Top 40+ Amazon Redshift Interview Questions and Answers

Amazon Redshift Interview Questions and Answers

Amazon Redshift Interview Questions and Answers

AWS Redshift is a powerful, petabyte-scale, highly managed cloud-based data warehousing solution. It processes and handles structured and unstructured data in exabytes (1018 bytes). The most common use cases of Redshift include large-scale data migration, log analysis, processing real-time analytics, joining multiple data sources, and many more.

1. What is Amazon Redshift?

Amazon Redshift is a fully managed, petabyte-scale data warehouse service provided by Amazon Web Services (AWS). It allows users to easily analyze data using SQL and Business Intelligence (BI) tools. Redshift is optimized for fast querying and can handle petabyte-scale data warehouses, making it a popular choice for organizations with large amounts of data to analyze. It is designed to be cost-effective and easy to use, with features such as automatic data compression and columnar storage to help reduce storage requirements and improve query performance. Redshift is also highly scalable, with the ability to add or remove nodes as needed to accommodate changes in data volume or query workloads.

2. What are the benefits of using AWS Redshift?

The major benefits provided by AWS Redshift include:

  • In-built security with end-to-end encryption.
  • Multiple query support that provides significant query speed upgrades.
  • It provides an easy-to-use platform that is similar to MySQL and provides the usage of PostgreSQL, ODBC, and JDBC.
  • It offers Automated backup and fast scaling with fewer complications.
  • It is a cost-effective warehousing technique.

3. How do list tables in Amazon Redshift?

The ‘Show table’ keyword lists the tables in Amazon Redshift. It displays the table schema along with table and column constraints. Syntax:

SHOW TABLE [schema.]table_name

4. Why use an AWS Data Pipeline to load CSV into Redshift? And How?

AWS Data Pipeline facilitates the extraction and loading of CSV(Comma Separated Values) files. Using AWS Data Pipelines for CSV loading eliminates the stress of putting together a complex ETL system. It offers template activities to perform DML(data manipulation) tasks efficiently.

To load the CSV file, we must copy the CSV data from the host source and paste that into Redshift via RedshiftCopyActivity.

5. How far Redshift is better in performance as compared to other data warehouse technologies?

Amazon Redshift is the easiest and fastest cloud data warehouse which facilitates 3 times better price performance than other data warehouses. Redshift offers fast query performance at a comparatively modest cost to firms where datasets range in size from gigabytes to exabytes.

6. How to connect a private Redshift cluster?

By selecting option NO, you access your private IP address within the VPC. Bu doing this, you execute the public IP address. Now, the way of its access is through the VPC.

One more method most people use to connect to a private database is by using port forwarding by a Bastion server.

7. How are Amazon RDS, DynamoDB, and Redshift different?

RDS – RDS’s storage limit depends on which engine you’re running, but it tops out at 64 TB using Amazon Aurora. SQL accommodates 16 TB, and all the other engines allow for 32TB. 

Redshift – Redshift’s max capacity is much higher at 2PB. 

DynamoDB – DynamoDB has limitless storage capacity..

8. How to use an AWS Data Pipeline to load CSV into Redshift?

You can also extract and load your CSV files using the AWS Data Pipeline. The advantage of using the AWS Data Pipeline for loading is that you won’t have to worry about putting together a complicated ETL system. You can use template activities to carry out data manipulation tasks more efficiently.

Copy your CSV data from your host source into Redshift with the RedshiftCopyActivity. This template uses Amazon RDS, Amazon EMR, and Amazon S3 to copy data.

9. Where and When Redshift can be used?

Big customers are heading towards service on the data warehouse today. Redshift can be used in different sectors, and business use cases seeking a data warehouse cloud service with features such as cost savings, efficient dynamic query engine, security, etc.

Clients looking for moving from on-premise to cloud model, PaaS model. The traditional setup of servers and data centers for a company was a headache. This requires upfront planning, estimation, prediction of servers, type of servers, etc., and eventually takes months to come to a conclusion. Any wrong estimation or decision can lead to over or short of the estimated capacity and financial loss or shortage of resources. Following are business use cases or industries where Redshift can be used:-

  • Consolidation of accounting data: Redshift can be used to consolidate the data to see the company’s financial position at the company level. Redshift math, analytic, and date functions along with user-in-built functions to derive various formulas and complex customized calculations with optimized performance are very valuable features for accounting
  • Build Data Lake for pricing data: Redshift’s columnar storage is the best fit for time series data.
  • Supply chain management: To query and analyze huge volumes of data features like parallel processing with powerful node types make Redshift a good option

10. What are materialized views in Redshift?

A precomputed result set is stored in a materialized view, which is based on a SQL query over one or more base tables. You may query a materialized view using SELECT queries in the same way how you can query other tables or views in the database.


[ BACKUP { YES | NO } ][ table_attributes ][ AUTO REFRESH { YES | NO } ]
AS query

11. What are the top features of Redshift?

  • Redshift uses columnar storage, data compression, and zone maps to reduce the amount of I/O needed to perform queries.
  • It uses a massively parallel processing data warehouse architecture to parallelize and distribute SQL operations.
  • Redshift uses machine learning to deliver high throughput based on your workloads.
  • Redshift uses result caching to deliver sub-second response times for repeat queries.
  • Redshift automatically and continuously backs up your data to S3. It can asynchronously replicate your snapshots to S3 in another region for disaster recovery.

12. How can you verify your disk space usage in a Redshift cluster?

Using the stv partitions table, run the following query:

  sum(capacity)/1024 as capacity_gbytes,sum(used)/1024 as used_gbytes,(sum(capacity) - sum(used))/1024 as free_gbytes
  stv_partitions where part_begin=0;

The output will look like below:


13. How do we load data into Redshift?

Several methods are available to load data into Redshift, but the commonly used 3 methods are:

  1. The Copy command is used to load data into AWS Redshift.
  2. Use AWS services to load data into Redshift.
  3. Use the Insert command to load data into Redshift.

14. What is Redshift Spectrum? What data formats does Redshift Spectrum support?

Redshift Spectrum is released by AWS(Amazon Web Services) as a companion to Amazon Redshift. It uses Amazon Simple Storage Service (Amazon S3) to run SQL queries against the data available in a data lake. Redshift Spectrum facilitates the query processing against gigabytes to exabytes of unstructured data in Amazon S3, and no ETL or loading is required in this process. Redshift Spectrum is used to produce and optimize a query plan. Redshift Spectrum supports various structured and semi-structured data formats, including AVRO, TEXTFILE, RCFILE, PARQUET, SEQUENCE FILE, RegexSerDe, JSON, Geok, Ion, and ORC. Amazon suggests using columnar data formats like Apache PARQUET to improve performance and reduce cost.

15. How do I use Amazon S3 Bucket to load CSV into Redshift?

To load a CSV file from an Amazon S3 bucket into Amazon Redshift, you can follow these steps:

  1. Create an Amazon S3 bucket and upload the CSV file to it.
  2. Create an Amazon Redshift cluster and database.
  3. Create a database table in Redshift to hold the data from the CSV file.
  4. Use the COPY command to load the data from the S3 bucket into the Redshift table. The COPY command has the following syntax:
    COPY table_name FROM 's3://bucket_name/file_name.csv'
    CREDENTIALS 'aws_access_key_id=access_key;aws_secret_access_key=secret_key'

Replace table_name with the name of the Redshift table, bucket_name with the name of the S3 bucket, and file_name.csv with the name of the CSV file. Replace access_key and secret_key with your AWS access key and secret key, respectively.

  1. Execute the COPY command to load the data from the S3 bucket into the Redshift table.

Note that you will need to have the appropriate permissions and access to the S3 bucket and Redshift cluster in order to execute the COPY command. You may also need to set up VPC and security group rules to allow the Redshift cluster to access the S3 bucket

16. What are the key differences between SQL Server and Amazon Redshift?

Here are some key differences between SQL Server and Amazon Redshift:

FeatureSQL ServerAmazon Redshift
DeploymentOn-premises or cloudCloud
Data modelRelationalRelational
Database engineMicrosoft SQL ServerPostgreSQL
ScalabilityLinearLinear or columnar
PerformanceFastVery fast
Integration with other AWS servicesLimitedStrong

SQL Server is a traditional, on-premises or cloud-based database management system with a relational data model and a powerful database engine. It is fast and flexible, but can be expensive to scale and may not integrate well with other AWS services.

Amazon Redshift is a fully managed, cloud-based data warehousing service with a relational data model and a PostgreSQL-based database engine. It is designed for fast query performance and low cost, and has strong integration with other AWS services such as Amazon S3 and Amazon EMR. However, it may not have all of the features and capabilities of SQL Server.

17. How will the price of Amazon Redshift vary?

The Amazon Redshift pricing depends upon the type of node chosen by the customer to build his cluster. It mainly offers two types of nodes that differ in terms of storage and computation:

  1. Dense Compute Nodes

These optimized computing nodes offer RAM of up to 244GB and SSDs of up to 2.5 terabytes. The lowest spec price for dc2.larges varies from 0.25$ to 0.37$ per hour, and the highest spec price for dc2.8x varies from 4.8$ to 7$ per hour.

  1. Dense Storage Nodes

These nodes provide high storage capacity in two versions- a basic version(ds2.xlarge) with up to 2 TB HDDs and a higher version(ds2.8xlarge) with up to 16 TB HDDs. The cost of the basic version varies from 0.85$ to1.4$ per hour, and for the higher version is 6$ to 11$.

18. What is a data warehouse and how does AWS Redshift help?

A data warehouse is designed as a warehouse where the data from the systems and other sources generated by the organization are collected and processed.

A high-level data warehouse has three-tier architecture:

  1. In the bottom tier, we have the tools which cleanse and collect the data.
  2. In the middle level, we have tools to transform the data using the Online Analytical Processing Server.
  3. At the top level, we have different tools where data analysis and data mining are carried out at the front end.

As data grows continuously in an organization and the company constantly has to update its expensive storage servers. Here AWS Redshift is generated in the cloud-based warehouses offered by Amazon where businesses store their data.

19. Is there any support for time zones in Redshift while storing data?

Timezones aren’t supported by Redshift while storing data.

All times data are stored without timezone information and are considered to be UTC.

When you insert a value into a TIMESTAMPTZ, for example, the timezone offset is applied to the timestamp to convert it to UTC, and the corrected timestamp is saved. The original timezone information is not kept.

We’ll need an extra column to store the timezone if you wish to keep track of it.

20. How does Amazon Redshift apply Pricing?


  • You pay for the number of bytes scanned by RedShift Spectrum
  • You pay a per-second billing rate based on the type and number of nodes in your cluster.
  • You can reserve instances by committing to using Redshift for a 1 or 3-year term and save costs.

 21. How do we execute SQL files on Redshift?

You can be done this job by using a simple Python script running on an EC2 to set up a JDBC connection to Redshift. When it is done, execute the queries in the your.SQL file

22. What problems have you faced while working with Amazon Redshift?

  • Majority of the people face the problem of queries that are very slow and take a lot of time answering it.
  • Another problem that is seemed is on the dashboard. The dashboard is too slow.
  • Another problem in Amazon Redshift is the “black box”. It is very difficult to observe ‘what’s going on’.

23. What are the benefits of Amazon Redshift?

AWS Redshift has below main benefits compared to other options :

  1. AWS Redshift is easy to operate: you can find a choice to build a cluster in the AWS Redshift Console. Only press and leave the rest on the Redshift computer program. Just complete the correct information and start the cluster. The cluster is now ready to be used, for example, to control, track and scale Redshift.
  2. Cost Effective: Because there is no need to set up, the cost of this warehouse is reduced to 1/10th.
  3. Scaling of Warehouse is very easy: You just have to resize the cluster size by increasing the number of compute nodes.
  4. High performance: It uses techniques such as column storage and large simultaneous processing techniques to produce high efficiency and responsiveness times.

24. What are clusters in Redshift? How do I create and delete a cluster in AWS redshift?

Computing resources in Amazon Redshift data warehouse are called nodes which are further arranged in a group known as a cluster.

This cluster contains at least one database and it works on the Amazon Redshift engine.

To create a Cluster, you have to follow these steps: –
  • The very first step to creating a cluster is to open the Amazon ECS console
  • After that, you need to select the region to use which you can find from the navigation bar.
  • When it is done, select cluster in the navigation panel.
  • Then, select Create Cluster can be seen on the Cluster page.
  • At last, you should select the selection compatibility which might be networking, EC2 Linux+ networking, or EC2 window + networking.
To delete a cluster in AWS, follow these steps: –

The very first step to delete a cluster is to need you to open the Amazon Redshift console.

  • After that, select the Cluster which you want to remove from the navigation panel
  • When it is done, on the Configuration tab of the cluster details page and then select Cluster, and after that select the Delete option.
  • Before going through the end, you need to do some final steps one of the following in the Delete Cluster dialog box.
  • You must choose YES to remove the cluster in creating a snapshot and then take the last snapshot. And then you give the name to that snapshot. And finally, select the delete option.
  • Or you can choose NO to delete in creating a snapshot without the taking final snapshot and then select the delete option.

 25. How to Stop/Start the Redshift cluster?

You can Start the Redshift cluster by using the following steps:

  • In the Redshift Snapshots, select the snapshot of the cluster that you want to restore.
  • Select the Restore option on the Dropdown “Actions” Snapshot menu.
  • Complete the configuration details, then click the “Restore” button at the bottom right.

You can Stop the Redshift cluster by using the following steps:

  • Select the cluster you want to stop from the AWS Console.
  • Select the “Delete” option on the Dropdown “Cluster” menu.
  • Enter the Snapshot name.
  • Click on Stop.

Amazon Redshift Interview Questions and Answers

26. How do you query Amazon Redshift to show your table data?

Below is the command to list tables in a public schema :

FROM pg_table_def
WHERE schemaname = 'public'
ORDER BY employee;

# Below is the command to describe the columns from a table called table_data

FROM pg_table_def
WHERE tablename = 'employee'AND schemaname = 'public'

27. Why should I use Amazon Redshift over an on-premises data warehouse?

On-premises data warehouses require a considerable amount of time and resources to manage, especially for large datasets. In addition, the financial costs of constructing, maintaining, and increasing self-managed on-site data warehouses are very high.

As your data expands, you must continuously exchange what data to load into your data warehouse and what data to store in order to control costs, keep ETL complexity low, and deliver good results. Amazon Redshift not only greatly decreases the expense and operating overhead of a data center, but with Redshift Bandwidth, it also makes it easy to analyze vast volumes of data in its native format without forcing you to load the data.

28. Explain the architecture of Amazon Redshift?

An Amazon Redshift data repository is a business-class relational database query and administration system. It provides connection of clients with a great number of applications including reporting, business intelligence (BI), and analytics tools.

Amazon Redshift has great storage and excellent query performance with an aggregation of column data storage, massively parallel processing, and targeted data compression encoding schemes. It is all about the architecture of Redshift system architecture

29. List some Pros and Cons of Amazon Redshift.

Pros of Amazon Redshift

  • It offers network isolation.
  • It offers result caching.
  • It integrates with third-party tools.
  • It offers a consistent backup for your data.

Cons of Amazon Redshift

  • It does not work as a live app database.
  • 2. It is a little behind the times with its Postgre setup.
  • 3. Your performance levels decrease as the clusters increase.
  • 4. There are no stored procedures available to you in Amazon Redshift.

30. What is Redshift Spectrum?

RedShift Spectrum :

  • Enables you to run queries against exabytes of data in S3 without having to load or transform (ETL) any data.
  • Redshift Spectrum doesn’t use Enhanced VPC Routing.
  • If you store data in a columnar format, Redshift Spectrum scans only the columns needed by your query, rather than processing entire rows.
  • If you compress your data using one of Redshift Spectrum’s supported compression algorithms, less data is scanned.

Redshift Spectrum scales up to thousands of instances if needed, so queries run fast, regardless of the size of the data. In addition, you can use exactly the same SQL for Amazon S3 data as you do for your Amazon Redshift queries and connect to the same Amazon Redshift endpoint using the same BI tools.

Redshift Spectrum lets you split storage and compute, allowing you to scale each of them independently. You can set up as many Amazon Redshift clusters as you need to query your Amazon S3 data lake, providing high availability and unlimited concurrence. Redshift Spectrum gives you the right to store your data wherever you want, in the format you want, and to have it ready for processing when you need it.

If you are making a query, the Amazon Redshift SQL Endpoint creates and optimizes a query plan. Amazon Redshift describes what data is local and what is in Amazon S3, creates a plan to reduce the amount of Amazon S3 data that needs to be read, and requests Redshift Spectrum workers from a shared resource pool to read and process data from Amazon S3.

31. What is Amazon Redshift managed storage?

Amazon Redshift managed storage is available with RA3 node types which allows you to scale and pay for computing and storing separately so that you can configure your cluster based on your computing needs.

It automatically uses high-performance SSD-based local storage as a Tier-1 cache and takes advantage of optimizations such as data block temperature, data blockage, and workload patterns to deliver high performance while scaling storage automatically to Amazon S3 as required without requiring action.

32. How does Amazon Redshift simplify data warehouse management?

Amazon Redshift handles the work necessary to set up, run and scale a data center.

It provides infrastructure power, automating ongoing administrative tasks such as backup and patching, and monitoring nodes and drives to recover from failures. For Redshift Spectrum, Amazon Redshift handles all the computing infrastructure, load balancing, planning, scheduling, and execution of your queries for data stored in Amazon S3.

33. What are Database Querying Options available in Amazon Redshift?

Database Querying Options :

  • Connect to your cluster through a SQL client tool using standard ODBC and JDBC connections.
  • Connect to your cluster and run queries on the AWS Management Console with the Query Editor.

34. Which query language is used by Amazon Redshift?

SQL (Structured Query Language) is used by Amazon Redshift

35. How do you manage security in Amazon Redshift?

Security :

  • By default, an Amazon Redshift cluster is only accessible to the AWS account that creates the cluster.
  • Use IAM to create user accounts and manage permissions for those accounts to control cluster operations.
  • If you are using the EC2-VPC platform for your Redshift cluster, you must use VPC security groups.
  • If you are using the EC2-Classic platform for your Redshift cluster, you must use Redshift security groups.
  • When you provision the cluster, you can optionally choose to encrypt the cluster for additional security. Encryption is an immutable property of the cluster.
  • Snapshots created from the encrypted cluster are also encrypted.

36. What are the different options for monitoring Amazon Redshift?

Monitoring :

  • Use the database audit logging feature to track information about authentication attempts, connections, disconnections, changes to database user definitions, and queries run in the database. The logs are stored in S3 buckets.
  • Redshift tracks events and retains information about them for a period of several weeks in your AWS account.
  • Redshift provides performance metrics and data so that you can track the health and performance of your clusters and databases. It uses CloudWatch metrics to monitor the physical aspects of the cluster, such as CPU utilization, latency, and throughput.
  • Query/Load performance data helps you monitor database activity and performance.
  • When you create a cluster, you can optionally configure a CloudWatch alarm to monitor the average percentage of disk space that is used across all of the nodes in your cluster, referred to as the default disk space alarm.

37. What are Cluster Snapshots in Amazon Redshift?

Cluster Snapshots :

  • Point-in-time backups of a cluster. There are two types of snapshots: automated and manual. Snapshots are stored in S3 using SSL.
  • Redshift periodically takes incremental snapshots of your data every 8 hours or 5 GB per node of data change.
  • Redshift provides free storage for snapshots that is equal to the storage capacity of your cluster until you delete the cluster. After you reach the free snapshot storage limit, you are charged for any additional storage at the normal rate.
  • Automated snapshots are enabled by default when you create a cluster. These snapshots are deleted at the end of a retention period, which is one day, but you can modify it. You cannot delete an automated snapshot manually.
  • By default, manual snapshots are retained indefinitely, even after you delete your cluster.
  • You can share an existing manual snapshot with other AWS accounts by authorizing access to the snapshot.
  • You can configure Amazon Redshift to automatically copy snapshots (automated or manual) for a cluster to another AWS Region. For automated snapshots, you can also specify the retention period to keep them in the destination AWS Region. The default retention period for copied snapshots is seven days.
  • If you store a copy of your snapshots in another AWS Region, you can restore your cluster from recent data if anything affects the primary AWS Region. You can configure your cluster to copy snapshots to only one destination AWS Region at a time.

38. What is Amazon Redshift ODBC?

The Amazon Redshift ODBC Driver allows you to connect with live Amazon Redshift data, directly from applications that support ODBC connectivity. It is also helpful to read, write, and update Amazon Redshift data through a standard ODBC Driver interface.

39. What are the Limits per Region in Amazon Redshift?

  • The maximum number of tables is 9,900 for large and xlarge cluster node types and 20,000 for 8xlarge cluster node types.
  • The number of user-defined databases you can create per cluster is 60.
  • The number of concurrent user connections that can be made to a cluster is 500.
  • The number of AWS accounts you can authorize to restore a snapshot is 20 for each snapshot and 100 for each AWS KMS key.

 40. What are the limitations of Amazon Redshift?

Amazon Redshift has a few limitations that users should be aware of:

  1. Redshift is a data warehousing service and is not designed for real-time, low-latency workloads. It may not be suitable for applications that require fast writes or reads.
  2. Redshift is based on the PostgreSQL database engine and does not support all features of PostgreSQL or other database engines.
  3. Redshift has a maximum cluster size of 128 nodes. If you need to scale beyond this limit, you will need to use multiple clusters or a different database service.
  4. Redshift only supports a limited set of data types, including numeric, character, and date/time types. It does not support complex data types such as arrays or objects.
  5. Redshift does not support transactions or ACID compliance. This means that it is not suitable for applications that require transactional consistency.
  6. Redshift has a maximum table size of 1.6 TB and a maximum column size of 65535 bytes. If you need to store larger amounts of data or longer columns, you will need to use a different database service.
  7. Redshift has a maximum of 20,000 tables per cluster and a maximum of 10,000 columns per table. If you need to create more tables or columns, you will need to use a different database service.
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