Scenario: You are tasked with designing a real-time analytics application using Apache Flink. Which feature of Apache Flink would you utilize for exactly-once processing semantics?
- Checkpointing
- Savepoints
- State TTL (Time-To-Live)
- Watermarking
Checkpointing in Apache Flink is the feature used for ensuring exactly-once processing semantics. Checkpoints capture the state of the application at regular intervals, allowing Flink to recover from failures and guaranteeing that each record is processed exactly once, even in the presence of failures or restarts.
Which storage solution in the Hadoop ecosystem is designed for handling small files and is used as a complementary storage layer alongside HDFS? ________
- HBase
- Hadoop Archives (HAR)
- Hive
- Kudu
Kudu is a storage solution in the Hadoop ecosystem specifically designed for handling small files efficiently. It serves as a complementary storage layer alongside Hadoop Distributed File System (HDFS) and is optimized for workloads involving random access to data, such as time-series data or small analytical queries.
How does Data Lake architecture facilitate data exploration and analysis?
- Centralized data storage, Schema-on-read approach, Scalability, Flexibility
- Data duplication, Data redundancy, Data isolation, Data normalization
- Schema-on-write approach, Predefined schemas, Data silos, Tight integration with BI tools
- Transactional processing, ACID compliance, Real-time analytics, High consistency
Data Lake architecture facilitates data exploration and analysis through centralized storage, a schema-on-read approach, scalability, and flexibility. This allows users to analyze diverse data sets without predefined schemas, promoting agility and innovation.
How does Kafka ensure fault tolerance and high availability?
- Enforcing strict data retention policies
- Implementing strict message ordering
- Increasing network bandwidth
- Replication of data across multiple brokers
Kafka ensures fault tolerance and high availability by replicating data across multiple brokers. This redundancy ensures that if one broker fails, data can still be retrieved from other replicas, ensuring continuity.
Scenario: A large organization is facing challenges in ensuring data consistency across departments. How can a data governance framework help in addressing this issue?
- By conducting regular data audits and implementing access controls to enforce data integrity.
- By defining standardized data definitions and establishing data stewardship roles to oversee data quality and consistency.
- By deploying real-time data synchronization solutions to maintain consistency across distributed systems.
- By implementing data encryption techniques to prevent unauthorized access and ensure data security.
A data governance framework can help address challenges in ensuring data consistency across departments by defining standardized data definitions, formats, and structures. It involves establishing data governance policies and procedures to ensure consistent data interpretation and usage across the organization. Additionally, assigning data stewardship roles and responsibilities can help oversee data quality and consistency, ensuring that data is accurate, complete, and reliable across departments.
________ is a distributed messaging system often used with Apache Flink for data ingestion.
- Apache Hadoop
- Apache Kafka
- Apache Storm
- RabbitMQ
Apache Kafka is a distributed messaging system known for its high throughput, fault tolerance, and scalability. It is commonly used with Apache Flink for data ingestion, acting as a durable and scalable event streaming platform. Kafka's distributed architecture and support for partitioning make it well-suited for handling large volumes of data and real-time event streams, making it an integral component in many modern data processing pipelines.
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.