AWS Data Migration Service (DMS) is a Database Migration service provided by Amazon. Using DMS, you can migrate your data from one Database to another Database. It supports both, Homogeneous and Heterogeneous Database Migration. DMS also supports migrating data from the on-prem Database to AWS Database services.
As a fully managed service, Amazon Aurora saves you time by automating time-consuming operations like provisioning, patching, backup, recovery, and failure detection and repair.
Amazon Redshift is a cloud-based, fully managed petabyte-scale data warehousing service. Starting with a few hundred gigabytes of data, you may scale up to a petabyte or more. This allows you to gain fresh insights for your company and customers by analyzing your data.
In this article, you will be introduced to AWS DMS. You will understand the steps to load data from Amazon Aurora to Redshift using AWS DMS. You also explore the pros and cons associated with this method. So, read along to gain insights and understand the loading of data from Aurora to Redshift using AWS DMS.
What is Amazon Aurora?
Amazon Aurora is a popular database engine with a rich feature set that can import MySQL and PostgreSQL databases with ease. It delivers enterprise-class performance while automating all common database activities. As a result, you won’t have to worry about managing operations like data backups, hardware provisioning, and software updates manually.
Amazon Aurora offers great scalability and data replication across various zones thanks to its multi-deployment tool. As a result, consumers can select from a variety of hardware specifications to meet their needs. The server-less functionality of Amazon Aurora also controls database scalability and automatically upscales or downscales storage as needed. You will only be charged for the time the database is active in this mode.
Key Features of Amazon Aurora
Amazon Aurora’s success is aided by the following features:
- Exceptional Performance: The Aurora database engine takes advantage of Amazon’s CPU, memory, and network capabilities thanks to software and hardware improvements. As a result, Aurora considerably exceeds its competition.
- Scalability: Based on your database usage, Amazon Aurora will automatically scale from a minimum of 10 GB storage to 64 TB storage in increments of 10 GB at a time. This will have no effect on the database’s performance, and you won’t have to worry about allocating storage space as your business expands.
- Backups: Amazon Aurora offers automated, incremental, and continuous backups that don’t slow down your database. This eliminates the need to take data snapshots on a regular basis in order to keep your data safe.
- High Availability and Durability: Amazon RDS continuously monitors the health of your Amazon Aurora database and underlying Amazon Elastic Compute Cloud (Amazon EC2) instance. In the event of a database failure, Amazon RDS will automatically resume the database and associated activities. With Amazon Aurora, you don’t need to replay database redo logs for crash recovery, which cuts restart times in half. Amazon Aurora also isolates the database buffer cache from the database process, allowing it to survive a database restart.
- High Security: Aurora is integrated with AWS Identity and Access Management (IAM), allowing you to govern what your AWS IAM users and groups may do with specific Aurora resources (e.g., DB Instances, DB Snapshots, DB Parameter Groups, DB Event Subscriptions, DB Options Groups). You can also use tags to restrict what activities your IAM users and groups can take on groups of Aurora resources with the same tag (and tag value).
- Fully Managed: Amazon Aurora will keep your database up to date with the latest fixes. You can choose whether and when your instance is patched with DB Engine Version Management. You can manually stop and start an Amazon Aurora database with a few clicks. This makes it simple and cost-effective to use Aurora for development and testing where the database does not need to be up all of the time. When you suspend your database, your data is not lost.
- Developer Productivity: Aurora provides machine learning capabilities directly from the database, allowing you to add ML-based predictions to your applications using the regular SQL programming language. Thanks to a simple, efficient, and secure connectivity between Aurora and AWS machine learning services, you can access a wide range of machine learning algorithms without having to build new integrations or move data around.
What is Amazon Redshift?
Amazon Redshift is a petabyte-scale data warehousing service that is cloud-based and completely managed. It allows you to start with a few gigabytes of data and work your way up to a petabyte or more. Data is organised into clusters that can be examined at the same time via Redshift. As a result, Redshift data may be rapidly and readily retrieved. Each node can be accessed individually by users and apps.
Many existing SQL-based clients, as well as a wide range of data sources and data analytics tools, can be used with Redshift. It features a stable architecture that makes it simple to interface with a wide range of business intelligence tools.
Each Redshift data warehouse is fully managed, which means administrative tasks like backup creation, security, and configuration are all automated.
Because Redshift was designed to handle large amounts of data, its modular design allows it to scale easily. Its multi-layered structure enables handling several inquiries at once simple.
Slices can be created from Redshift clusters, allowing for more granular examination of data sets.
Key Features of Amazon Redshift
Here are some of Amazon Redshift’s important features:
- Column-oriented Databases: In a database, data can be organised into rows or columns. Row-orientation databases make up a large percentage of OLTP databases. In other words, these systems are built to perform a huge number of minor tasks such as DELETE, UPDATE, and so on. When it comes to accessing large amounts of data quickly, a column-oriented database like Redshift is the way to go. Redshift focuses on OLAP operations. The SELECT operations have been improved.
- Secure End-to-end Data Encryption: All businesses and organisations must comply with data privacy and security regulations, and encryption is one of the most important aspects of data protection. Amazon Redshift uses SSL encryption for data in transit and hardware-accelerated AES-256 encryption for data at rest. All data saved to disc is encrypted, as are any backup files. You won’t need to worry about key management because Amazon will take care of it for you.
- Massively MPP (Multiple Processor Parallelization): Redshift, like Netezza, is an MPP appliance. MPP is a distributed design approach for processing large data sets that employs a “divide and conquer” strategy among multiple processors. A large processing work is broken down into smaller tasks and distributed among multiple compute nodes. To complete their calculations, the compute node processors work in parallel rather than sequentially.
- Cost-effective: Amazon Redshift is the most cost-effective cloud data warehousing alternative. The cost is projected to be a tenth of the cost of traditional on-premise warehousing. Consumers simply pay for the services they use; there are no hidden costs. You may discover more about pricing on the Redshift official website.
- Scalable: Amazon Redshift, a petabyte-scale data warehousing technology from Amazon, is scalable. Redshift from Amazon is simple to use and scales to match your needs. With a few clicks or a simple API call, you can instantly change the number or kind of nodes in your data warehouse, and scale up or down as needed.
What is AWS Data Migration Service (DMS)?
Using AWS Data Migration Service (DMS) you can migrate your tables from Aurora to Redshift. You need to provide the source and target Database endpoint details along with Schema Names. DMS uses a Replication Instance to process the Migration task. In DMS, you need to set up a Replication Instance and provide the source and target endpoint details. Replication Instance reads the data from the source and loads the data into the target. This entire processing happens in the memory of the Replication Instance. For migrating a high volume of data, it is recommended to use Replication Instances of higher instance classes.
To explore more about AWS DMS, visit here.
Why Move Data from Amazon Aurora to Redshift?
Aurora is a row-based database, therefore it’s ideal for transactional queries and web apps. Do you need to check for a user’s name using their id? Aurora makes it simple. Do you want to count or average all of a user’s widgets? Redshift excels in this area. As a result, if you want to utilize any of the major Business Intelligence tools on the market today to analyze your data, you’ll need to employ a data warehouse like Redshift. You can use Hevo for this to make the process easier.
Methods to Move Data from Aurora to Redshift
You can easily move your data from Aurora to Redshift using the following 2 methods:
Method 1: Move Data from Aurora to Redshift Using Hevo Data
Hevo Data, an Automated Data Pipeline helps you directly transfer data from Aurora to Redshift in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. You can seamlessly ingest data from your Amazon Aurora PostgreSQL database using Hevo Pipelines and replicate it to a Destination of your choice.
While you unwind, Hevo will take care of retrieving the data and transferring it to your destination Warehouse. Unlike AWS DMS, Hevo provides you with an error-free, fully managed setup to move data in minutes. You can check a detailed article to compare Hevo vs AWS DMS.
Refer to these documentations for detailed steps for integration of Amazon Aurora to Redshift.
The following steps can be implemented to connect Aurora PostgreSQL to Redshift using Hevo:
- Step 1) Authenticate Source: Connect Aurora PostgreSQL as the source to Hevo’s Pipeline.
- Step 2) Configure Destination: Configure your Redshift account as the destination for Hevo’s Pipeline.
Check out what makes Hevo amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Auto Schema Mapping: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data from Aurora PostgreSQL files and maps it to the destination schema.
- Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use for aggregation.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
With continuous Real-Time data movement, Hevo allows you to combine Aurora PostgreSQL data along with your other data sources and seamlessly load it to Redshift with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial!
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Method 2: Move Data from Aurora to Redshift Using AWS DMS
Using AWS DMS, perform the following steps to transfer your data from Aurora to Redshift:
- Step 1: Let us create a table in Aurora (Table name redshift.employee). We will move the data from this table to Redshift using DMS.
- Step 2: We will insert some rows in the Aurora table before we move the data from this table to Redshift.
- Step 3: Go to the DMS service and create a Replication Instance.
- Step 4: Create source and target endpoint and test the connection from the Replication Instance.
Once both the endpoints are created, it will look as shown below:
- Step 5: Once Replication Instance and endpoints are created, create a Replication task. The Replication task will take care of your migration of data.
- Step 6: Select the table name and schema, which you want to migrate. You can use % as wildcards for multiple tables/schema.
- Step 7: Once setup is done, start the Replication task.
- Step 8: Once the Replication task is completed, you can see the entire details along with the assessment report.
- Step 9: Now, since the Replication task has completed its activity, let us check the data in Redshift to know whether the data has been migrated.
As shown in the steps above, DMS is pretty handy when it comes to Replicating data from Aurora to Redshift but it requires performing a few manual activities.
Pros of Moving Data from Aurora to Redshift using AWS DMS
- Data movement is secure as Data Security is fully managed internally by AWS.
- No Database downtime is needed during the Migration.
- Replication task setup requires just a few seconds.
- Depending upon the volume of Data Migration, users can select the Replication Instance type and the Replication task will take care of migrating the data.
- You can migrate your data either in Full mode or in CDC mode. In case your Replication task is running, a change in the data in the source Database will automatically reflect in the target database.
- DMS migration steps can be easily monitored and troubleshot using Cloudwatch Logs and Metrics. You can even generate notification emails depending on your rules.
- Migrating data to Redshift using DMS is free for 6 months.
Cons of Moving Data from Aurora to Redshift using AWS DMS
- While copying data from Aurora to Redshift using AWS DMS, it does not support SCT (Schema Conversion Tool) for your Automatic Schema conversion which is one of the biggest demerits of this setup.
- Due to differences in features of the Aurora Database and Redshift Database, you need to perform a lot of manual activities for the setup i.e. DMS does not support moving Stored Procedures since in Redshift there is no concept of Stored Procedures, etc.
- Replication Instance has a limitation on storage limit. It supports up to 6 TB of data.
- You cannot migrate data from Aurora from one region to another region meaning both the Aurora Database and Redshift Database should be in the same region.
Overall the DMS approach of replicating data from Aurora to Redshift is satisfactory, however, you need to perform a lot of manual activities before the data movement. Few features that are not supported in Redshift have to be handled manually as SCT does not support Aurora to Redshift data movement.
In a nutshell, if your manual setup is ready and taken care of you can leverage DMS to move data from Aurora to Redshift. You can also refer to our other blogs where we have discussed Aurora to Redshift replication using AWS Glue and AWS Data Pipeline.
Hevo Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. Hevo caters to 100+ data sources (including 40+ free sources) and can seamlessly transfer your data from Aurora PostgreSQL to Redshift within minutes. Hevo’s Data Pipeline enriches your data and manages the transfer process in a fully automated and secure manner without having to write any code. It will make your life easier and make data migration hassle-free.
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