data modeling, and data warehousing. Databricks-Certified-Data-Engineer-Associate-JPN exam also covers Databricks-specific tools and technologies, such as Databricks Delta Lake and Databricks Runtime. Passing the exam requires a deep understanding of these topics and the ability to apply that knowledge in real-world scenarios. Successful candidates will have demonstrated their ability to design and implement reliable, scalable, and efficient data pipelines using Databricks.
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| Section | Objectives |
|---|---|
| Data Ingestion and ELT Development | - ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data |
| Data Governance and Quality | - Data access control and governance - Unity Catalog basics - Data quality concepts and management |
| Data Processing and Transformations | - PySpark DataFrame transformations - Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) |
| Productionizing Data Pipelines | - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs |
| Databricks Lakehouse Platform Fundamentals | - Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage |
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