Tech Mahindra Data Engineer Interview Questions and Answers for Freshers

To remove duplicate rows from BigQuery, use the DISTINCT keyword. To find the month of a given date, use the EXTRACT function.

• To remove duplicate rows, use SELECT DISTINCT * FROM table_name;

• To find the month of a given date, use SELECT EXTRACT(MONTH FROM date_column) AS month_name FROM table_name;

• Make sure to replace 'table_name' and 'date_column' with the appropriate values in your query.

The operator used in Composer to move data from GCS to BigQuery is the GCS to BigQuery operator.

• The GCS to BigQuery operator is used in Apache Airflow, which is the underlying technology of Composer.

• This operator allows you to transfer data from Google Cloud Storage (GCS) to BigQuery.

• You can specify the source and destination parameters in the operator to define the data transfer process.

Code to square each element in the input array.

• Iterate through the input array and square each element.

• Store the squared values in a new array to get the desired output.

BigQuery architecture includes storage, execution, and optimization components for efficient query processing.

• BigQuery stores data in Capacitor storage system for fast access.

• Query execution is distributed across multiple nodes for parallel processing.

• Query optimization techniques include partitioning tables, clustering tables, and using query cache.

• Using partitioned tables can help eliminate scanning unnecessary data.

• Clustering tables based on certain columns can improve que...read more

Bigtable is a NoSQL database for real-time analytics, while BigQuery is a fully managed data warehouse for running SQL queries.

• Bigtable is a NoSQL database designed for real-time analytics and high throughput, while BigQuery is a fully managed data warehouse for running SQL queries.

• Bigtable is used for storing large amounts of semi-structured data, while BigQuery is used for analyzing structured data using SQL queries.

• Bigtable is suitable for real-time data processing and hig...read more

RDD vs dataframe vs dataset in PySpark

• RDD (Resilient Distributed Dataset) is the basic abstraction in PySpark, representing a distributed collection of objects

• Dataframe is a distributed collection of data organized into named columns, similar to a table in a relational database

• Dataset is a distributed collection of data with the ability to use custom classes for type safety and user-defined functions

• Dataframes and Datasets are built on top of RDDs, providing a more structured...read more