Explore key concepts of integrating big data technologies like Hadoop and Spark with enterprise analytics platforms. This quiz covers connectors, data import, architecture, and performance to boost your understanding of big data integration strategies.
Which of the following technologies is commonly used as a connector for integrating enterprise analytics with Hadoop-based data sources?
Explanation: ODBC is frequently used as a connector to access data from Hadoop environments in analytics solutions. JSON and HTML are data formats and markup languages, not connectors. CSS is used for styling web pages and is unrelated to data integration.
When importing large datasets from Hadoop for analytical reporting, which underlying execution model processes the data in parallel across clusters?
Explanation: MapReduce is a processing model that divides tasks and distributes them across computer clusters, making it well-suited for large-scale data processing in Hadoop environments. KeyValue refers to a data storage structure, NoSQL describes a type of database, and PivotTable is a tool for summarizing data, not a parallel execution model.
Which benefit best describes using Spark as a data source for enterprise analytics integration?
Explanation: Spark is known for in-memory processing, which allows much faster queries and analytics compared to disk-based engines, and is suited for real-time analytics. Automatic dashboard creation and encryption of all credentials are not default features specific to Spark, while converting data to CSV is only one possible output and not a primary advantage.
What is the primary purpose of configuring an external table when integrating analytics with a distributed big data environment?
Explanation: External tables allow analytics platforms to access and query data that remains in distributed storage systems, rather than copying it locally. Chart generation and log storage are unrelated, while updating desktop software is not connected to big data integration.
During integration, what does schema mapping help achieve when loading data from Hadoop into an analytics schema?
Explanation: Schema mapping ensures that columns from the Hadoop source correctly correspond to fields in the analytics schema, crucial for correct data representation. Compressing data, deleting temp files, and managing drivers are not related to matching data fields.
Which action helps improve query performance when importing from large-scale Hadoop environments?
Explanation: Applying filters at the data source reduces the data volume transferred and processed, significantly enhancing performance. Upgrading browsers, disabling firewalls, or converting reports to images do not affect the efficiency of big data queries.
What does the Direct Query method do when connecting enterprise analytics to a big data platform?
Explanation: Direct Query allows analytics tools to run queries directly on big data sources in real time, without importing the data. It does not delete data, encrypt presentation files, or merge dashboards by default.
Why is partitioning large tables by date commonly used in big data environments integrated with analytics tools?
Explanation: Partitioning by date allows queries to scan only the necessary data slices, making time-based reports much faster. Dashboard icons, number of users, and data duplication are unaffected by date partitioning.
Which authentication approach is often used to securely connect analytics platforms to big data clusters?
Explanation: Kerberos is a widely-used protocol in big data environments, enabling secure authentication between services and users. The other options are misspelled or incorrect versions of the protocol and do not exist in the context.
What is a main advantage of scheduling regular data refreshes when integrating analytics with cloud-based big data sources?
Explanation: Scheduled data refreshes update analytics reports with the latest available data from cloud sources, ensuring accuracy. Reducing users, locking data, or disabling backups are not benefits of data refresh scheduling.