Most Frequently asked Redshift Interview Questions and Answers (2024) | TechGeekNext>>

Most Frequently asked Redshift Interview Questions and Answers (2024)

In this post, questions from Amazon Redshift will be answered for Experienced and Freshers. We're trying to share our experience and learn how to help you make progress in your career.


Q: What is Amazon Redshift?

AWS Redshift is a Cloud-based data warehouse service. Redshift is a petabyte scale, powerful and fully managed relational data warehousing service.

  • A fully managed, petabyte-scale data warehouse service.
  • Redshift can expand your data warehouse queries to your data lake. You can run analytical queries against petabytes of data stored locally in Redshift, and directly against exabytes of data stored in S3.
  • RedShift is an OLAP type of DB.
An Amazon Redshift data warehouse is a collection of computing resources called nodes, which are organized into a group called a cluster. Each cluster runs an Amazon Redshift engine and contains one or more databases. Amazon Redshift provides efficient storage and faster database efficiency through specific properties such as massively parallel processing, columnar data storage, and multiple encoding schemes for data compression. Redshift can connect to JDBC client applications with ODBC connections. It is based on the PostgreSQL standard.

Q: 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. Amazon Web Services Data Pipelining With Redshift

Q: Where and When Redshift can be used?

Big customers are heading towards service on data warehouse today. Redshift can be used in different sectors, 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. Traditional setup of servers, data centers for a company was 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 short 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 company level. Redshift math, analytic, date functions along with user in-built functions to derive various formula and complex customized calculations with optimized performance are very valuable features for accounting
  • Build Data Lake for pricing data : Redshift's columnar storage is best fit for time series data.
  • Supply chain management : To query and analyze huge volume of data feature like parallel processing with powerful node types make Redshift a good option

Q: What is materialized views in Redshift?

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


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

Q: What are 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.

Q: 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;

Output will look like below:

capacity_gbytes  | used_gbytes | free_gbytes
382              | 22          | 370

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Q: How do I use Amazon S3 Bucket to load CSV into Redshift?

Using an S3 bucket is one of the easiest ways to load CSV files into Amazon Redshift. It consists of two stages: importing the CSV files into S3 and then loading the data from S3 into Amazon Redshift.

  1. Make a manifest file with the CSV data you want to load. Upload the files to S3 and gzip them if possible.
  2. Once the file has been loaded onto S3, use the COPY command to copy it from S3 and into the desired table. Your code will have the following structure if you used gzip:
    COPY <schema-name>.<table-name> (<ordered-list-of-columns>) FROM '<manifest-file-s3-url>'
    CREDENTIALS'aws_access_key_id=<key>;aws_secret_access_key=<secret-key>' GZIP MANIFEST;

    The CSV keyword is important here to enable Amazon Redshift recognise the file format. Any column configurations or row headers that should be excluded must also be specified, as seen below:

    COPY table_name (col1, col2, col3, col4)
    FROM 's3://<your-bucket-name>/load/file_name.csv'
    credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'
    -- Ignore the first line
    COPY table_name (col1, col2, col3, col4)
    FROM 's3://<your-bucket-name>/load/file_name.csv'
    credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'

Q: What is a data warehouse and how does AWS Redshift helps?

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.

At 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 growing 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.

Q: Is there any support for timezones 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.

Q: How 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.

Q: What are benefits of Amazon Redshift?

AWS Redshift has below main benefits compare to other options :
  1. AWS Redshift is easy to operation : you can find an 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 such techniques such as column storage and large simultaneous processing techniques to produce high efficiency and responsiveness times.

Q: How 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';

Q: 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.

Q: 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, requests Redshift Spectrum workers from a shared resource pool to read and process data from Amazon S3.

Q: What is Amazon Redshift managed storage?

Amazon Redshift managed storage is available with RA3 node types which allows you to scale which 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 block age and workload patterns to deliver high performance while scaling storage automatically to Amazon S3 as required without requiring action.

Q: How does Amazon Redshift simplify data warehouse management?

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

It providing infrastructure power, automating on-going 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.

Q: 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.

Q: How 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.

Q: What are different option of monitoring in 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.

Q: 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.

Q: What are 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.

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