AWS Data Engineering
Data lakes, ETL pipelines, and analytics infrastructure on AWS. We build the data foundation that turns raw information into assets your business can use for reporting, decisions, and AI.
Talk to a Data Engineering Specialist
Trusted by Companies Worldwide
Your Data Is Growing Faster Than Your Ability to Use It
Data is everywhere but usable nowhere
You have data in databases, S3 buckets, APIs, and third-party tools, but no central place to query and analyze it
ETL jobs are fragile and slow
Your data pipelines break, run late, or produce inconsistent results. Nobody wants to touch them
Reporting depends on engineers
Business teams can't get numbers without asking a developer to write a query
No clear data permissions
Sensitive data exposed where it shouldn't be, no audit trail for who accessed what
You want AI features but your data isn't ready
Tools like AWS Bedrock Knowledge Base need structured, clean data. Yours is scattered and poorly organized
What We Deliver
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A Structured Data Lake on S3
A central repository with clear zones for raw, processed, and ready-to-use data. Lake Formation handles permissions and governance so the right people access the right data.
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Reliable ETL pipelines
Serverless data pipelines on AWS Glue that extract from your sources, transform and validate your data, and load it where it needs to go. Scheduled or event-driven. They run without babysitting.
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Self-service analytics
Amazon Athena for SQL queries directly on your data lake. Amazon QuickSight for dashboards and reports that business teams can use without engineering help.
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Your data as an AI knowledge base
We structure your data lake to work with AWS Bedrock Knowledge Base so your AI agents and assistants reference real business data — not generic content.
AWS Services We Use
Data lake storage
Serverless ETL
Governance and permissions
SQL queries on S3
Dashboards and BI
AI knowledge source
Who This Is For
SaaS companies with growing data
Your product generates data but there is no pipeline to aggregate and analyze it
Teams with broken ETL jobs
Existing pipelines are unreliable and nobody wants to maintain them
Companies preparing for AI
You want to use AWS Bedrock but your data isn't clean or structured enough
Startups building data-driven products
You need a data foundation that supports reporting and AI from the start
Why Choose Perfsys?
- Data pipelines that run reliably without constant babysitting
- Built on AWS-native services: S3, Glue, Lake Formation, Athena
- Your data structured and queryable for analytics and AI
- Scales with your data volume, not just sized for today
Our Achievements
Our Achievements
30+
International clients
across Media, Software & Technology, Financial Services, Energy, Logistics
10+
Years Experience
in AWS & DevOps
70+
Projects completed
delivered successfully
Media
Software & Technology
Financial Services
Energy
Logistics
What our clients say
Perfsys is trusted by startups and SMBs worldwide for delivering scalable, reliable, and cost-optimized AWS cloud solutions.
FAQs
Common questions about data lakes, ETL pipelines, and analytics infrastructure on AWS.
A centralized storage on Amazon S3 where you keep all your data in its original format. Lake Formation adds governance and permissions on top. Unlike a warehouse, you store everything first and decide how to query it later.
A basic data lake with ETL pipelines takes 3 to 6 weeks. Adding BI dashboards and AI integration adds 2 to 4 weeks. Depends on number of data sources and transformation complexity.
Yes. AWS Bedrock Knowledge Base connects directly to S3. The cleaner your data, the more accurate your AI responses. This is one of the strongest reasons to invest in a proper data lake.
S3 storage is around $0.023/GB/month. Glue ETL is billed per run. Athena charges per query. Most early-stage data lakes cost $50–$200/month. We optimize with Parquet formats and partitioning to reduce costs.
Usually not. Athena lets you query S3 directly without a warehouse. For most startups this is enough and much cheaper. If you need sub-second performance later, Redshift can complement your lake.
Yes. We audit existing ETL jobs, find bottlenecks, and either refactor them or rebuild on Glue with proper error handling and monitoring.
That's the normal starting point. We build pipelines that consolidate data from databases, APIs, S3, and third-party tools into one structured data lake.
Ready to Get Your Data in Order?
Talk to an AWS Select Tier Consulting Partner about data lakes, ETL pipelines, and analytics on AWS.
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