When it comes to storing and processing large volumes of data in the cloud, Amazon Web Services and Microsoft Azure are the two dominant players. Both offer managed data lake services, but they take different architectural approaches and come with distinct pricing models, ecosystem integrations, and tooling. This article compares AWS Lake Formation and Azure Data Lake to help DevOps and data engineering teams make an informed decision.
What Is a Data Lake?
A data lake is a centralized repository designed to store structured, semi-structured, and unstructured data at scale, typically in raw format. Unlike a data warehouse, which enforces a schema before ingestion, a data lake follows a schema-on-read approach. This makes it flexible enough to support diverse workloads including machine learning, real-time analytics, and batch processing.
For machine learning in particular, co-locating training data with compute resources is critical for performance. Cloud providers address this by bundling storage and compute into cohesive data lake platforms.
AWS Lake Formation
AWS Lake Formation is Amazon’s managed service for building, securing, and managing data lakes. Under the hood, it relies on Amazon S3 as its primary storage layer. Lake Formation sits on top of S3 and adds a governance and cataloging layer — including fine-grained access controls, data cataloging via AWS Glue, and automated ETL (extract, transform, load) workflows.
Key capabilities of AWS Lake Formation include:
- Centralized access control: Column- and row-level security policies applied consistently across analytics services such as Amazon Athena, Redshift Spectrum, and EMR.
- Data ingestion and transformation: Built-in blueprints and integration with AWS Glue for crawling, cataloging, and transforming data from relational databases, application logs, and streaming sources.
- ML readiness: Direct compatibility with Amazon SageMaker and other AWS ML services, making it straightforward to feed cataloged datasets into training jobs.
- S3 storage tiers: Because Lake Formation uses S3, teams benefit from S3’s full range of storage classes — from S3 Standard to S3 Glacier — allowing cost optimization based on access frequency.
Azure Data Lake Storage
Microsoft’s Azure Data Lake Storage (ADLS) is a cloud-native storage service optimized for big data analytics workloads. ADLS Gen2, the current generation, combines the scalability of Azure Blob Storage with a hierarchical namespace and HDFS-compatible interface, making it compatible with the broad Hadoop ecosystem including tools like Apache Spark, Hive, and Pig.
Key capabilities of Azure Data Lake Storage include:
- Hadoop compatibility: The HDFS-compatible API means existing big data workloads can be migrated to Azure with minimal code changes.
- Azure HDInsight integration: HDInsight brings managed Hadoop, Spark, Kafka, and HBase clusters directly to your data lake, enabling large-scale distributed processing.
- Azure Data Factory: A fully managed ETL and data integration service that connects to hundreds of on-premises and cloud data sources, providing similar pipeline capabilities to AWS Glue.
- Role-based and ACL security: ADLS Gen2 supports both Azure RBAC and POSIX-style ACLs at the file and directory level.
- Synapse Analytics integration: Azure Synapse provides a unified analytics workspace combining data warehousing, big data, and data integration on top of ADLS.
Storage Pricing: A Direct Comparison
Storage cost is often the first metric teams evaluate, but pricing structures differ enough between the two providers that a direct comparison requires care. The figures below are drawn from the original 2019 source article and should be verified against current published pricing before any procurement decision, as cloud prices change frequently.
AWS S3 pricing (as referenced in the source, 2019):
- S3 Standard: ~$0.023 per GB for the first 50 TB/month, with lower rates for higher tiers.
- S3 Standard-Infrequent Access: approximately $0.0125 per GB.
- S3 Glacier: as low as ~$0.004 per GB per month for archival storage.
Azure Data Lake Storage Gen1 pricing (as referenced in the source, 2019):
- Committed pricing: approximately $2,900/month for 100 TB and $26,000/month for 1 PB.
- Pay-as-you-go: starting at approximately $0.039 per GB, decreasing in higher tiers.
Based on these figures, AWS S3 appears to have a lower per-GB storage cost than Azure Data Lake Gen1 at equivalent volumes. However, the total cost of ownership depends on far more than raw storage rates — compute costs, egress fees, support tiers, licensing, and the engineering time required to integrate and maintain each platform all factor in significantly.
ETL and Data Integration
Both platforms provide managed ETL services that allow teams to ingest, clean, and transform data before it reaches the lake or before it is consumed by analytics workloads.
AWS Glue is the primary ETL service in the AWS ecosystem. It automatically crawls data sources, infers schemas, populates the AWS Glue Data Catalog, and generates Spark-based ETL scripts. Lake Formation builds on this catalog to enforce access policies.
Azure Data Factory is Microsoft’s equivalent and supports a broader set of native connectors to on-premises systems, SaaS applications, and third-party databases. Its visual pipeline authoring environment appeals to teams with mixed engineering and analyst profiles.
Machine Learning and Analytics Ecosystem
The data lake is rarely the end destination — it feeds downstream analytics and ML workflows.
On AWS, Amazon SageMaker provides end-to-end ML capabilities including data labeling, model training, hyperparameter tuning, and managed deployment. SageMaker reads training data directly from S3, making Lake Formation a natural upstream dependency.
On Azure, Azure Machine Learning integrates with ADLS and Azure Synapse. Teams already invested in Microsoft tools — including Power BI for visualization and Azure Databricks for Spark-based processing — will find the Azure ecosystem cohesive.
If your organization has an existing AWS footprint or is already using SageMaker, Lake Formation is the path of least resistance. If your stack is Microsoft-heavy or you rely heavily on the Hadoop ecosystem, ADLS Gen2 with Synapse or HDInsight is likely a better fit.
Key Considerations Beyond Price
Storage cost per GB is an important data point, but it should not be the sole driver of your decision. Consider the following when evaluating either platform:
- Existing cloud investment: Migrating between cloud providers is expensive in terms of engineering time and egress fees. Extend your current platform where possible unless there is a compelling reason to switch.
- Ecosystem compatibility: Teams using Apache Spark, Hadoop, or Hive heavily may prefer ADLS Gen2’s HDFS-compatible interface. Teams already using AWS services will benefit from Lake Formation’s native integrations.
- Governance requirements: Both platforms offer fine-grained access control, but the implementation differs. Evaluate how each maps to your organization’s compliance and auditing requirements.
- Data ingress and egress costs: Uploading data to either cloud is typically free, but downloading or transferring between regions carries charges that can significantly affect total cost at petabyte scale.
- Support and SLAs: Enterprise support contracts, guaranteed uptime SLAs, and the availability of professional services should be evaluated alongside technical capabilities.
Bottom Line
AWS Lake Formation and Azure Data Lake Storage are both capable, production-grade platforms for building and managing data lakes at scale. Based on published storage pricing (as of the original 2019 comparison), AWS S3 offers a lower per-GB cost, which matters at petabyte scale. However, the better platform for your organization depends on your existing cloud investments, preferred analytics tooling, Hadoop ecosystem dependencies, and the specific regulatory and governance controls you need to enforce.
For teams starting from scratch, both platforms deserve a proof-of-concept evaluation. For teams already committed to one cloud provider, the switching cost almost always outweighs any storage price delta — focus instead on optimizing storage tiers and data lifecycle policies within your existing platform.
Original source article: datacenterknowledge.com (2019). Pricing figures cited are from the original 2019 article and may no longer reflect current pricing. Always verify costs against the official AWS and Azure pricing calculators before making procurement decisions.