PwC Big Data Engineer Interview Questions, Process, and Tips

Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

• Apache Spark is designed for speed and ease of use in processing large amounts of data.

• It can run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.

• Spark provides high-level APIs in Java, Scala, Python, and R, and an opt...read more

Core components of Spark include Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.

• Spark Core: foundation of the Spark platform, provides basic functionality for distributed data processing

• Spark SQL: module for working with structured data using SQL and DataFrame API

• Spark Streaming: extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams

• MLlib...read more

Implement fault tolerance by using checkpointing, replication, and monitoring mechanisms.

• Enable checkpointing in Spark Streaming to save the state of the computation periodically to a reliable storage like HDFS or S3.

• Use replication in Kafka to ensure that data is not lost in case of node failures.

• Monitor the health of the Kafka and Spark clusters using tools like Prometheus and Grafana to detect and address issues pro

Hive Architecture is a data warehousing infrastructure built on top of Hadoop for querying and analyzing large datasets.

• Hive uses a language called HiveQL which is similar to SQL for querying data stored in Hadoop.

• It organizes data into tables, partitions, and buckets to optimize queries and improve performance.

• Hive metastore stores metadata about tables, columns, partitions, and their locations.

• Hive queries are conver...read more

Vectorization is the process of converting data into a format that can be easily processed by a computer's CPU or GPU.

• Vectorization allows for parallel processing of data, improving computational efficiency.

• It involves performing operations on entire arrays or matrices at once, rather than on individual elements.

• Examples include using libraries like NumPy in Python to perform vectorized operations on arrays.

• Vectorizati...read more

Partition in Hive is a way to organize data in a table into multiple directories based on the values of one or more columns.

• Partitions help in improving query performance by allowing Hive to only read the relevant data directories.

• Partitions are defined when creating a table in Hive using the PARTITIONED BY clause.

• Example: CREATE TABLE table_name (column1 INT, column2 STRING) PARTITIONED BY (column3 STRING);

Functions in SQL are built-in operations that can be used to manipulate data or perform calculations within a database.

• Functions in SQL can be used to perform operations on data, such as mathematical calculations, string manipulation, date/time functions, and more.

• Examples of SQL functions include SUM(), AVG(), CONCAT(), UPPER(), LOWER(), DATE_FORMAT(), and many others.

• Functions can be used in SELECT statements, WHERE ...read more

Rank, Dense_rank, and row_number are window functions used in SQL to assign a rank to each row based on a specified order.

• Rank function assigns a unique rank to each row based on the specified order.

• Dense_rank function assigns a unique rank to each row without any gaps based on the specified order.

• Row_number function assigns a unique sequential integer to each row based on the specified order.