AWS Redshift is one of the most popular data warehousing solutions in the market today. It is capable of handling data on an exabytes scale. It can be used for data warehouse use cases like processing real-time analytics, combining multiple data sources, log analysis, etc. Redshift can execute operations on an enormous volume of data at lightning speed. This is made possible by its Massively Parallel Processing (MPP) technology. Its MMP technology enables users to manage massive data volumes efficiently. At the same time, it costs only a fraction of what platforms like Oracle, Azure, IBM, Teradata, etc., charge for comparable products.
AWS RedShift has a plethora of benefits and features to offer. But, to determine whether it is suitable for your application infrastructure, you have to make a well-informed decision as changing your data warehousing platform at a later stage would prove to be extremely cumbersome and time-consuming. So here is a detailed understanding of AWS RedShift to help you make the right choice for your business.
What is AWS RedShift and Why is it so special?
AWS Redshift is a data warehousing platform that enables applications to process structured and unstructured data. It is built by Amazon Web Solutions (AWS), a data warehousing solution from Amazon Web Services. Redshift is way ahead of its time when compared to competitors due to its ability to handle huge volumes of data. It possesses the capability to process structured and unstructured data in the range of exabytes. The service is usually used for applications which require large-scale data warehousing capabilities. At times, developers also use the service for large-scale data migrations needed in digital transformation or app modernization processes.
Just like all the other AWS services, RedShift can be deployed with just a few clicks. The major highlight of RedShift is that it encrypts all the data to prevent security breaches. Applications that use RedShift for data warehousing have excellent capabilities to generate valuable insights from a large amount of data. Applications that require extensive data and Machine Learning solutions usually have RedShift as a part of their infrastructure. Despite all these complexities that it handles, RedShift has an easy-to-use interface that is similar to the rest of the AWS services. It enables users to start a new cluster in just a couple of minutes, and infrastructure deployment and management are a piece of cake.
Redshift possesses a column-oriented OLAP-style (Online Analytical Processing) database. Its infrastructure is based on PostgreSQL version 8.0.2. This enables users to process regular SQL queries through Redshift as well. But this is just a cherry on the top of all RedShift features. The fast delivery to queries on large-scale databases is the stand-out feature of Redshift.
RedShift enables fast querying and response through Massively Parallel Processing design (MPP). MPP is a design that involves connecting a large number of computer processors in parallel to increase the processing capabilities of the system manifold while using less power and time. Computations that usually take hours to complete can be done within seconds through the MPP system. When all these processors are interconnected through the cloud, processors located across multiple servers are used to deliver a process.
The Benefits of Using AWS Redshift
AWS RedShift is an extremely powerful solution for data warehousing and processing. Apart from that, it costs only a fraction of the cost of its competitors like Teradata and Oracle. This is a significant advantage for most users. In addition to cost, there are several other benefits to using Redshift.
The MPP technology used in RedShift is one-of-a-kind. Its capability to deliver speedy outputs on large data sets is unparalleled. At present, there is no cloud service provider that can match the processing speed offered by RedShift at the same costs. There are solutions that provide higher processing speeds, but they cost almost 12 times more than RedShift. Additionally, the processing speed offered by RedShift is efficient and more than adequate for the data processing requirements of enterprise applications.
Data Encryption and Security
Data encryption is a default capability for any RedShift operation. Users decide which operations need encryption and those that don’t. AWS also provides an additional layer of security for the platform. Amazon provides provisions like access control and an option for a virtual private cloud.
Use Your Own Tools
RedShift is based on PostgreSQL. So, all operations are query-based. Needless to say that all types of SQL queries are compatible with it. You can integrate any BI tool with RedShift seamlessly.
Intelligent Optimization of Data
When the dataset is humongous, there are multiple ways to query data even for the same set of parameters. Each method and command will have a different level of data utilization. RedShift offers tools and suggestions to get the most out of your data. It helps you improve your queries and also provides tips to improve your database. This will help you further speed up your operations.
No matter how smart our systems become, there are always going to be a few monotonous, repetitive tasks that we have to perform on a daily basis, to keep the systems running smoothly. RedShift offers functionalities to help you eliminate the need for repetitive manual tasks. You can set processes within RedShift and automate the repetition as required. These tasks are mainly administrative in nature like generating daily, weekly, or monthly reports, resource and cost auditing, and regular maintenance tasks to clean up your data.
Concurrent Scaling and Query Volume Management
RedShift has an added capability of automatically scaling up and scaling down to support concurrent workloads. As a result, there is zero downtime during sudden increases in workloads. Enterprises usually opt for pay-as-you-use cloud services, as scaling down when additional cloud space is not required to save costs.
The MPP technology is extremely efficient in handling thousands of queries simultaneously. So, when there are several concurrent workloads, the MPP technology will ensure the query response time does not slow down the system in any shape or form. It will dynamically allocate processing and memory resources to handle higher demand.
AWS Integration and RedShift APIs
The critical feature of any solution is interoperability and RedShift excels in this aspect. It has a robust API supported by extensive and detailed documentation. As a result, data engineers can seamlessly integrate RedShift with any platform.
As RedShift is an Amazon solution, it comes with pre-built integrations with all AWS tools and services. So, users can effortlessly set up integrations with all AWS tools when required.
Being an AWS solution, RedShift comes with its own perks. RedShift comes with easy deployment capabilities, just like all AWS tools. A RedShift cluster can be deployed in any part of the world from anywhere within minutes. Deploying RedShift does not require additional skills, and there is no learning curve involved. Any IT professional with just a little experience with cloud deployments could deploy RedShift clusters.
Data backup is another default functionality in AWS RedShift. It automatically backs up data at regular intervals for disaster recovery. The backups ensure business continuity without downtime in the event of any faults, failures, or data corruption. In addition, these backups are spread across different locations. So this eliminates the risk of location-based failures as well.
When to use Amazon Redshift?
Amazon Redshift is used when the data to be analyzed is humongous. The MPP technology used to design RedShift can be leveraged only at scale. For RedShift to be a suitable solution, your data warehousing needs have to be at least at a petabyte-scale. Sure, you can use RedShift for smaller data requirements, but the time and costs involved in building the supporting infrastructure and design for RedShift are viable only when the size of the database is large. Using RedShift for smaller databases is like using a sword to slice tomatoes for your sandwich.
Apart from processing huge amounts of data, there are other capabilities that make RedShift an ideal choice for certain use cases. It is essential to understand when it is preferable to make RedShift a part of your application infrastructure and when it is not. Choosing a data warehousing solution is not a decision one can alter in a day.
Here are some ideal use cases where AWS RedShift could be your best choice.
Applications today need to make quick decisions based on real-time data available to them and offer quick solutions based on that data. This is called event-centric architecture. The data collected by most applications is not meant just to be stored. Users today expect an immediate service based on real-time data and their preferences.
For example, consider a food order and delivery application. The application will have to suggest dishes and restaurants based on the users’ preferences. This will require users’ historical data of past orders. At the same time, the application will have to check which of the restaurants are delivering food in the user’s location and how much time it will take to deliver. Here, real-time analytics come in – the traffic on the road, order surge at that time, the distance between the restaurant and the user’s location, what route the delivery partner should take to avoid traffic, and many more. All this data has to be calculated quickly in real-time, including the delivery cost with the surge charges during peak hours. The same situation is applicable for cab service applications like Uber.
So, if you have an application that requires a lot of historical data analytics as well as real-time analytics, RedShift is the go-to solution for you.
Combining multiple data sources
Usually, there are three types of data in any database – structured data, unstructured data, and semi-structured data. Simple applications usually store semi-structured data and structured data, which is adequate for their functioning. But for large-scale and complex applications, there is unstructured data present as well. When multiple types of data are present in the database, the application needs to process all of them separately to gain better insights. Unfortunately, business Intelligence (BI) tools lack the capability to handle varied data types from different sources. Amazon RedShift does not have this limitation. It can process all forms of data simultaneously in real-time. So, if your application requires processing a lot of data of different types from multiple sources, it is advisable to choose RedShift for your infrastructure.
Business Intelligence Capabilities
Data collected by the application is handled by a lot of different people in the organization, not necessarily engineers or data scientists. They will not be familiar with the data science tools and methods. So, they usually rely on detailed reports or information dashboards with an easy-to-use interface. If such business intelligence capabilities fall under your requirements, you can use AWS RedShift as it offers seamless integrations with popular BI tools like Amazon QuickSight and other third-party tools.
In the example of a food delivery application, the application is required to use historical user data for behavior analysis. Every time a user places an order through the application, the application automatically stores the data. The data is then analyzed to offer better food preferences and restaurant suggestions. For example, the user’s delivery address, preferred food cuisines, usual time of ordering lunch and dinner, etc., are forecasted based on the collected data. This is called behavior analytics. It provides reliable information about how the user uses the application, how they interact with it, their duration of usage, what they click on, and a lot more.
The data for behavioral analytics is collected from multiple sources and devices, including the web application used on the desktop, tablet, and mobile application. All the collected data is then aggregated and analyzed to gain behavioral insights. This requires coalescing complex datasets and computing a lot of data. If you require behavioral analytics as a part of your application, RedShift is the way to go.
You can also use RedShift for traditional data warehousing. But considering the capabilities of RedShift, it would be a complex solution for a simple problem. Simpler data warehousing solutions would likely be better suited for that.
Implementing the AWS Redshift Deployment system
Deploying RedShift clusters is extremely simple. But, while implementing the AWS RedShift deployment pipeline, the main challenge is to ensure that the new database deployment pipeline does not trip existing ones due to the changes in API/UI. Therefore, it is preferable to choose an SQL-based source code deployment approach that is straightforward in design. You can also leverage an AWS codepipeline designed explicitly for database changes as it would help maintain the same code-base across different environments and is easy to debug. In addition, it allows each newly added change to be maintained in a separate location so that changes can be recorded and rolled back or deleted in the future if needed (similar to API/UI changes).
The first step towards solving this problem was maintaining the .SQL changes in a separate folder from the API/UI changes. This allows the AWS codePipeline to deploy the SQL changes directly to the database without affecting other API/UI changes and yielding zero downtime for changes without any dependency on API/UI.
This design involves minimal code in the SQL-based repository and few basic AWS components. Take a look at how the entire solution works.
A quick overview of the key components
- Script – Migration-version generator:
We wrote a script that generates a .SQL file with the file-name as the current-time (preferably in milliseconds). It also contains a query that inserts a record into the “migration-version” table containing the newly created file-name and the current time-stamp (to help us differentiate later on when this migration was created and executed). The main reason to hold the migrations in a table is to keep track of the migration scripts that are executed on a DB, time of execution and that are not yet executed.
This is used in detecting code changes (specifically the folder consisting the .SQL files) and triggering the code-pipeline when changes are being done. The new SQL files (from step-1) are picked up by the code-pipeline as an artifact to pass it to the lambda (step-3) where the contents in the files are executed. An AWS-CodeBuild setup includes the buildspec.yml file where the folder to be monitored and the artifacts to be taken are mentioned.
- AWS-Lambda Function:
The lambda executes the statements/queries from the .SQL file received as code-build artifacts. It does so in a jiffy and hence is extremely short-lived. It has a connection established with the target database and parses the file contents as raw strings. These strings are executed against the DB, waits to collect the returned results and logs it in cloudwatch.
The above architecture achieves the requirement as follows:
- The Migration-version generator (a python script file) creates a new file with the current timestamp and loads the file into the desired folder (folder marked for SQL deployment).
- Any commit/merge/push to the repository (any repository supported by the AWS code-build such as GitHub, BitBucket, GitLab, etc,.) are picked up immediately by the AWS-CodeBuild with the help of buildspec file, and this in turn starts the code-pipeline.
- The code-pipeline picks up the files added/changed in the recent deployment and pases it to the lambda function as an artifact.
- The Lambda function reads the .SQL file, parses it to pick up the individual SQL statements and runs them against the target DB.
- The results of the SQL execution are logged in cloudwatch and also returned as lambda-response. It is to be noted that the SQLs being executed will also contain the INSERT query into the migration-version table.
This way, the files that have completed/failed execution for any DB can be identified from the entries available in the migration-version table. The SQL changes that are no longer needed can be added as a rollback query in a new .SQL file and executed (example, if a view created long ago needs to be dropped, then add a “Drop View view_name” statement in new file) or the existing file can directly be deleted (if it was not previously invoked).
Advantages of this design
- Zero downtime for the DB server
- Takes only a couple of seconds to complete the deployment
- API/UI and DB are uncoupled and hence one can be deployed without affecting the other system
- Simple architecture that is easy to maintain and debug