Full-stack database infrastructure engineering across the GCC estate

Full-stack database infrastructure engineering means owning every layer a query touches: storage and the operating system kernel, engine internals, the connection layer, application access patterns and observability. MinervaDB delivers it across relational, NoSQL, in-memory, analytical, federation and vector engines as one coherent practice, which is what lets GCC data leaders govern one operating model.

Below the SQL prompt

A full-stack mandate means the work does not stop at the database. We instrument the Linux kernel with eBPF tooling to capture I/O latency histograms, lock contention and scheduler stalls that engine metrics never reveal. The example correlates block-device latency with PostgreSQL checkpoint activity, a class of problem that looks like a slow query and lives in the storage stack.

# Block I/O latency histogram for 10 s during a suspected checkpoint storm
sudo biolatency-bpfcc -D 10 1

# Correlate with PostgreSQL checkpoint activity (PostgreSQL 17+: pg_stat_checkpointer)
psql -c "SELECT num_timed, num_requested, write_time, sync_time, buffers_written
         FROM pg_stat_checkpointer;"

On PostgreSQL 16 and earlier the checkpoint counters live in pg_stat_bgwriter (checkpoints_timed, checkpoints_req, checkpoint_write_time). Test tooling on a non-production host first.

This whole-stack visibility converts an escalation such as “the database is slow” into an evidence-backed root cause and a fix that holds under load, which is the difference between infrastructure engineering and reactive administration.

Analytics and data platform engineering

GCC data leaders increasingly own the analytics and AI platform, not just the operational databases. We engineer real-time and batch platforms that move data from PostgreSQL, MySQL and MongoDB into ClickHouse, Snowflake, BigQuery, Redshift or Databricks through change-data-capture pipelines rather than hand-rolled ETL, and unify it in a governed lakehouse.

Real-time analytics with ClickHouse and Trino

For event-scale analytics, GCC data leaders get ClickHouse deployed with the table engine, partitioning and ordering key chosen from the query pattern, so aggregations over billions of rows return in milliseconds. The schema below is a MergeTree design for time-series telemetry, a pattern used for CPG, BFSI and telecom observability workloads. ClickHouse engagements are delivered with our sister company ChistaDATA.

CREATE TABLE telemetry.events
(
    event_time  DateTime64(3, 'UTC'),
    tenant_id   UInt32,
    metric      LowCardinality(String),
    value       Float64,
    dims        Map(LowCardinality(String), String)
)
ENGINE = MergeTree
PARTITION BY toYYYYMM(event_time)
ORDER BY (tenant_id, metric, event_time)
TTL toDateTime(event_time) + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

Where GCC data leaders need queries that span a transactional PostgreSQL database, an object store and a ClickHouse warehouse, Trino acts as the federation layer so analysts use one SQL interface without copying data. The GCC delivers a unified analytics experience while each dataset stays on the engine best suited to it. Related: data analytics platform engineering and data engineering.

Cloud database infrastructure and DBaaS

Most estates run by GCC data leaders are hybrid, with self-managed engines beside managed cloud services. MinervaDB engineers cloud database infrastructure across all three hyperscalers and the major data platforms, choosing the managed service that fits each workload and operating it to the same SLA as the self-hosted fleet.

Cloud engineering for GCC data leaders is opinionated about cost: right-sized instances, scheduled non-production environments, tiered and lifecycle storage, and reserved or committed-use pricing modelled from your billing data. The recommendation follows the workload and the economics, not a partnership incentive, which is the independence GCC data leaders need when reporting to global finance.

GCC data leaders: frequently asked questions