By: Manpinder Singh, Enterprise AI & Cloud Architect 2020
Before “lakehouse” became a common architectural term, enterprise teams were already exploring ways to centralize data in durable object storage, separate compute from storage, and use multiple engines on shared datasets. On-premises Hadoop clusters initially met batch processing needs, but as requirements expanded around storage flexibility, broad user access, and consistent data oversight, these systems showed their limitations.
In my experience driving large-scale cloud transformation programs across multiple industries, teams often required analytics platforms that support varied processing engines, integrate cleanly with cloud storage, and apply access and governance policies in a uniform way. As data programs expanded, priorities shifted toward agility, predictable operating patterns, and reduced operational overhead.
Moving analytics to AWS offered a path to address these priorities with services supporting Apache Spark, SQL-based analysis, and warehouse-style workloads. This transition helped organizations create architectures that match present-day needs and anticipate future growth.
The Limitations of Enterprise Hadoop-Centric Analytics
Hadoop-based platforms provided early value, but their built-in limitations quickly restricted scalability. The Hadoop Distributed File System (HDFS) required ongoing monitoring and careful hardware planning. MapReduce slowed batch workloads with repeated disk operations. Spark offered faster processing, but running it within traditional Hadoop clusters meant persistent management overhead.
As streaming, SQL, and machine learning workloads increased, the tight connection between storage and compute limited flexibility. Scattered policies and metadata made consistent data oversight difficult.
To resolve these issues, enterprises turned to AWS analytics services built around S3-backed storage, managed compute, and centralized metadata. The following table highlights key differences:
| Capability | Legacy Hadoop | AWS Analytics Platform |
| Scalability | Hardware-bound, fixed capacity | Scales storage and compute independently; elastic provisioning |
| Cost Model | High upfront hardware and maintenance costs | Usage-based pricing, on-demand, and Spot options |
| Data Governance | Multi-system controls, fragmented metadata | Centralized via Glue Data Catalog, Lake Formation, IAM |
| Operational Overhead | Substantial cluster management | Managed services reduce administrative load |
| Workload Diversity | Strong for batch; limited for interactive or streaming | Supports SQL, Spark, batch, streaming, ML |
| Reliability | Sensitive to node failures; complex recovery | Durability from S3 and managed service SLAs |
These gaps led enterprises to adopt a different architectural approach using AWS analytics services.
Modernizing the Analytics Stack Using Core AWS Services
AWS provides several analytics services that work together as part of a modern data strategy. The AWS analytics decision guide illustrates how these services integrate around Amazon S3, which functions as large-scale storage for batch, interactive, and streaming workloads.
Diagram showing the AWS analytics services
Source: Choosing an AWS analytics service | AWS
The following services form the core of most modernization efforts:
Amazon EMR: Managed Compute for Distributed Processing
EMR streamlines Spark and Hadoop operations by handling provisioning, configuration, and scaling, reducing manual system management. Spot and Reserved Instances help teams match compute to workload patterns while controlling cost.
AWS Glue: Data Integration and Centralized Metadata
AWS Glue provides a serverless environment for ETL, metadata, and data preparation. The Glue Data Catalog maintains consistent schemas across services, supporting data discoverability and oversight. Its schema evolution capabilities suit modern architectures with dynamic, diverse datasets.
Amazon Athena: Serverless SQL for S3-Based Data Lakes
Athena enables direct SQL queries on S3 data without infrastructure setup. It handles various formats (JSON, CSV, Parquet, ORC) and integrates with the Glue Data Catalog for metadata. Its serverless, pay-per-query design makes it a strong choice for ad-hoc analysis.
Amazon Redshift: Scalable Cloud Data Warehouse for Curated Layers
Redshift supports reporting and advanced analytics at high throughput. Federated queries extend access across Redshift tables and S3 datasets, allowing organizations to centralize curated data while analyzing raw or refined lake data.
Designing Architectures with Decoupled Storage and Compute
A modern AWS analytics pipeline typically follows this flow: ingestion → storage → processing → analytics. The modern data analytics reference architecture illustrates this pattern, showing how services like AWS DMS, Kinesis, Amazon MSK, and AWS IoT Core move data into S3. Lake Formation governs access, and the Glue Data Catalog captures metadata.
Reference architecture diagram showing how to build data analytics pipelines using a Modern Data Analytics approach to derive insights from the data.
Source: Modern Data Analytics Reference Architecture | AWS
With storage and compute decoupled, engines like EMR, Athena, Redshift Spectrum, Flink, and SageMaker all process the same datasets. This setup means:
- Compute adjusts to demand, not tied to storage limits.
- Clusters run only during active jobs, minimizing maintenance.
- Multiple engines share one dataset, reducing data copies.
- Governance and metadata apply consistently across services.
This design formed the basis for early AWS lakehouse models. Teams could store data once, use different engines as needed, and manage access and governance from one place. Expanding analytics did not require additional complexity.
Performance Tuning Strategies for Spark-Based Workloads on AWS
Apache Spark relies on in-memory processing to cut down on disk operations. On Amazon EMR, Spark benefits from managed provisioning and scaling. This reduces infrastructure management and allows attention to shift toward workload performance.
Several practices help improve performance:
1. Use columnar formats: Parquet and ORC reduce I/O by storing data by column. EMR and Athena support these formats, allowing Spark to scan less data and apply filters more efficiently.
2. Scale compute with demand: EMR autoscaling adds capacity for heavy processing and scales back after, lowering costs.
3. Keep schemas consistent: Glue Data Catalog provides a shared schema reference for Spark and Athena.
4. Partition data for common queries: Organizing S3 data to match query patterns allows faster reads.
These methods support ETL, analytics, and data preparation workflows across the platform.
Structuring Analytics-Ready Data Layers in AWS
Enterprises structure data lakes into layers to bring order to ingestion and consumption. Raw data remains unchanged for reprocessing, refined data applies cleaning and normalization, and curated data supports analytics and reporting. This structure clarifies data readiness and removes ambiguity as platforms expand.
Concepts such as schema-on-read and schema evolution support this layered approach. Schema-on-read lets analysts apply structure when querying, instead of requiring strict contracts at ingestion. Schema evolution supports those changes by allowing fields to be added or updated without interrupting jobs that depend on the data.
Metadata, tracked in the Glue Data Catalog, links datasets and manages access. As platforms scale, open table formats like Iceberg, Hudi, and Delta Lake add features such as transactional updates and versioning.
Security and Access Control Considerations
Security controls influence every part of an analytics platform. Strong identity foundations restrict each request to only the necessary data. Centralized identity, short-lived credentials, and clear separation of duties limit potential misuse. Data protection uses encryption in transit and at rest, plus access policies based on dataset sensitivity.
Full traceability is essential for monitoring system activity. Comprehensive logging and audit trails support real-time monitoring and investigations. Applying controls across the network, compute, applications, and data creates multiple protection layers.
Automation enhances consistency. Managed services and infrastructure-as-code reduce configuration errors, keep permissions up to date, and limit unnecessary data access. These practices make investigation and operations more reliable and strengthen security across the cloud data platform.
Managing Cost Efficiency in Large-Scale AWS Analytics
Cost optimization requires deliberate planning. Teams benefit from knowing which jobs run steadily, which spike, and which can tolerate interruption. This knowledge informs compute and storage choices, avoiding excess provisioning. A consumption-based model limits the idle capacity that was common in static Hadoop clusters.
Detailed billing lets teams assign costs to workloads or departments, connecting usage patterns to spending. This visibility supports better forecasts and aligns resources with business needs.
Regular review is essential. As workloads and data grow, retiring unused datasets, resizing environments, and adopting pricing models that reflect actual use keep costs under control while maintaining capability.
Lessons Learned from Operating Production Analytics Workloads
Operational analytics depend on a continuous flow of information about system health. In the AWS operational analytics reference architecture, designs use logs, traces, and metrics for troubleshooting, monitoring, and improvement. This allows engineers to troubleshoot, monitor trends, and improve performance.
Operational analytics reference architecture
Source: Reference architecture | AWS
Across my work supporting enterprise-scale analytics, several patterns proved valuable:
- Strong observability shortens investigation times and improves reliability.
- Automated, predictable pipelines simplify ingestion and transformation.
- Consistent metadata supports cross-team collaboration and incident resolution.
- Regular review of operational practices enhances system stability.
These patterns continue to influence how enterprises design and maintain analytics environments at scale, guiding decisions in architecture, governance, and operational planning.
Conclusion and Key Takeaways
Modernizing analytics from Hadoop clusters to AWS lakehouse architectures enables management of larger datasets, unified governance, and reduced operational effort. Central S3 storage, shared metadata, and flexible compute services support diverse workloads without relying on persistent clusters.
Enterprises considering modernization should use a best-practices checklist based on the pillars of the Well-Architected framework:
- Operational Excellence: Treat operations as code and refine processes using workload insights.
- Security: Apply least-privilege access, encryption, and continuous monitoring.
- Reliability: Design for recovery, manage changes carefully, and test failover scenarios.
- Performance Efficiency: Match engines to workload needs and choose efficient data formats.
- Cost Optimization: Align usage with demand and track spending regularly.
- Sustainability: Minimize waste using data lifecycle policies and appropriate compute sizing.
Modernization efforts succeed when architecture decisions clearly support business priorities and build a platform that handles both current and future analytics requirements.
About the Author:
Manpinder Singh is an enterprise AI and cloud architect specializing in AWS analytics modernization, governed lakehouse architecture, and large-scale cloud data strategies. Recognized for contributions to the field through modernization roadmaps, architecture workshops, and technical leadership across industries, he brings a practitioner’s lens to analytics transformation.
References:
- Dsouja, J. (2019, April 17). Big Data Hadoop vs. Traditional RDBMS. TDAN. https://tdan.com/big-data-hadoop-vs-traditional-rdbms
- Choosing an AWS analytics service. (n.d.). AWS. https://docs.aws.amazon.com/decision-guides/latest/analytics-on-aws-how-to-choose/analytics-on-aws-how-to-choose.html
- Amazon Redshift. (n.d.). AWS Architecture Blog. https://aws.amazon.com/blogs/architecture/category/analytics/amazon-redshift-analytics/
- Modern Data Analytics Reference Architecture on AWS Diagram. (n.d.). AWS. https://docs.aws.amazon.com/architecture-diagrams/latest/modern-data-analytics-on-aws/modern-data-analytics-on-aws.html
- What is Apache Spark? (n.d.). Amazon Web Services. https://aws.amazon.com/what-is/apache-spark/
- Setting up permissions for open table storage formats in Lake Formation. (n.d.). AWS Lake Formation. https://docs.aws.amazon.com/lake-formation/latest/dg/otf-tutorial.html
- Security. (n.d.). Data Analytics Lens. https://docs.aws.amazon.com/wellarchitected/latest/analytics-lens/security.html
- Cost optimization. (n.d.). Data Analytics Lens. https://docs.aws.amazon.com/wellarchitected/latest/analytics-lens/cost-optimization.html
- Reference architecture. (n.d.). AWS Well-Architected Framework. https://docs.aws.amazon.com/wellarchitected/latest/analytics-lens/section-17.html
- Pillars of the Well-Architected Framework. (n.d.). Data Analytics Lens. https://docs.aws.amazon.com/wellarchitected/latest/analytics-lens/well-architected-design-principles.html