Bigdata and Hadoop Developer Interview Questions and Answers

Hadoop data architect is responsible for designing and implementing the data architecture for Hadoop-based solutions.

• Designing and implementing data architecture for Hadoop-based solutions

• Ensuring data is stored efficiently and securely

• Optimizing data processing and retrieval

• Working with other teams to ensure data integration and compatibility

• Examples: designing a data lake architecture for a large retail company, implementing a real-time data processing pipeline for a financ...read more

Debugging a Spark application involves analyzing logs, using the Spark UI, and employing tools like breakpoints and local testing.

• Check Spark Logs: Review the executor and driver logs for error messages and stack traces that can provide insights into failures.

• Use Spark UI: Access the Spark Web UI to monitor job execution, view stages, and identify bottlenecks or failed tasks.

• Local Testing: Run Spark applications locally with a smaller dataset to isolate issues before deployin...read more

Basic transformations on DataFrames include filtering, selecting, and aggregating data for analysis.

• Filtering: Use 'filter()' to select rows based on conditions. Example: df.filter(df['age'] > 30).

• Selecting: Use 'select()' to choose specific columns. Example: df.select('name', 'age').

• Aggregating: Use 'groupBy()' and 'agg()' for summary statistics. Example: df.groupBy('gender').agg({'salary': 'mean'}).

• Adding Columns: Use 'withColumn()' to create new columns. Example: df.withCo...read more

Hive optimization techniques improve query performance by optimizing data storage and query execution.

• Partitioning tables based on commonly used columns to reduce data scanned during queries

• Using bucketing to evenly distribute data across files for faster query processing

• Using appropriate file formats like ORC or Parquet for efficient storage and retrieval

• Optimizing joins by broadcasting smaller tables or using map-side joins

• Tuning query execution parameters like parallelism ...read more

Hive is a data warehousing tool built on top of Hadoop for querying and analyzing large datasets stored in Hadoop Distributed File System (HDFS).

• Hive uses a SQL-like query language called HiveQL to process data.

• It translates HiveQL queries into MapReduce jobs to execute on Hadoop.

• Hive organizes data into tables, partitions, and buckets for efficient querying.

• It supports external tables for data stored outside of HDFS.

• Hive provides metadata storage in a relational database lik...read more