How does Apache Flink handle event time processing?
- Implements sequential processing
- Relies on batch processing techniques
- Uses synchronized clocks for event ordering
- Utilizes watermarks and windowing
Apache Flink handles event time processing by utilizing watermarks and windowing techniques. Watermarks are markers that signify the progress of event time within the stream and are used to trigger computations based on the completeness of the data. Windowing enables the grouping of events into time-based or count-based windows for aggregation and analysis. By combining watermarks and windowing, Flink ensures accurate and efficient event time processing, even in the presence of out-of-order events or delayed data arrival.
In a relational database, a join that returns all rows from both tables, joining records where available and inserting NULL values for missing matches, is called a(n) ________ join.
- Cross join
- Inner join
- Left join
- Outer join
An outer join in a relational database returns all rows from both tables, joining records where available and inserting NULL values for missing matches. This includes both left and right outer joins.
The ETL process often involves loading data into a ________ for further analysis.
- Data Lake
- Data Mart
- Data Warehouse
- None of the above
In the ETL process, data is frequently loaded into a Data Warehouse, a central repository where it can be organized, integrated, and analyzed for business insights.
How do data modeling tools like ERWin or Visio support reverse engineering in the context of existing databases?
- Data lineage tracking, Data migration, Data validation, Data cleansing
- Data profiling, Data masking, Data transformation, Data visualization
- Importing database schemas, Generating entity-relationship diagrams, Metadata extraction, Schema synchronization
- Schema comparison, Code generation, Query execution, Database optimization
Data modeling tools like ERWin or Visio support reverse engineering by enabling tasks such as importing existing database schemas, generating entity-relationship diagrams, extracting metadata, and synchronizing the schema with changes made in the tool.
________ is a data extraction technique that involves querying data from web pages and web APIs.
- Data Wrangling
- ETL (Extract, Transform, Load)
- Streaming
- Web Scraping
Web Scraping is a data extraction technique that involves querying data from web pages and web APIs. It allows for automated retrieval of data from various online sources for further processing and analysis.
________ involves comparing data across multiple sources or systems to identify discrepancies and inconsistencies.
- Data integration
- Data profiling
- Data reconciliation
- Data validation
Data reconciliation involves comparing data from different sources or systems to ensure consistency and accuracy. It helps identify discrepancies, such as missing or mismatched data, between datasets. This process is crucial in data integration projects to ensure that data from various sources align properly and can be combined effectively.
Apache Flink's ________ API enables complex event processing and time-based operations.
- DataSet
- DataStream
- SQL
- Table
Apache Flink's DataStream API is designed for processing unbounded streams of data, enabling complex event processing and time-based operations such as windowing and event-time processing. It provides high-level abstractions for expressing data transformations and computations on continuous data streams, making it suitable for real-time analytics and stream processing applications.
Scenario: You are working on a project where data integrity is crucial. Your team needs to design a data loading process that ensures data consistency and accuracy. What steps would you take to implement effective data validation in the loading process?
- Data Profiling
- Referential Integrity Checks
- Row Count Validation
- Schema Validation
Referential integrity checks ensure that relationships between data tables are maintained, preventing orphaned records and ensuring data consistency. By verifying the integrity of foreign key relationships, this step enhances data accuracy and reliability during the loading process.
Apache MapReduce divides tasks into ________ and ________ phases for processing large datasets.
- Input, Output
- Map, Reduce
- Map, Shuffle
- Sort, Combine
Apache MapReduce divides tasks into Map and Reduce phases for processing large datasets. The Map phase handles input data and generates key-value pairs, while the Reduce phase aggregates and processes these pairs.
Scenario: Your organization is experiencing performance issues with its ETL pipeline, resulting in delayed data processing. As an ETL specialist, what steps would you take to diagnose and address these performance issues?
- Analyze and optimize data ingestion and loading processes.
- Implement data partitioning and sharding strategies.
- Increase hardware resources such as CPU and memory.
- Review and optimize data transformation logic and SQL queries.
To address performance issues in an ETL pipeline, reviewing and optimizing data transformation logic and SQL queries is essential. This involves identifying inefficient queries or transformations and optimizing them for better performance.