Table of Contents

1. Why Genomics Teams Choose Lakehouse Architecture
2. Understanding D23.io and the Modern Data Stack
3. Apache Iceberg: The Foundation for Petabyte Genomics Data
4. Partitioning Strategy for Genomics Datasets
5. Trino as Your Query Engine: Performance at Scale
6. Apache Superset for Genomics Analytics Visualisation
7. Governance, Security, and Compliance
8. Claude-Assisted Query Writing for Genomics Analytics
9. Implementation Roadmap and Next Steps
10. Real-World Outcomes and Cost Implications

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Why Genomics Teams Choose Lakehouse Architecture

Genomics organisations face a unique data challenge: they generate petabytes of raw sequencing data, variant annotations, clinical metadata, and research outputs that traditional data warehouses simply cannot handle cost-effectively. A genomics team at a mid-size biotech firm might ingest 500 terabytes of raw FASTQ files annually, plus billions of variant records, expression matrices, and patient phenotypes. Moving all of this through a traditional ETL pipeline into a centralised warehouse costs millions in compute and storage, and the rigid schema often breaks when research protocols evolve.

Lakehouse architecture—a hybrid of data lake flexibility and warehouse structure—solves this problem. Instead of forcing genomics data into predefined schemas, you store raw and processed data in cloud object storage (S3, Azure Blob, GCS) whilst maintaining ACID transactions, schema enforcement, and query optimisation through an open table format like Apache Iceberg. This approach lets genomics teams:

• Store petabytes without warehouse licensing costs
• Query data in place using SQL, avoiding expensive data movement
• Evolve schemas as research questions change
• Enforce governance through role-based access and audit logs
• Reduce time-to-insight from weeks to hours

D23.io, a modern data orchestration and lakehouse platform, is purpose-built to simplify this architecture for teams that need production-grade reliability without DevOps overhead. When paired with Apache Iceberg for ACID-compliant storage, Trino for distributed SQL querying, and Apache Superset for interactive analytics, genomics organisations can build a complete analytics stack that scales from gigabytes to petabytes without architectural rework.

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Understanding D23.io and the Modern Data Stack

D23.io is a cloud-native data platform that abstracts away the complexity of building and maintaining a lakehouse. Rather than managing Kubernetes clusters, Spark jobs, and object storage buckets manually, D23.io provides a managed environment where you define data pipelines, transformations, and governance policies through a declarative interface.

For genomics teams, D23.io offers several critical advantages:

Managed Infrastructure: D23.io handles cluster provisioning, auto-scaling, and fault tolerance. Your team focuses on data quality and analytics, not infrastructure patching.

Native Iceberg Integration: D23.io natively supports Apache Iceberg tables, meaning you get ACID transactions, time-travel queries, and hidden partitioning out of the box. This is essential when genomics pipelines need to reprocess data or audit changes to variant calls.

Query Engine Flexibility: D23.io integrates with multiple query engines—Trino, Spark SQL, and others—allowing your team to choose the right tool for each workload. Exploratory queries might use Trino for speed; heavy transformations might use Spark.

Cost Transparency: D23.io’s consumption-based pricing means you pay only for compute and storage you use. A genomics team processing 100TB of variants monthly pays a fraction of traditional data warehouse costs.

When you combine D23.io with Trino and Superset, you create a stack where raw genomics data flows into Iceberg tables, analysts query those tables via Trino using SQL, and non-technical researchers visualise results through Superset dashboards. This separation of concerns—storage, compute, and presentation—is the foundation of modern data architecture.

PADISO has worked with genomics and bioinformatics teams to architect exactly this stack. Our AI & Agents Automation service includes designing query layers that let researchers ask questions in plain English, which Claude translates into optimised SQL. This approach cuts query-writing time by 60% and makes analytics accessible to domain experts without SQL skills.

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Apache Iceberg: The Foundation for Petabyte Genomics Data

Apache Iceberg is an open-source table format that brings database-like semantics to cloud object storage. Unlike traditional data lakes where data governance is loose and schema evolution is painful, Iceberg enforces schema, tracks data lineage, and enables atomic updates—critical for genomics where data quality and reproducibility are non-negotiable.

Why Iceberg for Genomics

Genomics datasets evolve constantly. Your initial variant table might have 50 columns; six months later, you’ve added functional annotations, population frequencies, and clinical significance scores. With Iceberg, you add columns without rewriting the entire dataset. Iceberg’s schema evolution handles this transparently.

Iceberg also solves the “late-arriving data” problem common in genomics. A sample sequenced in January might have reanalysis results added in March. With Iceberg’s time-travel feature, you can query the data as it existed on any date, enabling reproducible research and audit trails.

Key Iceberg Features for Petabyte Genomics Workloads:

• ACID Transactions: Multiple pipelines can write to the same Iceberg table safely. If a reanalysis pipeline crashes mid-write, it rolls back automatically.
• Hidden Partitioning: You define partitioning logic once; Iceberg handles it transparently. No more manually creating partition columns that clutter your schema.
• Snapshot Isolation: Queries see consistent snapshots of data, even whilst writes are happening. Researchers get accurate results without locking tables.
• Schema Evolution: Add, rename, or reorder columns without rewriting data.
• Data Compaction: Iceberg tracks small files and merges them efficiently, preventing the “small-files problem” that plagues traditional data lakes.

Shopify’s migration of petabytes from Hive to Iceberg using Trino demonstrated the performance gains achievable. Similarly, Ancestry optimises a 100-billion-row Iceberg table with best practices that directly apply to genomics datasets, including partition pruning, clustering, and statistics collection.

Iceberg on D23.io

D23.io manages Iceberg table creation, versioning, and optimisation. When your genomics pipeline ingests raw sequencing data, D23.io automatically:

1. Writes data to Iceberg tables in cloud storage
2. Compacts small files in the background
3. Collects statistics for query optimisation
4. Enforces schema and data quality rules
5. Maintains audit logs for compliance

This hands-off approach means your team doesn’t manage Iceberg metadata or worry about table bloat—common pain points when teams run Iceberg manually.

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Partitioning Strategy for Genomics Datasets

Partitioning is critical for petabyte-scale genomics analytics. A poorly partitioned Iceberg table forces Trino to scan terabytes of irrelevant data; a well-partitioned table lets Trino skip 99% of data and return results in seconds.

Natural Partitioning for Genomics Data

Genomics datasets have natural partitioning dimensions that align with how researchers query data:

By Chromosome and Region: Variant tables are almost always queried by chromosome (chr1, chr2, etc.) and genomic region (start-end coordinates). Partition by chromosome first, then by region ranges (e.g., 0-1M, 1M-2M, etc.). This lets researchers query a single chromosome without touching data for other chromosomes.

Partition Scheme:
  /chromosome=1/region_bin=0/
  /chromosome=1/region_bin=1/
  /chromosome=2/region_bin=0/
  ...

By Sample or Individual: Clinical genomics tables often group data by patient ID or sample identifier. Partition by sample_id so queries filtering to a single patient skip irrelevant samples.

By Analysis Date: Genomics pipelines run reanalysis frequently (new reference genomes, updated annotations). Partition by analysis_date so researchers can query the latest run or compare across runs.

By Data Type: Separate partitions for raw sequencing (FASTQ), aligned reads (BAM), variants (VCF), and annotations. This prevents mixing different data types and allows independent scaling of storage and compute.

Iceberg’s Hidden Partitioning Advantage

Traditional partitioned tables (Hive, Delta) require partition columns in your schema. This clutters queries: SELECT * FROM variants WHERE chromosome = '1' AND region_bin = 0. With Iceberg’s hidden partitioning, the partition columns are metadata; your schema stays clean:

SELECT * FROM variants WHERE chromosome = '1' AND position BETWEEN 1000000 AND 2000000

Iceberg automatically prunes partitions without you managing partition columns explicitly. This is especially valuable for genomics where domain experts write queries and shouldn’t need to understand partitioning logic.

Partition Sizing for Petabyte Workloads

Each partition should contain roughly 100GB–1TB of data. Too small (< 10GB per partition), and metadata overhead becomes significant. Too large (> 10TB per partition), and partition pruning loses effectiveness.

For a genomics team storing 10 petabytes of variant data:

• 23 chromosomes × 100 region bins per chromosome = 2,300 partitions
• Each partition ≈ 4.3TB
• Trino can prune to relevant partitions in milliseconds

D23.io’s partition management automatically suggests optimal partition sizes based on your data volume and query patterns. You define the partitioning logic; D23.io handles rebalancing as your data grows.

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Trino as Your Query Engine: Performance at Scale

Trino is a distributed SQL query engine designed for federated queries across multiple data sources. For genomics teams using Iceberg, Trino is the ideal query layer: it understands Iceberg’s partition pruning, supports complex genomics queries (window functions, recursive CTEs, array operations), and scales to petabyte datasets without breaking a sweat.

Why Trino for Genomics Queries

Partition Pruning: Apache Iceberg Trino: Modern Data Lakehouse Explained details how Trino leverages Iceberg’s metadata to skip irrelevant partitions. A query filtering to chromosome 1 doesn’t scan chromosomes 2–22.

Complex Analytics: Genomics queries often involve:

• Window functions (allele frequency rankings within populations)
• Array operations (exploding variant arrays into individual calls)
• Recursive queries (ancestry trees in population genetics)
• Statistical functions (chi-square tests, p-value calculations)

Trino supports all of these natively, whereas some traditional warehouses require workarounds.

Cost Efficiency: Trino is open-source and runs on commodity hardware. Unlike proprietary query engines with per-seat licensing, Trino’s costs scale with compute usage, not users. A genomics team with 50 analysts pays the same for compute as one with 5.

Trino Architecture on D23.io

D23.io manages Trino clusters automatically. You define the cluster size (small for dev, large for production), and D23.io:

1. Provisions worker nodes
2. Configures Iceberg connectors
3. Tunes memory and parallelism settings
4. Handles failover and recovery

Your genomics team submits queries via the Trino JDBC driver, command-line interface, or integrated tools like Superset. Trino distributes the query across workers, each processing a subset of partitions in parallel.

Query Optimisation for Petabyte Genomics Workloads

Predicate Pushdown: Always filter early. A query like:

SELECT chromosome, position, ref, alt, allele_frequency
FROM variants
WHERE chromosome = '1' AND position BETWEEN 1000000 AND 2000000

lets Trino skip 95% of partitions before reading any data.

Columnar Storage: Iceberg stores data in Parquet format, which is columnar. Queries selecting 5 columns from a 100-column table read only those 5 columns, not the entire row.

Statistics and Cost-Based Optimisation: Trino uses table statistics to choose optimal query plans. D23.io automatically collects and updates statistics, so Trino always has accurate cardinality estimates.

Caching: D23.io caches frequently accessed partitions in memory, reducing query latency for interactive dashboards and repeated queries.

The official Trino documentation on Apache Iceberg integration provides detailed configuration options for tuning Trino for your genomics workloads.

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Apache Superset for Genomics Analytics Visualisation

Trino executes queries fast, but genomics researchers need to visualise results. Apache Superset is an open-source business intelligence tool that connects to Trino and lets researchers build dashboards, run ad-hoc queries, and explore data interactively.

Superset as Your BI Layer

Superset sits between Trino and your genomics team. Researchers define semantic layers (views, calculated fields, aggregations) in Superset, then build dashboards without touching SQL. Non-technical domain experts can filter by chromosome, sample, or analysis date using UI controls.

Key Superset Features for Genomics:

• SQL Lab: Write and test Trino queries interactively, save results, and build dashboards from query results
• Semantic Layer: Define views and calculated fields once; reuse across dashboards (e.g., “allele frequency” as a calculated field across 50 dashboards)
• Caching: Cache query results for 1 hour, reducing redundant Trino queries and costs
• Row-Level Security: Restrict researchers to their own samples or studies
• Alerts: Notify teams when variants of interest appear or quality metrics drop

Connecting Superset to Trino on D23.io

The official guide for connecting Apache Superset to Trino walks through the setup:

1. Install Superset (Docker, Kubernetes, or managed service)
2. Add a Trino database connection: trino://user:password@trino-host:8080/iceberg
3. Import Iceberg tables as Superset datasets
4. Configure caching and row-level security
5. Build dashboards

D23.io can provision Superset as part of your lakehouse stack, or you can run it independently and point it to D23.io’s Trino endpoint. Most genomics teams prefer the latter for flexibility—they keep Superset separate from the data platform so they can upgrade, customise, or migrate independently.

Real-World Superset Dashboards for Genomics

A typical genomics team builds Superset dashboards like:

Variant Discovery Dashboard: Shows newly discovered variants by chromosome, gene, and functional impact. Filters by allele frequency, population, and analysis date. Researchers click a variant to see detailed annotations.

Sample QC Dashboard: Tracks sequencing depth, coverage uniformity, and contamination across samples. Flags samples failing quality thresholds.

Population Genetics Dashboard: Visualises allele frequency distributions, Hardy-Weinberg equilibrium, and linkage disequilibrium across populations.

Clinical Genomics Dashboard: Links variants to phenotypes, disease associations, and drug responses. Restricted to clinicians via row-level security.

PADISO’s The $50K D23.io Consulting Engagement: What’s Inside outlines how we architect semantic layers and dashboards in Superset for fixed-fee engagements. Genomics teams typically invest 4–6 weeks to build a production semantic layer and 10–15 core dashboards.

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Governance, Security, and Compliance

Genomics data is sensitive. Patient samples contain genetic information that, if breached, could identify individuals or reveal health status. Regulatory frameworks like HIPAA (US), GDPR (EU), and Australian Privacy Act require governance controls: access logs, encryption, data retention policies, and audit trails.

Apache Iceberg + Trino + Superset + D23.io provides the foundation, but you must layer on governance policies.

Access Control and Row-Level Security

Iceberg Permissions: Define who can read, write, or modify tables. D23.io integrates with cloud IAM (AWS IAM, Azure AD, GCP IAM) so researchers authenticate via corporate credentials. A researcher in the cardiology team can read cardiology samples but not oncology samples.

Trino Catalog Security: Trino supports role-based access control (RBAC) at the catalog, schema, and table levels. You define roles (e.g., cardiology_analyst, genomics_admin) and grant permissions.

Superset Row-Level Security: Restrict dashboard data by user. A researcher sees only samples from their study or institution. Superset applies filters transparently when they query the dashboard.

Data Encryption and Privacy

Encryption at Rest: D23.io stores Iceberg tables on cloud object storage (S3, Azure Blob, GCS) with encryption enabled by default. Genomics data is encrypted using AES-256.

Encryption in Transit: Trino and Superset communicate over TLS/SSL. Data never moves unencrypted.

Pseudonymisation: Store sample identifiers separately from phenotypes. Use a separate key management system to map identifiers to samples. This way, if the data warehouse is breached, attackers don’t immediately know which samples belong to which individuals.

Audit Logging and Compliance

Query Audit Logs: D23.io logs every query executed on Iceberg tables: who ran it, when, what data they accessed. Genomics teams use these logs to demonstrate HIPAA compliance during audits.

Data Lineage: Iceberg tracks data lineage—which pipeline produced which table, which transformations created which columns. Researchers can trace a result back to raw sequencing data.

Retention Policies: Define how long data is kept. Iceberg’s snapshot isolation lets you retain historical snapshots for compliance without storing duplicate data.

SOC 2 and ISO 27001 Readiness

If your genomics organisation is subject to SOC 2 Type II or ISO 27001 audits (common for clinical genomics or precision medicine companies), D23.io’s built-in governance features accelerate compliance. PADISO’s Security Audit (SOC 2 / ISO 27001) service helps teams audit their D23.io deployments and implement additional controls like multi-factor authentication, IP whitelisting, and data masking.

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Claude-Assisted Query Writing for Genomics Analytics

One of the most innovative applications of agentic AI in genomics is using Claude (or similar large language models) to translate natural language questions into optimised SQL queries. Instead of requiring researchers to know Trino SQL syntax, they ask questions in plain English, and Claude generates the query.

Why Claude for Genomics Queries

Genomics domain experts—molecular biologists, geneticists, clinicians—often lack SQL skills. Training them on Trino syntax takes weeks and creates bottlenecks. With Claude, a researcher can ask:

“Show me the allele frequency of rs1234567 across European populations, stratified by age group.”

Claude understands genomics terminology (allele frequency, populations, stratification) and translates this into a correct Trino query:

SELECT 
  population,
  age_group,
  COUNT(*) as sample_count,
  SUM(CASE WHEN genotype LIKE '%1%' THEN 1 ELSE 0 END) * 2.0 / (COUNT(*) * 2) as allele_frequency
FROM variants v
JOIN samples s ON v.sample_id = s.id
WHERE v.rsid = 'rs1234567'
  AND s.population IN ('EUR', 'EUR_SUBPOP')
GROUP BY population, age_group
ORDER BY population, age_group

PADISO’s Agentic AI + Apache Superset: Letting Claude Query Your Dashboards explores this pattern in detail. We’ve implemented Claude-assisted query writing for genomics teams, reducing query-writing time by 60% and enabling non-technical researchers to self-serve analytics.

Architecture: Claude + Trino + Superset

The architecture is straightforward:

1. Query Interface: Researcher types a natural language question into Superset’s SQL Lab or a custom web interface.
2. Claude Translation: Claude receives the question, table schema, and column descriptions (provided via a system prompt). Claude generates a Trino SQL query.
3. Validation: The system validates the query (checks syntax, estimated cost) before execution.
4. Execution: Trino executes the query, returns results.
5. Caching: Results are cached in Superset for 1 hour, reducing redundant Trino queries.

Prompt Engineering for Genomics

For Claude to generate correct genomics queries, you must provide detailed context in the system prompt:

You are an expert Trino SQL query writer for genomics data.

Table Schemas:
- variants: chromosome, position, ref, alt, rsid, allele_frequency, populations (array), annotations (struct)
- samples: sample_id, individual_id, population, age_group, phenotype, sequencing_date
- genes: gene_id, gene_name, chromosome, start, end, biotype

Column Descriptions:
- allele_frequency: frequency of alternate allele (0–1)
- populations: array of population codes (e.g., ['EUR', 'AFR', 'EAS'])
- annotations: struct containing functional impact, CADD score, ClinVar significance

Common Queries:
- Allele frequency stratified by population: GROUP BY population
- Variants in a gene: JOIN variants v WITH genes g ON v.chromosome = g.chromosome AND v.position BETWEEN g.start AND g.end
- Sample filtering: JOIN variants WITH samples ON v.sample_id = s.sample_id

Always:
- Use explicit JOINs
- Filter by chromosome first (partition pruning)
- Aggregate at the lowest granularity needed
- Avoid SELECT * (specify columns)

With this context, Claude generates queries that are not only correct but also optimised for Trino’s execution engine.

Limitations and Safeguards

Claude is powerful but not infallible. For genomics, implement safeguards:

Query Cost Limits: Estimate query cost before execution. If a query would scan > 1TB, require manual approval.

Dry-Run Validation: Trino can explain query plans without executing them. Validate the plan before running the query.

Sensitive Data Masking: If Claude’s query would expose sensitive data (e.g., individual-level genotypes), mask or redact results.

Audit Logging: Log all Claude-generated queries and results for compliance audits.

PADISO’s Agentic AI vs Traditional Automation: Which AI Strategy Actually Delivers ROI for Your Startup discusses when agentic AI (like Claude-assisted queries) is worth the investment versus traditional automation. For genomics teams with 20+ analysts, the ROI is typically positive within 3–6 months.

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Implementation Roadmap and Next Steps

Building a petabyte-scale genomics analytics stack on D23.io + Iceberg + Trino + Superset is a 12–16 week project for a mid-size genomics team. Here’s a realistic roadmap:

Phase 1: Foundation (Weeks 1–4)

Week 1–2: Data Ingestion

• Set up D23.io account and cloud storage (S3, Azure Blob, or GCS)
• Define Iceberg table schema for your genomics data (variants, samples, annotations)
• Build initial data pipelines to ingest raw sequencing data, VCF files, and sample metadata
• Implement data quality checks (missing values, schema validation, referential integrity)

Week 3–4: Partitioning and Optimisation

• Analyse query patterns from your existing data warehouse or logs
• Design Iceberg partitioning scheme (chromosome, region, analysis date)
• Run initial data loads and benchmark query performance
• Collect table statistics for Trino cost-based optimisation

Phase 2: Query and Analytics Layer (Weeks 5–8)

Week 5–6: Trino Setup

• Provision Trino cluster on D23.io (or independently, pointing to D23.io’s Iceberg tables)
• Configure Iceberg connector and table metadata caching
• Test query performance on representative genomics queries
• Tune Trino memory, worker count, and parallelism settings

Week 7–8: Superset Deployment

• Install and configure Apache Superset
• Connect Superset to Trino
• Import Iceberg tables as Superset datasets
• Build semantic layer (views, calculated fields, aggregations)
• Create 5–10 core dashboards (variant discovery, sample QC, population genetics)

Phase 3: Governance and Security (Weeks 9–12)

Week 9–10: Access Control

• Implement Trino RBAC (define roles, grant permissions)
• Configure Superset row-level security
• Integrate with corporate identity provider (Azure AD, Okta, etc.)
• Test access controls with pilot users

Week 11–12: Compliance and Auditing

• Enable audit logging in D23.io, Trino, and Superset
• Implement data retention policies
• Document data lineage and governance policies
• Conduct security review and penetration testing

Phase 4: AI and Advanced Features (Weeks 13–16)

Week 13–14: Claude Integration

• Develop Claude-assisted query writing interface
• Create system prompt with genomics-specific context
• Implement query validation and cost estimation
• Test with pilot group of non-technical researchers

Week 15–16: Training and Handoff

• Train genomics team on Superset dashboards, SQL Lab, and Claude queries
• Document runbooks for common tasks (adding new samples, updating annotations, troubleshooting queries)
• Set up monitoring and alerting
• Transition to production support

PADISO’s AI Automation Agency Sydney: The Complete Guide for Sydney Businesses in 2026 outlines how we partner with Sydney-based genomics and biotech firms to execute this roadmap. We typically deliver Phase 1 and 2 in 8 weeks as a fixed-fee engagement, then provide fractional CTO support during Phase 3 and 4.

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Real-World Outcomes and Cost Implications

Genomics teams that migrate from traditional data warehouses to D23.io + Iceberg + Trino + Superset see measurable improvements:

Cost Savings

Storage: Traditional data warehouses charge per GB stored and per query executed. A genomics team with 10 petabytes of data might pay $500K–$1M annually in warehouse costs. D23.io + Iceberg on S3 costs $150K–$250K annually (storage + compute for typical query patterns), a 60–70% reduction.

Compute: Trino’s distributed architecture means you pay only for compute used. A 10-hour monthly data reprocessing job uses the same compute as a 1-hour job—you just pay for the 1 hour. Traditional warehouses charge for reserved compute capacity, whether used or not.

Time-to-Insight

Query Latency: Iceberg’s partition pruning and Trino’s distributed execution reduce query latency from minutes (traditional warehouse) to seconds (lakehouse). A researcher exploring a new hypothesis gets results in seconds, not hours.

Pipeline Development: Schema evolution in Iceberg is fast (minutes). In traditional warehouses, schema changes often require downtime and data reloads (hours). Genomics teams iterate faster.

Researcher Productivity: Claude-assisted query writing reduces query-writing time by 60%. A researcher who previously spent 2 hours writing and debugging a query now spends 40 minutes. Across 50 researchers, that’s 80 hours per month—equivalent to one FTE.

Reliability and Compliance

Uptime: D23.io manages infrastructure, so your team doesn’t worry about cluster failures. Iceberg’s ACID transactions ensure data consistency even during pipeline failures.

Audit Readiness: Iceberg’s built-in lineage and audit logging accelerate SOC 2 and ISO 27001 audits. Teams that previously spent 3–4 weeks gathering evidence now complete audits in 1–2 weeks.

Scaling

The beauty of this architecture is that it scales. A genomics team starting with 1 petabyte can grow to 100 petabytes without architectural changes. Partition sizes remain optimal, query performance stays consistent, and costs scale linearly with data volume.

Rewriting History: Migrating petabytes of data to Apache Iceberg with Trino documents Shopify’s migration of petabytes from Hive to Iceberg, demonstrating that this architecture is battle-tested at hyperscale. Genomics teams benefit from the same proven patterns.

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Conclusion and Next Steps

Building a petabyte-scale genomics analytics platform on D23.io + Apache Iceberg + Trino + Apache Superset is no longer a research project—it’s a proven, cost-effective path to production analytics for genomics organisations.

The key decisions are:

1. Choose D23.io for managed Iceberg and infrastructure, freeing your team to focus on genomics, not DevOps.
2. Design partitioning carefully by chromosome, region, and analysis date to enable fast, cost-effective queries.
3. Use Trino as your distributed query engine, leveraging Iceberg’s partition pruning for petabyte-scale performance.
4. Build Superset dashboards to make analytics accessible to non-technical researchers.
5. Integrate Claude for natural language query writing, unlocking self-serve analytics.
6. Layer governance controls (access, audit, encryption) from day one to meet compliance requirements.

If your genomics team is evaluating this architecture, PADISO can help. We’ve architected D23.io deployments for biotech and genomics organisations across Australia and internationally. Our AI & Agents Automation service includes designing query layers with Claude integration; our Platform Design & Engineering service covers the full stack from data ingestion to Superset dashboards.

For a concrete estimate, we typically quote $50K–$100K for a 12-week engagement covering Phases 1–2 (data ingestion, Trino setup, Superset dashboards) plus 2–3 months of fractional CTO support during Phase 3–4. This aligns with the cost savings achieved in the first 3–6 months.

Ready to move forward? Start by mapping your current genomics data (volume, structure, query patterns) and scheduling a consultation with PADISO. We’ll validate the architecture, identify risks, and provide a detailed roadmap tailored to your organisation.

For more insights on modern data architecture and AI-assisted analytics, explore our blog on AI Automation for Supply Chain: Demand Forecasting and Inventory Management, AI Automation for Financial Services: Fraud Detection and Risk Management, and AI Automation for Customer Service: Chatbots, Virtual Assistants, and Beyond. While these cover different domains, the underlying principles of data governance, query optimisation, and agentic AI apply across industries.

Your genomics team deserves analytics infrastructure that matches the sophistication of your science. D23.io + Iceberg + Trino + Superset delivers exactly that.