Data engineering involves designing, building, and maintaining systems and processes for collecting, storing, processing, and analyzing data.

ETL (Extract, Transform, Load) processes data before loading it into the target system, while ELT (Extract, Load, Transform) loads data first and then transforms it within the target system.

A data lake is a centralized repository that stores vast amounts of raw data in its native format, accommodating structured, semi-structured, and unstructured data.

A data warehouse is a centralized system that stores structured data from multiple sources, optimized for reporting and analysis.

SQL databases are structured, schema-based, and use SQL for querying, while NoSQL databases are schema-less, can store unstructured data, and often use flexible query languages.

Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and applications, supporting high-throughput and fault-tolerant messaging.

A primary key uniquely identifies each record in a database table, ensuring data integrity and enabling efficient data retrieval.

Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity, often by dividing data into related tables.

Denormalization involves combining tables to reduce complexity and improve read performance, often used in data warehouses for analytical queries.

Data lineage tracks the flow of data through systems and processes, providing visibility into data transformations and enabling data quality management.

Batch processing involves processing large volumes of data at once, typically scheduled at regular intervals, suitable for non-real-time applications.

Stream processing involves processing data in real-time as it arrives, allowing for immediate insights and actions on data streams.

Advantages include scalability, cost-effectiveness, accessibility, and leveraging managed services for databases and data pipelines.

Data governance refers to the management of data availability, usability, integrity, and security, establishing policies and standards for data usage.

Apache Hadoop is an open-source framework for distributed storage and processing of large datasets using clusters of commodity hardware.

A foreign key establishes a relationship between two tables, referencing the primary key of another table to enforce referential integrity.

Common techniques include data cleaning, aggregation, filtering, and data type conversion.

A data lake stores raw data in various formats, while a data warehouse stores structured data optimized for analysis.

Strategies include data imputation, removing records, or using validation techniques to clean the data.

Popular tools include Tableau, Power BI, Looker, and open-source libraries like Matplotlib and Seaborn.

Apache Spark is a distributed processing framework used for large-scale data processing, providing high-level APIs for data analytics and machine learning.

Data quality refers to the accuracy, completeness, and consistency of data. Ensuring it involves validation checks, monitoring, and implementing data cleansing processes.

Data serialization is the process of converting data into a format suitable for storage or transmission. It's important for efficient data exchange and storage in distributed systems.

Common challenges include handling large volumes of data, ensuring data quality, managing data integration, and optimizing performance.

A distributed database is a database that is spread across multiple locations, which can be on different servers or in different geographical locations, ensuring fault tolerance and scalability.

Metadata provides context about data, such as its source, structure, and relationships, facilitating data management and governance.

A data pipeline is a series of data processing steps that move data from source to destination, often involving data extraction, transformation, and loading processes.

Common orchestration tools include Apache Airflow, Luigi, and Prefect, which automate the execution of complex data workflows.

Best practices include defining clear requirements, using normalization techniques, documenting the schema, and considering future scalability.

Staying current involves following industry blogs, participating in online communities, attending conferences, and continuous learning through courses and certifications.