How can you choose the best data format and standard for your project?

2 Semi-structured data formats

Semi-structured data formats are designed to store data that has some structure, but not as rigid or predefined as structured data formats. They allow more flexibility and variability in the data schema, such as nested, hierarchical, or dynamic elements. Some of the most popular semi-structured data formats are JSON, XML, YAML, and Avro. Semi-structured data formats are more expressive and adaptable than structured data formats, and they can support complex and evolving data models. However, they also have some drawbacks, such as being harder to parse, validate, and query, and requiring more metadata and schemas. Semi-structured data formats are suitable for scenarios where you need to store complex, hierarchical, or non-relational data that can be processed and transformed using various tools and frameworks.

3 Unstructured data formats

Unstructured data formats are designed to store data that has no structure or schema at all, such as text, images, audio, video, or binary files. Unstructured data formats are the most flexible and diverse of all data formats, and they can store any kind of data without any constraints or limitations. However, they also have some drawbacks, such as being difficult to analyze, extract, and integrate with other data sources, and requiring more storage space and processing power. Unstructured data formats are suitable for scenarios where you need to store raw, unprocessed, or heterogeneous data that can be accessed and processed using specialized tools and techniques.

4 Data standards

Data standards are rules or guidelines that define how data should be formatted, structured, encoded, exchanged, or interpreted. Data standards can be general or specific, formal or informal, open or proprietary, depending on the purpose and scope of the data. Some of the most common data standards used in data engineering are CSV, JSON, XML, Avro, Parquet, ORC, Protobuf, Thrift, HDF5, NetCDF, GeoJSON, and GeoTIFF. Data standards can help improve the quality, consistency, and interoperability of data, and they can facilitate the communication and collaboration between different systems and stakeholders. However, they also have some challenges, such as being diverse, complex, and evolving, and requiring compliance and maintenance. Data standards are suitable for scenarios where you need to share, transfer, or integrate data across different platforms, applications, or domains.

5 How to evaluate data formats and standards

In order to select the best data format and standard for your project, you must evaluate them based on the specific data characteristics, requirements, and objectives. To compare and contrast different data formats and standards, you can look at the data type, size, structure, performance, and interoperability. The data type could be structured, semi-structured, or unstructured; numeric, textual, spatial, temporal, or multimedia; simple, complex, or dynamic. The data size should be considered in terms of small, medium, or large; static or streaming; compressed or uncompressed. Additionally, the data structure could be flat, nested, or hierarchical; relational or non-relational; schema-on-read or schema-on-write. Moreover, the performance should take into account latency, throughput, bandwidth; CPU usage; memory consumption; and disk resources. Finally, the interoperability should consider compatibility with other data sources and systems; ease and reliability of data exchange and integration; as well as adherence to standards and best practices. By evaluating these criteria for each option available to you and looking at their trade-offs and drawbacks in comparison to their benefits, you can determine which one is best suited for your data engineering needs and goals.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?