Data WarehousingIndustry: Enterprise Business Intelligence 2021 - 2023

Greenfield AWS Redshift Data Warehouse & S3 Lakehouse Build

Q: Why was AWS Redshift chosen for this greenfield data warehouse?

AWS Redshift was selected for its native integration with AWS S3, Redshift Spectrum capability for querying raw data lake files directly, exceptional petabyte-scale query performance, and cost-effective RA3 node scaling.

Q: How were disparate data sources unified into the data warehouse?

Automated ingestion scripts using PySpark and Apache Sqoop extracted schema data from relational databases and SaaS APIs into S3. Airflow DAGs then cleaned, normalized, and transformed the data into dimensional data marts inside Redshift.

Designed and built an enterprise AWS Redshift Data Warehouse from scratch, unifying disparate data streams into sub-second BI reporting dashboards.

Focused detail of a modern server rack with blue LED indicators in a data center. — Photo: panumas nikhomkhai / Pexels

100%

Data Source Integration

Unification of disparate ERP, CRM, and transactional SQL data sources

<1 sec

Query Execution Speed

Sub-second response time for complex executive BI queries

24/7

Reporting Automated

Continuous, automated data refresh for executive decision making

01 · Challenge

The problem on the ground

Read the full brief

Business leaders lacked a single source of truth; operational data was scattered across legacy databases, flat files, and third-party SaaS APIs, requiring manual spreadsheet consolidation that took days.

02 · Solution

How we engineered it

Read the approach

Architected a modern AWS data lakehouse architectureusing PySpark and Apache Sqoop to ingest raw data into an S3 Data Lake, transformed via dbt/Airflow into dimensional star-schema data marts inside AWS Redshift.

03 · Architecture

What's running under the hood

See the execution breakdown

Ingestion Engine: Deployed PySpark and Sqoop jobs for high-throughput batch and incremental database extraction.

S3 Staging Lake: Established raw, cleaned, and curated S3 storage layers with Parquet columnar compression.

Redshift Data Marts: Modeled dimensional Fact and Dimension tables optimized with sort and distribution keys.

BI Integration: Connected AWS QuickSight and Power BI directly to Redshift for real-time executive reporting.

04 · Results

What changed for the client

See the outcomes

Eliminated 100% of manual spreadsheet reporting efforts across finance and operations.

Delivered sub-second query response times for executive dashboards.

Created a central data governance model with role-based access security.

Enabled self-serve data analytics for business analysts across departments.

Technical Q&A

Why was AWS Redshift chosen for this greenfield data warehouse?

AWS Redshift was selected for its native integration with AWS S3, Redshift Spectrum capability for querying raw data lake files directly, exceptional petabyte-scale query performance, and cost-effective RA3 node scaling.

How were disparate data sources unified into the data warehouse?

Automated ingestion scripts using PySpark and Apache Sqoop extracted schema data from relational databases and SaaS APIs into S3. Airflow DAGs then cleaned, normalized, and transformed the data into dimensional data marts inside Redshift.

Planning a similar data warehousing project?

Consult directly with our platform engineers to assess your workload and get a zero-downtime architecture plan.

Consult Engineers