/REPORTING & DATA WAREHOUSING

When 70,000 daily transactions need to make sense in real time.

Custom reporting engines and data warehousing solutions built for a fintech payment processor and a fully regulated German commercial bank, giving every stakeholder exactly the data they need, when they need it.

/OVERVIEW

REPORTING INFRASTRUCTURE BUILT FOR REGULATED FINTECH.

DATA WAREHOUSING

REAL-TIME REPORTING

ETL

KAFKA

REGULATORY COMPLIANCE

POSTGRESQL

Evolt worked with two clients simultaneously: a fintech company processing crypto, card, and SEPA retail payments, and a fully regulated German commercial bank. Both were dealing with the same core problem — managing high volumes of transaction data and generating reliable, real-time reports for a wide range of stakeholders including merchants, regulators, analytics teams, and marketing departments. The architecture challenges were similar; the implementation details were not.

/THE CHALLENGE

WHAT THE CLIENTS NEEDED US TO BUILD.

Financial institutions at this scale don't just need data. They need the right data, formatted correctly, delivered on time, to the right person, with full regulatory compliance. Both clients lacked the reporting infrastructure to do that reliably.

The complexity came from several directions: live dashboards with real-time filtering, automated weekly and monthly settlement statements, bookkeeping integration, and the need to serve completely different stakeholder groups from a single system. Off-the-shelf solutions couldn't handle the specificity required.

GOALS

Build custom reporting engines capable of generating reports on demand and on a schedule

Integrate data from PostgreSQL, data warehouses, and multiple external services

Enable live dashboard filtering alongside automated daily, weekly, and monthly report pipelines

Tailor reports to distinct stakeholder groups: merchants, regulators, analytics teams, and marketing

Ensure full regulatory compliance across both client environments

Implement enterprise-grade security across all data at rest and in transit

Build custom reporting engines capable of generating reports on demand and on a schedule

Integrate data from PostgreSQL, data warehouses, and multiple external services

Enable live dashboard filtering alongside automated daily, weekly, and monthly report pipelines

Tailor reports to distinct stakeholder groups: merchants, regulators, analytics teams, and marketing

Ensure full regulatory compliance across both client environments

Implement enterprise-grade security across all data at rest and in transit

/TECH STACK

TECH STACK

Programming

Python | Golang | Java | TypeScript

Database

PostgreSQL | AWS Redshift

Streaming

Kafka | RabbitMQ | Pulsar

Scheduling

Celery with RabbitMQ

Storage

S3 | AWS Glue

Data Warehousing and ETL

Apache NiFi | Apache Hive

Programming

Python | Golang | Java | TypeScript

Database

PostgreSQL | AWS Redshift

Streaming

Kafka | RabbitMQ | Pulsar

Scheduling

Celery with RabbitMQ

Storage

S3 | AWS Glue

Data Warehousing and ETL

Apache NiFi | Apache Hive

/THE SOLUTION

ONE ARCHITECTURE. TWO HIGHLY REGULATED ENVIRONMENTS.

Evolt built custom reporting engines and data warehousing infrastructure for both clients, with shared architectural principles and implementation details tuned to each organisation's specific requirements.

Data integration

Data was pulled from PostgreSQL databases and external services using Python and Java connectors, cleaned and transformed via ETL workflows in Apache NiFi, and loaded into the data warehouse. Kafka and RabbitMQ handled real-time data streaming to keep the reporting engine continuously fed.

Live reporting

Real-time dashboards were built with live filtering capabilities, giving stakeholders an up-to-the-minute view of transactions, settlements, and financial activity without waiting for batch processing cycles.

Scheduled reporting

Automated pipelines generated and delivered daily, weekly, and monthly reports using distributed Celery clusters with RabbitMQ as the broker. Report types covered settlement reports, financial statements, transaction summaries, regulatory compliance reports, and marketing analytics.

Data warehouse optimisation

Schemas in Apache Hive were designed and optimised for fast data retrieval at high volume. Indexing strategies and data partitioning ensured query performance held up even at 50,000 to 70,000 daily transactions.

Stakeholder-specific reporting

Reports were scoped to individual stakeholder needs. Merchants received detailed transaction histories. Regulators received compliance-formatted output. Analytics and marketing teams had access to their own tailored views, all controlled through role-based access.

Security

Role-based and least-privilege access controls are enforced throughout the entire system. All data is encrypted at rest and in transit, with continuous database auditing and network traffic monitoring running at all times. Tight firewall rule-sets cover all publicly exposed endpoints, and every component is maintained with current security patches.