Flashcards

Data Ingestion

Data ingestion using Apache Kafka and Flume

Learn to collect, transmit, and process large datasets from various sources like log files, sensors, social media using Apache Kafka and Flume.

5 minutes 5 Questions

Data Ingestion is a foundational process in big data ecosystems where data from various sources is collected and imported into storage systems for further processing, analysis, and storage. As a Big Data Engineer, understanding data ingestion is crucial because it serves as the entry point for all …

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Big Data Engineer - Data Ingestion Example Questions

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Question 1

What is a common way to ingest structured data into a Hadoop cluster?

Using Apache Kafka to publish data to a message queue for later consumption by Hadoop Using Apache Sqoop to transfer data from a relational database to Hadoop Manually copying data files from a local machine to the Hadoop cluster Using Apache NiFi to create a real-time data flow to Hadoop Using Apache Flume to collect and aggregate log data Using Apache Storm to continuously ingest streaming data into Hadoop

Correct Answer: Using Apache Sqoop to transfer data from a relational database to Hadoop

Apache Sqoop is a tool designed to transfer data between Hadoop and relational databases. It is used to import or export data to/from Hadoop while maintaining the structure and relation between the data. This is a common ingestion practice for transferring structured data from a traditional RDBMS to Hadoop for analytical purposes.

Question 2

What is the primary purpose of data ingestion in a data lake?

Converting data to a specific format for easy query and visualization Merging data from various sources into a single file Deleting unnecessary data to save storage space Storing processed data for production use Storing raw data from various sources in a central location for further processing and analysis Transforming data for immediate use by business analysts

Correct Answer: Storing raw data from various sources in a central location for further processing and analysis

Data ingestion is the process of bringing data from various sources into a centralized data storage or processing system such as a data lake. The primary purpose of data ingestion in a data lake is to store raw data in its original format, preserving its potential value for future processing and analysis. This enables data scientists and business analysts to perform exploratory analysis at a later stage if needed, which can lead to new insights and opportunities that may not have been immediately available.

Question 3

What is the common approach to handling data ingestion errors?

Data partitioning and shuffling Data compression and decompression Retry and failure logging Data transformation and validation Data filtering and sorting Data aggregation and summarization

Correct Answer: Retry and failure logging

Retry and failure logging is a common approach to handling data ingestion errors. It involves retrying failed ingestion jobs with the same or modified configurations, while logging relevant error messages and job statistics for debugging and troubleshooting purposes

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