What are the interview questions for Hadoop developer?

What are the interview questions for Hadoop developer?

Hadoop MapReduce Interview Questions

• What is “MapReduce”?
• What are the main configuration parameters in a “MapReduce” program?
• State the reason why we can’t perform “aggregation” (addition) in mapper?
• What is the purpose of “RecordReader” in Hadoop?
• Explain “Distributed Cache” in a “MapReduce Framework”.

What is interview questions for big data?

Big Data Interview Questions & Answers

• Define Big Data and explain the Vs of Big Data.
• How is Hadoop related to Big Data?
• Define HDFS and YARN, and talk about their respective components.
• What do you mean by commodity hardware?
• Define and describe the term FSCK.
• What is the purpose of the JPS command in Hadoop?

How do I prepare for a big data interview?

So, consider the following tips to makes sure you’re prepared for your next big data job interview:

1. Know your audience.
2. Know your story.
3. Dress for success.
4. Have standard answers ready.
5. Ask good questions.
6. Test for success.
7. Practice, practice, practice.
8. Follow up.

Which tool imports each table of the Rdbms in Hadoop and considers each row of the table as a record in HDFS?

Sqoop Import tool
While it comes to import tables from RDBMS to HDFS we use Sqoop Import tool. Generally, we can consider that each row in a table is a record in HDFS. Also, when we talk about text files all records are there as text data. However, when we talk about Avro and sequence files all records are there as binary data here.

Why pig is faster than Hive?

PIG was developed as an abstraction to avoid the complicated syntax of Java programming for MapReduce. On the other hand HIVE, QL is based around SQL, which makes it easier to learn for those who know SQL. AVRO is supported by PIG making serialization faster.

What is Hadoop in Big Data?

Apache Hadoop is an open source framework that is used to efficiently store and process large datasets ranging in size from gigabytes to petabytes of data. Instead of using one large computer to store and process the data, Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly.

What is an example of Big Data TQ answer?

tracking the work hours of 100 employees with a real-time dashboard. entering and tracking a company’s daily transaction records in a spreadsheet. sending user survey responses from various store branches to a single central database.

What is Hadoop in big data?

What is spark vs Hadoop?

Apache Hadoop and Apache Spark are both open-source frameworks for big data processing with some key differences. Hadoop uses the MapReduce to process data, while Spark uses resilient distributed datasets (RDDs).

Why sqoop is used in Hadoop?

Apache Sqoop is designed to efficiently transfer enormous volumes of data between Apache Hadoop and structured datastores such as relational databases. It helps to offload certain tasks, such as ETL processing, from an enterprise data warehouse to Hadoop, for efficient execution at a much lower cost.

Why is spark used?

Spark is a general-purpose distributed data processing engine that is suitable for use in a wide range of circumstances. Tasks most frequently associated with Spark include ETL and SQL batch jobs across large data sets, processing of streaming data from sensors, IoT, or financial systems, and machine learning tasks.