API, MQTT, Parquet, CSV or Excel: choosing a delivery model

The right dataset delivered the wrong way creates as much friction as no data at all. Format and cadence should be chosen around how your teams consume data, not around what is easiest to export. Here is how to decide.

Start with the consumer, not the file

Ask who uses the data and how. An analyst opening a spreadsheet has very different needs from a streaming application reacting to events. The destination (a data warehouse, a model, a dashboard, an operational system) should determine the format and cadence, and the delivery model should be defined around your environment, not the supplier’s.

Formats, in plain terms

• Parquet, columnar and compressed; ideal for large analytical datasets and warehouse ingestion.
• CSV, universal and simple; good for interchange and moderate volumes.
• Excel, for business users who work directly with the data.
• JSON, flexible, nested structures; common for application integration.
• API, on-demand, query-driven access integrated into your systems.
• MQTT & streams, lightweight messaging for telemetry and event-driven, real-time use.

Match the cadence to the decision

Cadence should follow the speed of the decision the data supports. A monthly strategic review does not need a real-time feed; a grid-balancing or logistics-routing system does. Options range from one-off datasets and historical backfills, through daily, weekly and monthly batches and scheduled feeds, to near-real-time and real-time streams.

Don’t forget interface and security

Delivery is more than a file format. SFTP, secure file delivery, databases, cloud storage environments and custom enterprise interfaces all matter, as do the security controls around them. The interface is part of the requirement: define it alongside format and cadence.

A simple decision path

Large analytical volumes into a warehouse: Parquet in scheduled batches. Application integration: JSON over an API. Operational, event-driven systems: MQTT or streams in near-real-time or real-time. Business users: Excel or CSV. Most real projects combine more than one, and a managed supply partner can deliver the same data through several models at once.

Format deep-dive: when each wins

Parquet wins for large analytical datasets loaded into a warehouse or lakehouse: columnar storage and compression make it efficient to scan and cheap to store. CSV remains the universal interchange format, simple and readable, but it loses types and struggles at very large scale. Excel is right when humans, not pipelines, are the consumer. JSON suits nested, application-facing data. APIs serve on-demand, query-driven access; MQTT and streams serve continuous, event-driven consumption. Choosing by consumer and volume, rather than habit, avoids costly mismatches downstream.

Designing delivery for change

A first delivery is easy; a feed that stays reliable for years is the real test. Build for change from the start: version the schema so additions do not break consumers, define how breaking changes are communicated and migrated, monitor freshness and volume, and provide a replay or backfill path for gaps. These operational details, not the file format, are what determine whether a recurring feed is trustworthy.

A delivery decision checklist

• Who consumes the data, and is it a person, a warehouse, an application or an operational system?
• What latency does the decision actually need, one-off, batch, near-real-time or real-time?
• Which format and interface match that consumer and volume?
• What are the security and residency requirements for the channel?
• How are schema changes versioned and communicated?
• What SLA, monitoring and remediation back the feed?