The continuous improvement in machine learning algorithms has made data one of the key assets for businesses. Data is consumed in large volumes from data platforms and applications, creating a need for scalable storage and processing technologies to leverage this data.
This has led to the emergence of data mesh, a paradigm shift in modern data architecture that allows data to be considered a product. As a result, data architectures are being designed with distributed data around business domains with a focus on the quality of data being produced and shared with consumers.
Domain-Driven Design for Scalable Data Architecture
In the Domain Driven Design, or DDD, software design approach, the solution is divided such that the domains align with business capabilities, organizational boundaries, and software. This is a deviation from the traditional approach, where technologies are at the core of data architecture and not business domains.
Data mesh is a modern architectural pattern that can be built using a service such as AWS Lake Formation. The AWS modern data architecture allows architects and engineers to:
- Build scalable data lakes rapidly
- Leverage a broad and deep collection of purpose-built data services
- Be compliant by providing unified data access, governance, and security
Why You Need a Data Mesh
Businesses should be able to store structured and unstructured data at any scale, which can be available for different internal and external uses. Data lakes may require time and effort to ingest data and be unable to meet the varied and increasing business use cases. Often businesses try to cut costs and maximize value by planning one-time data ingestion into their data lake consuming it several times. But what they truly need is a scalable data lake architecture that scales. This adds value and provides continuous, real-time data insights to improve competitive advantage and accelerate growth.
By designing a data mesh on the AWS Cloud, businesses can experience the following benefits:
- Data sharing and consumption across multiple domains within the organization is simplified.
- Data producers can be onboarded at any time without the need for maintaining the entire data-sharing process. Data producers can continue with collecting, processing, storing, and onboarding data from their data domain into the data lake as and when needed.
- This can be done without incurring additional costs or management overhead.
- It assures security and consistency, thereby enabling external data producers also to be included and data shared with them in the data lake.
- Data insights can be gained continuously, in real-time, without disruptions
Features of AWS Data Architecture for Scalability
A data producer collects, processes, stores, and prepares for consumption. In the AWS ecosystem, the data is stored in Amazon Simple Storage Service (Amazon S3) buckets with multiple data layers if required. AWS services such as AWS Glue and Amazon EMR can be used for data processing.
AWS Lake Formation facilitates the data producer to share the processed data with the data consumer based on the business use cases. As the data produced grows, the number of consumers also increases. The earlier approach to managing this data-sharing manual is ineffective and prone to errors and delays. Developing an automated or semi-automated approach to share and manage data and access is an alternative, but also limited in effectiveness as it needs time and effort to design and build the solutions and also ensure security and governance. Over a period of time, it can become complicated and difficult to manage.
The data lake itself may become a bottleneck and not grow or scale. This will require redesigning and rebuilding the data lake to overcome the bottleneck and lead to increased utilization of cost, time, and resources.
This hurdle can be overcome using AWS Auto Scaling, which monitors applications and provides a predictable and cost-effective performance through automatic adjustment of capacity. It has a simple and powerful user interface that enables building plans for scaling resources such as Amazon ECS tasks, Amazon EC2 instances, Amazon DynamoDB tables and indexes, Amazon Aurora Replicas, and Spot Fleets. It provides recommendations for optimizing performance and costs. Users of Amazon EC2 Auto Scaling can combine it with AWS Auto Scaling to scale resources used by other AWS services as needed.
Benefits of AWS Auto Scaling
Some of the benefits of using AWS Auto Scaling include:
- Quick Setup for Scaling: A single, intuitive interface allows the setting of target utilization levels for different resources. A centralized control negates the need for navigating to other consoles.
- Improves Scaling Decisions: By building scaling plans using AWS Auto Scaling, businesses can automate the use of different resources by different groups based on demand. This helps with balancing and optimizing performance and costs. With AWS Auto Scaling, all scaling policies and targets can be created automatically based on need, adding or removing capacity in real time based on changes in demand.
- Automated Performance Management: AWS Auto Scaling helps to optimize application performance and availability, even in a dynamic environment with unpredictable and constantly changing workloads. By continuously monitoring applications, it ensures optimal performance of applications, increasing the capacity of constrained resources during a spike in demand to maintain the quality of service.
- Pay Per Use: Utilization and cost efficiencies of AWS services can be optimized as businesses pay only for the resources they need.
Indium to Enable Modern Data Architecture on AWS Ecosystem
Indium Software has demonstrated capabilities in AWS ecosystem, having delivered more than 250 data, ML, and DevOps solutions in the last 10+ years.
Our team consists of more than 370 data, ML, and DevOps consultants, 50+ AWS-certified engineers, and experienced technical leaders delivering solutions that break barriers to innovation. We work closely with our customers to deliver solutions based on the unique needs of the business.
How can I scale AWS resources?
AWS offers different options for scaling resources.
- Amazon EC2 Auto Scaling ensures access to the correct number of Amazon EC2 instances for handling the application load.
- Application Auto Scaling API that allows defining scaling policies for automatic scaling of AWS resources. It also allows scheduling scaling actions on a one-time or recurring basis.
- AWS Auto Scaling facilitates the automatic scaling of multiple resources across multiple services.
What is a scaling plan?
The collection of instructions for scaling for different AWS resources is called a scaling plan. Two key parameters for this are resource utilization metric and incoming traffic metric.