A Guide to Manage Amazon Machine Image: From Cloud to the On-Premises

Published on Feb 22, 2023

Why do people opt for on-premise storage

Uploading an Amazon Machine Image (AMI) to Amazon Simple Storage Service (S3) and downloading it to your on-premises machine can be useful for creating backups, sharing images with others, or moving images between regions. In this article, we will explain the process of uploading an AMI to S3 and downloading it to your data center, how to create an AMI from an on-premises backup, and how to launch an instance from that AMI.

Benefits of maintaining AMI on-premise data center

Compliance and security: Some organizations are required to keep specific data within their data centers for compliance or security reasons. Keeping AMIs in an on-premises data center allows them to maintain control over their data and ensure that it meets their compliance and security requirements.

Latency and bandwidth: Keeping AMIs in an on-premises data center can reduce the latency and bandwidth required to access the images since they are stored closer to the instances that will use them. This can be especially beneficial for firms with high traffic or large numbers of instances and also to avoid data transfer charges.

Cost savings: By keeping AMIs in an on-premises center, organizations can avoid the costs associated with storing them in the cloud. This can be especially beneficial for companies with large numbers of images or with high storage requirements.

Backup and Disaster Recovery: A copy of the AMI allows organizations to have an additional layer of backup and disaster recovery. In case of an unexpected event in the cloud, the firm can launch an instance from an on-premises copy of the AMI.

It’s important to note that keeping AMIs in an on-premises data center can also have some disadvantages, such as increased maintenance and management costs, and reduced flexibility. Organizations should weigh the benefits and drawbacks carefully before deciding to keep AMIs in an on-premises data center.

Uploading AMI to S3 bucket using AWS CLI

To upload an AMI to S3, you will need to have an AWS account and the AWS Command Line Interface (CLI) installed on your local machine.

Step 1: Locate the AMI that you want to upload to S3 by going to the EC2 Dashboard in the AWS Management Console and selecting “AMIs” from the navigation menu.

Step 2: Use the AWS ec2 create-store-image-task command to create a task that exports the image to S3. This command requires the image-id of the instance and the S3 bucket you want to store the image in.

Step 3: Use the AWS ec2 describe-import-image-tasks command to check the status of the task you just created.

Once the task is complete, the AMI will be stored in the specified S3 bucket.

Downloading the AMI from the S3 bucket

Now that the AMI has been uploaded to S3, here’s how you can download it to your local machine.

Use the AWS s3 cp command to copy the AMI from the S3 bucket to your local machine. This requires the S3 bucket and key where the AMI is stored and the local file path where you want to save the AMI.

Or else you can use the AWS S3 console to download the AMI file from the S3 bucket.

By following these steps, you should be able to successfully upload an AMI to S3 and download it to your local machine. This process can be useful for creating backups, sharing images with others, or moving images between regions.

It’s important to note that uploading and downloading large images may take some time, and may incur some costs associated with using S3 and EC2 instances. It’s recommended to check the costs associated before proceeding with this process.

Creating AMI from the local backup in another AWS account

To create AMI from the local backup in another AWS account, you will need to have an AWS account and the AWS Command Line Interface (CLI) installed on your local machine. Then, upload your local AMI backup on S3 on another AWS account

Step 1: Locate the backup that you want to create an AMI from. This backup should be stored in an S3 bucket in the format of an Amazon Machine Image (AMI).

Step 2: Use the AWS ec2 create-restore-image-task command to create a task that imports the image to EC2. This requires the object key of the image in S3, the S3 bucket where the image is stored, and the name of the new image.

Creating AMI from the local backup in another AWS account

Step 3: Use the AWS ec2 describe-import-image-tasks command to check the task status you just created.

Once the task is complete, the AMI will be available in your EC2 Dashboard.

Now the AMI has been created, let’s discuss the process of launching an instance from that AMI.

Step 1: Go to the EC2 Dashboard in the AWS Management Console and select “Instances” from the navigation menu.

Step 2: Click the “Launch Instance” button to start the process of launching a new instance.

Step 3: Select the newly created AMI from the list of available AMIs.

Step 4: Configure the instance settings as desired and click the “Launch” button.

Step 5: Once the instance is launched, you can connect to it using SSH or Remote Desktop.

Conclusion

In this article, we learned about the process of uploading and downloading an Amazon Machine Image (AMI) to Amazon Simple Storage Service (S3) and downloading it to an on-premises machine. We dived into the benefits of maintaining AMIs in an on-premises data center, including compliance and security, reduced latency and bandwidth, cost savings, and backup and disaster recovery. The steps for uploading an AMI to S3 using the AWS Command Line Interface (CLI) and downloading it from S3 were explained in detail. Finally, the process of creating an AMI from a local backup in another AWS account was discussed and demonstrated.

Hope you found this article helpful and interesting.

Want to read more such content?

Check out our blog: Implementing a Clean Architecture with Nest.JS

About the Author:

Suraj works as a Software Engineer at Mantra Labs. He’s responsible for designing, building, and maintaining the infrastructure and tools needed for software development and deployment. Suraj works closely with both development and operations teams to ensure that the software is delivered quickly and efficiently. During his spare time, he loves to play cricket and explore new places.

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.