MinervaDB × Amazon Web Services

AWS Data Platform Engineering, Migration & Managed Operations

MinervaDB helps enterprises design, migrate, optimize, and operate the AWS data stack — Amazon Aurora, RDS for PostgreSQL and MySQL, Redshift, DynamoDB, S3, and Glue — pairing two decades of database performance engineering with deep AWS expertise to turn fragmented estates into a governed, fast, and cost-disciplined data foundation.

Why MinervaDB for AWS

A Database Engineering Partner for the AWS Data Stack

AWS gives data teams more choice than any other cloud, and that is both its strength and its trap. Aurora and RDS deliver managed PostgreSQL and MySQL, Redshift handles the warehouse, DynamoDB covers key-value at scale, S3 and Lake Formation anchor the lake, and Glue stitches it together. The breadth is genuine, and it is exactly why so many enterprises standardize on AWS. It is also why the bill climbs faster than anyone planned, and why a query that should take seconds drags on for minutes. AWS makes provisioning effortless; it does not make the engineering decisions for you. Someone still has to size the instances, design the schema, tune the queries, and pick the right service for each workload. That is the work we do.

MinervaDB engineers have spent careers inside storage engines, query optimizers, and the cost-versus-performance tradeoffs that decide whether a data platform is an asset or a liability. We bring that same discipline to AWS. Where many consultancies stop at provisioning resources and wiring up a QuickSight dashboard, our engineers reason about Aurora instance sizing, Redshift distribution and sort keys, query plans, and the consumption patterns that quietly drive the bill. The result is an AWS estate that is fast where it needs to be, governed where it must be, and economical everywhere.

The Partnership

Centralized Data Management and Agility on AWS

The pairing is straightforward in principle. AWS supplies a broad, mature data platform: managed relational databases in Aurora and RDS, a columnar warehouse in Redshift, a managed lakehouse on S3 and Lake Formation, NoSQL at scale in DynamoDB, and ingestion and transformation through Glue, Kinesis, and the Database Migration Service. MinervaDB supplies the engineering discipline to make that platform perform predictably, stay governed, and cost what it should. Together the result is a data foundation where transactional and analytical workloads run on a coherent estate, silos collapse, and the analytics and application teams stop fighting the infrastructure.

In practice, enterprises rarely start from a clean slate. There is an on-premise database approaching end of support, a warehouse straining under reporting load, a data lake that drifted into a swamp, and an AWS account where spend has crept up quarter after quarter with no clear owner. Our job is to engineer the path from that reality to an AWS estate the organization can trust and afford. That means deliberate choices about instance sizing, warehouse design, partitioning, governance structure, and the operational tooling that keeps the platform healthy after the consultants leave.

If you want the broader context for how we think about data infrastructure, our approach to full-stack database infrastructure engineering carries directly into AWS: the same rigor on measurement, the same refusal to guess, and the same insistence that an architecture must be operable and affordable, not just impressive on a slide.

We lead with engineering rather than strategy decks because AWS rewards teams that understand what happens beneath the abstraction. An Aurora instance sized one tier too large bills around the clock for capacity nobody uses. A Redshift table with the wrong distribution key shuffles data across nodes on every join. An Athena query against unpartitioned S3 scans far more than it should, and you pay for every byte. The difference between an AWS bill that is defensible and one that is alarming is almost always engineering judgment, and that is precisely what we bring.

Our Capabilities

How We Engineer the AWS Data Platform

Our AWS practice is organized around six capability areas that span the full lifecycle of a modern data estate. Each maps to a stage most enterprises move through, and each is delivered by senior engineers rather than handed to a junior bench. These are not rigid phases you complete once. A mature estate revisits all six continuously as data volumes grow, new sources arrive, query patterns shift, and budgets tighten. We design with that reality in mind, so the foundation laid early does not have to be torn out later.

A word on data storage, because it is where many AWS builds quietly go wrong. Teams default to one service for everything — usually because it is familiar — and then fight its limits for years. A high-throughput key-value workload belongs in DynamoDB, not a relational instance bent out of shape to handle it. A heavy analytical aggregation belongs in Redshift or Athena, not an over-scaled Aurora instance straining under reporting load. A transactional workload belongs in Aurora or RDS, properly sized and tuned. Matching the workload to the right store, and tuning that store properly, is unglamorous work that pays off every single day the platform runs. We hold the line on it because it is the difference between an estate that scales and one that becomes a recurring incident.

Our Solutions

Engineering Accelerators for AWS

Repeatable problems deserve repeatable solutions. Over many engagements we have packaged the work that recurs into a set of accelerators — opinionated frameworks and tooling that shorten time to value while keeping the build maintainable. None of these replace engineering judgment; they encode it. An accelerator gets a team to a sensible default quickly, and then our engineers adapt it to the specifics of the environment. That balance matters, because a framework applied blindly is just a faster way to accumulate technical debt.

Engagement Model

From Assessment to Managed Operations

We meet enterprises wherever the AWS journey currently stands — greenfield build, stalled migration, or a platform that works but costs too much — and move through four phases. The phases are deliberately lightweight at the front. We would rather spend two weeks understanding the real workloads and the real cost drivers than a quarter producing a strategy nobody implements. Most engagements show a tangible win inside the first month, which is what earns the trust to do the deeper work.

Performance & Cost

Where AWS Cost Is Won or Lost

An AWS bill that surprises the CFO almost always traces back to engineering choices: over-sized Aurora and RDS instances, Redshift tables with poor distribution, Athena queries scanning unpartitioned S3, idle resources left running, and storage retention set longer than the data warrants. We treat these as solvable engineering problems. The example below shows the kind of routine analysis we use to keep an Amazon Aurora or RDS PostgreSQL instance fast and economical — surfacing the queries and missing indexes that dominate resource usage before they dominate the bill.

-- Top queries by total time on Aurora / RDS PostgreSQL
-- (requires the pg_stat_statements extension)
SELECT
  ROUND(total_exec_time::numeric, 0)              AS total_ms,
  calls,
  ROUND(mean_exec_time::numeric, 2)               AS avg_ms,
  ROUND(100.0 * total_exec_time /
        SUM(total_exec_time) OVER (), 1)           AS pct_of_total,
  LEFT(query, 120)                                 AS query_preview
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;

-- Large tables doing sequential scans (prime indexing candidates)
SELECT
  schemaname, relname,
  seq_scan, idx_scan,
  pg_size_pretty(pg_relation_size(relid))          AS table_size,
  n_live_tup                                       AS row_estimate
FROM pg_stat_user_tables
WHERE seq_scan > idx_scan
  AND pg_relation_size(relid) > 100 * 1024 * 1024  -- larger than 100 MB
ORDER BY pg_relation_size(relid) DESC;

The second query is the one that tends to surprise people. Large tables accumulating sequential scans are usually missing an index on a column the application filters on constantly, and adding it can turn a multi-second query into a sub-millisecond one. Beyond query and index work, we right-size Aurora and RDS instances to actual load, use Aurora Serverless and reserved instances where the usage pattern justifies them, tune Redshift distribution and sort keys, and partition S3 so Athena scans a fraction of the data. None of this is exotic, but doing it consistently — and measuring the effect — is what separates a platform that scales gracefully from one that becomes a budget line nobody can explain. For a deeper look at the methodology behind this, see our writing on database performance engineering.

We also watch the parts of the bill that are easy to ignore. Aurora and RDS instances over-provisioned for a peak that happens twice a year, Redshift clusters left running overnight where Serverless or pause-and-resume would do, snapshots and backups retained far longer than policy requires, cross-AZ and cross-region data transfer that a better architecture would avoid, and S3 storage classes left at Standard for data nobody touches all add up. A cost audit usually surfaces a handful of these within the first week, and the savings from fixing them often fund the rest of the engagement. Our view is simple: every dollar spent on AWS should be traceable to a workload someone can name. When it is not, that is an optimization waiting to happen.

Governance & Security

Governance That Auditors and Engineers Both Accept

Governance often fails because it is designed for one audience and resented by the other. Security teams want control and provable compliance; engineers want to ship without friction. The AWS governance stack, applied well, gives both. We implement the Glue Data Catalog and Lake Formation for cataloging, lineage, and fine-grained data access, AWS IAM and IAM Identity Center for identity, KMS for key management, and database-level controls such as encryption at rest and in transit, row-level security, and audit logging. AWS documents the building blocks thoroughly in the AWS security documentation; the engineering judgment is in how those blocks are assembled for a real organization.

For enterprises with residency and regulatory obligations — and most of the organizations we work with carry them — we engineer data placement, network isolation through VPCs and PrivateLink, and access policies to satisfy frameworks such as SOC 2, ISO 27001, HIPAA, GDPR, and India’s DPDP Act. The goal is an audit-ready posture that is enforced by the platform rather than by a policy document nobody reads. CloudTrail and CloudWatch give us the access history and operational detail needed to answer audit questions without a scramble.

DataOps is the other half of keeping an AWS estate trustworthy. We bring software engineering discipline to data pipelines and database changes: version control and CI/CD through CodePipeline and CodeBuild, infrastructure as code with CloudFormation or Terraform, automated testing of transformations and schema changes before they reach production, and observability that captures pipeline runs, data quality assertions, and end-to-end lineage. When a downstream QuickSight report looks wrong at 9am, the team should be able to trace it to the exact upstream change in minutes, not spend a day guessing. That capability is engineered in deliberately, and it pays for itself the first time a production issue is resolved before the business even notices.

Analytics & AI

Engineering Data That Is Ready for AI

AWS has invested heavily in bringing machine learning and generative AI to the data, with SageMaker for the full ML lifecycle and services like Amazon Bedrock and Q opening up generative AI on top of enterprise data. The appeal is obvious: keep the data in one governed place and build analytics and AI on top of it. The catch is equally familiar. AI is only as good as the data underneath it, and a model trained on inconsistent or poorly modeled data produces confident nonsense at scale.

Our work here is unglamorous and essential. We build pipelines that are observable and incremental rather than monolithic batch jobs, structure the data so feature engineering is repeatable, and apply the same governance and quality discipline to AI inputs as to any other production data. When an organization wants to use SageMaker or Bedrock for forecasting, document processing, or natural-language analytics, the value depends entirely on whether the underlying data is trustworthy and well-modeled. We make sure it is, so the AI initiative rests on engineering rather than hope.

Industries

Where We Apply AWS Data Engineering

The AWS data pattern is industry-agnostic, but the workloads and the regulatory weight are not. We have engineered AWS data platforms across sectors where data volume, query latency, and compliance all matter at once.

In banking and financial services, the work centers on risk, fraud, and regulatory reporting, where lineage and access control are non-negotiable and a late report has consequences. In pharmaceuticals and healthcare, it is migrating on-premise platforms onto AWS while satisfying strict compliance, and building analytics on Redshift that researchers and commercial teams can trust. In consumer goods and retail, it is demand forecasting, customer analytics, and the relentless need to unify data from dozens of source systems — often migrating a legacy platform onto AWS along the way. In manufacturing and energy, it is high-volume operational and IoT data streaming through Kinesis into the lake for analysis. Across all of these, the engineering principles are the same even when the domain is not, which is exactly why a performance-led, vendor-neutral partner tends to outperform a team that knows the tool but not the economics beneath it.

Why It Matters

Why a Specialist Engineering Partner Pays Off

It is tempting to treat an AWS build as a staffing problem — add a few contractors, follow the platform documentation, and the rest will follow. It rarely does. The documentation describes what is possible, not what is wise for a specific estate under specific constraints. The decisions that determine whether a platform is fast, governed, and affordable are made early and are expensive to reverse: which service holds which workload, how Redshift is distributed and sorted, how instances are sized, how migration risk is contained, and how spend is governed. Getting those right the first time is worth far more than the day rate of the people making them.

That is the case for working with MinervaDB on AWS. We are not generalists who learned the platform last quarter, and we are not a reseller optimizing for consumption. We are database engineers who have spent careers making data systems fast, reliable, and secure, and we apply that same standard to the AWS data stack. The outcome a data leader can take to the board is a platform that does what the business needs, costs what it should, and keeps doing so after we hand over the keys.

Customer Outcomes

Outcomes We Engineer

A few representative engagement patterns, drawn from the kinds of problems enterprises bring to us. Specifics are generalized to respect confidentiality, but the shape of each is true to the work. What they have in common is a starting point of frustration — a platform that was supposed to simplify things and instead added cost or confusion — and an ending where the data finally became an asset the business could rely on.

Insights

Thought Leadership & Resources

Our engineers write about the work. A selection of guides and resources on building and operating the AWS data platform.

FAQ

Frequently Asked Questions

How MinervaDB Can Help

Let’s build an AWS data estate that performs and pays off

Whether you are starting a greenfield estate on Aurora and Redshift, rescuing a stalled migration from Oracle or SQL Server, or trying to bring a runaway AWS bill back under control, MinervaDB brings senior database engineers who design, migrate, optimize, and operate the platform end to end — with measured results, not slideware.

We work vendor-neutral, tune for cost as seriously as for speed, and stay on through 24×7 managed operations so reliability and economics remain engineered long after go-live.

Talk to our AWS engineering team →