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.
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:
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:
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.
Some of the benefits of using AWS Auto Scaling include:
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AWS offers different options for scaling resources.
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.
By Ankit Kumar Ojha
By Uma Raj
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