Introduction to Data Engineering Flashcards
What is Data Engineering?
Data Engineering is the field of dealing with large-scale data collection, storage, and retrieval, involving the development and maintenance of architectures like databases and processing systems.
What are the key responsibilities of a Data Engineer?
Data Engineers are responsible for managing and organizing data, transforming, cleansing, and ensuring its integrity for analysis. They develop, construct, test, and maintain data architectures.
Which programming languages are commonly used in Data Engineering?
Common programming languages in Data Engineering include SQL for database management, Python for data manipulation and analysis, and sometimes Java or Scala, particularly in big data environments.
What is the significance of ‘big data’ in Data Engineering?
In Data Engineering, ‘big data’ refers to large, complex data sets. The significance lies in the engineer’s ability to prepare and process this data for analysis, which can lead to valuable insights for businesses and organizations.
What are some common tools and technologies used in Data Engineering?
Common tools include SQL for database querying, Hadoop and Apache Spark for big data processing, ETL (Extract, Transform, Load) tools, and data warehousing solutions like Amazon Redshift or Google BigQuery.
How does the skill set of a Data Engineer differ from that of a Data Scientist?
Data Engineers typically have strong software engineering skills with expertise in database design and large-scale processing systems. Data Scientists, on the other hand, have skills in statistics, machine learning, and data visualization.
In what way does the output of a Data Analyst differ from a Data Scientist?
Data Analysts usually provide more straightforward, descriptive analytics and reporting based on existing data, whereas Data Scientists deliver more complex, predictive and prescriptive insights, often creating models to predict future trends.
What are the key responsibilities of a Data Analyst?
Data Analysts primarily focus on processing and performing statistical analysis on existing datasets. They interpret data, analyze results, and provide ongoing reports, often using tools like Excel, SQL, or BI tools.
What is the main role of a Data Scientist?
A Data Scientist’s main role is to analyze and interpret complex data to help make informed decisions. They use advanced statistical techniques, machine learning, and predictive modeling to uncover insights and trends.
What is a Data Pipeline?
A data pipeline is a set of actions that extract data from various sources, transform it into a format suitable for analysis, and load it into a final storage system.
What are the key components of a Data Pipeline?
Key components include data sources, data extraction tools, data transformation processes, data storage destinations, and often orchestration tools to manage the workflow.
How do you start building a Data Pipeline?
Begin by defining the data sources and the type of data you need. Next, decide how to extract this data, determine the transformations needed to make the data useful, and choose where to store the processed data.
What is ETL in the context of Data Pipelines?
ETL stands for Extract, Transform, Load. It’s a process where data is extracted from various sources, transformed into a format that can be analyzed, and then loaded into a data warehouse or other systems.
What is a Real-Time Data Pipeline?
A real-time data pipeline processes data immediately as it is generated, without delay, enabling instant data analysis and decision-making.
How does a Real-Time Data Pipeline differ from a Batch Data Pipeline?
Unlike batch data pipelines, which process data in periodic batches, real-time pipelines handle data continuously and immediately as it arrives.
