AWS Data Engineering Services. Turn Raw Data Into Business Decisions

One senior engineer at MSCLOUDTECH owns the AWS data engineering end to end, from ingestion through to the query layer your analysts use. That means S3 and Lake Formation for the lake, Glue and EMR for the transforms, Kinesis or MSK when events have to land in under a second, and Athena or Redshift for the analytics on top. Which of those you get depends on your data volume, latency, and budget rather than a fixed template. Scoping starts with a free architecture review and the build is fixed-price.

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.