50+ Gcp Data Engineer Interview Questions and Answers

Bigquery is a cloud-based data warehousing tool used for analyzing large datasets quickly. Pubsub is a messaging service, Dataflow is a data processing tool, and Cloud Storage is a scalable object storage service.

• Bigquery is used for analyzing large datasets quickly

• Pubsub is a messaging service used for asynchronous communication between applications

• Dataflow is a data processing tool used for batch and stream processing

• Cloud Storage is a scalable object storage service used f...read more

IAM is Identity and Access Management, SA is Service Account, BigQuery is a data warehouse, QlikSense is a data visualization tool, GitHub is a version control system, Spark is a distributed computing framework, Airflow is a workflow automation tool, Bigtable is a NoSQL database, Cloud Composer is a managed workflow orchestration service, Pub/Sub is a messaging service.

• IAM is used to manage access to resources in Google Cloud Platform.

• SA is a special Google account that repre...read more

Real-time data services can be used to migrate datawarehouse with GCP services.

• Use Cloud Dataflow to ingest and transform data in real-time

• Use Cloud Pub/Sub to stream data to BigQuery or Cloud Storage

• Use Cloud Dataproc to process data in real-time

• Use Cloud Composer to orchestrate data pipelines

• Use Cloud Spanner for real-time transactional data

• Use Cloud SQL for real-time relational data

• Use Cloud Bigtable for real-time NoSQL data

Google Cloud BigQuery is a fully-managed, serverless data warehouse that uses a distributed architecture for processing and analyzing large datasets.

• BigQuery uses a distributed storage system called Capacitor for storing and managing data.

• It uses a distributed query engine called Dremel for executing SQL-like queries on large datasets.

• BigQuery separates storage and compute, allowing users to scale compute resources independently.

• It supports automatic data partitioning and clu...read more

Data pipelines in GCP can be created using various tools like Dataflow, Dataproc, and Cloud Composer.

• Choose the appropriate tool based on the use case and data volume

• Define the data source and destination

• Create a pipeline using the chosen tool and define the data transformations

• Test and deploy the pipeline

• Monitor and troubleshoot the pipeline for any issues

select * from table limit 100 is faster

• Using 'select * from table' retrieves all rows from the table, which can be slower if the table is large

• Using 'select * from table limit 100' limits the number of rows retrieved, making it faster

• Limiting the number of rows fetched can improve query performance

To find out errors in a failed BigQuery query, check the query job history and error message details.

• Check the query job history in the BigQuery console for details on the failed query.

• Look for error messages in the job history to identify the specific issue that caused the query to fail.

• Review the query syntax and data being queried to troubleshoot common errors such as syntax errors or data type mismatches.

Dataproc is used for processing large datasets in Hadoop/Spark, while Cloud Composer is used for orchestrating workflows and managing pipelines.

• Use Dataproc for processing large datasets in Hadoop/Spark

• Use Cloud Composer for orchestrating workflows and managing pipelines

• Dataproc is ideal for running big data processing jobs, while Cloud Composer is suitable for managing complex workflows

I have used various services in GCP including BigQuery, Dataflow, Cloud Storage, and Pub/Sub.

• BigQuery for data warehousing and analytics

• Dataflow for data processing and ETL

• Cloud Storage for storing and accessing data

• Pub/Sub for messaging and event-driven architectures

I have used modules like pandas, numpy, matplotlib, and sklearn in Python for data manipulation, analysis, visualization, and machine learning tasks.

• pandas - for data manipulation and analysis

• numpy - for numerical computing and array operations

• matplotlib - for data visualization

• sklearn - for machine learning tasks

Use 'bq show' command to display the schema of a table in BigQuery.

• Use 'bq show' command followed by the dataset and table name to display the schema.

• The schema includes the column names, data types, and mode (nullable or required).

• Example: bq show project_id:dataset.table_name

Python code to trigger a dataflow job in cloud function

• Use the googleapiclient library to interact with the Dataflow API

• Authenticate using service account credentials

• Submit a job to Dataflow using the projects.locations.templates.launch endpoint

Use GCP Dataflow to transfer files between GCS buckets

• Create a Dataflow pipeline using Apache Beam to read from source bucket and write to destination bucket

• Use GCS connector to read and write files in Dataflow pipeline

• Set up appropriate permissions for Dataflow service account to access both buckets

Use SQL to find keys present in table A but not in table B (old copy of A).

• Use a LEFT JOIN to combine tables A and B based on the key column

• Filter the results where the key column in table B is NULL

• This will give you the keys present in table A but not in table B

Use Python's slicing feature to display a string in reverse order.

• Use string slicing with a step of -1 to reverse the string.

• Example: 'hello'[::-1] will output 'olleh'.

To schedule a job to trigger every hour in Airflow, you can use the Cron schedule interval

• Define a DAG (Directed Acyclic Graph) in Airflow

• Set the schedule_interval parameter to '0 * * * *' to trigger the job every hour

• Example: schedule_interval='0 * * * *'

List and tuple are both used to store collections of data, but they have some differences.

• Lists are mutable while tuples are immutable

• Lists use square brackets [] while tuples use parentheses ()

• Lists are typically used for collections of homogeneous data while tuples are used for heterogeneous data

• Lists have more built-in methods than tuples

The GCP services used in our project include BigQuery, Dataflow, Pub/Sub, and Cloud Storage.

• BigQuery for data warehousing and analytics

• Dataflow for real-time data processing

• Pub/Sub for messaging and event ingestion

• Cloud Storage for storing data and files

BigQuery does not have fixed slots, it dynamically allocates resources based on the query requirements.

• BigQuery does not have a fixed number of slots like traditional databases.

• It dynamically allocates resources based on the query requirements.

• The number of slots available for a query can vary depending on the complexity and size of the query.

• BigQuery's serverless architecture allows it to scale automatically to handle large workloads.

Various data sources such as databases, APIs, files, and streaming services are used for data ingestion and processing.

• Databases (e.g. MySQL, PostgreSQL)

• APIs (e.g. RESTful APIs)

• Files (e.g. CSV, JSON)

• Streaming services (e.g. Kafka, Pub/Sub)

Materialized view in BigQuery is a precomputed result set stored as a table for faster query performance.

• Materialized views store the results of a query and can be used to speed up query performance by avoiding the need to recompute the same result multiple times.

• They are updated periodically to reflect changes in the underlying data.

• Materialized views are particularly useful for complex queries that involve aggregations or joins.

• Example: CREATE MATERIALIZED VIEW my_materiali...read more

Code to find max number of product by customer

• Iterate through each customer's purchases

• Keep track of the count of each product for each customer

• Find the product with the maximum count for each customer

Bigquery handles various types of jobs including querying, loading, exporting, and copying data.

• Querying data for analysis and reporting

• Loading data into Bigquery for storage and processing

• Exporting data from Bigquery to other systems

• Copying data within Bigquery or to other destinations

GCP main components include Compute Engine, Cloud Storage, BigQuery, and Dataflow for various uses.

• Compute Engine - Virtual machines for running workloads

• Cloud Storage - Object storage for storing data

• BigQuery - Data warehouse for analytics

• Dataflow - Stream and batch processing of data

Partitioning is dividing data into smaller, manageable parts. Clustering is grouping similar data together. Types include range, hash, list, and composite partitioning.

• Partitioning divides large tables into smaller, more manageable parts based on a chosen criteria.

• Clustering groups together rows with similar values for one or more columns to improve query performance.

• Types of partitioning include range partitioning, hash partitioning, list partitioning, and composite partitio...read more

Types of NoSQL databases in GCP include Firestore, Bigtable, and Datastore.

• Firestore is a flexible, scalable database for mobile, web, and server development.

• Bigtable is a high-performance NoSQL database service for large analytical and operational workloads.

• Datastore is a highly scalable NoSQL database for web and mobile applications.

Using bq commands to create a table and load a CSV file in Google BigQuery

• Use 'bq mk' command to create a new table in BigQuery

• Use 'bq load' command to load a CSV file into the created table

• Specify schema and source format when creating the table

• Specify source format and destination table when loading the CSV file

• Example: bq mk --table dataset.table_name schema.json

• Example: bq load --source_format=CSV dataset.table_name data.csv

Leaf nodes are the bottom nodes in a tree structure, while columnar storage stores data in columns rather than rows.

• Leaf nodes are the end nodes in a tree structure, containing actual data or pointers to data.

• Columnar storage stores data in columns rather than rows, allowing for faster query performance on specific columns.

• Columnar storage is commonly used in data warehouses and analytics databases.

• Leaf nodes are important for efficient data retrieval in tree-based data struc...read more

Generators and decorators are features in Python. Generators are functions that can pause and resume execution, while decorators are functions that modify other functions.

• Generators are functions that use the yield keyword to return values one at a time, allowing for efficient memory usage.

• Decorators are functions that take another function as input and return a new function with added functionality.

• Generators can be used to iterate over large datasets without loading everyth...read more

Partitioning is dividing data into smaller parts based on a key, while clustering is storing data together based on similar values.

• Partitioning is used to improve query performance by reducing the amount of data that needs to be scanned.

• Clustering is used to physically store related data together on disk to improve query performance.

• In BigQuery, partitioning can be done based on a date column, while clustering can be done based on one or more columns to group related data tog...read more

SQL joins are used to combine rows from two or more tables based on a related column between them.

• SQL joins are used to retrieve data from multiple tables based on a related column between them

• Types of SQL joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• In BigQuery, joins can be performed using standard SQL syntax

• Example: SELECT * FROM table1 INNER JOIN table2 ON table1.column = table2.column

GCP offers different storage classes for varying performance and cost requirements.

• Standard Storage: for frequently accessed data

• Nearline Storage: for data accessed less frequently

• Coldline Storage: for data accessed very infrequently

• Archive Storage: for data stored for long-term retention

SQL optimization techniques focus on improving query performance by reducing execution time and resource usage.

• Use indexes to speed up data retrieval

• Avoid using SELECT * and instead specify only the columns needed

• Optimize joins by using appropriate join types and conditions

• Limit the use of subqueries and instead use JOINs where possible

• Use EXPLAIN to analyze query execution plans and identify bottlenecks

Indexing in SQL is used to improve the performance of queries by creating indexes on columns in tables.

• Indexes can be created on columns that are frequently used in WHERE, JOIN, and ORDER BY clauses.

• Indexes can speed up query performance by allowing the database to quickly locate rows based on the indexed columns.

• Primary keys automatically create a unique index on the column(s) specified.

• Examples: CREATE INDEX idx_name ON table_name(column_name);

• Examples: DROP INDEX idx_name ...read more

Window functions in BigQuery are used to perform calculations across a set of table rows related to the current row.

• Window functions allow you to perform calculations on a set of rows related to the current row

• They are used with the OVER() clause in SQL queries

• Common window functions include ROW_NUMBER(), RANK(), and NTILE()

• They can be used to calculate moving averages, cumulative sums, and more

Nearline is for data accessed less frequently, while coldline is for data accessed very infrequently.

• Nearline storage is designed for data that is accessed less frequently but still needs to be readily available.

• Coldline storage is for data that is accessed very infrequently and is stored at a lower cost.

• Nearline storage has a higher retrieval cost compared to coldline storage.

• Examples of nearline storage include Google Cloud Storage Nearline, while examples of coldline stora...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 applications.

• BigQuery is a fully managed data warehouse that allows users to run SQL queries on large datasets.

• BigTable is optimized for high-speed reads and writes, making it suitable for real-time data processing.

• BigQuery is optimized for running complex SQL queries on...read more

Cloud Composer is another orchestration tool in GCP

• Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow

• It allows you to author, schedule, and monitor workflows that span across GCP services

• Cloud Composer provides a rich set of features like DAGs, plugins, and monitoring capabilities

• It integrates seamlessly with other GCP services like BigQuery, Dataflow, and Dataproc

Cloud Functions are event-driven functions that run in response to cloud events.

• Serverless functions that automatically scale based on demand

• Can be triggered by events from various cloud services

• Supports multiple programming languages like Node.js, Python, etc.

SCD stands for Slowly Changing Dimension and Merge is a SQL operation used to update or insert data in BigQuery.

• SCD is used to track changes to data over time in a data warehouse

• Merge in BigQuery is used to perform insert, update, or delete operations in a single statement

• Example: MERGE INTO target_table USING source_table ON condition WHEN MATCHED THEN UPDATE SET col1 = value1 WHEN NOT MATCHED THEN INSERT (col1, col2) VALUES (value1, value2)

Different storage types in GCP include Cloud Storage, Persistent Disk, Cloud SQL, Bigtable, and Datastore.

• Cloud Storage: object storage for storing and accessing data from Google Cloud

• Persistent Disk: block storage for virtual machine instances

• Cloud SQL: fully-managed relational database service

• Bigtable: NoSQL wide-column database service for large analytical and operational workloads

• Datastore: NoSQL document database for web and mobile applications

Lazy evaluation in Spark delays the execution of transformations until an action is called.

• Transformations in Spark are not executed immediately, but are stored as a directed acyclic graph (DAG) of operations.

• Actions trigger the execution of the DAG, allowing for optimizations like pipelining and avoiding unnecessary computations.

• Lazy evaluation helps in optimizing the execution plan and improving performance by delaying the actual computation until necessary.

BigQuery is used for analyzing large datasets and running complex queries, while SQL is used for querying databases.

• BigQuery is used for analyzing large datasets quickly and efficiently

• SQL is used for querying databases to retrieve specific data

• BigQuery can handle petabytes of data, making it ideal for big data analysis

• SQL can be used to perform operations like filtering, sorting, and aggregating data

Dataflow function to split sentence

• Use the Split transform in Dataflow to split the sentence into words

• Apply ParDo function to process each word individually

• Use regular expressions to handle punctuation and special characters

Recursion function to calculate factorial of a number

• Define a function that takes an integer as input

• Base case: if input is 0, return 1

• Recursive case: return input multiplied by factorial of input-1

• Example: factorial(5) = 5 * factorial(4) = 5 * 4 * factorial(3) = ... = 5 * 4 * 3 * 2 * 1 = 120

Partitioning is dividing data into smaller chunks for efficient storage and retrieval, while clustering is organizing data within those partitions based on a specific column.

• Partitioning is done at the storage level to distribute data across multiple nodes for better performance.

• Clustering is done at the query level to physically group data based on a specific column, improving query performance.

• Example: Partitioning by date in a sales database can improve query performance b...read more

I chose TCS for its reputation, global presence, diverse opportunities, and focus on innovation.

• TCS is a renowned company with a strong reputation in the IT industry

• TCS has a global presence with offices in multiple countries, providing opportunities for international exposure

• TCS offers diverse opportunities for career growth and development in various domains

• TCS is known for its focus on innovation and cutting-edge technologies, which aligns with my career goals