The earliest computers had memory between 3 to 45 kilobytes. Later on with the increased usage of computing devices, the need for more storage capacity also increased.
Albeit this, Gordon Moore in 1965 gave the Moore’s law. As per this, the speed of processors and storage capacity will double every two years. The law has held true for the longest period of time.
With the inception of the Internet, the world came closer and the amount of services and user data generated also increased. This required server-based storage that was hosted on-premise.
However, it wasn’t enough considering modern services required huge capacities, dynamic architecture, and scalability, and soon came cloud storage services.
These were essential for many software development companies and their customers for smooth operations. They allowed huge storage capacities with the capability to remotely access data.
Today, we’ll be talking about Amazon Redshift which is a popular cloud data warehousing service and is used by multiple large corporations.
In this article, we will extrapolate data on how Amazon Redshift can be used for data migration and data warehousing services, therefore, let’s start!
What is Cloud Data Warehousing?
A cloud data warehouse is a public cloud that is used by organizations to store their information. It lets organizations manage large amounts of data making it quickly accessible.
It is a perfect solution for any company that is looking for data agility, flexibility, and ease of usage. Cloud data warehousing is the process of storing organizational data in these cloud data warehouses.
Below are the key features of using cloud data warehouses:
- Stores data in one place and lets users access it anywhere
- It offers extensive data integration capabilities
- It is scalable in nature
- It offers columnar storage and in-memory caching for high-performance
- It comes with extensive tools to manage data
What is Amazon Redshift?
Amazon Redshift is a cloud data warehousing service that has several capabilities to offer its users such as:
- Capability to store huge volumes of data
- It breaks down data into siloes
- Gather real-time and predictive analytics
- 5X better price performance in comparison to its competitors
- Reliable infrastructure
- Access to data without infrastructure management
How Amazon Redshift Works? - Steps to Use Amazon Redshift for Data Warehousing and Analytics
Amazon Redshift primarily uses SQL to perform its operations. It uses SQL queries to analyze structured as well as semi-structured data for the following data pools:
- Data Warehouses
- Operational Databases
- Data Lakes
In order to migrate data using Amazon Redshift for data warehousing and analytics, we have mentioned the steps below:
Source: Amazon
Step 1:
Create a cluster of virtual computers by creating an Amazon Redshift cluster. This will help you store data and run queries.
Step 2:
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Load data from different sources such as Amazon S3, Amazon RDS, and on-premise data warehouse to Amazon Redshift
Step 3:
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Define tables, columns, and relationships between the data to create a data warehouse schema
Step 4:
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Now, use SQL queries in Amazon Redshift to perform operations.
Step 5:
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Analyze your data using the different tools that are provided by Amazon Redshift.
Additional Tips Related to Data Migration for Amazon Redshift
Here is some additional information related to each step that is mentioned below in order to have an effective migration process with zero hindrances.
For Creating a Cluster:
- Specify the number of nodes
- Make sure to input the type of node
- Enter the storage capacity
- Region of the cluster
Loading Data to Amazon Redshift:
- Use AWS Data Pipeline to automatically load data from a variety of sources
- Use the Amazon Redshift Import/Export window to load data from Amazon S3
- Use the copy command to load data from a variety of sources
Creating a Data Warehouse Schema:
While designing the data warehouse schema, keep the objective of your business in mind.
Running Queries on Amazon Redshift:
SQL features include multiple queries that include joins, aggregation, and subqueries.
Data Analysis:
Once the data is loaded, some of the popular tools that one can use for the ordeal are Amazon QuickSight, Amazon Athena, and Amazon EMR.
Examples of Cloud Data Warehousing using Amazon Redshift
Here are some of the customers of Amazon Redshift that have availed satisfactory service from the platform. Let’s check their case studies:
Nasdaq – Scaling 30 to 70 Billion Records Per Day
Nasdaq is a multinational financial services and technology company that operates in a total of 27 markets. It has over 4000 companies enlisted and provides critical technology to market infrastructure operators that are located in 50 countries.
In 2014, Nasdaq shifted from a legacy system to a cloud-based infrastructure using AWS (Amazon Web Services) and Amazon Redshift. Between the years 2014 and 2018, Nasdaq’s cluster grew to 70 nodes because of the expansion in the North American market.
The company required a scalable solution for this drastic change and Amazon Redshift proved to be a critical partner in this situation.
Zynga – Doubled ETL to 5.8 TB for Daily Game Data
Zynga is one of the most popular social game development companies in the world. It is behind some really popular games such as Words with Friends, Zynga Poker, and Farmville. It has a user base of approximately 70 million active users in a month.
Considering the company’s mission which is to connect the entire world together, the company heavily relies on analytics. For this, they migrated their data warehouse to Amazon Redshift.
After the migration, Zynga was able to double the extract, transform, and enhance ETL performance. This enabled them to process up to 5.8 TB of daily generated game data.
Data that is used for long-term analysis and experiments to enhance the player’s experience.
Wrapping Up!
Multiple software development companies will tell you that data warehousing is the need of the hour. With terabytes of data being generated on a daily basis, companies are required to store and access this data real-time.
While creating an on-premise architecture requires deployment and continuous maintenance cost, services like Amazon Redshift are dynamic in nature both in terms of pricing and scalability.
Adding to it, they actively support their users and make it easier for them to migrate data at the snap of a finger.
