Building a Financial Data Pipeline for Commodities: Schema Design, Retention, and Query Patterns

Hook: Tame cost, complexity, and latency for commodity tick data now

You run trading systems or market analytics on wheat, corn, and soy ticks and you are tired of unpredictable infrastructure bills, slow analytical queries, and flaky ingestion at market open. The hard truth in 2026 is that naive storage of raw ticks at full fidelity indefinitely will break budgets and slow you down. This guide gives a practical schema, retention plan, and query patterns tailored to commodity ticks, with explicit tradeoffs between cost and speed and a clear OLTP to OLAP path you can implement this week.

Executive summary: What to do first

• Design for reads first Define the primary query patterns you need now and in 6 months.
• Use a hot/warm/cold tiered architecture Hot for real-time trading, warm for recent analytics, cold for historical research and audit.
• Keep raw ticks short term Raw tick retention 7 to 30 days depending on latency SLAs; materialize downsampled series for longer retention.
• OLTP for ingest, OLAP for analytics Use write-optimized streaming + small-time-series store to capture ticks, then materialize to a columnar OLAP engine or Parquet lake for bulk queries.
• Monitor cost per query and storage Track cost per TB and per 1000 queries monthly; automate TTLs and downsampling pipelines.

Why commodity ticks are different in 2026

Market infrastructure has evolved through late 2024 and 2025. By 2026 common patterns include serverless OLAP with tiered object storage, vectorized query engines like ClickHouse and Apache Pinot for subsecond aggregations, and wider adoption of table formats such as Apache Iceberg and Delta for reliable historical retention. For commodity markets, you also see heavier use of streaming enrichment at ingest for provenance and regulatory metadata. These trends let you separate short lived raw fidelity storage from long term analytics cost effectively.

Data model and schema design for wheat, corn, soy ticks

Start with a compact, normalized schema that supports both low-latency lookups and columnar aggregation. The schema below assumes an integer id for symbol and exchange to reduce redundancy and enable compression in columnar stores.

Core tables

Use three logical tables: tick_events for raw events, tick_1s for per-second rollups, and ohlcv_daily for long term analytics.

tick_events columns (write optimized):

• ts_epoch_ms bigint // event timestamp in ms
• symbol_id int // 1=wheat 2=corn 3=soy or reference table
• exchange_id smallint
• price double
• size int
• side tinyint // 0=unknown 1=buy 2=sell
• flags smallint // market maker, corrected trade etc
• recv_ts_ms bigint // ingestion arrival time
• source varchar // feed id or connection id

tick_1s columns (materialized):

• ts_epoch_s int // second bucket
• symbol_id int
• exchange_id smallint
• open double
• high double
• low double
• close double
• volume bigint
• num_trades int
• vwap double

ohlcv_daily columns (aggregated):

• date date
• symbol_id int
• exchange_id smallint
• open double
• high double
• low double
• close double
• volume bigint
• open_interest bigint // if available

Normalization and dictionary encoding

Keep a small reference table for symbol and exchange metadata. Replace textual symbols with integers at ingest. Columnar stores will compress these IDs aggressively with dictionary encoding and give you huge savings for multi-year cold storage.

Partitioning, indexing, and storage layout

For time-series, partition by day and symbol. For high frequency markets like commodity ticks, additional bucketing by time interval per day prevents hot partitions at market open.

• Partition keys day, symbol_id. Example partition path: 2026-01-12/symbol_1.
• Bucket key for hot storage: modulo of minute or 5 minute slice, e.g., minute_of_day % 10 to spread writes.
• Indexing For OLTP ingest store, use time-series-friendly indices such as TimescaleDB hypertable and chunk time ranges. For OLAP, rely on columnar engine indices like MergeTree primary key (ts, symbol_id).

Retention policy and downsampling strategy

The most important lever to control cost is retention and controlled lossiness through downsampling. Below is a practical multi-tier retention plan tuned for commodity ticks.

Recommended retention tiers

• Hot raw ticks Keep full fidelity ticks on fast SSD for 7 to 30 days. Typical SLA: sub-50ms ingestion to query latency. Use this for order reconstruction, regulatory lookback, and immediate P&L.
• Warm rollups Keep per-second and per-minute rollups in a columnar OLAP store for 1 to 12 months depending on analytic needs. These support VWAP, TWAP, intraday patterns, and backtesting windows.
• Cold aggregates Store daily OHLCV and longer aggregates in compressed Parquet / Iceberg with 3 to 7 years retention or according to compliance needs. Cost optimized for bulk analytical queries.

Downsampling recipe

1. Ingest raw ticks to a short-lived write store with guaranteed ordering by ts_epoch_ms.
2. Continuously compute per-second rollups using stream processing (Flink, Kafka Streams, or materialized views in Timescale/ClickHouse).
3. Compute per-minute and per-5-minute rollups from per-second materialized tables as a batch or streaming compaction job.
4. Materialize daily OHLCV at EOD and archive to object storage with table format for fast partition pruning.
5. Apply TTL to raw ticks after hot window: move to cold Parquet or delete if not needed for compliance.

OLTP vs OLAP: when to use which

The choice is not binary. Use OLTP where low-latency writes and point reads dominate. Use OLAP where analytical throughput and cost per scan are priorities.

OLTP systems

Use for: ingest, last price queries, order reconstruction, immediate trade validations.

• Examples: TimescaleDB, PostgreSQL with partitioning, Kafka + fast state store.
• Strengths: transactional semantics, low-latency single-row reads and writes.
• Weaknesses: expensive for full table scans and long-term storage at scale.

OLAP systems

Use for: large aggregations, backtests, market analytics across months or years.

• Examples: ClickHouse, Apache Pinot, Snowflake, Trino on Iceberg/Delta, BigQuery for ad hoc large scans.
• Strengths: columnar compression, vectorized execution, cheap scans over large volumes.
• Weaknesses: higher ingest complexity, eventual consistency for streamed materialized views.

Hybrid patterns

A common and safe architecture in 2026 is hybrid: ingest with an OLTP friendly path, stream into a message bus, and have consumers that write both to a hot OLTP store and into an OLAP store. Tools like Materialize, ClickHouse Kafka engine, and managed ClickHouse cloud accelerate this pattern.

Query patterns and example queries

Design your schema to make these common query patterns fast. Below are canonical query patterns and pseudo SQL for ClickHouse and TimescaleDB styles.

1. Latest price per symbol

SELECT symbol_id, argMax(price, ts_epoch_ms) AS last_price
FROM tick_events
WHERE symbol_id IN (1,2,3)
GROUP BY symbol_id

2. VWAP for last N minutes

SELECT sum(price*size)/sum(size) AS vwap
FROM tick_events
WHERE symbol_id = 1
  AND ts_epoch_ms >= now_ms() - 5*60*1000

3. Intraday minute bars from rollups

SELECT ts_epoch_s, open, high, low, close, volume
FROM tick_1s
WHERE symbol_id = 2
  AND ts_epoch_s BETWEEN to_unix_timestamp('2026-01-15 09:00:00')
                  AND to_unix_timestamp('2026-01-15 16:00:00')
ORDER BY ts_epoch_s

4. Backtest window spanning warm and cold

For backtests that span months, read per-minute materialized tables in OLAP. If minute data missing, fall back to daily aggregates for the older window.

Cost vs speed tradeoffs: numbers and examples

Real numbers help decisions. These are sample estimates based on 2026 price levels for cloud resources and conservative compression factors.

Assumptions

• Average tick payload after normalization: 32 bytes raw, 8 bytes in compressed column store per column on average
• Ticks per second across all symbols during day: 100k for a diversified commodity feed; peak 1M/s at market open for large customers
• Cloud object storage: 1 USD per TB per month cold
• Managed OLAP compute: 2 to 8 USD per node hour depending on provider

Example cost calculation, 100k ticks/s sustained

Raw storage/day = 100k ticks/s * 86,400 s * 32 bytes ≈ 276 GB uncompressed. Columnar compressed warm store could be 2x to 6x smaller depending on dedupe and dictionary, say 60 GB/day.

If you keep raw ticks hot for 14 days you need ~3.9 TB fast storage. At 0.10 USD/GB-month for SSD this can cost hundreds to low thousands per month in cloud. Move older data to cold Parquet at 1 USD/TB-month to cut costs.

Key takeaway

Shorten hot retention and aggressively downsample to reduce compute costs for OLAP queries. The biggest wins are moving from row-based raw retention to columnar rollups and compressed Parquet with partition pruning.

Operational considerations and monitoring

You need observability for ingestion, TTL, downsampling success, and query performance. Instrument everything and automate actions when thresholds breach.

Essential metrics

• Ingestion lag: max and p99 between source event ts and write ts
• Partition size and growth by day and symbol
• Downsampling job success rate and latency
• Query latency percentiles for key queries (latest price, VWAP, overnight batch)
• Storage costs by tier and cost per query

Tools

Use Prometheus and Grafana for metrics; OpenTelemetry for distributed traces; cost monitoring via cloud billing and custom dashboards. For alerting, automate archive or scale actions when hot partitions exceed size thresholds.

Design systems that assume failure and automate movement between tiers. Manual housekeeping is a silent cost drain.

Practical implementation patterns and code snippets

Below are implementation patterns you can adopt quickly.

Stream to dual-sink pattern

1. Producer publishes ticks to Kafka topic raw_ticks.
2. Stream app consumes and writes enriched events to hot OLTP (TimescaleDB or low-latency ClickHouse table).
3. Same stream app emits aggregated per-second events to an OLAP ingestion stream or writes batch Parquet files to object storage.

Downsample job pseudocode

-- pseudo SQL for materialize per-second from raw
INSERT INTO tick_1s
SELECT floor(ts_epoch_ms/1000) AS ts_epoch_s,
       symbol_id,
       exchange_id,
       first(price) AS open,
       max(price) AS high,
       min(price) AS low,
       last(price) AS close,
       sum(size) AS volume,
       count(*) AS num_trades,
       sum(price*size)/sum(size) AS vwap
FROM tick_events
WHERE ts_epoch_ms BETWEEN :start_ms AND :end_ms
GROUP BY ts_epoch_s, symbol_id, exchange_id;

Case study: Applying the plan to wheat, corn, soy

A mid-sized commodity analytics firm in late 2025 moved from an all-raw retention model to a tiered model. They had 200k ticks/s at peak and were storing 30 TB of raw data monthly. After implementing key changes below they reduced monthly storage cost by 78% and cut median query time for intraday analytics from 4s to 120ms.

What they changed

• Normalized symbols to ids and applied dictionary encoding in OLAP.
• Kept raw ticks hot for 10 days instead of 90.
• Materialized per-second and per-minute rollups and served most analytics from those tables.
• Archived historical daily OHLCV to Iceberg on S3 for compliance and research.
• Added Prometheus dashboards for ingestion lag and partition sizes; auto-archived partitions older than 10 days.

Result metrics

• Storage cost reduced by 78%
• Median analytic query latency dropped 96%
• Operational overhead dropped by 60% due to automated TTL and archiving

Advanced strategies and 2026 trends to adopt

As we move through 2026, consider adopting these advanced strategies for further gains.

• Compute-separation with table formats Use Iceberg/Delta with Trino/Presto or Spark for cheap compute-on-demand over cold storage.
• Serverless OLAP Providers now offer serverless ClickHouse-like engines which can scale compute independently of storage for unpredictable backtests.
• Vectorized time-series functions Many engines now provide built-in TWAP, VWAP, and rolling windows optimized for latency sensitive queries.
• Model-aware downsampling Keep full-fidelity ticks that are anomalous or contain large changes; otherwise downsample conservatively.
• Selective retention driven by business value Not all symbols are equal. Keep longer history for core traded contracts and downsample older, less important symbols more aggressively.

Checklist to implement in 30 days

1. Inventory query patterns and decide retention for raw ticks, per-second, per-minute, and daily aggregates.
2. Implement symbol and exchange dictionary normalization at ingest.
3. Deploy dual-sink stream pipeline to hot OLTP and OLAP or object storage.
4. Implement automated TTLs and archive jobs for partitions older than your hot window.
5. Set up Prometheus/Grafana dashboards and alerts for ingestion lag and partition growth.

Actionable takeaways

• Do not keep raw ticks forever in write-optimized stores. Plan a TTL and aggregation strategy up front.
• Start with per-second materialization; it unlocks most intraday analytics with minimal cost.
• Measure cost per query and per TB monthly, and optimize retention to meet business ROIs.
• Adopt a hybrid OLTP/OLAP pipeline with streaming enrichment and materialized rollups.

Next steps and call to action

If you manage commodity market data for wheat, corn, or soy, start by running a 30 day pilot: instrument ingestion metrics, deploy per-second materialization, and apply a 14 day hot TTL. If you want a hands-on review of your pipeline, reach out for a tailored architecture review. We help teams migrate raw tick archives to a tiered, cost predictable platform and implement downsampling pipelines that cut costs while retaining analytical fidelity.

Contact us at theplanet.cloud for a free cost and performance audit and a 30 day migration plan that targets a measurable reduction in your monthly bill without compromising latency.

Related Reading

• Design a Child Theme for High-Performance Mobile: Lessons from Lightweight Linux and Android Skin Rankings
• Proof Your Dough Like a Pro Using Heated Pads and Hot-Water Bottles
• Carry-On Cocktail: The Best Travel Syrups and Bottles That Meet Airline Rules
• A Runner’s Guide to Launching a Paid Channel: Lessons from Entertainment Execs
• When Crypto Treasury Strategies Go Wrong: What Merchants Should Learn from Michael Saylor