The global datasphere will balloon to 175 zettabytes in 2025, and nearly 80% of that data will go cold within months of creation. That’s not just a technical challenge for businesses. It’s a financial and strategic one.

For small and medium-sized businesses (SMBs), the question is: how to retain vital information like compliance records, backups, and historical logs without bleeding budget on high-cost active storage?

This is where Amazon S3 Glacier comes in. With its ultra-low costs, high durability, and flexible retrieval tiers, the purpose-built archival storage solution lets you take control of long-term data retention without compromising on compliance or accessibility.

This guide breaks down what S3 Glacier is, how it works, when to use it, and how businesses can use it to build scalable, cost-efficient data strategies that won’t buckle under tomorrow’s zettabytes.

Key takeaways:

• Purpose-built for archival storage: Amazon S3 Glacier classes are designed to reduce costs for infrequently accessed data while maintaining durability.
• Three storage class options: Instant retrieval, flexible retrieval, and deep archive support varying recovery speeds and pricing tiers.
• Lifecycle policy automation: Amazon S3 lifecycle rules automate transitions between storage classes, optimizing cost without manual oversight.
• Flexible configuration and integration: Amazon S3 Glacier integrates with existing Amazon S3 buckets, IAM policies, and analytics tools like Amazon Redshift Spectrum and AWS Glue.
• Proven benefits across industries: Use cases from healthcare, media, and research confirm Glacier’s role in long-term data retention strategies.

What is Amazon S3 Glacier storage, and why do SMBs need it?

Amazon Simple Storage Service (Amazon S3) Glacier is an archival storage class offered by AWS, designed for long-term data retention at a low cost. It’s intended for data that isn’t accessed frequently but must be stored securely and durably, such as historical records, backup files, and compliance-related documents.

Unlike Amazon S3 Standard, which is built for real-time data access, Glacier trades off speed for savings. Retrieval times vary depending on the storage class used, allowing businesses to optimize costs based on how soon or how often they need to access that data.

Why it matters for SMBs: For small and mid-sized businesses modernizing with AWS, Amazon S3 Glacier helps manage growing volumes of cold data without escalating costs. Key reasons for implementing the solution include:

• Cost-effective for inactive data: Pay significantly less per GB compared to other Amazon S3 storage classes, ideal for backup or archive data that is rarely retrieved.
• Built-in lifecycle policies: Automatically move data from Amazon S3 Standard or Amazon S3 Intelligent-Tiering to Amazon Glacier or Glacier Deep Archive based on rules with no manual intervention required.
• Seamless integration with AWS tools: Continue using familiar AWS APIs, Identity and Access Management (IAM), and Amazon S3 bucket configurations with no new learning curve.
• Durable and secure: Data is redundantly stored across multiple AWS Availability Zones, with built-in encryption options and compliance certifications.
• Useful for regulated industries like healthcare: Healthcare SMBs can use Amazon Glacier to store medical imaging files, long-term audit logs, and compliance archives without overcommitting to active storage costs.

Amazon S3 Glacier gives SMBs a scalable way to manage historical data while aligning with cost-control and compliance requirements.

How can SMBs choose the right Amazon S3 Glacier class for their data?

The Amazon S3 Glacier storage classes are purpose-built for long-term data retention, but not all archived data has the same access or cost requirements. AWS offers three Glacier classes, each designed for a different balance of retrieval time and storage pricing.

For SMBs, choosing the right Glacier class depends on how often archived data is accessed, how quickly it needs to be retrieved, and the overall storage budget.

1. Amazon S3 Glacier Instant Retrieval

Amazon S3 Glacier Instant Retrieval is designed for rarely accessed data that still needs to be available within milliseconds. It provides low-cost storage with fast retrieval, making it suitable for SMBs that occasionally need immediate access to archived content.

Specifications:

• Retrieval time: Milliseconds
• Storage cost: ~$0.004/GB/month
• Minimum storage duration: 90 days
• Availability SLA: 99.9%
• Durability: 99.999999999%
• Encryption: Supports SSE-S3 and SSE-KMS
• Retrieval model: Immediate access with no additional tiering

When it’s used: This class suits SMBs managing audit logs, patient records, or legal documents that are accessed infrequently but must be available without delay. Healthcare providers, for instance, use this class to store medical imaging (CT, MRI scans) for emergency retrieval during patient consultations.

2. Amazon S3 Glacier Flexible Retrieval

Amazon S3 Glacier Flexible Retrieval is designed for data that is infrequently accessed and can tolerate retrieval times ranging from minutes to hours. It offers multiple retrieval options to help SMBs manage both performance and cost, including a no-cost bulk retrieval option.

Specifications:

• Storage cost: ~$0.0036/GB/month
• Minimum storage duration: 90 days
• Availability SLA: 99.9%
• Durability: 99.999999999%
• Encryption: Supports SSE-S3 and SSE-KMS
• Retrieval tiers:
  
  • Expedited: 1–5 minutes ($0.03/GB)
  • Standard: 3–5 hours ($0.01/GB)
  • Bulk: 5–12 hours (free per GB)
• Provisioned capacity (optional): $100 per unit/month

When it’s used: SMBs performing planned data restores, like IT service providers handling monthly backups, or financial teams accessing quarterly records, can benefit from this class. It's also suitable for healthcare organizations restoring archived claims data or historical lab results during audits.

3. Amazon S3 Glacier Deep Archive

Amazon S3 Glacier Deep Archive is AWS’s lowest-cost storage class, optimized for data that is accessed very rarely, typically once or twice per year. It’s designed for long-term archival needs where retrieval times of up to 48 hours are acceptable.

Specifications:

• Storage cost: ~$0.00099/GB/month
• Minimum storage duration: 180 days
• Availability SLA: 99.9%
• Durability: 99.999999999%
• Encryption: Supports SSE-S3 and SSE-KMS
• Retrieval model:
  
  • Standard: ~12 hours ($0.0025/GB)
  • Bulk: ~48 hours ($0.0025/GB)

When it’s used: This class is ideal for SMBs with strict compliance or regulatory needs but no urgency in data retrieval. Legal firms archiving case files, research clinics storing historical trial data, or any business maintaining long-term tax records can use Deep Archive to minimize ongoing storage costs.

By selecting the right Glacier storage class, SMBs can control storage spending without sacrificing compliance or operational needs. 

How to successfully set up and manage Amazon S3 Glacier storage?

Setting up Amazon S3 Glacier storage classes requires careful planning of bucket configurations, lifecycle policies, and access management strategies. Organizations must consider data classification requirements, access patterns, and compliance obligations when designing Amazon S3 Glacier storage implementations.

The management approach differs significantly from standard Amazon S3 storage due to retrieval requirements and cost optimization considerations. Proper configuration ensures optimal performance while minimizing unexpected costs.

Step 1: Creating and configuring Amazon S3 buckets

S3 bucket configuration for Amazon S3 Glacier storage classes requires careful consideration of regional placement, access controls, and lifecycle policy implementation. Critical configuration parameters include:

• Regional selection: Choose regions based on data sovereignty requirements, disaster recovery strategies, and network latency considerations for retrieval operations
• Access control policies: Implement IAM policies that restrict retrieval operations to authorized users and prevent unauthorized cost generation
• Versioning strategy: Configure versioning policies that align with minimum storage duration requirements (90 days for Instant/Flexible, 180 days for Deep Archive)
• Encryption settings: Enable AES-256 or AWS KMS encryption for compliance with data protection requirements

Bucket policy configuration must account for the restricted access patterns associated with Amazon S3 Glacier storage classes. Standard S3 permissions apply, but organizations should implement additional controls for retrieval operations and related costs.

Step 2: Uploading and managing data in Amazon S3 Glacier storage classes

Direct uploads to Amazon S3 Glacier storage classes utilize standard S3 PUT operations with appropriate storage class specifications. Key operational considerations include:

• Object size optimization: AWS applies default behavior, preventing objects smaller than 128 KB from transitioning to avoid cost-ineffective scenarios
• Multipart upload strategy: Large objects benefit from multipart uploads, with each part subject to minimum storage duration requirements
• Metadata management: Implement comprehensive tagging strategies for efficient object identification and retrieval planning
• Aggregation strategies: Consider combining small files to optimize storage costs, where minimum duration charges may exceed data storage costs

Large-scale migrations often benefit from AWS DataSync or AWS Storage Gateway implementations that optimize transfer operations. Organizations should evaluate transfer acceleration options for geographically distributed data sources.

Step 3: Restoring objects and managing retrieval settings

Object restoration from Amazon S3 Glacier storage classes requires explicit restoration requests that specify retrieval tiers and duration parameters. Critical operational parameters include:

• Retrieval tier selection: Choose appropriate tiers based on urgency requirements and cost constraints
• Duration specification: Set restoration duration (1-365 days) to match downstream processing requirements
• Batch coordination: Plan bulk restoration operations to avoid overwhelming downstream systems
• Cost monitoring: Track retrieval costs across different tiers and adjust strategies accordingly

Restored objects remain accessible for the specified duration before returning to the archived state. Organizations should coordinate restoration timing with downstream processing requirements to avoid re-restoration costs. 

How to optimize storage with Amazon S3 Glacier lifecycle policies?

Moving beyond basic Amazon S3 Glacier implementation, organizations can achieve significant cost optimization through the strategic configuration of S3 lifecycle policies. These policies automate data transitions across storage classes, eliminating the need for manual intervention while ensuring cost-effective data management throughout object lifecycles.

Lifecycle policies provide teams with precise control over how data is moved across storage classes, helping to reduce costs without sacrificing retention goals. For Amazon S3 Glacier, getting the configuration right is crucial; even minor missteps can result in higher retrieval charges or premature transitions that impact access timelines.

Translating strategy into measurable savings starts with how those lifecycle rules are configured.

1. Lifecycle policy configuration fundamentals

Amazon S3 lifecycle policies automate object transitions through rule-based configurations that specify transition timelines and target storage classes. Organizations can implement multiple rules within a single policy, each targeting specific object prefixes or tags for granular control and management.

Critical configuration parameters include:

• Transition timing: Objects in Standard-IA storage class must remain for a minimum of 30 days before transitioning to Amazon S3 Glacier
• Object size filtering: Amazon S3 applies default behavior, preventing objects smaller than 128 KB from transitioning to avoid cost-ineffective scenarios
• Storage class progression: Design logical progression paths that optimize costs while maintaining operational requirements
• Expiration rules: Configure automatic deletion policies for objects reaching end-of-life criteria

2. Strategic transition timing optimization

Effective lifecycle policies require careful analysis of data access patterns and cost structures across storage classes. Two-step transitioning approaches (Standard → Standard-IA → Amazon S3 Glacier) often provide cost advantages over direct transitions.

Optimal transition strategies typically follow these patterns:

• Day 0-30: Maintain objects in the Standard storage class for frequent access requirements
• Day 30-90: Transition to Standard-IA for reduced storage costs with immediate access capabilities
• Day 90+: Implement Amazon S3 Glacier transitions based on access frequency requirements and cost optimization goals
• Day 365+: Consider Deep Archive transition for long-term archival scenarios

3. Policy monitoring and cost optimization

Billing changes occur immediately when lifecycle configuration rules are satisfied, even before physical transitions complete. Organizations must implement monitoring strategies that track the effectiveness of policies and their associated costs.

Key monitoring metrics include:

• Transition success rates: Monitor successful transitions versus failed attempts
• Cost impact analysis: Track storage cost reductions achieved through lifecycle policies
• Access pattern validation: Verify that transition timing aligns with actual data access requirements
• Policy rule effectiveness: Evaluate individual rule performance and adjust configurations accordingly

What type of businesses benefit the most from Amazon S3 Glacier?

Amazon S3 Glacier storage classes are widely used to support archival workloads where cost efficiency, durability, and compliance are key priorities. Each class caters to distinct access patterns and technical requirements. 

The following use cases, drawn from AWS documentation and customer case studies, illustrate practical applications of these classes across different data management scenarios.

1. Media asset archival (Amazon S3 Glacier Instant Retrieval)

Amazon S3 Glacier Instant Retrieval is recommended for archiving image hosting libraries, video content, news footage, and medical imaging datasets that are rarely accessed but must remain available within milliseconds. The class provides the same performance and throughput as Amazon S3 Standard while reducing storage costs.

Snap Inc. serves as a reference example. The company migrated over two exabytes of user photos and videos to Instant Retrieval within a three-month period. Despite the massive scale, the transition had no user-visible impact. In several regions, latency improved by 20-30 percent. This change resulted in annual savings estimated in the tens of millions of dollars, without compromising availability or throughput.

2. Scientific data preservation (Amazon S3 Glacier Deep Archive)

Amazon S3 Glacier Deep Archive is designed for data that must be retained for extended periods but is accessed infrequently, such as research datasets, regulatory archives, and records related to compliance. With storage pricing at $0.00099 per GB per month and durability of eleven nines across multiple Availability Zones, it is the most cost-efficient option among S3 classes. Retrieval options include standard (up to 12 hours) and bulk (up to 48 hours), both priced at approximately $0.0025 per GB.

Pinterest is one example of Deep Archive in practice. The company used Amazon S3 Lifecycle rules and internal analytics pipelines to identify infrequently accessed datasets and transition them to Deep Archive. This transition enabled Pinterest to reduce annual storage costs by several million dollars while meeting long-term retention requirements for internal data governance.

How Cloudtech helps SMBs solve data storage challenges?

SMBs don’t need to figure out Glacier class selection on their own. AWS partners like Cloudtech can help them assess data access patterns, retention requirements, and compliance needs to determine the most cost-effective Glacier class for each workload. From setting up automated Amazon S3 lifecycle rules to integrating archival storage into ongoing cloud modernization efforts, Cloudtech ensures that SMBs get the most value from their AWS investment.

A recent case study around a nonprofit healthcare insurer illustrates this approach. They faced growing limitations with its legacy on-premises data warehouse, built on Oracle Exadata. The setup restricted storage capacity, leading to selective data retention and delays in analytics.

Cloudtech designed and implemented an AWS-native architecture that eliminated these constraints. The new solution centered around a centralized data lake built on Amazon S3, allowing full retention of both raw and processed data in a unified, secure environment.

To support efficient data access and compliance, the architecture included:

• AWS Glue for data cataloging and metadata management
• Amazon Redshift Spectrum for direct querying from Amazon S3 without the need for full data loads
• Automated Redshift backups stored directly in Amazon S3 with custom retention settings

This minimized data movement, enabled near real-time insights, and supported healthcare compliance standards around data availability and continuity.

Outcome: By transitioning to managed AWS services, the client removed storage constraints, improved analytics readiness, and reduced infrastructure overhead. The move also unlocked long-term cost savings by aligning storage with actual access needs through Amazon S3 lifecycle rules and tiered Glacier storage classes.

Similarly, Cloudtech is equipped to support SMBs with varying storage requirements. It can help businesses with:

• Storage assessment: Identifies frequently and infrequently accessed datasets to map optimal Glacier storage classes
• Lifecycle policy design: Automates data transitions from active to archival storage based on access trends
• Retrieval planning: Aligns retrieval time expectations with the appropriate Glacier tier to minimize costs without disrupting operations
• Compliance-focused configurations: Ensures backup retention, encryption, and access controls meet industry-specific standards
• Unified analytics architecture: Combines Amazon S3 with services like AWS Glue and Amazon Redshift Spectrum to improve visibility without increasing storage costs

Whether it’s for healthcare records, financial audits, or customer history logs, Cloudtech helps SMBs build scalable, secure, and cost-aware storage solutions using only AWS services.

Conclusion

Amazon S3 Glacier storage classes, Instant Retrieval, Flexible Retrieval, and Deep Archive, deliver specialized solutions for cost-effective, long-term data retention. Their retrieval frameworks and pricing models support critical compliance, backup, and archival needs across sectors.

Selecting the right class requires alignment with access frequency, retention timelines, and budget constraints. With complex retrieval tiers and storage duration requirements, expert configuration makes a measurable difference. Cloudtech helps organizations architect Amazon S3 Glacier-backed storage strategies that cut costs while maintaining scalability, data security, and regulatory compliance.

Book a call to plan a storage solution that fits your operational and compliance needs, without overspending.

FAQ’s 

1. What is the primary benefit of using Amazon S3 Glacier?

Amazon S3 Glacier provides ultra-low-cost storage for infrequently accessed data, offering long-term retention with compliance-grade durability and flexible retrieval options ranging from milliseconds to days.

2. Is the Amazon S3 Glacier free?

No. While Amazon Glacier has the lowest storage costs in AWS, charges apply for storage, early deletion, and data retrieval based on tier and access frequency.

3. How to change Amazon S3 to Amazon Glacier?

Use Amazon S3 lifecycle policies to automatically transition objects from standard classes to Glacier. You can also set the storage class during object upload using the Amazon S3 API or console.

4. Is Amazon S3 no longer global?

Amazon S3 remains a regional service. Data is stored in selected AWS Regions, but can be accessed globally depending on permissions and cross-region replication settings.

5. What is a vault in Amazon S3 Glacier?

Vaults were used in the original Amazon Glacier service. With Amazon S3 Glacier, storage is managed through Amazon S3 buckets and storage classes, rather than separate vault structures.

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Small and medium-sized businesses (SMBs) seek to derive value from their data without heavy infrastructure investment, and Amazon Athena offers a simple yet powerful solution. As a serverless query service, Athena allows teams to run SQL queries directly on data stored in Amazon S3, eliminating the need for provisioning servers, setting up databases, or managing ETL pipelines.

Many SMBs use Athena to generate fast insights from CSVs, logs, or JSON files stored in S3. For example, a healthcare provider can use Athena to analyze patient encounter records stored in S3, helping operations teams track appointment volumes, identify care gaps, or monitor billing anomalies in real time.

This guide will go deeper into how Athena works, how it integrates with AWS Glue for automated schema discovery, and how SMBs can get started quickly while keeping costs predictable and usage efficient.

Key takeaways: 

• Run interactive SQL queries directly on Amazon S3 without managing any servers or infrastructure.
• Only pay for the data scanned, making it cost-effective for SMBs with varying workloads.
• Works seamlessly with AWS Glue, IAM, QuickSight, and supports multiple data formats like Parquet, JSON, and CSV.
• Ideal for log analysis, ad-hoc queries, BI dashboards, clickstream analysis, and querying archived data.
• Read-only access, no DML support, performance depends on partitioning and query optimization.

What is AWS Athena and why does it matter for SMBs?

Amazon Athena is a serverless, distributed SQL query engine built on Trino (formerly Presto) that lets businesses query data directly in Amazon S3 using standard SQL, without provisioning infrastructure or building ETL pipelines.

It runs queries in parallel across partitions of business data, which drastically reduces response times even for multi-terabyte datasets. Its schema-on-read model means SMB teams can analyze raw files like CSV, JSON, or Parquet without needing to ingest or transform them first.

Key technical benefits for SMBs include:

• Efficient querying with columnar formats: When SMBs store data in Parquet or ORC, Athena scans only the relevant columns, significantly reducing the amount of data processed and cutting query costs.
• Tight AWS integration: Athena connects to the AWS Glue Data Catalog for automatic schema discovery, versioning, and metadata management, allowing SMBs to treat their Amazon S3 data lake like a structured database.
• Federated query support: SMB teams can run SQL queries across other AWS services (e.g., DynamoDB, Redshift) and external sources (e.g., MySQL, BigQuery) using connectors through AWS Lambda, enabling cross-source analytics without centralizing all their data.
• Security and compliance: Access is controlled via IAM, and all data in transit and at rest can be encrypted. Athena logs queries and results to CloudTrail and CloudWatch, helping regulated industries meet audit requirements.

Example: A diagnostics lab stores lab results in Parquet files on Amazon S3. With Amazon Athena + AWS Glue, they create a unified SQL-accessible view without loading data into a database. Analysts query test volume by region, check turnaround time patterns, and even flag anomalies, all with no infrastructure or long reporting delays.

The bottom line? Amazon Athena gives SMBs the power of big data analytics—on-demand, with no overhead—making it ideal for lean teams that need insights fast without building a data warehouse.

How to get started with AWS Athena? step-by-step

To get started, businesses need to organize their data in Amazon S3, define schemas using AWS Glue Data Catalog, and run queries using standard SQL. With native integration into AWS services, fine-grained IAM access controls, and a pay-per-query model, Athena makes it easy to unlock insights from raw data with minimal setup. It's ideal for teams needing quick, cost-effective analytics on logs, reports, or structured datasets.

Setting up AWS Athena is relatively straightforward, even for businesses with limited technical expertise:

1. Set up an AWS account

To begin using Amazon Athena, an active AWS account is required. The user must ensure that Identity and Access Management (IAM) permissions are correctly configured. Specifically, the IAM role or user should have the necessary policies to access Athena, Amazon S3 (for data storage and query results), and AWS Glue (for managing metadata). 

It's recommended to attach managed policies like AmazonAthenaFullAccess, AmazonS3ReadOnlyAccess, and AWSGlueConsoleFullAccess or create custom policies with least-privilege principles tailored to the organization’s security needs. These permissions allow users to create databases, define tables, and execute queries securely within Athena.

2. Create an S3 bucket for data storage

Before running queries with Athena, data must reside in Amazon S3, as Athena is designed to analyze objects stored there directly. This setup step includes bucket creation, data upload, and optimization for query performance.

Navigate to the Amazon S3 console and click “Create bucket.”

• Assign a globally unique name.
• Choose a region close to the workloads or users for latency optimization.
• Enable settings like versioning, encryption (e.g., SSE-S3 or SSE-KMS), and block public access if required.

Upload datasets in supported formats: Athena supports multiple formats, including CSV, JSON, Parquet, ORC, and Avro.

• For better performance and lower cost, use columnar formats like Parquet or ORC. 
• Store large datasets in compressed form to reduce data scanned per query.

Organize data into partitions: Structuring data into logical partitioned folders (e.g., s3://company-data/patient-records/year=2025/month=07/) enables Athena to prune unnecessary partitions during query execution.

This improves performance and significantly reduces query costs, as Athena only scans relevant subsets of data.

Set appropriate permissions: Ensure IAM roles or users have the necessary S3 permissions to list, read, and write objects in the bucket. Fine-grained access control improves security and limits access to authorized workloads only.

3. Set up AWS Glue Data catalog (optional)

AWS Glue helps SMBs manage metadata and automate schema discovery for datasets stored in Amazon S3, making Athena queries more efficient and scalable.

• The team creates a Glue database to store metadata about the S3 datasets.
• A crawler is configured to scan the S3 bucket and automatically register tables with defined schemas.
• Once cataloged, these tables appear in Athena, enabling immediate, structured SQL queries without manual schema setup.

4. Access AWS Athena console

Once the data is in Amazon S3 and cataloged (if applicable), the team navigates to the Amazon Athena console via the AWS Management Console.

• They configure a query result location, specifying an S3 bucket where Athena will store the output of all executed queries.
• This is a mandatory setup step, as Athena requires an S3 destination for query results before any SQL can be run.

5. Run the first query

With the environment configured, the team can begin querying data:

• In the Athena console, they select the relevant database and table, automatically available if defined in AWS Glue.
• They write a query using standard SQL to analyze data directly from Amazon S3.
• On executing the query via “Run Query,” Athena scans the underlying data and returns the results in the console and the designated S3 output location.

6. Review and optimize queries

As the team begins using Athena, it’s important to ensure queries remain efficient and cost-effective:

• Implement partitioning on commonly filtered columns (e.g., date or region) to reduce the amount of data scanned.
• Limit query scope by selecting only necessary columns and applying precise WHERE clauses to minimize data retrieval.
• Use the Athena console to monitor query execution time, data scanned, and associated costs, enabling optimization and proactive cost control.

7. Explore advanced features (optional)

Once the foundational setup is complete, SMBs can begin using Amazon Athena’s more advanced capabilities:

• Optimize with efficient data formats: Use columnar formats like Parquet or ORC to reduce scan size and improve query performance for large datasets.
• Visualize with Amazon QuickSight: Connect Athena to Amazon QuickSight for interactive dashboards and business intelligence without moving data.
• Run federated queries: Extend Athena’s reach by querying external data sources, such as MySQL, PostgreSQL, or on-prem databases—via federated connectors and AWS Lambda.

These features allow teams to scale their analytics capabilities while keeping operational overhead low.

Practical use cases for Amazon Athena in business operations

Amazon Athena is more than a business tool. It is a practical engine for answering real business questions directly from raw data in S3, without the delays of setting up infrastructure or ETL pipelines. 

For SMBs, it offers a fast path from storage to insight, whether you’re analyzing patient records in healthcare, transaction logs in retail, or system logs in SaaS platforms. Because it speaks standard SQL and integrates natively with AWS Glue and QuickSight, teams can start querying within minutes, with no heavyweight setup, and no data wrangling bottlenecks.

1. Log analysis and monitoring

Amazon Athena is ideal for analyzing server logs and application logs stored in Amazon S3. It allows businesses to perform real-time analysis of logs without needing to set up complex infrastructure.

Example: A retail SMB collects logs from its web servers, tracking user activity and transaction details. Using Athena, the business can query logs to identify trends such as peak shopping hours or common search queries. This data helps the company optimize its website for better user engagement and targeted promotions.

2. Data warehousing and business intelligence

Amazon Athena can be used as a lightweight data warehousing solution, particularly for SMBs that do not require the heavy infrastructure of a full-scale data warehouse. It integrates easily with Amazon QuickSight to deliver business intelligence (BI) dashboards and reports.

Example: A small e-commerce business uses Athena to run SQL queries on sales data stored in Amazon S3. The results are then visualized in Amazon QuickSight to track key performance indicators (KPIs), such as daily sales, revenue per product, and customer acquisition rates, allowing the business to make data-driven decisions.

3. Ad-hoc data queries

For businesses that need to quickly analyze large datasets without the overhead of setting up and maintaining a dedicated analytics system, Athena provides an efficient and cost-effective solution.

Example: A small marketing firm has large datasets containing customer demographics and campaign performance metrics. With Athena, the firm can run ad-hoc queries to analyze customer behavior and assess the effectiveness of different marketing campaigns in real-time, without needing a permanent infrastructure.

4. Clickstream analysis

Athena is a popular tool for analyzing clickstream data, which helps businesses understand user behavior on websites or apps. By querying clickstream data in real-time, companies can optimize user experiences and improve conversion rates.

Example: A media company collects clickstream data that tracks how users interact with its online content. Using Athena, the company can quickly query this data to identify which articles or videos are most popular, what content leads to longer engagement, and where users drop off. This allows the company to tailor content strategies to improve user retention.

5. Data archiving and backup analysis

Businesses often archive large amounts of historical data that may not be frequently accessed but still need to be searchable. Athena makes it easy to query these archives to retrieve specific data when needed.

Example: A healthcare provider stores patient records and historical treatment data in S3. Though this data is rarely accessed, it still needs to be searchable in case of audits or legal requests. Athena allows the provider to run quick queries on archived data, retrieving necessary records without the need for time-consuming data retrieval processes.

Amazon Athena’s versatility makes it an excellent tool for a wide range of use cases, from log analysis to financial reporting. Its serverless nature and cost-effective pricing model ensure that even SMBs with limited technical resources can harness the power of data analytics without the need for complex infrastructure.

How does Cloudtech help SMBs benefit from Amazon Athena?

Deploying Amazon Athena effectively requires the right data architecture, schema management, and cost governance to ensure long-term value. That’s where Cloudtech comes in. As an AWS Advanced Tier Partner focused exclusively on SMBs, Cloudtech helps them harness Athena with a strategic, scalable approach tailored to real business needs.

Here’s how Cloudtech supports the Amazon Athena project:

• Smart data lake setup: Cloudtech builds secure, well-partitioned data lakes on Amazon S3 and integrates AWS Glue for schema discovery, enabling faster, cost-efficient queries with minimal manual setup.
• Query performance optimization: By implementing best practices like columnar formats (Parquet/ORC), partitioning, and bucket strategies, Cloudtech ensures that every Athena query is fast and budget-aware.
• Secure, unified data access: Cloudtech configures federated queries across Amazon S3, on-prem, and third-party sources while enforcing fine-grained IAM policies, so teams can query broadly without compromising security.
• Insights without the overhead: Cloudtech connects Athena to Amazon QuickSight or other BI tools, enabling business teams to explore data visually, without needing SQL or data engineering support.

Whether it is a healthcare provider analyzing claims data or a fintech firm building on-demand dashboards, Cloudtech ensures that Amazon Athena is implemented not just as a tool, but as a foundation for faster, smarter, and more secure decision-making.

Conclusion

Amazon Athena offers SMBs a powerful, serverless solution to analyze data directly from Amazon S3 using standard SQL, eliminating infrastructure complexities and reducing costs. Its fast performance and seamless integration with AWS services enable businesses to gain timely insights and make data-driven decisions efficiently. By following simple setup steps, SMBs can leverage Athena for diverse use cases, from log analysis to business intelligence, enhancing operational agility and scalability.

Cloudtech’s expertise in AWS infrastructure optimization and cloud modernization complements this approach by helping businesses streamline operations and maximize cloud investments.

Talk to our cloud experts or reach us on (332) 222 7090).

FAQs

1. How can AWS Athena help small and midsize businesses reduce their data analytics costs?

AWS Athena’s serverless, pay-per-query pricing model means SMBs only pay for the data they actually scan, eliminating upfront infrastructure costs and allowing flexible, cost-efficient analytics that scale with business needs.

2. What are the key steps for SMBs to start using Athena without a large IT team?

SMBs can quickly begin by setting up an AWS account, creating an S3 bucket for data storage, configuring IAM permissions, and using the Athena console or AWS Glue Data Catalog to define schemas and run SQL queries, all without managing servers.

3. What types of data formats does AWS Athena support for querying?

Athena supports a wide range of data formats including CSV, JSON, Apache Parquet, ORC, and Avro, enabling flexible querying of structured, semi-structured, and unstructured data directly in Amazon S3.

4. Can Athena be integrated with other AWS services for enhanced analytics?

Yes, Athena integrates seamlessly with AWS Glue for metadata management, Amazon QuickSight for data visualization, and supports federated queries to access data across multiple sources, enhancing overall analytics capabilities.

5. How does Cloudtech assist SMBs in maximizing their AWS Athena deployment?

Cloudtech offers cloud modernization services including AWS infrastructure optimization, data management, and application modernization, helping businesses streamline Athena usage, improve scalability, enhance security, and control costs effectively.

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Amazon Elastic Kubernetes Service (EKS) is AWS’s fully managed Kubernetes solution, designed to simplify container orchestration for businesses. This helps in aiming to modernize the application delivery. 

AWS EKS automates the setup and management of the Kubernetes control plane, enabling businesses, including multiple small and medium-sized businesses (SMBs), to deploy, scale, and secure containerized workloads without the operational overhead of managing infrastructure. 

According to AWS, SMBs using EKS can reduce container management time by up to 80%, allowing teams to focus on innovation and faster releases. 

This comprehensive guide covers EKS architecture, deployment strategies, cost optimization, security best practices, and practical steps for SMBs to harness the full potential of Amazon EKS on AWS.

Key Takeaways:

• Fully Managed Kubernetes with EKS: Amazon Elastic Kubernetes Service (EKS) is a fully managed Kubernetes service that simplifies container deployment, scaling, and operations on AWS.
• Automated Control Plane Management: AWS manages the Kubernetes control plane (provisioning, patching, scaling).
• Flexible Deployment Options: Choose from self-managed nodes, managed node groups, serverless with Fargate, or hybrid with EKS Anywhere.
• Security and Optimization Tools: Secure your cluster with IAM + RBAC, enable autoscaling, optimize resource limits, monitor logs/metrics, and automate backups.
• Ideal for Modern Workloads: Perfect for web applications, microservices, machine learning deployments, and DevOps CI/CD pipelines.

What is Amazon Elastic Kubernetes Service (EKS)?

Amazon Elastic Kubernetes Service (EKS) is a fully managed Kubernetes service provided by AWS, designed to simplify the deployment, operation, and scaling of containerized applications using Kubernetes. 

EKS integrates seamlessly with other AWS services such as Amazon EC2, AWS Fargate, Amazon IAM, and Amazon VPC, allowing organizations to leverage AWS’s security, networking, and monitoring capabilities. It supports both cloud and on-premises deployments, making it suitable for hybrid and multi-cloud strategies. Amazon EKS is certified Kubernetes-conformant, ensuring compatibility with existing Kubernetes tools and applications.

Example: A healthcare SMB might use Amazon EKS to run its microservices-based EHR platform. EKS manages the control plane, while backend services (e.g., patient record APIs, billing, and authentication) run in isolated pods on EC2 instances. With IAM roles for service accounts and Amazon CloudWatch integration, the company achieves HIPAA-aligned security, real-time monitoring, and automated scaling without managing Kubernetes masters manually.

The key features of Amazon EKS include:

• Fully managed Kubernetes control plane: EKS automatically provisions and manages the Kubernetes control plane (including etcd and API servers), removing the need to manage Kubernetes internals yourself.
• Seamless compute flexibility: Running workloads on Amazon EC2, AWS Fargate, or a mix of both, enabling cost-optimized or serverless deployments depending on your use case.
• Native AWS integration: EKS works out of the box with AWS services like IAM (role-based access), VPC (networking), CloudWatch (monitoring), EBS (storage), and AWS Load Balancer Controller for ingress.
• Secure and compliant by design: Supports IAM roles for service accounts, private cluster endpoints, and encryption via KMS, making it suitable for regulated workloads (e.g., healthcare, fintech).
• Hybrid and on-prem support with EKS Anywhere: Deploy Kubernetes clusters on your own infrastructure using EKS Anywhere, while maintaining consistency with your cloud-native EKS deployments.

How Amazon EKS works: core architecture explained

The architecture of Amazon Elastic Kubernetes Service (EKS) is designed to facilitate easy deployment, management, and scaling of containerized applications, while ensuring high availability and security. The architecture consists of several key components that work together to run Kubernetes workloads efficiently on AWS. Here’s an overview of the primary elements:

1. Control plane: The control plane is responsible for managing the overall state, scheduling, and health of the Kubernetes cluster. It runs critical components across multiple availability zones for high availability and fault tolerance.

• Includes api server, ETCD, scheduler, and controller manager
• Distributed across at least three availability zones
• Automatically monitored and scaled by AWS
• Isolated within an amazon vpc for security
• Ensures consistent uptime and resilience

High availability: AWS automatically replicates the control plane across multiple Availability Zones (AZs) for fault tolerance, ensuring that the Kubernetes control plane remains available even if one AZ fails.

2. Data plane (worker nodes): The data plane consists of the worker nodes (EC2 instances or AWS Fargate) that run your containerized workloads. These nodes communicate with the control plane and execute the applications as Kubernetes pods.

• Supports both managed node groups and self-managed EC2 nodes
• Each node runs kubelet and kube-proxy for pod management and networking
• Responsible for running and scaling application pods
• Integrates with AWS services for compute and storage

Worker nodes communicate with the control plane via the Kubernetes API, allowing them to receive workloads (pods) and report their status back.

3. Networking and load balancing: Networking components connect the cluster to internal and external resources, manage traffic, and ensure secure communication. Load balancing distributes incoming application traffic efficiently.

• Integrates with Amazon VPC, subnets, and security groups
• Uses elastic load balancers (ALB, NLB) for distributing traffic
• Supports service discovery and DNS integration
• Enables secure, scalable communication between services

4. Authentication, authorization, and security: AWS EKS uses both Kubernetes and AWS IAM for access control and security, ensuring only authorized users and services interact with cluster resources.

• Uses IAM roles and policies for fine-grained access control
• Supports Kubernetes RBAC for internal permissions
• Enables network policies and encryption for data protection
• Integrates with AWS security services for compliance

5. Monitoring and auto scaling: EKS provides built-in tools for monitoring cluster health and performance, as well as features for automatic scaling of both control and data planes.

• Integrates with Amazon CloudWatch, Prometheus, and AWS CloudTrail for monitoring
• Supports auto scaling groups for worker nodes
• Enables cluster logging and event tracking
• Provides insights for optimizing performance and cost

The architecture of Amazon EKS provides scalability and security for containerized applications. To fully maximize its potential, businesses need optimized cloud infrastructure, and AWS partners like Cloudtech can help them achieve this.

Choosing the right Amazon EKS deployment model 

Amazon EKS offers flexible deployment models that suit a variety of business and technical needs. Whether businesses are running latency-sensitive apps, scaling quickly during seasonal spikes, or migrating legacy systems, there’s an EKS deployment model that fits.

Each approach offers different trade-offs in terms of control, automation, cost, and complexity. The key is aligning the model with a business’ workload behavior, team expertise, and long-term growth plans.

Even if a business is starting small or preparing to scale, there's a proven path for their Kubernetes journey on AWS:

1. Self-managed Amazon EC2 nodes

Some SMBs need precise control over their infrastructure—whether it’s to meet compliance needs, support custom software, or integrate with legacy systems. With self-managed nodes, teams provision and manage their own EC2 instances for Kubernetes workloads.

Example use case: A financial services SMB running latency-sensitive trading applications chooses self-managed nodes to fine-tune performance using Amazon EC2 instances with high-memory and high-CPU configurations. They also install third-party security agents that aren't supported on managed node groups.

Why it works: This approach gives SMBs full control over operating system versions, instance types, and patch cycles, critical for maintaining regulatory compliance and performance SLAs.

2. Amazon EKS managed node groups

For most SMBs, managing Amazon EC2 infrastructure manually isn’t worth the time or risk. Managed Node Groups handle provisioning, updates, and scaling automatically, which is ideal for businesses that want Kubernetes benefits without deep infrastructure management.

Example use case: A retail e-commerce startup moves their monolithic app to microservices and uses managed node groups to host their shopping cart, payment gateway, and customer profile services.

Why it works: SMBs can scale nodes automatically during sales events without worrying about patching or provisioning. This frees up the small DevOps teams to focus on improving application performance instead of managing servers.

3. Amazon EKS on Fargate (serverless)

Fargate abstracts away the Amazon EC2 layer completely. Businesses can define what resources their pods need, and AWS runs them. This method is perfect for unpredictable or bursty workloads.

Example use case: A SaaS product for HR compliance uses Fargate to run their document scanning microservices. These services only run when a user uploads a file, so maintaining a dedicated EC2 instance would have been costly and inefficient.

Why it works: Fargate’s pay-per-use model ensures businesses only pay for compute when needed. There is no infrastructure to manage, making it ideal for a lean engineering team with unpredictable workloads.

4. Hybrid deployment with Amazon EKS Anywhere

Some SMBs operate in industries like healthcare or government where not all data can live in the cloud. EKS Anywhere lets teams run Kubernetes clusters on-prem while maintaining consistent tooling and governance via EKS.

Example use case: A regional healthcare provider wants to modernize its patient record system but needs to keep records on-premises due to HIPAA data residency rules. They use EKS Anywhere to containerize parts of their system while keeping sensitive data in their local datacenter.

Why it works: SMBs can modernize application delivery while meeting compliance, and their teams can use the same EKS tools across both environments, simplifying operations.

5. Multi-cluster deployments

Some businesses serve customers in multiple regions or require high availability. EKS allows workloads to run in multiple clusters across different regions or availability zones.

Example use case: A media streaming platform runs its recommendation engine and content delivery services across clusters in North America and Europe. Each cluster serves users close to its region to minimize latency.

Why it works: Multi-cluster deployments reduce downtime risk and improve user experience globally. They can +isolate workloads for performance, compliance, or maintenance without disrupting the whole system.

6. Amazon EKS with Amazon RDS and AWS services

Many SMBs run stateful applications like ERP, CRM, or billing systems, which need persistent storage and relational databases. EKS integrates cleanly with managed services like RDS, DynamoDB, S3, and ElastiCache.

Example use case: An SMB offering an online invoicing platform uses Amazon RDS for storing customer data, Amazon S3 for file uploads, and Amazon ElastiCache to speed up dashboard performance. The core app logic runs in EKS.

Why it works: This hybrid model lets them scale stateless and stateful components independently. They don’t have to manage database patching or backups. RDS handles it while EKS ensures high availability for the app logic.

Each of these deployment models has its place. SMBs can start with managed node groups or Fargate to reduce complexity, then expand to hybrid or multi-cluster architectures as they grow or face new compliance and latency demands. The key is to match the deployment model with your application’s behavior, business needs, and team skillsets.

Best practices for managing Amazon EKS

Effectively managing Amazon EKS means more than just running containers. It requires a structured approach to security, cost control, resilience, and automation. Adhering to these best practices helps ensure that the EKS cluster remains secure, reliable, cost-effective, and scalable. 

1. Secure access and workload permissions

Kubernetes on EKS introduces new layers of access, from users to pods to AWS services. Managing this securely is critical, especially for SMBs handling customer data or regulated workloads.

Tools to use:

• AWS IAM for authentication
• Kubernetes RBAC for role enforcement
• IAM Roles for Service Accounts (IRSA)
• AWS CloudTrail + AWS Config for auditing

Example: A fintech SMB deploying a payments microservice needs to access Amazon S3 securely. Instead of embedding credentials in the container, they use IRSA so the pod can access Amazon S3 with temporary permissions. Combined with RBAC, they prevent dev teams from accessing sensitive audit logs, and AWS CloudTrail logs all access attempts. This setup reduces risk and simplifies compliance with PCI-DSS.

2. Automate scaling and optimize resource use

Manually sizing Kubernetes clusters often leads to overprovisioning or performance issues. EKS supports autoscaling at both the pod and node level, letting you optimize cost and availability dynamically.

Tools to use:

• Kubernetes Horizontal Pod Autoscaler (HPA)
• Cluster Autoscaler
• Amazon EC2 Spot Instances
• AWS Fargate for serverless workloads

Example: An SMB running a marketing analytics platform sees large traffic spikes during campaign launches. By enabling HPA, they ensure pods scale up automatically. For compute nodes, they use a mix of on-demand and Spot Instances, cutting infrastructure costs by over 50%. Low-priority workloads (like daily reports) run on AWS Fargate, which auto-scales with zero server overhead.

3. Monitor, log, and trace everything

Without full visibility into your workloads, it’s hard to troubleshoot issues or optimize performance. Monitoring ensures you're alerted before users feel the impact.

Tools to use:

• Amazon CloudWatch for metrics and logs
• AWS X-Ray for request tracing
• CloudWatch Alarms and Dashboards

Example: An e-commerce SMB running its checkout service on EKS notices random latency during peak hours. With Amazon CloudWatch, they spot high memory usage. Using AWS X-Ray, they trace the issue to slow database queries in one pod. After optimizing queries and increasing memory limits, the checkout latency drops by 60%, and future issues are caught early via Amazon CloudWatch alarms.

4. Plan for high availability and disaster recovery

Downtime, even for a few minutes, can result in lost revenue or customer trust. EKS offers built-in features to help you recover from failures and stay available across regions.

Tools to use:

• Multi-AZ deployments in EKS
• Velero (for backup/restore)
• Amazon Route 53 for failover
• Amazon S3 for storing backup artifacts

Example: A healthcare SMB hosts their appointment system on EKS. They use Velero to back up Kubernetes resources daily to Amazon S3, and deploy worker nodes across three Availability Zones. During an AZ failure, workloads are automatically rescheduled, and Amazon Route 53 redirects traffic without downtime. Their recovery drill confirms they can restore a full cluster in under 30 minutes.

5. Keep everything updated and automate deployments

Outdated clusters or node groups expose you to security vulnerabilities and performance bugs. Keeping EKS and workloads current ensures stability, especially as your architecture evolves.

Tools to use:

• EKS version tracking in AWS Console
• Managed Node Group upgrades
• CI/CD pipelines (e.g., CodePipeline, GitHub Actions)
• Blue/green or canary deployments with Kubernetes

Example: A SaaS startup regularly pushes new features to production. They use GitHub Actions to build and deploy container images, with canary deployments to test updates on 10% of traffic. Managed node groups are updated quarterly, and each new image is verified in staging using the same CI/CD pipeline. This reduces downtime risk while accelerating feature delivery.

Need help managing EKS the right way? Cloudtech brings deep AWS expertise and an SMB-first approach to Kubernetes operations, from building secure clusters to scaling production workloads and setting up observability.

How does Cloudtech help SMBs succeed with Amazon EKS?

Adopting Amazon EKS offers incredible scalability and flexibility, but only when it’s designed, deployed, and managed with the right expertise. For small and medium-sized businesses, that’s where Cloudtech comes in.

Here’s how Cloudtech helps SMBs get the most from EKS:

• Simplified EKS setup: Cloudtech handles everything from provisioning your EKS clusters to integrating IAM, VPCs, and monitoring, so the teams can focus on delivering features, not managing infrastructure.
• Cost-effective, right-sized architecture: Cloudtech helps businesses choose the best deployment model, whether it’s AWS Fargate, Managed Node Groups, or hybrid, and right-size workloads using tools like AWS Compute Optimizer and Savings Plans, ensuring performance without waste.
• Secure by default: From pod-level IAM roles to network policies and Kubernetes RBAC, Cloudtech enforces AWS security best practices from day one, helping SMBs meet compliance requirements like HIPAA or SOC 2.
• CI/CD and automation built in: Cloudtech implements automated pipelines (GitHub Actions, CodePipeline) and infrastructure as code (CloudFormation, Terraform) to make your deployments repeatable, testable, and fast.
• Observability and support: Cloudtech configures Amazon CloudWatch, AWS X-Ray, and log aggregation so your team has real-time visibility. We also offer long-term support, including cost optimization reviews and performance tuning.

Cloudtech’s AWS-certified team specializes in helping SMBs modernize with EKS, securely, efficiently, and with a roadmap that scales as your business grows. 

Conclusion

In conclusion, Amazon EKS offers a powerful, scalable, and secure solution for managing containerized applications, providing businesses with the flexibility to deploy and scale workloads efficiently. By leveraging the robust architecture and seamless integrations with AWS services, organizations can optimize their Kubernetes environments for performance and cost savings. However, to truly maximize the benefits of EKS, it’s essential to have a well-optimized cloud infrastructure. 

Cloudtech offers specialized services in data modernization, application modernization, and infrastructure & resiliency to help SMBs streamline operations, enhance security, and drive cost efficiency. 

Contact Cloudtech today to unlock the full potential of your EKS deployments and accelerate your cloud journey. 

FAQs

1. Is Amazon EKS suitable for small and medium-sized businesses, or is it just for large enterprises?

‍EKS is absolutely SMB-friendly. While originally built for scale, its managed architecture reduces the operational burden for lean teams. Features like Fargate (for serverless workloads) and managed node groups let SMBs start small, scale predictably, and avoid hiring specialized Kubernetes staff upfront.

2. How can I avoid overpaying for infrastructure when using EKS?

‍Right-sizing is key. Use AWS tools like Compute Optimizer and Auto Scaling Groups to match resources to actual usage. Fargate is great for spiky workloads, and Spot Instances work well for batch jobs. Cloudtech also helps SMBs configure cost guardrails from day one using tagging, budgets, and usage policies.

3. What makes EKS different from other Kubernetes services like GKE or AKS?

‍EKS offers native integration with the AWS ecosystem, so networking (VPC), identity (IAM), storage (Amazon S3, EBS), and observability (Amazon CloudWatch) are baked in. That gives SMBs better security, centralized control, and less tool sprawl compared to multi-platform setups.

4. Can EKS run hybrid workloads or be used for on-prem deployments?

‍ A: Yes. With EKS Anywhere, a business can deploy Kubernetes on their own hardware while using the same tools, security policies, and dashboards from AWS. It’s ideal for SMBs in healthcare, finance, or manufacturing with local compliance or latency requirements.

5. How does Cloudtech support SMBs during and after EKS adoption?

‍Cloudtech goes beyond basic setup. They tailor the cluster design to a business’ workload type (e.g., microservices, batch, ML), automate deployments with CI/CD, and enforce security with IAM, RBAC, and encryption. Post-deployment, they monitor cost, performance, and availability, ensuring EKS remains efficient and sustainable as the business grows.

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