Top 10 AWS Redshift Interview Questions in 2022
This article was published as a part of the Data Science Blogathon.
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.
Redshift is an OLAP(Online Analytical Processing) database that gathers valuable insights from huge data. It is based on the PostgreSQL standard and can connect the JDBC client applications with ODBC connections. The properties like Massively Parallel Processing (MPP) technology, columnar data storage, and multiple encoding schemes for data compression provide Redshift with the strength of executing the humongous volume of data efficiently and at a faster speed. Like many other Amazon Web Services(AWS), Redshift can be deployed with just a few clicks and provides an easy-to-use interface.
This blog will discuss the frequently asked interview questions that might help you gain knowledge about Redshift and prepare you for the next interview.
RedShift Interview Questions
Q1: What is Redshift in AWS?
Amazon Web Service(AWS) Redshift is a fully managed big data warehouse service in the cloud that is rapid and potent enough to process and manage data in the range of exabytes. Redshift is built to handle large-scale data sets and database migrations by the company ParAccel (later acquired by Actian). It uses massive parallel processing (MPP) technology and provides a cost-effective and efficient data solution. The famous usage of Redshift is acquiring the latest insight for business and customers.
Q2: 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.
Q3: 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.
Q4: How to 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
Q5: How are Amazon RDS, DynamoDB, and Redshift different?
Below are the major differences:
The available Amazon RDS engines include Oracle, MySQL, SQL Server, PostgreSQL, etc., while the DynamoDB engine is NoSQL, and Amazon Redshift supports the Redshift(adapted PostgreSQL) as a database engine.
RDS facilitates 6 terabytes per instance, Redshift supports 16 terabytes per instance, and DynamoDB provides unlimited storage.
RDS is used for traditional databases, while Redshift is famous for data warehousing DynamoDB is the database for dynamically modified data.
Multi-Availability Zone Replication
RDS acts as an additional service while Multi-AZ replication for Redshift is Manual and for DynamoDB is Built-in.
Q6: How far Redshift is better in performance as compared to other data warehouse technologies?
Q7: How do we load data into Redshift?
Several methods are available to load data into Redshift, but the commonly used 3 methods are:
The Copy command is used to load data into AWS Redshift.
Use AWS services to load data into Redshift.
Use the Insert command to load data into Redshift.
Q8: 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.
Q9: 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:
Dense Compute Nodes
These optimized computing nodes offer RAM up to 244GB and SSDs 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.
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$.
Q10: What are the limitations of Amazon Redshift?
It cannot be used as a live app database due to the slow processing speed of web apps.
There is no way to enforce uniqueness in AWS Redshift on inserted data.
It supports the parallel loading only for Amazon EMR, relational DynamoDB, and Amazon S3.
In this blog, we have seen some of the important interview questions that can be asked in AWS Redshift interviews. We had discussed a basic combination of theoretical and practical questions, but that’s not it. This blog will give you a basic understanding of what type of questions you expect. However, it’s recommended apart from these Redshift interview questions; you also practice the SQL commands to develop more understanding of data processing and transformations. The key takeaways from the above AWS Redshift questions are:
We learned about what is Redshift in AWS and how it is beneficial for the user.
We have seen how can we load CSV in Redshift using the data pipeline.
We understand how Redshift differs from RDS and DynamoDB.
We got an understanding of how we can show tables.
We have also discussed the basics of Redshift Spectrum and the limitations of Redshift.
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