Moving Data into Hadoop Cognitive Class Exam Answers

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Introduction to Moving Data into Hadoop

Moving data into Hadoop involves several techniques and considerations to ensure efficiency and reliability. Here’s an introduction to the process:

1. Understanding Hadoop Basics

• Hadoop Ecosystem: Hadoop is an open-source framework designed for distributed storage and processing of large datasets across clusters of computers.
• Components: It includes HDFS (Hadoop Distributed File System) for storage and MapReduce (or other processing frameworks like Apache Spark) for computation.

2. Types of Data for Hadoop

• Structured, Semi-Structured, and Unstructured: Hadoop can handle various types of data, including structured data (like relational databases), semi-structured data (JSON, XML), and unstructured data (text, logs).

3. Methods for Moving Data

• Apache Sqoop: A tool designed to efficiently transfer bulk data between Apache Hadoop and structured datastores such as relational databases.
• Apache Flume: A distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
• Apache Kafka: A distributed streaming platform that can publish, subscribe to, store, and process streams of records in real-time.
• Direct Data Ingestion: Using HDFS commands or APIs to directly load data into Hadoop.

4. Considerations

• Data Volume: Handling large volumes of data efficiently is a core requirement.
• Data Formats: Ensure compatibility between the data format and the Hadoop ecosystem tools.
• Network Bandwidth: Transfer speeds can influence the choice of tools and methods.
• Data Consistency: Ensuring that data integrity is maintained during the transfer process.

5. Best Practices

• Incremental Loading: Where possible, transfer only the changes since the last load to reduce transfer time and resources.
• Compression: Compress data during transfer to reduce network overhead and storage costs.
• Error Handling and Monitoring: Implement mechanisms to monitor data movement, detect errors, and ensure data consistency.

6. Security Considerations

• Encryption: Secure data transfer using encryption techniques to protect sensitive information.
• Access Control: Implement strict access controls to prevent unauthorized access to data during transfer.

7. Tools and Technologies

• Hadoop Command Line Tools: Hadoop provides various command-line utilities (hadoop fs, hadoop distcp) for moving data.
• Third-Party Integrations: Many third-party tools and connectors are available for seamless data integration with Hadoop.

8. Integration with Data Pipelines

• Data Orchestration Tools: Use tools like Apache Airflow, Apache NiFi, or commercial solutions for managing complex data workflows and scheduling data transfers.

9. Conclusion

• Moving data into Hadoop involves leveraging tools and techniques that ensure efficient, secure, and reliable transfer of data from various sources. Understanding the types of data, the volume, and the desired outcome are essential in choosing the right method and ensuring successful integration into the Hadoop ecosystem.

Moving Data into Hadoop Cognitive Class Certification Answers

Module 1 – Lord Scenario Quiz Answers

Question 1: What is Data at rest?

• Data that is being transferred over
• Data that is already in a file in some directory
• Data that hasn’t been used in a while
• Data that needs to be copied over

Question 2: Data can be moved using BigSQL Load. True or false?

• True
• False

Question 3: Which of the following does not relate to Flume?

• Pipe
• Sink
• Interceptors
• Source

Module 2 – Using Sqoop Quiz Answers

Question 1: Sqoop is designed to

• export data from HDFS to streaming software
• read and understand data from a relational database at a high level
• prevent “bad” data in a relational database from going into Hadoop
• transfer data between relational database systems and Hadoop

Question 2: Which of the following is NOT an argument for Sqoop?

• –update-key
• –split-from
• –target-dir
• –connect

Question 3: By default, Sqoop assumes that it’s working with space-separated fields and that each record is terminated by a newline. True or false?

• True
• False

Module 3 – Flume Overview Quiz Answers

Question 1: Avro is a remote procedure call and serialization framework, developed within a separate Apache project. True or false?

• True
• False

Question 2: Data sent through Flume

• may have different batching but must be in a constant stream
• may have different batching or a different reliability setup
• must be in a particular format
• has to be in a constant stream

Question 3: A single Avro source can receive data from multiple Avro sinks. True or false?

• True
• False

Module 4 – Using Flume Quiz Answers

Question 1: Which of the following is NOT a supplied Interceptor?

• Regex extractor
• Regex sinker
• HostType
• Static

Question 2: Channels are:

• where the data is staged after having been read in by a source and not yet written out by a sink
• where the data is staged after having been read in by a sink and not yet written out by a source
• where the data is staged after having been written in by a source and not yet read out by a sink
• where the data is staged after having been written in by a sink and not yet written out by a source

Question 3: One property for sources is selector.type? True or false?

• True
• False

Moving Data into Hadoop Final Exam Answers

Question 1: The HDFS copyFromLocal command can be used to

• capture streaming data that you want to store in Hadoop
• ensure that log files which are actively being used to capture logging from a web server are moved into Hadoop
• move data from a relational database or data warehouse into Hadoop
• None of the above

Question 2: What is the primary purpose of Sqoop in the Hadoop architecture?

• To “catch” logging data as it is written to log files and move it into Hadoop
• To schedule scripts that can be run periodically to collect data into Hadoop
• To import data from a relational database or data warehouse into Hadoop
• To move static files from the local file system into HDFS
• To stream data into Hadoop

Question 3: A Sqoop JDBC connection string must include

• the name of the database you wish to connect to
• the hostname of the database server
• the port that the database server is listening on
• the name of the JDBC driver to use for the connection
• All of the above

Question 4: Sqoop can be used to either import data from relational tables into Hadoop or export data from Hadoop to relational tables. True or false?

• True
• False

Question 5: When importing data via Sqoop, the imported data can include

• a collection of data from multiple tables via a join operation, as specified by a SQL query
• specific rows and columns from a specific table
• all of the data from a specific table
• All of the Above

Question 6: When importing data via Sqoop, the incoming data can be stored as

• Serialized Objects
• JSON
• XML
• None of the Above

Question 7: Sqoop uses MapReduce jobs to import and export data, and you can configure the number of Mappers used. True or false?

• True
• False

Question 8: What is the primary purpose of Flume in the Hadoop architecture?

• To “catch” logging data as it is written to log files and move it into Hadoop
• To schedule scripts that can be run periodically to collect data into Hadoop
• To import data from a relational database or data warehouse into Hadoop
• To move static files from the local file system into HDFS
• To stream data into Hadoop

Question 9: When you create the configuration file for a Flume agent, you must configure

• an Interceptor
• a Sink
• a Channel
• a Source
• All of the above

Question 10: When using Flume, a Source and a Sink are “wired together” using an Interceptor. True or false?

• True
• False

Question 11: Flume agents can run on multiple servers in the enterprise, and they can communicate with each other over the network to move data. True or false?

• True
• False

Question 12: Possible Flume channels include

• The implementation of your own channel
• File Storage
• Database Storage
• In Memory
• All of the Above

Question 13: Flume provides a number of source types including

• Elastic Search
• HBase
• Hive
• HDFS
• None of the Above

Question 14: Flume agent configuration is specified using

• CSV
• a text file, similar to the Java.properties format
• JSON
• XML, similar to Sqoop configuration

Question 15: To pass data from a Flume agent on one node to another, you can configure an Avro sink on the first node and an Avro source on the second. True or false?

• True
• False