Amazon Redshift is one of the most prominent data warehousing leaders across companies of all industries and sizes, providing applications in analytics, reporting, business intelligence, etc.
Using Amazon redshift will allow you to retrieve, compare, and evaluate large amounts of data in multiple-stage operations to deliver the desired outcome. Forming part of the giant cloud-computing platform Amazon Web Services, this fully-managed petabyte-scale data warehouse enables high performance and efficient storage thanks to a combination of characteristics we'll discuss through this article.
Table of Content:
- Amazon Redshift Overview and new software developers transition guide.
- How to design and build your database
- How to Load and query your data
- How to maintain and manage your database?
- What are the best practices for developing your data warehouse's databases?
- How Can Intermix Help you?
Enjoying This Article?
Receive great content weekly with the Xplenty Newsletter!
Amazon Redshift Overview and new software developers transition guide.
There are seven major components of Amazon Redshift's architecture.
- Data applications
You can use Amazon Redshift with several data applications: ETLs, analytics, and business intelligence reporting tools. Additionally, if you are used to writing SQL, then Redshift will be no harder to use since it is based on industry-standard Postgres, so it shares a lot of similarities with Postgres, and its query language is significantly similar to SQL. Thus, you can connect to the client applications by using industry-standard JDBC and ODBC drivers for PostgreSQL
Clusters are one of the fundamental components in Redshift's architecture. It is composed of two types of nodes: Leader node and compute node, which we'll detail below. If a cluster has more than a single compute node, it also has a leader node that handles the different interactions and data applications communications.
- Leader nodes
The leader node parses and develops query execution plans that enable the compiled code's distribution to the compute nodes and assigns data to each compute node. Only compute nodes with referenced tables get involved in the execution. Otherwise, all other queries run exclusively on the leader node.
- Compute nodes
Amazon Redshift's cluster contains at least one compute node. This type of node executes the assigned compiled code and returns the results to leader nodes to be aggregated.
- Node slices
Slices are partitions of compute nodes with an allocated percentage of the node's memory and disk space. The slices concept helps with workload management, where each slice works in parallel to process a part of the node's workload.
- Internal network
Redshift creates a separate, isolated, and high-speed internal network for the cluster's nodes to communicate and perform their operations, benefiting from the proximity and high-bandwidth connections.
Amazon Redshift is an enterprise-class relational database query and management system, thus the compatibility with other RDBMS applications. Besides allowing traditional OLTP functions, Redshift is built for high-performance analysis and reporting of huge datasets. Each Redshift cluster is composed of one or more databases.
You can read more on AWS Redshift Architecture: Clusters & Nodes & Data Apps, oh my!
How to design and build your database
Data understanding is the main and crucial step that will set up your database design on a solid foundation. Based on your data understanding, you can leverage many circumstances to your advantage as well as being able to make informed decisions and a clear implementation vision. Data knowledge helps you set control over various parameters, including your expected data storage and even how well queries perform. It's important to take time to fully understand your data and the existing relationships before you go on to building your queries.
Prior to creating your database, you need to set up your amazon redshift's cluster and ensure that everything is up and running and well connected to your SQL client tool. A single cluster can host multiple databases.
Let's say you want to build a database for your e-commerce business; first, you'll need to connect to your initial database created when you launched your cluster, and you want to execute the following command:
create database e-commerce;
You can use any name for your database with a limit of 127 bytes names that contain no reserved words. The e-commerce database you've created is only accessible by the master user created when you launched Redshift's cluster. You can add other users' accounts by executing the CREATE USER command.
For example, to create a user named manager with password ProductsManager, issue the following command:
create user manager password 'ProductsManager';
Integrate Your Data Today!
Try Xplenty free for 14 days. No credit card required.
How to load your data and query your database?
- Load data by yourself
At this point, you have a database called e-commerce, and you are connected to it. Now you'll need to create your tables and then load your data.
Let's create a customers table:
create table customers( customerid integer not null distkey sortkey, username char(8), firstname varchar(30), lastname varchar(30), city varchar(30), state char(2), email varchar(100), phone char(14));
You can use the same syntax to create the rest of your tables, then use the COPY command to load your tables with data stored in an Amazon S3 bucket, DynamoDB, etc. Data is usually loaded into AWS Redshift via the COPY command, which leverages the Amazon Redshift massively parallel processing architecture to read and write files in parallel.
- Partner with a data integration service
There are many ETL and data integration services that can enable you to load your data to Redshift easily and without worrying about development time and issues. Some recommended industry leaders are Xplenty, Stitch, Talend, etc.
After loading your data, you will be able to query your database using the standard SQL commands, including SELECT, INSERT, and DROP.
How to maintain and manage your database?
Database management and maintenance might not be the most exciting part of the process, yet it is one of the essential parts of an effective database implementation. This step is an iterative process that enforces many aspects of your implementation; it includes:
- Setting a backup and recovery plan.
- Constantly maintaining your tables.
- Efficiently managing your workloads.
- Regularly optimizing your queries.
This step is what ensures you'll get the desired outcomes out of your database implementation as well as an increased performance that benefits from the leading factors behind Redshift's increased performance, which are:
- Massively parallel processing,
- columnar data storage
- targeted data compression encoding schemes.
What are the best practices for developing your data warehouse's databases?
To maximize query performance, these recommendations can assist you with your Redshift's journey:
- When you design your tables: let COPY choose compression encodings, define the primary key and foreign key constraints, use the smallest possible column size, use date/time data types for date columns, etc.
- If you load data: use a COPY command to load data, use a single COPY command to load from multiple files, split your load data into multiple files, use a staging table to perform a merge, etc.
- When you design queries: Avoid using select all, use a CASE expression to perform complex aggregations, don't use cross-joins unless absolutely necessary, use a WHERE clause to restrict the dataset if possible, add predicates to filter tables that participate in joins, etc.
How Can Intermix Help you?
Enjoying This Article?
Receive great content weekly with the Xplenty Newsletter!
Intermix is performance management for data teams to optimize their Amazon Redshift cluster for cost and performance. The AWS Console is good for real-time monitoring of cluster health metrics. With intermix.io, you get historical trends and the ability to drill-down into individual users and models. Intermix enables you to gain insights and make informed decisions to scale the cluster efficiently and truly understand the end-user experience.