The use of ________ can help optimize ETL processes by reducing the amount of data transferred between systems.
- Change Data Capture
- Data Encryption
- Snowflake Schema
- Star Schema
Change Data Capture (CDC) is a technique used to identify and capture changes made to data in source systems, allowing only the modified data to be transferred, thus optimizing ETL processes.
Apache Spark supports ________ APIs, which allow for easier integration with various data sources.
- Machine Learning
- SQL
- Streaming
- Unified
Apache Spark supports Unified APIs, which provide a consistent interface for programming Spark applications across different languages like Scala, Java, Python, and R. These APIs simplify integration with various data sources and enable developers to write code in their preferred language.
Scenario: Your team is tasked with designing an ETL process for a large retail company. They want to integrate data from various sources, including transactional databases, online sales platforms, and social media. What factors would you consider when designing the data extraction phase of the ETL process?
- Data governance policies, data security measures, data compression techniques, data validation procedures
- Data modeling techniques, data partitioning strategies, data archiving policies, data synchronization mechanisms
- Data transformation requirements, data integration tools, target system compatibility, data encryption techniques
- Data volume and frequency, source system complexity, network bandwidth availability, data extraction methods
When designing the data extraction phase of the ETL process, it's crucial to consider factors such as data volume and frequency, source system complexity, network bandwidth availability, and appropriate data extraction methods. These considerations ensure efficient and reliable extraction of data from diverse sources.
In Apache Airflow, a ________ is a unit of work or task that performs a specific action in a workflow.
- DAG (Directed Acyclic Graph)
- Executor
- Operator
- Sensor
In Apache Airflow, an "Operator" is a unit of work or task that performs a specific action within a workflow. Operators can perform tasks such as transferring data, executing scripts, or triggering external systems. They are the building blocks of workflows in Airflow, allowing users to define the individual actions to be performed.
Data ________ involves breaking down large datasets into smaller chunks to distribute the data loading process across multiple servers or nodes.
- Normalization
- Partitioning
- Replication
- Serialization
Data partitioning involves breaking down large datasets into smaller chunks to distribute the data loading process across multiple servers or nodes, enabling parallel processing and improving scalability and performance.
A ________ schema is a type of schema in Dimensional Modeling where dimension tables are normalized into multiple related tables.
- Constellation
- Galaxy
- Snowflake
- Star
A Snowflake schema is a type of schema in Dimensional Modeling where dimension tables are normalized into multiple related tables, creating a more complex but potentially more efficient structure for querying data.
Which strategy involves delaying the retry attempts for failed tasks to avoid overwhelming the system?
- Constant backoff
- Exponential backoff
- Immediate retry
- Linear backoff
Exponential backoff involves increasing the delay between retry attempts exponentially after each failure. This strategy helps prevent overwhelming the system with retry attempts during periods of high load or when dealing with transient failures. By gradually increasing the delay, it allows the system to recover from temporary issues and reduces the likelihood of exacerbating the problem.
What is the primary goal of normalization in database design?
- Improve data integrity
- Maximize redundancy
- Minimize redundancy
- Optimize query performance
The primary goal of normalization in database design is to improve data integrity by minimizing redundancy, ensuring that each piece of data is stored in only one place. This helps prevent inconsistencies and anomalies.
The process of removing inconsistencies and errors from data before loading it into a data warehouse is known as ________.
- Data Cleansing
- Data Integration
- Data Migration
- Data Wrangling
Data Cleansing involves identifying and correcting errors or inconsistencies in data to ensure accuracy and reliability before loading it into a data warehouse.
Scenario: Your team is tasked with designing ETL processes for a data warehouse project. How would you ensure data quality during the ETL process?
- Apply referential integrity constraints
- Implement data validation checks
- Perform data profiling
- Use incremental loading techniques
Ensuring data quality during the ETL process involves implementing data validation checks. These checks verify the accuracy, completeness, and consistency of the data being loaded into the data warehouse. By validating data against predefined rules and constraints, potential errors or discrepancies can be identified and addressed, thereby enhancing the overall quality of the data.