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+# Composable data ecosystem
+
+Ibis exists in a broader composable data ecosystem. [The Composable Codex by
+Voltron Data](https://voltrondata.com/codex) is the result of years of
+experience and hours of writing by experts in the field, providing an in-depth
+introduction the composable data systems. We'll take a look at:
+
+- [Apache Arrow](https://github.com/apache/arrow)
+- [Apache Arrow Database Connectivity (ADBC)](https://arrow.apache.org/adbc/current/index.html)
+- [Substrait](https://substrait.io/)
+
+and how they fit in with Ibis.
+
+## Overview
+
+Ibis is the portable Python dataframe API, supporting many backends. This is
+achieved by decoupling the API from the execution engine. While Ibis already
+relies on standards like Apache Arrow today, we expect the composable data
+ecosystem to mature and broader adoption of adjacent projects going forward.
+This will allow Ibis to simplify its implementation and improve performance for
+backends that support these standards.
+
+[The first chapter of The Composable Codex provides a great overview of where
+these projects fit
+in](https://voltrondata.com/codex/standards-over-silos#1-2-3-a-composable-ecosystem):
+
+![Standards](images/standards.png)
+
+And a table explaining the standards:
+
+| Label | Types of standards | Standards |
+| --- | --- | --- |
+| A | Intermediate representation | [Substrait](https://substrait.io) allows any user-interface that produces Substrait to pass the compute operations to a Substrait-consuming execution engine. You could swap any Substrait compatible user interfaces or execution engine. |
+| B | Connectivity | [Arrow Database Connectivity (ADBC)](https://arrow.apache.org/adbc/current/index.html) ensures that no matter where the computation is performed the data will be returned in the Arrow format. You can swap your execution engine and know that your downstream code will still work. |
+| C | Data memory layout | The [Apache Arrow in-memory data format](https://arrow.apache.org/docs/format/Columnar.html) ensures that the data can pass from the storage to the engine (and even across the systems in a distributed environment) and back to the user without slowing down to serialize and deserialize. |
+
+## History
+
+The composable data ecosystem has been envisioned for some time. [Wes
+McKinney](https://wesmckinney.com) has been instrumental in the development of
+the composable data ecosystem, co-founding Voltron Data, Apache Arrow, and
+initially creating Ibis. [Wes looked back on 15 years on the road to composable
+data systems](https://wesmckinney.com/blog/looking-back-15-years/) and gave some
+motivation for Ibis in his infamous ["Apache Arrow and the '10 Things I Hate
+About pandas'"](https://wesmckinney.com/blog/apache-arrow-pandas-internals/).
+
+Ibis started as a pandas-like API for Apache Impala, but has since expanded to
+support many backends. It currently leverages open-source projects like
+[SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy) and
+[SQLGlot](https://github.com/tobymao/sqlglot) to work with many backends. While
+these projects are great, they rely on backend-specific SQL that does not
+constitute a standard. Going forward, we expect ADBC and Substrait to be the
+standards for connectivity and intermediate representation, respectively.
+
+## Apache Arrow
+
+Ibis uses [Apache Arrow](https://arrow.apache.org/) to provide a common data
+format for data interchange between Ibis and backends. Many backends also use
+Apache Arrows as their in-memory data format.
+
+### Dataframe interchange protocol
+
+Ibis supports [the dataframe interchange
+protocol](https://data-apis.org/dataframe-protocol/latest/purpose_and_scope.html)
+for data interchange between with other Python dataframe libraries and
+visualization libraries. This is largely efficient because most libraries
+support Apache Arrow as their in-memory data format, making interchange between
+them cheap and efficient.
+
+## Apache Arrow Database Connectivity (ADBC)
+
+[Apache Arrow Database Connectivity
+(ADBC)](https://arrow.apache.org/docs/format/ADBC.html) is a relatively new
+standard for database connectivity. It is a low-level protocol for exchanging
+data between a client and a database. It is a successor to ODBC and JDBC, and is
+designed to be a more modern and performant alternative to these standards.
+
+While Ibis does not currently use ADBC for its backends, as the project matures
+we expect an increase in performance and a decrease in complexity for backends
+that support ADBC.
+
+## Substrait
+
+[Substrait](https://substrait.io/) is a relatively new standard for
+cross-language serialization of relational algebra. It is intended as an
+intermediary representation between the user interface and other points in the
+data system. Ibis can already compile expressions to Substrait which can then be
+executed by Substrait-consuming backends. Support today is limited but, like
+ADBC, we expect the project to mature and for Ibis to leverage it more in the
+future.
+
+### Why not SQL?
+
+Structured Query Language (SQL) is not a standard. There is a commonly
+referenced [ANSI Standard for
+SQL](https://blog.ansi.org/sql-standard-iso-iec-9075-2023-ansi-x3-135) that you
+can pay a lot of money to access and most execution engines claim to support.
+However, most execution engines extend or subtly deviate from the standard and
+in practice it is not possible to simply reuse SQL from one execution engine on
+another. This leads to notoriously difficult database migrations and vendor
+lock-in.
+
+### Why Substrait?
+
+Substrait, unlike SQL, is not intended as a user interface. Instead, a user
+interface like Ibis in Python or dplyr in R would compile to Substrait and pass
+it to a Substrait-consuming execution engine. This allows the user interface to
+be decoupled from the execution engine, allowing for more flexibility and
+portability.
+
+## Going forward
+
+Going forward, Ibis intends to leverage other standards in the broader
+composable data ecosystem to simplify its implementation and improve
+performance.
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