Cloud Data Infrastructure: How to Build a Scalable Foundation for AI.

AI projects do not run successfully due to ineffectual models but because there was no proper infrastructure to support it.

Have you ever encountered any of the following failures while working on your role as the Lead Data Engineer:

• Scaling restrictions with your AI models and data pipelines
• Disassociation of your teams and their data sources due to different data storage methodologies
• No definitive way to measure your AI project benefits against its associated costs (ROI)
• Models that are being trained using inaccurately defined data sets

Cloud data infrastructures become invaluable in this instance.

Cloud Data Infrastructure is more than just moving all your data to the cloud, it means creating a scalable, reliable, and AI Ready foundation to assist you with your data processing, machine learning (ML) and analytics initiatives.

The following breakdown will assist you with understanding:

• What Cloud Data Infrastructure means to you as a Data Engineer
• The foundation components of a Modern Cloud Data Infrastructure Architecture
• How to determine the best Cloud Service Provider for your company
• Ways you can lower your overall Cloud Data Infrastructure 'project' costs
• The Best Practices related to creating a scalable AI data architecture

Understanding Cloud Data Infrastructure (Cloud Data Infrastructure Definition)

Within Cloud Data Infrastructure, the focus is on the data and how it is processed, moved, stored, and secured - all of which reside within the Cloud Space.

How do We Define Cloud Data Infrastructure?

Cloud Data Infrastructure is a foundational architecture that provides the ability to run analytics, data platforms and AI pipelines in the cloud with efficiency.

Core Capabilities

• Self-service capabilities
• Data accessibility and autonomy for all users.
• Advanced analytics and machine learning capabilities
• Create sophisticated models quickly.
• Security at all levels within the system.

Cloud Data Infrastructure and Modern Workloads

AI workloads will require all the core components of cloud data infrastructure, including real-time data streams and distributed processing across different storage and compute systems.

Real-Time Data Processing

The main goal of inference is to have low latency.

Feature Engineering Pipeline

An important part of the success of a feature engineering pipeline is that it has a set of reusable and consistent features.

Some of the best data cloud solutions with AI capabilities include:

• Databricks
• Snowflake
• BigQuery

Each of the features of those platforms will have the following attributes:

• data processing
• machine learning
• analytics

Comparing Providers’ Data Infrastructure Solutions

Cloud platforms (AWS, Azure, GCP) that are publicly available have different capabilities for analytics.

If you’re comparing 1 cloud provider’s infrastructure to another provider’s infrastructure for 1 BIG DATA analytics solution, be sure to consider:

• Performance
• Cost
• Scalability
• Integration

Real-Time Data Ingestion in The Cloud

Why Real-Time ingestion of data is important

The uses cases for Real-Time ingestion include:

• Personalization
• Fraud Detection
• Operational Analytics

How does Real-Time Data ingestion work?

Real-Time Data ingestion works on:

• Event Streaming
• Message Queues
• Other Technologies

Technologies used

• Kafka
• Amazon Kinesis
• Google Cloud Pub/Sub

How to Optimize Cost for Cloud Data Infrastructure

Cost is a major concern for data executives.

Main Cost Factors

• Compute
• Storage volume
• Data transfer

Ways to Optimize your Cloud Storage Costs

• Tiered Storage
• Optimize data formats (e.g.: Parquet, ORC)
• Delete unused data

More Cost Optimization ideas

• Use Auto-Scaling
• Use Spot Instances
• Monitor all resource usage

How to access pricing

• AWS Pricing Calculator
• Azure Pricing Calculator
• Google Cloud Pricing Calculator

Bottom-line: Cost Optimization will be ongoing, not just once.

Best Practices for Security in Cloud Data Infrastructure

All data across various Cloud Service Providers (CSPs) will have an organized and secure method of storage.

Authentication and authorization management

Managing access control to data.

Encryption

1. Infrequently changing data
2. Changing data

Data security monitoring

Tracking recognition of threatening activities or anomalies.

Hybrid Cloud Data Security Best Practices

1. Centralized identity system(s)
2. Zero-trust architecture
3. Regular audits of access

Constraining and defining a cloud data infrastructure - the largest challenges

• Data warehouses
• Different teams have separate systems of course.
• Complicated systems
• Governance
• Skills

You need to have a certain level of competence in your organization when it comes to building and running cloud solutions.

Takeaways

In order to be successful in building the right infrastructure you must have both the technical and organizational team in alignment.

Building a scalable cloud data infrastructure - Step-by-step

Step 1: Defining your use cases

• Analytics
• Artificial Intelligence & Machine Learning
• Real-time processing

Step 2: Developing your architecture

• Lake-house
• Data Mesh
• Hybrid model

Step 3: Choose your tools

• Expertise
• How fast you will scale

Step 4: Your access governance

• Set data access rules
• Establish data standards

Step 5: Continue to optimize

Continually monitor performance and cost.

What should new ventures or startup companies consider

For new ventures or startups they should have the following top three objectives to consider:

This will facilitate a solution with more simplicity than traditional SQL or relational database systems.

This provides customers with lower cost to obtain and build their own solutions than traditional point-to-point systems.

This can allow for customers to take advantage of their pricing model to purchase a solution based upon the usage of the solution, giving them a better rate than they may have expected.

Cloud Data Infrastructure vs. Traditional Data Systems - Major Differences

Cloud: Faster innovation and less operation expense than using data centres.

The Future of Cloud Data Infrastructure

1. AI ready infrastructure
2. Automation
3. Unified data platforms
4. Treating data products as reusable items

Conclusion: the development of the foundation for Artificial Intelligence

For today's modern data engineering teams, the cloud-based data infrastructure should not be thought of as a layer of functionality only but ultimately as the foundation to enable the capability for future innovation and adopting AI capabilities.

The best cloud-based data architectures have the following:

• Scalability
• Cost-effective
• Secure
• AI ready

As the Data Engineering Lead responsible for designing data infrastructures that work not only for now, but also for the future use cases of your organisation's data, you must understand that your cloud data infrastructure is your business strategy in the age of artificial intelligence.

At Logiciel Solutions, we help enterprise customers create a scalable, cloud-based data infrastructure geared toward Artificial Intelligence-driven use cases. This includes working with our clients to design the right architecture and keep their overall infrastructure costs down.

If you want to build or develop your next data platform in the correct manner, this would be the time.