Amazon Web Services
AWS Data Lake Implementation Services
Design and build a governed, open data lake on Amazon S3 that serves analytics, reporting and machine learning from a single, trusted foundation.
Build a Data Lake That Stays Useful
Amazon S3 makes it cheap and simple to store almost unlimited data, which is exactly why many data lakes turn into data swamps: thousands of folders, unclear owners, duplicated files and permissions managed bucket by bucket. Modern AWS data lakes look different. Open table formats such as Apache Iceberg add transactions, schema evolution and time travel, Amazon S3 Tables provides managed Iceberg storage, the AWS Glue Data Catalog describes every table, and AWS Lake Formation controls access down to columns and rows.
Hanoi Technology designs and builds data lakes using these capabilities, connected to the lakehouse architecture of Amazon SageMaker and query engines such as Athena, Redshift and EMR. We focus on ingestion reliability, data quality and governance, so the lake becomes the place teams trust rather than avoid.
The Challenge
Why Data Lakes Disappoint
Most problems stem from missing structure, not from storage limits.
The Data Swamp Effect
Raw files land without catalog entries, documentation or ownership, and analysts cannot tell which version of a dataset is correct.
Fragmented Access Control
Permissions handled through a mix of bucket policies and IAM roles become impossible to audit and block safe self-service access.
Slow and Costly Queries
Small files, missing partitions and row-based formats make Athena and Spark queries slow and scan far more data than needed.
Hard Updates and Deletes
Plain Parquet or CSV files make record-level updates, late-arriving data and privacy deletion requests difficult to handle.
Business Value
What a Modern AWS Data Lake Delivers
One Foundation for Many Engines
Iceberg tables in S3 can be queried by Athena, Redshift, EMR, AWS Glue and third-party engines without copying data.
Fine-Grained, Central Governance
Lake Formation permissions and tag-based access control grant access to databases, tables, columns and rows from one place.
Reliable Updates and Time Travel
Iceberg supports inserts, updates, deletes, schema evolution and querying previous snapshots for audits and debugging.
Lower Storage and Query Costs
S3 storage classes, compaction, partitioning and columnar formats reduce both storage and per-query scan costs.
Ready for ML and AI
Data scientists access curated tables directly from SageMaker Unified Studio and SageMaker AI for training and feature engineering.
Discoverable Data Assets
The Glue Data Catalog and SageMaker Catalog make datasets searchable with business descriptions and ownership.
Design for Consumers, Not Just for Storage
A data lake earns its keep when analysts, engineers and data scientists can find, trust and use what it contains. We design zones, table standards and permissions around the people who consume data, and treat ingestion as the start of the job rather than the end.
Our Services
Hanoi Technology’s End-to-End AWS Data Lake Services
Architecture, engineering and governance for data lakes on AWS.
Data Lake Architecture
We design storage zones, table formats, catalog structure, account layout and integration with Redshift, Athena and SageMaker based on your workloads.
- Reference architecture
- Zone and naming standards
- Account and security design
Ingestion Pipelines
We build batch ingestion with AWS Glue, streaming with Amazon Kinesis Data Streams, Amazon Data Firehose and Amazon MSK, and zero-ETL integrations from supported databases and applications.
- Batch and streaming pipelines
- CDC ingestion
- Pipeline monitoring
Iceberg Tables and S3 Tables
We implement Apache Iceberg tables in general-purpose buckets or Amazon S3 Tables, with compaction, snapshot management and partitioning strategies.
- Table design standards
- Maintenance automation
- Performance benchmarks
Transformation and Data Quality
We build curated layers with Glue, EMR Serverless or dbt, and add AWS Glue Data Quality rules that stop bad data from reaching consumers.
- Transformation jobs
- Data quality rules
- Quality dashboards
Governance and Security
We configure Lake Formation permissions, LF-Tags, encryption with AWS KMS, Amazon Macie for sensitive data discovery and audit logging with CloudTrail.
- Permission model
- Sensitive data classification
- Audit trail
Catalog and Self-Service Access
We publish data assets in the Glue Data Catalog and SageMaker Catalog with business metadata, and enable governed self-service in SageMaker Unified Studio.
- Business catalog
- Data access workflows
- User onboarding guide
Our Approach
How We Implement Your Data Lake
Incremental delivery that produces usable data early.
- 01
Define Use Cases and Sources
We identify the analytics and ML use cases the lake must serve first, and inventory the sources they require.
Deliverables: Prioritized use cases · Source inventory
- 02
Design the Architecture
We design zones, table formats, accounts, permissions and integration with consuming services.
Deliverables: Architecture blueprint · Security design
- 03
Build the Foundation
We deploy storage, catalog, governance and pipeline frameworks as code, and ingest the first priority sources.
Deliverables: Foundation as code · First datasets available
- 04
Deliver Curated Data Products
We transform and model data for the first use cases, add quality checks and publish datasets to consumers.
Deliverables: Curated datasets · Quality checks
- 05
Scale and Operate
We onboard further sources and domains, monitor costs and quality, and train internal teams.
Deliverables: Operations runbooks · Training sessions
Use Cases
AWS Data Lake Use Cases
Concrete scenarios where a governed data lake pays off.
Customer 360 Analytics
CRM, web events, orders and support tickets combine into curated customer tables queried by Athena and visualized in Quick Sight.
Clickstream and Event Analytics
Application events stream through Kinesis and Firehose into Iceberg tables, enabling analysis of user journeys within minutes.
Regulatory Data Retention
Historical transaction records are stored cost-effectively in S3 with lifecycle policies, while Iceberg time travel supports audit queries.
Machine Learning Feature Store
Curated tables feed SageMaker AI training jobs and features, ensuring models use the same definitions as reports.
Privacy Request Handling
Iceberg row-level deletes make it practical to remove a customer’s records across tables when a deletion request arrives.
Operational Database Offload
Zero-ETL integrations replicate Aurora data into the lakehouse, so analytics no longer burden production databases.
Technology
Data Lake Technologies on AWS
AWS services and open formats we build with.
- Amazon S3
- Amazon S3 Tables
- Apache Iceberg
- AWS Glue
- AWS Glue Data Catalog
- AWS Glue Data Quality
- AWS Lake Formation
- Amazon Athena
- Amazon EMR Serverless
- Amazon Kinesis Data Streams
- Amazon Data Firehose
- Amazon MSK
- Amazon SageMaker Unified Studio
- Amazon Macie
- AWS KMS
- dbt
Platforms
Engines That Work with Your AWS Data Lake
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · E-commerce and retail
Personalized shopping experience for a US fashion retailer
A fast-growing US fashion and lifestyle retailer had plenty of traffic but a 2.1% conversion rate. We combined a recommendation engine with a simpler path from discovery to checkout.
Read the Full StoryMore Success Stories
Real estate
+38%lead conversion rateProperty discovery and lead conversion for a US real estate marketplace
A US marketplace connecting buyers, sellers and agents had plenty of traffic but few enquiries. We built personalized property discovery, AI valuation and a one-tap route to contact an agent.
Logistics
−25%operational costsRoute optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
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CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Turn S3 into a Trusted Data Foundation
Book a free consultation to discuss your sources, use cases and governance needs. We will propose an architecture and a first increment that delivers usable data quickly.
AWS Data Lake FAQ
Should we use Apache Iceberg or plain Parquet files?
For most new data lakes we recommend Iceberg tables. Iceberg adds ACID transactions, schema and partition evolution, time travel and efficient updates and deletes on top of Parquet files. It is supported by Athena, Redshift, EMR, Glue and many third-party engines. Plain Parquet remains reasonable for append-only, rarely changing data, but most organizations eventually need updates, deletes or schema changes that Iceberg handles far better.
What is the difference between S3 Tables and Iceberg in regular S3 buckets?
Amazon S3 Tables are a bucket type designed for Iceberg, with table-level resources, built-in maintenance such as compaction and snapshot management, and integration with AWS analytics services. Iceberg tables in general-purpose buckets give you more control but require you to run maintenance jobs yourself. We choose based on your engines, governance model and how much table maintenance your team wants to own.
How does Lake Formation fit with IAM?
IAM controls which AWS principals can call which services, while Lake Formation manages data permissions at the database, table, column and row level for data in the Glue Data Catalog. Using Lake Formation, and tag-based access control with LF-Tags in particular, lets you grant analysts access to specific data without editing bucket policies. We design the model so that both layers work together and remain auditable.
How long does it take to build a data lake on AWS?
A foundation with core storage, catalog, governance and ingestion framework, plus the first few sources and curated datasets, typically takes two to three months. Further sources and domains are added in increments afterward. Starting with specific use cases, rather than trying to ingest everything at once, delivers value early and keeps the lake organized from the beginning.
What drives data lake costs?
Main cost elements are S3 storage, data processing with Glue or EMR, query costs in Athena based on data scanned, streaming services, and data transfer. Good design reduces all of them: columnar formats and compaction reduce scans, lifecycle policies move cold data to cheaper storage classes, and incremental processing avoids reprocessing unchanged data. We provide cost estimates during design and monitoring after go-live.
Can a data lake replace our data warehouse?
For some workloads, yes. Lakehouse patterns with Iceberg allow warehouse-style SQL and updates directly on S3 data. Many organizations still keep Amazon Redshift for high-concurrency BI and complex workloads, querying the same lake data through Redshift Spectrum or Iceberg support. The lakehouse architecture of Amazon SageMaker unifies both, and we help decide which workloads belong where.