In normalization, the process of breaking down a large table into smaller tables to reduce data redundancy and improve data integrity is called ________.
- Aggregation
- Compaction
- Decomposition
- Integration
Normalization involves decomposing a large table into smaller, related tables to eliminate redundancy and improve data integrity by reducing the chances of anomalies.
When considering scalability, what does the term "sharding" refer to in a distributed database system?
- Adding more replicas of the same data
- Horizontal partitioning of data
- Replicating data across multiple nodes
- Vertical partitioning of data
Sharding in a distributed database system involves horizontally partitioning data across multiple servers or nodes. Each shard contains a subset of the overall data, enabling better scalability by distributing the data workload and reducing the burden on individual nodes. This approach facilitates handling large volumes of data and accommodating increased read and write operations in a distributed environment.
________ is a feature in streaming processing frameworks that allows for saving intermediate results to persistent storage.
- Buffering
- Caching
- Checkpointing
- Snapshotting
Checkpointing is a critical feature in streaming processing frameworks that enables fault tolerance and state recovery by periodically saving intermediate processing results to durable storage. This mechanism allows the system to resume processing from a consistent state in case of failures or system restarts, ensuring data integrity and reliability in continuous data processing pipelines.
A well-defined data ________ helps ensure that data is consistent, accurate, and reliable across the organization.
- Architecture
- Ecosystem
- Governance
- Infrastructure
A well-defined data governance framework helps ensure that data is consistent, accurate, and reliable across the organization by establishing policies, standards, and processes for managing data throughout its lifecycle. This includes defining data quality standards, data classification policies, data access controls, and data stewardship responsibilities. By implementing a robust data governance framework, organizations can improve data quality, enhance decision-making, and ensure regulatory compliance.
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.
________ 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.
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.
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.
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.
Scenario: Your company is planning to implement a new data warehouse solution. As the data engineer, you are tasked with selecting an appropriate data loading strategy. Given the company's requirements for near real-time analytics, which data loading strategy would you recommend and why?
- Bulk Loading
- Change Data Capture (CDC)
- Incremental Loading
- Parallel Loading
Change Data Capture (CDC) captures only the changes made to the source data since the last extraction. This approach ensures near real-time analytics by transferring only the modified data, reducing the processing time and allowing for quicker insights.