Summary
This guide provides a glossary of key terms and concepts related to data engineering. It defines data engineering as the practice of designing, building, and maintaining systems for data collection, storage, and processing. The guide outlines several important processes, including ETL (Extract, Transform, Load), which is crucial for preparing data for analysis. It describes data pipelines, which are essential for moving data from sources to destinations, and differentiates between batch and stream processing methods. The document also explains various storage solutions such as data warehouses, data lakes, and data marts, along with database types like relational and NoSQL databases. Additionally, it covers concepts like data governance, data quality, and data integration, emphasizing the importance of data transformation and modeling. Tools and frameworks such as Airflow, Apache Spark, and Hadoop are mentioned, highlighting their roles in data engineering. The glossary serves as a comprehensive reference for professionals in the field.
