Data Engineering Fundamentals Quiz: Test Your Skills

What is the primary focus of data engineering in the context of data science?

What are the responsibilities of a data engineer in an enterprise data analytics team?

What is the goal of data acquisition in data engineering?

What is the purpose of deploying sophisticated analytics programs in data engineering?

What is the primary question being asked in regression problems in data analytics?

What is the role of data engineers in obtaining data?

What is the role of Sales Calls in the context of Deals Closed?

What are the three main stages of Data Preparation?

What is the concept that suggests 'garbage in, garbage out' during data processing?

What are the six dimensions of Data Quality?

What is the primary purpose of data transformation during data preprocessing?

What does 'bit rot' refer to in the context of Data Quality?

Data Preprocessing

Data quality problems can occur due to dirty data, transformations, data integration, and "bit rot" (old data losing value/accuracy over time)
Data quality dimensions:
- Accuracy: data is recorded correctly
- Completeness: all relevant data is recorded
- Uniqueness: entities are recorded once
- Timeliness: data is kept up to date
- Consistency: data agrees with itself

ETL (Extract, Transform, Load)

Extract: obtain data from sources (file, database, event log, website, HDFS…)
Transform: modify data at the source, sink, or in a staging area
Load: load data into a sink (Python, R, SQLite, RDBMS, NoSQL store, files, HDFS…)

Data Engineering

Data engineering: focuses on practical applications of data collection and analysis
Data engineer: responsible for managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution throughout the organization
Data engineer responsibilities:
- Developing, constructing, testing, and maintaining architectures
- Aligning architecture with business requirements
- Data acquisition and data set processing
- Improving data reliability, efficiency, and quality
- Deploying sophisticated analytics programs, machine learning, and statistical methods
- Preparing data for predictive and prescriptive modeling
- Finding hidden patterns in data and automating tasks

Data Collection

Obtaining data: collecting data from various sources
Data sources: files, databases, event logs, websites, HDFS, etc.
Regression problems: identifying relationships between predictors and outcomes

Learn about the role of data engineering in data science, its practical applications, and the responsibilities of a data engineer in an enterprise data analytics team.