Cloud Data Warehousing (Redshift, BigQuery, Snowflake) Questions and Answers for Viva

Answer-1: Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud provided by AWS.

Answer-2: Google BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility.

Answer-3: Snowflake is a cloud-native data platform that offers data warehousing, data lakes, and data sharing in a single solution.

Answer-4: Redshift stores data in a columnar format for high compression and fast query performance.

Answer-5: BigQuery uses a serverless architecture, allowing users to run SQL queries without managing infrastructure.

Answer-6: Snowflake uses a multi-cluster shared data architecture separating storage and compute for scalability.

Answer-7: BigQuery uses on-demand (per query) pricing and flat-rate pricing models.

Answer-8: Redshift Spectrum allows users to run queries against exabytes of data in S3 without loading it into Redshift.

Answer-9: Snowflake natively supports semi-structured data like JSON, Avro, and Parquet using its VARIANT data type.

Answer-10: Federated querying allows BigQuery to query data stored in external sources like Cloud Storage, Sheets, and Cloud SQL.

Answer-11: A virtual warehouse in Snowflake is a compute cluster used to perform queries, loading, and other DML operations.

Answer-12: Yes, Redshift offers concurrency scaling and RA3 nodes with managed storage to scale resources dynamically.

Answer-13: BigQuery ML allows users to create and execute machine learning models using SQL queries directly in BigQuery.

Answer-14: Yes, Snowflake is available on AWS, Azure, and Google Cloud, offering cross-cloud functionality.

Answer-15: A Redshift cluster consists of a leader node and one or more compute nodes to process and store data.

Answer-16: Snowflake uses role-based access control (RBAC), data encryption at rest and in transit, and integration with external identity providers.

Answer-17: A dataset in BigQuery is a container that holds tables, views, and routines.

Answer-18: Snowflake's data sharing allows secure, real-time sharing of data across accounts without duplication.

Answer-19: Yes, BigQuery supports partitioned and clustered tables for performance and cost optimization.

Answer-20: A materialized view in Redshift stores precomputed results to improve query performance on large datasets.

Answer-21: Snowflake?s multi-cluster architecture handles high concurrency by automatically provisioning clusters as needed.

Answer-22: SQL is the primary language for querying BigQuery, but it also supports integration with Python, Java, and R.

Answer-23: Data can be loaded using COPY commands from S3, DynamoDB, or through third-party ETL tools.

Answer-24: Stages are storage locations used to load or unload data in Snowflake.

Answer-25: It removes infrastructure management overhead and automatically handles scaling and performance tuning.

Answer-26: Yes, Redshift supports semi-structured data using the SUPER data type and PartiQL.

Answer-27: Snowflake stores data in cloud object storage in an optimized, compressed, and columnar format.

Answer-28: A job is an action executed by BigQuery such as running a query, loading or exporting data.

Answer-29: Redshift automatically takes snapshots and allows users to create manual snapshots for backup and restore.

Answer-30: Snowpipe is a continuous data ingestion service that automatically loads data into Snowflake from cloud storage.

Answer-31: Yes, BigQuery allows you to schedule SQL queries to run on a recurring basis.

Answer-32: The Redshift Data API allows developers to interact with Redshift using HTTPS, removing the need for JDBC/ODBC.

Answer-33: Snowflake bills compute separately for each virtual warehouse by the second of usage.

Answer-34: Yes, it integrates with tools like Looker, Tableau, and Power BI using standard connectors.

Answer-35: Time Travel allows users to access historical data (up to 90 days) for recovery and auditing purposes.

Answer-36: BigQuery supports very large tables, with limits measured in petabytes, practically constrained by quotas and pricing.

Answer-37: Yes, Redshift supports scalar and SQL user-defined functions to extend SQL capabilities.

Answer-38: Zero-copy cloning allows you to create instant copies of databases, schemas, or tables without duplicating data.

Answer-39: Using AWS CloudWatch, Redshift Console, and the EXPLAIN command for query plans.

Answer-40: It allows real-time analytics and reporting using third-party tools directly connected to Snowflake.

Answer-41: A view is a saved SQL query that acts as a virtual table and can be queried like a regular table.

Answer-42: RA3 nodes let you scale compute and storage independently, with managed storage backed by S3.

Answer-43: All data is encrypted using AES-256 at rest and in transit, with automatic key management.

Answer-44: BigQuery supports schema evolution like adding columns but requires careful handling for other modifications.

Answer-45: Yes, Redshift Spectrum supports fine-grained access control via AWS Lake Formation integration.

Answer-46: Micro-partitions are Snowflake?s internal method of dividing data into small chunks for efficient storage and retrieval.

Answer-47: Reservation pricing allows you to purchase dedicated query slots at a flat rate for predictable workloads.

Answer-48: Using IAM, VPC, SSL encryption, and database authentication with user roles and groups.

Answer-49: Yes, virtual warehouses can be paused and resumed to save on compute costs.

Answer-50: External tables allow querying data in external sources like Cloud Storage without loading it into BigQuery.