The shift towards big data has created a need for tools that can handle big database management. Big data has also ushered in the rise of cloud computing platforms. Of the cloud providers offering products and services in this market, Amazon Web Services is at the top of the charts. This is because Amazon Web Services (AWS) has become a fixture among data professionals for machine learning, artificial intelligence, business analytics, and database management.
With dozens of data science tools to choose from, AWS is a one-stop-shop for beginners and more experienced practitioners within the realm of data science, database design, and development. Amazon Redshift is the premier product for creating data warehouses within Amazon Web Services (AWS). Read on to learn more about how to incorporate Amazon Redshift into your data science toolkit.
What is Amazon RedShift?
Amazon Redshift is an AWS database management service that provides storage of data through SQL relational databases. In contrast to other products within the Amazon ecosystem, such as Amazon RDS, Redshift was developed for the creation and maintenance of data warehouses and data lakes. Instead of storing data in one database, data warehouses allow Data Scientists to work with multiple databases that share a data type. Data lakes allow you to work with databases that include different data types.
Data warehouses and lakes are useful for businesses and data science teams that need to access their data storage system from multiple computers or workplaces. Amazon Redshift ensures that data stored in the cloud is made readily available and accessible. It allows users to easily query across databases, add new nodes, and migrate data within the system. Redshift ML incorporates automation and artificial intelligence by allowing users to train machine learning models with the SQL programming language.
When Should You Use Amazon Redshift?
Amazon Redshift is most commonly used for big database management. Big database management utilizes a collection of databases to hold a certain type of data, or for a specific purpose. Big database management makes it easier to add storage to your database management system over time.
When working on a new project that is anticipated to grow in volume, size, and/or data types, it is important to use tools that can grow with the project. Big database management systems like Amazon Redshift are not considered to be suitable for small data, or any projects that have less than a hundred gigabytes, as the system can go up to a petabyte or more of storage.
For businesses that do require large data storage capabilities, Amazon Redshift is a great option. Amazon Redshift offers all of the benefits of cloud systems and big databases, making it suitable data science professionals and companies shifting into cloud computing. It’s also useful for incorporating cloud-based nodes into an already-established data warehouse or lake.
Getting Started with Amazon Redshift
Now that you have some background on Amazon Redshift, you can start using it by signing up for an AWS account. AWS currently offers a free tier of their products, which is an excellent way to get an introduction to Redshift and other database management tools from Amazon. If you already have an account with AWS or need to work with this system within a company or organization, then you can get started with Redshift by creating an account and also determining the cybersecurity settings for your computer system or network (firewalls, password protection, etc). Then you can begin connecting the clusters that you want to work within Redshift.
Within Amazon Redshift, clusters act as a method of organizing your databases, with each database in your data warehouse acting as a cluster, or node, in the system. Once you have identified the clusters that you plan on working with, you can begin learning the different features within the database management system. Redshift allows you to back up your data with replacement nodes, so getting started with the system means making a plan for how to back up that data in case of a system failure or database migration. Once a database management plan has been established, you are ready to keep working with this cloud-based computing system or explore other Amazon Web Services!
Interested in learning more about Amazon Web Services?
As one of the most popular cloud providers on the market, AWS is known for its database management systems and services for Data Scientists, business analysts, and Software Engineers. Noble Desktop offers a course on Cloud Computing with AWS for students and professionals who want to start learning Amazon Web Services. This course focuses on the role of cybersecurity in the development and maintenance of these popular database management systems.
Noble Desktop’s data science classes offer additional instruction around cloud computing, product development, and programming languages that correspond to working with database management systems. The Data Science Certificate includes training in Python and SQL to create a holistic approach to learning data science. Especially when working with Amazon Redshift, combining Python and SQL is instructive in collecting and organizing data across databases. Beginners in the field can use an introduction to AWS in order to further their overall data skills.