Managed data
What is Data as a Service (DaaS) and why it matters
A clear definition of Data as a Service (DaaS): what it includes, how it differs from building your own infrastructure and why more companies adopt it.
Read articleKey takeaways
- CSV is simple and universal but inefficient for large volumes.
- JSON is flexible and readable, ideal for semi-structured data and APIs.
- Parquet is columnar and compressed, optimal for large-scale analytics.
- The right format cuts storage cost and query time.
- Several formats coexist in a good architecture.
The format in which data is stored seems a minor technical detail, but it directly influences storage cost, query speed and the cloud bill. Knowing the options helps understand decisions with real economic impact.
What they are
Data formats define how data is encoded and stored on disk. The three most common in analytics are CSV, JSON and Parquet, each fitting different scenarios.
The three formats
- CSV: simple, universal and readable, but without types or compression; inefficient for large volumes.
- JSON: flexible and readable, ideal for semi-structured data and APIs; heavier than a columnar format.
- Parquet: columnar and compressed, designed for analytics: it reads only the needed columns and takes far less space.
CSV
SimpleInefficient at scale
JSON
FlexibleAPIs
Parquet
ColumnarCompressed
Why Parquet dominates in analytics
In analytical workloads over large volumes, Parquet makes a notable difference: being columnar, a query needing only three columns does not read the rest, which speeds up and cheapens. Its compression cuts storage significantly versus CSV or JSON.
How to choose
There is no universal "best" format: CSV for simple exchanges, JSON for semi-structured data and APIs, and Parquet to store and analyse at scale. In a well-designed architecture several coexist, and a managed service picks the most efficient for each stage.
The right format is a quiet but real lever on cloud cost and query speed.
In summary
CSV is simple but inefficient at scale, JSON flexible for APIs and semi-structured data, and Parquet columnar and compressed for large-scale analytics. The right format cuts storage cost and query time; in a good architecture several coexist, chosen per stage.
Sources & further reading
Frequently asked questions
Which is the best data format?
There is no universal one. CSV for simple exchanges, JSON for semi-structured data and APIs, and Parquet for analytics at scale due to its efficiency.
Why is Parquet so efficient?
It is columnar and compressed: a query reads only the needed columns and takes far less space, speeding up and cheapening analysis.
Does the format affect cost?
Yes. An efficient format like Parquet reduces storage and query time, which lowers the cloud bill in analytical workloads.
When should I use CSV?
For simple data exchanges where universality matters more than efficiency, not as analytical storage at scale.
When is JSON the right choice?
For semi-structured data and APIs, where its flexibility and readability are valuable, accepting it is heavier than columnar formats.
Can several formats coexist?
Yes, and they usually do. A good architecture uses the most efficient format at each stage; a managed service handles that choice.
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