Databricks database replication eliminated query processing lag.
Pythian used a strangler migration pattern to move Netezza pipelines to Databricks with zero billing disruption.
A regional US mobile network provider faced critical billing database delays as rapid subscriber growth overwhelmed its physical, unsupported Netezza database. To resolve this performance bottleneck without risking global operational downtime, the provider engaged Pythian to systematically replicate its transactional data using Databricks. Replicating these workloads successfully automated legacy pipelines with dbt and Apache Airflow, enabling network engineers to immediately optimize regional tower routing instead of troubleshooting physical server failures.
We faced a high-risk operational crisis with an aging appliance that simply could not handle our scaling log volumes. Pythian's incremental synchronization allowed us to test and validate our migration on Databricks with zero downtime and absolute data integrity."
Vice President of Network Infrastructure
Regional Mobile Network Provider
$1.8M
Annual operational savings
40%
TCO reduced
3–5x
Faster query performance
Accelerate database migration to achieve instant operational scale.
Physical hardware constraints delayed daily subscriber analytics.
Pythian transitioned legacy query logic to automate a rapid, risk-free migration into a Databricks lakehouse.
We had ten years of critical usage and billing logic locked inside outdated NZPLSQL procedures that had zero documentation. Pythian mapped every dependency and safely translated that legacy procedural code into Databricks Spark execution with zero unplanned downtime."
Vice President of Network Infrastructure
Regional Mobile Network Provider
Server disk failures stalled critical reporting
Two physical disk failures on the legacy Netezza appliance forced emergency manual restorations during critical billing runs, threatening revenue reconciliation.
Heavy subscriber traffic saturated database
Rapid regional network expansion generated millions of connection logs daily, overwhelming the on-premises database storage.
Undocumented NZPLSQL procedures halted migration
Over 400 legacy stored procedures lacked configuration tracking, making sudden platform transitions highly dangerous.
Big bang cutovers risked regional outages
Attempting a single-day cold database migration threatened to disrupt continuous cellular services for regional subscribers.
Established an operational performance baseline before data migration.
Pythian Data Engineers conducted a forensic technical assessment of the Netezza environment. Correcting physical disk data skew and zone map errors improved local query performance by 30% before the move. The forensic audit mapped the data lineage to ensure the team left redundant files behind, preventing post-migration cloud spend from inflating.
Replicated network workloads incrementally via parallel sync, eliminating downtime risks.
Engineering teams established a parallel database replication framework to migrate data step-by-step into Databricks. With this parallel setup, technical teams could test and validate transactional calculations against the Netezza baseline in real time. Running parallel operations eliminated the operational threat of system downtime or a single point of failure.
Automated telemetry workflows using Airflow and dbt to process millions of logs.
The team automated 85% of query migrations and manually rewrote complex recursive SQL into modular dbt models. By replacing legacy DataStage pipelines with Airflow, they introduced version-controlled tracking and automated validation tests, allowing telemetry ingestion to scale dynamically and process millions of connection logs without manual intervention.
Configured active compute guardrails within Databricks to prevent unexpected cost spikes.
Pythian's Managed Services team implemented automated scaling limits and query performance monitoring within the Databricks lakehouse. The guardrails protect against unexpected cost spikes while dynamically allocating compute resources during peak subscriber traffic hours. The telecom provider now secures elastic database scaling benefits alongside rigid cost control.
.svg){kind=small}
.svg){kind=small}