Scenario: A regulatory audit requires your organization to provide a comprehensive overview of data flow and transformations. How would you leverage metadata management and data lineage to address the audit requirements effectively?
- Depend solely on manual documentation for audit, neglect data lineage analysis, limit stakeholder communication
- Document metadata and data lineage, analyze data flow and transformations, generate comprehensive reports for audit, involve relevant stakeholders in the process
- Ignore metadata management and data lineage, provide limited data flow information, focus on compliance with regulatory requirements only
- Use generic templates for audit reports, overlook data lineage and metadata, minimize stakeholder involvement
Leveraging metadata management and data lineage involves documenting metadata and data lineage, analyzing data flow and transformations, and generating comprehensive reports for the audit. Involving relevant stakeholders ensures that the audit requirements are effectively addressed, providing transparency and compliance with regulatory standards.
What are the main components of a Data Lake architecture?
- Data ingestion, Storage, Processing, Security
- Data modeling, ETL, Reporting, Dashboards
- NoSQL databases, Data warehouses, Data marts, OLAP cubes
- Tables, Indexes, Views, Triggers
The main components of a Data Lake architecture typically include data ingestion, storage, processing, and security. These components work together to store and manage large volumes of diverse data efficiently.
In an ERD, what does a rectangle represent?
- Attribute
- Entity
- Process
- Relationship
In an Entity-Relationship Diagram (ERD), a rectangle represents an entity, which is a real-world object or concept that is distinguishable from other objects. It typically corresponds to a table in a database.
________ is a distributed storage and processing framework in the Hadoop ecosystem that provides high-level abstractions for processing large datasets.
- Flink
- HBase
- MapReduce
- Spark
MapReduce is a distributed storage and processing framework in the Hadoop ecosystem that provides high-level abstractions for processing large datasets. It operates by breaking down tasks into smaller, manageable chunks that are distributed across a cluster of machines for parallel processing. Although MapReduce was one of the early frameworks in the Hadoop ecosystem, it's still widely used for batch processing tasks in big data applications.
What is the significance of partitions in Apache Kafka?
- Enables parallel processing of messages
- Enhances data replication
- Facilitates encryption of data
- Improves data compression
Partitions in Apache Kafka enable parallel processing of messages by dividing the topic's data into multiple segments. This enhances throughput and scalability in data processing.
How does parallel processing enhance data transformation performance?
- By distributing workload across multiple processors simultaneously
- By optimizing network bandwidth usage
- By reducing the size of the dataset
- By serializing data processing tasks
Parallel processing enhances data transformation performance by distributing the workload across multiple processors simultaneously, thereby speeding up the processing time and improving overall efficiency.
Can you identify any specific scenarios where denormalization can lead to performance improvements over normalization?
- Complex data relationships
- OLAP (Online Analytical Processing) scenarios
- OLTP (Online Transaction Processing) scenarios
- Reporting and analytical queries
Denormalization can improve performance in scenarios such as reporting and analytical queries where data retrieval from multiple tables is common, as it reduces the need for complex joins and improves query performance.
What strategies can be employed to optimize index usage in a database?
- All of the above
- Regularly analyze and update statistics on indexed columns
- Remove indexes on frequently updated columns
- Use covering indexes to include all required columns in the index
To optimize index usage, it's essential to regularly analyze and update statistics on indexed columns, remove unnecessary indexes, and use covering indexes to avoid lookups to the main table, thereby improving query performance.
When is the use of regular expressions (regex) commonly applied in data transformation?
- Encrypting data
- Extracting patterns from unstructured data
- Filtering data
- Sorting data
Regular expressions (regex) are often used in data transformation to extract specific patterns or structures from unstructured data sources, facilitating the process of data parsing and extraction for further processing.
Which of the following is a key characteristic of distributed systems?
- Centralized control
- Fault tolerance
- Low network latency
- Monolithic architecture
Fault tolerance is a key characteristic of distributed systems, referring to their ability to continue operating despite individual component failures. Distributed systems are designed to handle failures gracefully by replicating data, employing redundancy, and implementing algorithms to detect and recover from faults without disrupting overall system functionality. This resilience ensures system availability and reliability in the face of failures, a crucial aspect of distributed computing.
Scenario: You are tasked with processing a large batch of log data stored in HDFS and generating summary reports. Which Hadoop component would you use for this task, and why?
- Apache Hadoop MapReduce
- Apache Kafka
- Apache Pig
- Apache Sqoop
Apache Hadoop MapReduce is ideal for processing large batch data stored in HDFS and generating summary reports. It provides a scalable and fault-tolerant framework for parallel processing of distributed data.
Which pipeline architecture is suitable for processing large volumes of data with low latency requirements?
- Batch architecture
- Lambda architecture
- Microservices architecture
- Streaming architecture
A streaming architecture is suitable for processing large volumes of data with low latency requirements. In a streaming architecture, data is processed in real-time as it arrives, allowing for immediate insights and actions on fresh data. This architecture is well-suited for use cases such as real-time analytics, fraud detection, and IoT data processing, where timely processing of data is crucial.