AWS Data Engineering
AWS Data Engineering Services. Turn Raw Data Into Business Decisions
Your data is scattered across databases, APIs, and files with no unified view. We build data pipelines and analytics platforms on AWS that deliver real-time insights to your team.
Sub-second data delivery
Unified storage layer
Dashboards your team owns
Common Data Challenges
Your Data is Broken
Most teams know they have a data problem. Here is what it actually looks like.
Data Silos
Critical data trapped in separate systems. Sales in one database, product usage in another, finance in a spreadsheet. No unified view.
Batch-Only Processing
Insights arrive 24 hours late. Decisions based on yesterday's data. By the time you see the trend, the opportunity is gone.
No Single Source of Truth
Conflicting numbers from different reports. Marketing says one thing, finance says another. Nobody trusts the data.
Data Services
What We Build
End-to-end data platforms on AWS. Every component production-grade.
Data Lake & Warehouse
S3 + Lake Formation + Redshift + Athena
Centralized storage for structured and unstructured data. Query petabytes with standard SQL. Fine-grained access control. Pay-per-query with Athena or dedicated performance with Redshift.
Real-Time Pipelines
Kinesis + MSK + Lambda + EventBridge
Stream data from any source in sub-second latency. Process events as they happen. React to changes instantly. Scale from hundreds to millions of events per second automatically.
ETL & Transformation
Glue + Step Functions + EMR + Spark
Clean, enrich, and transform raw data into analytics-ready datasets. Automated workflows that run on schedule or on-demand. Schema evolution, data quality checks, and lineage tracking built in.
Technology Stack
The AWS Data Technology Stack
Every layer of your data platform, covered. Battle-tested at scale.
Ingestion
- Kinesis Data Streams
- Amazon MSK (Kafka)
- AWS DMS
- AppFlow
- EventBridge
Storage
- S3 (Data Lake)
- Lake Formation
- Redshift
- DynamoDB
Processing
- AWS Glue (ETL)
- EMR (Spark)
- Lambda
- Step Functions
Analytics
- Athena (SQL)
- Redshift Spectrum
- OpenSearch
- Kinesis Analytics
Visualization
- QuickSight
- Grafana (Managed)
- Custom Dashboards
Governance
- Lake Formation Permissions
- Glue Data Catalog
- CloudTrail Audit
- Data Quality Rules
Use Cases
What Teams Build With Us
Real data platforms solving real problems. Not proof-of-concepts.
Real-Time Dashboards
Stream operational data from production systems into live dashboards. Monitor KPIs, detect anomalies, and trigger alerts in seconds, not hours. Built with Kinesis, Lambda, and QuickSight.
Customer 360 Views
Unify customer data from CRM, product, support, and billing into one profile. Every team sees the same customer. Built with Glue, Lake Formation, Redshift, and API layer.
Predictive Analytics
Feed clean, enriched data into ML models for demand forecasting, churn prediction, and recommendation engines. Built with Glue, SageMaker, and automated retraining pipelines.
Cost Analytics
Ingest AWS Cost & Usage Reports, normalize across accounts, and surface spend trends by team, service, and environment. Built with S3, Athena, and QuickSight.
AWS Data Engineering FAQ
Common questions about building data platforms on AWS.
We build on the full AWS data stack: S3 and Lake Formation for data lake storage, Glue and EMR for ETL and processing, Kinesis and MSK for real-time streaming, Redshift and Athena for analytics, and QuickSight for visualization. The exact combination depends on your data volume, latency requirements, and budget.
A production-ready data lake with ingestion pipelines, cataloging, and basic analytics typically takes 4-8 weeks. That includes S3 bucket architecture, Lake Formation permissions, Glue crawlers and ETL jobs, and Athena query layer. More complex setups with real-time streaming and ML pipelines take 8-12 weeks.
Yes. We follow an incremental migration approach. First we replicate your existing warehouse to Redshift or Athena using AWS DMS and Schema Conversion Tool. Then we validate data parity, migrate workloads in phases, and cut over once everything checks out. Most migrations complete in 6-12 weeks depending on data volume and complexity.
A data lake (S3 + Lake Formation) stores raw data in any format at low cost. You pay for storage and query only what you need. A data warehouse (Redshift) stores structured, pre-modeled data optimized for fast SQL queries. Most modern architectures use both: raw data lands in the lake, transformed data loads into the warehouse for analytics. We call this a lakehouse pattern.
For real-time pipelines we use Kinesis Data Streams or Amazon MSK (managed Kafka) for ingestion, Lambda or Kinesis Data Analytics for processing, and DynamoDB or OpenSearch for serving. Events flow end-to-end in under a second. We also set up dead-letter queues, monitoring, and automatic scaling so nothing gets lost during traffic spikes.
It depends on scope. A focused data lake build with 3-5 source integrations runs $15K-30K. A full analytics platform with real-time pipelines, data warehouse, and dashboards is $40K-80K. We scope every project with a fixed-price estimate upfront so there are no surprises. Start with a free architecture review to get a real number.
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