Data EngineeringDelivered

Finance Cockpit & Intelligent Collections on Cloudera

A big-data platform that cut financial-reporting errors by 50%

Operationalised a Cloudera-based big-data platform for a telecom operator’s finance function: 10 workflows and ~50 scripts across Spark, Impala, Hive and Oozie that raised operational efficiency 30% and cut reporting errors 50%.

PythonPySparkSQLApache SparkImpalaApache OozieHiveHadoopCloudera

Problem Statement

A finance analytics estate ran on un-optimised workflows and scripts. The result was inefficiency, inconsistent reporting and fragile data integrity, which undermined trust in the numbers the business ran on.

Un-optimised workflows and scripts caused operational inefficiency.
Limited analytical insight from a fragmented finance data platform.
Data inconsistencies threatened the accuracy of financial reporting.

Headline Outcomes

+30%optimised workflows

Operational efficiency

−50%data consistency

Reporting errors

Enrichedbig-data platform

Analytical insight

The Solution

A Cloudera data platform of 10 orchestrated workflows and roughly 50 scripts, with five major enhancements across Apache Spark, Impala, Hive, Oozie and Hadoop, built to enrich analytics and make financial reporting verifiably accurate.

Operationalised a Cloudera platform of 10 workflows and ~50 production scripts.

Shipped five major enhancements using PySpark, Impala, Apache Spark, Oozie and Hive.

Resolved data inconsistencies to cut financial-reporting errors by 50%.

Enriched analytical insight for confident, data-driven decision-making.

System Architecture

How the data flows

Source Finance Data

Collections & ledgers

Oozie Workflows

10 orchestrated jobs

Spark + Hive

~50 transform scripts

Impala

Interactive analytics

Finance Cockpit

Accurate reporting

Result 01

Tidied finance workflows and optimised script execution at scale.

Result 02

Halved reporting errors, restoring trust in financial data.

Result 03

Gave stakeholders deeper, faster analytical insight.

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