Coming soonAWS Data Lake Architecture: 7 Reliable Layers That Scale
A 2026 layer-by-layer guide to AWS data lakes: ingestion, S3 Tables and Iceberg, Glue Data Catalog, Glue 6.0 and EMR, Lake Formation, consumption and cost.
AWS Data Engineering Services
Data lakes, pipelines, analytics, streaming and AI-ready data on AWS, built as code by senior engineers.
AWS data engineering services
Data platforms on AWS that are fast, governed and affordable
Our AWS data engineering services cover the full path from source systems to trusted tables: ingestion, storage on Amazon S3 with open table formats, processing with AWS Glue, Amazon EMR and Athena, analytics with Amazon Redshift, streaming, governance and AI-ready data.
We are AWS-pragmatic. Some workloads belong in managed AWS services, others run better on Databricks on AWS. We help you choose deliberately, then build it properly, as code, with tests and monitoring.
Typical outcomes
- One governed data lake instead of copies in every team
- Pipelines that recover from failures without manual fixes
- Clear ownership and cost per data product
- A platform your engineers can extend on their own
What we build on AWS
AWS data engineering, end to end
Data lakes on Amazon S3
Bucket and prefix design, Apache Iceberg tables (including Amazon S3 Tables), the AWS Glue Data Catalog and lifecycle policies that keep storage tidy.
Glue and EMR pipelines
Spark jobs on AWS Glue, Amazon EMR or EMR Serverless, right-sized and observable, with job bookmarks, retries and data quality checks.
Redshift and Athena analytics
Warehouse and lake query layers for BI: Redshift Serverless or provisioned clusters, Athena on Iceberg, and the modeling that makes them fast.
Streaming with Kinesis and MSK
Amazon Kinesis Data Streams, Amazon MSK and Data Firehose, chosen for your throughput, latency and ecosystem needs, landing in the lake reliably.
Zero-ETL and CDC
Zero-ETL integrations into Amazon Redshift where they fit, and change data capture with AWS DMS or Kafka where you need more control.
Data for generative AI
Amazon Bedrock Knowledge Bases fed by governed pipelines, with the right chunking, metadata, vector store and evaluation.
How we build
Principles we apply on every AWS project
- Infrastructure as code. Terraform or AWS CDK for every bucket, role, job and catalog object, reviewed like application code.
- Least privilege. IAM roles per workload, Lake Formation permissions for data, encryption with AWS KMS everywhere.
- Open formats. Parquet and Apache Iceberg or Delta Lake, so any engine can read your data tomorrow.
- Observable by default. CloudWatch metrics, alarms and data quality results that tell you something broke before users do.
- Cost-aware. Tags, budgets and right-sized compute from day one, not after the first surprising bill.
Deliverables
- Architecture diagram and decision records
- Terraform modules and CI/CD pipelines
- Tested ingestion and transformation code
- Dashboards, alarms and runbooks
- Handover sessions for your team
Insights
Latest AWS data engineering guides
Coming soon
Coming soonAmazon S3 Tables: 7 Powerful Ways to Run Iceberg Better
What AWS manages for your Iceberg tables in S3 table buckets, how to connect Athena, Spark and Databricks, and what the pricing meters really cost.
Coming soonAWS Zero-ETL: 6 Powerful Integrations and Their Hidden Limits
What AWS zero-ETL replicates in 2026, the limits that break tables, what you still own after replication, and when DMS, Debezium or Lakeflow Connect fit better.
FAQ
AWS data engineering services: common questions
Which AWS services do you work with?
Mostly Amazon S3 and S3 Tables, AWS Glue, Amazon EMR, Amazon Athena, Amazon Redshift, Amazon Kinesis, Amazon MSK, Amazon Data Firehose, AWS Lake Formation, AWS DMS, Step Functions, Amazon MWAA, Amazon Bedrock and SageMaker, plus IAM, KMS and CloudWatch around them.
Should we use AWS Glue or Databricks?
It depends on workload size, team skills and governance needs. Glue is a good fit for lighter, AWS-native ETL; Databricks shines for heavy engineering, collaborative development, ML and BI on one platform. Many teams use both, and we help draw a sensible line between them.
Can you work in our existing AWS accounts?
Yes. We work in your accounts and repositories with the access you grant, following your security policies. Everything we build is yours, defined as code and documented.
Do you help reduce AWS data costs?
Yes. We break spend down by service, team and workload, then fix the biggest items first: right-sizing compute, storage tiering and lifecycle rules, better file layout, and removing duplicate pipelines.
How quickly can you start?
Usually within a couple of weeks of a first call. Most engagements begin with a short assessment so we agree on priorities and success measures before building anything.
Planning a data platform on AWS?
Get a senior engineer’s view on your architecture, risks and costs in a free 30-minute call.