Python is a programming language, ideal for data processing, automation, and analysis. Its intuitive syntax and vast libraries make it essential for data engineers to manage and interpret datasets effectively.

Error handling ensures that software operates smoothly even when unexpected issues arise. It’s crucial to handle discrepancies in data or processing errors to maintain data integrity and system reliability.

Amazon Web Services (AWS) is a cloud services platform, providing an array of tools and infrastructure for data storage, processing, and analysis. Mastering AWS means harnessing the power of the cloud to scale and optimize data pipelines.

SageMaker is a service dedicated to machine learning, enabling users to build, train, and deploy models at scale. Data engineers utilizing SageMaker can integrate sophisticated machine learning capabilities into their data pipelines.

Athena allows users to analyze data directly in Amazon S3 using standard SQL. For data engineers, Athena offers a swift and cost-effective way to run ad-hoc queries without the need for complex infrastructure, making data analysis more accessible.

Hadoop is a distributed framework designed to store and process vast amounts of data across clusters of computers. Leveraging Hadoop leads to scalable and fault-tolerant data management, making it a cornerstone in big data analytics.

Hive is a data warehousing infrastructure built on top of Hadoop, facilitating data summarization, querying, and analysis. With Hive, complex big data tasks become accessible, enabling data engineers to manage large datasets without deep programming expertise.

Amazon Aurora is a relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Aurora automatically scales, replicates, and patches, ensuring optimal performance.

Amazon Kinesis offers powerful solutions for real-time data streaming, ingestion, and processing. Whether ingesting application logs, analyzing real-time metrics, or responding to live events, Kinesis allows engineers to harness the potential of real-time data.

Storage concepts encompass the various methods and best practices used to save, retrieve, and manage data. Grasping these concepts is essential, as it determines how data is accessed, its speed of retrieval, and the cost implications of storage solutions.

Amazon Redshift is a fully managed data warehouse service in the cloud, optimized for online analytic processing (OLAP). It offers fast query performance making it a go-to solution for analyzing large datasets.

Normalization is the process of organizing data in a database to reduce redundancy. By structuring data in this way, databases become more efficient, making data retrieval and updates more streamlined.

OLTP (Online Transaction Processing) systems prioritize fast and reliable transactional tasks, while OLAP (Online Analytical Processing) focuses on complex queries and analytics over large volumes of data. They serve different database functions and cater to specific business needs.

Notebooks, like Jupyter and Zeppelin, are interactive tools that allow for live code execution, visualization, and data analysis in a unified document. They’re essential in exploratory data analysis, machine learning, and sharing insights in an interactive manner.

AWS SageMaker is a cloud service designed to empower users to build, train, and deploy machine learning models with ease. With its integrated tools and scalable environment, SageMaker simplifies the entire machine-learning workflow.

Elastic MapReduce (EMR) is a cloud framework that facilitates the processing of data. Leveraging EMR aids in efficiently running big data frameworks like Apache Hadoop and Apache Spark, without the operational overhead.