docs(presentations): update overview slides (#9685)

## Description of changes

mainly adding some composable data system slides for optional use at the
beginning. draft for now, want to review and make some more edits
potentially

also moves the slides into a directory

## Issues closed

docs/presentations/overview.qmd → docs/presentations/overview/index.qmd

@@ -9,27 +9,159 @@ format:
     footer: <https://ibis-project.org>
     # preview-links: true
     chalkboard: true
-    incremental: true
+    incremental: false
     # https://quarto.org/docs/presentations/revealjs/themes.html#using-themes
     theme: dark
     scrollable: true
     # smaller: true
 ---
 
-# what
+# composable data systems
+
+## A Python perspective
+
+["The Road to Composable Data Systems: Thoughts on the Last 15 Years and the Future"](https://wesmckinney.com/blog/looking-back-15-years) by Wes McKinney:
+
+> **pandas solved many problems that database systems also solve**, but almost no one in the data science ecosystem had the expertise to build a data frame library using database techniques. Eagerly-evaluated APIs (as opposed to “lazy” ones) make it more difficult to do efficient “query” planning and execution. **Data interoperability with other systems is always going to be painful**...
+
+## A Python perspective
+
+["The Road to Composable Data Systems: Thoughts on the Last 15 Years and the Future"](https://wesmckinney.com/blog/looking-back-15-years) by Wes McKinney:
+
+> ...**unless faster, more efficient “standards” for interoperability are created**.
+
+## Layers
+
+["The Composable Codex"](https://voltrondata.com/codex) by Voltron Data:
+
+![layers](img/layers.png)
+
+## Future
+
+["The Composable Codex"](https://voltrondata.com/codex) by Voltron Data:
+
+![future](img/future2.png)
+
+## Why composable data systems?
+
+Efficiency:
+
+- time
+- money
+- data mesh
+- engineering productivity
+- avoid vendor lock-in
+
+## How can you implement it? {.smaller}
+
+Choose your stack:
+
+:::: {.columns}
+
+::: {.column width="33%"}
+**UI**:
+
+- Ibis (Python)
+- dplyr (R)
+- SQL
+- ...
+:::
+
+::: {.column width="33%"}
+**Execution engine**:
+
+- DuckDB
+- DataFusion
+- Polars
+- Spark
+- Trino
+- ClickHouse
+- Snowflake
+- Databricks
+- Theseus
+- ...
+:::
+
+::: {.column width="33%"}
+**Storage**:
+
+- Iceberg
+- Delta Lake
+- Hudi
+- Hive-partitioned Parquet files
+- ...
+:::
+
+::::
+
+## Choose your stack (there's more) {.smaller}
+
+Additionally, choose tools for:
+
+**Orchestration**:
+
+- Airflow
+- Prefect
+- Dagster
+- Kedro
+- SQLMesh
+- dbt
+- ...
+
+**Ingestion**:
+
+- dlt
+- Airbyte
+- requests
+- Ibis
+- ...
+
+**Visualization**:
+
+- Altair
+- plotnine
+- Plotly
+- seaborn
+- matplotlib
+- ...
+
+**Dashboarding**:
+
+- Streamlit
+- Quarto dashboards
+- Shiny for Python
+- Dash
+- ...
+
+**Testing**:
+
+- Great Expectations
+- Pandera
+- Pytest
+- assert statements
+- ...
+
+**CLI**:
+
+- Click
+- Typer
+- argparse
+- ...
+
+# what is Ibis?
 
 ## Ibis is a Python library for:
 
 - exploratory data analysis (EDA)
 - analytics
 - data engineering
 - machine learning
-- building your own library (e.g. [Google BigFrames](https://github.com/googleapis/python-bigquery-dataframes))
+- building your own library
 - ...
 
 ::: {.fragment}
 ::: {.r-fit-text}
-development to production with the same API
+***development to production with the same API***
 :::
 :::
 
@@ -122,18 +254,19 @@ t.group_by("species", "island").agg(count=t.count()).order_by("count")
 
 :::
 
-## how it works
+## How it works
 
 Ibis compiles down to SQL or dataframe code:
 
 ```{python}
 #| echo: false
-
 import os
 import sys
-sys.path.append(os.path.abspath(".."))
+
+sys.path.append(os.path.abspath("../.."))
 
 from backends_sankey import fig
+
 fig.show()
 ```
 
@@ -199,7 +332,7 @@ Analyzing 10M+ rows from 4+ data sources.
 
 # why
 
-## dataframe lore
+## Dataframe lore {.smaller}
 
 ::: {.fragment .fade-in-then-semi-out}
 Dataframes first appeared in the `S` programming language (*in 1991!*), then evolved into the `R` programming language.
@@ -225,7 +358,7 @@ This leads to data scientists frequently "throwing their work over the wall" to
 But what if there were a new [standard](https://xkcd.com/927/)?
 :::
 
-## Ibis origins
+## Ibis origins {.smaller}
 
 ::: {.fragment .fade-left}
 from [Apache Arrow and the "10 Things I Hate About pandas"](https://wesmckinney.com/blog/apache-arrow-pandas-internals/) by Wes McKinney
@@ -235,7 +368,7 @@ from [Apache Arrow and the "10 Things I Hate About pandas"](https://wesmckinney.
 > ...in 2015, I started the Ibis project...to create a pandas-friendly deferred expression system for static analysis and compilation [of] these types of [query planned, multicore execution] operations. Since an efficient multithreaded in-memory engine for pandas was not available when I started Ibis, I instead focused on building compilers for SQL engines (Impala, PostgreSQL, SQLite), similar to the R dplyr package. Phillip Cloud from the pandas core team has been actively working on Ibis with me for quite a long time.
 :::
 
-## two world problem {auto-animate="true"}
+## Two world problem {auto-animate="true"}
 
 ::: {.nonincremental}
 :::: {.columns}
@@ -251,7 +384,7 @@ Python:
 ::::
 :::
 
-## two world problem {auto-animate="true"}
+## Two world problem {auto-animate="true"}
 
 ::: {.nonincremental}
 :::: {.columns}
@@ -271,7 +404,7 @@ Python:
 ::::
 :::
 
-## two world problem {auto-animate="true"}
+## Two world problem {auto-animate="true"}
 
 ::: {.nonincremental}
 :::: {.columns}
@@ -293,7 +426,7 @@ Python:
 ::::
 :::
 
-## two world problem {auto-animate="true"}
+## Two world problem {auto-animate="true"}
 
 ::: {.nonincremental}
 :::: {.columns}
@@ -317,7 +450,7 @@ Python:
 ::::
 :::
 
-## two world problem {auto-animate="true"}
+## Two world problem {auto-animate="true"}
 
 ::: {.nonincremental}
 :::: {.columns}
@@ -343,7 +476,7 @@ Python:
 ::::
 :::
 
-## two world problem {auto-animate="true"}
+## Two world problem {auto-animate="true"}
 
 ::: {.nonincremental}
 :::: {.columns}
@@ -375,19 +508,40 @@ SQL:
 
 ## Python dataframe history {.smaller}
 
+::: {.incremental}
+
 - **pandas** (2008): dataframes in Python
 - **Spark** (2009): distributed dataframes with PySpark
 - **Dask** (2014): distributed pandas dataframes
 - **Vaex** (2014): multicore dataframes in Python via C++
-- [**Ibis**]{style="color:#7C65A0"} (2015): dataframes in Python with SQL-like syntax
+- [**Ibis**]{style="color:#7C65A0"} (2015): backend-agnostic dataframes in Python
 - **cuDF** (2017): pandas API on GPUs
 - **Modin** (2018): pandas API on Ray/Dask
 - **Koalas** (2019): pandas API on Spark, later renamed "pandas API on Spark"
 - **Polars** (2020): multicore dataframes in Python via Rust
 - [**Ibis**]{style="color:#7C65A0"} (2022): Ibis invested in heavily by Voltron Data
 - **Snowpark Python** (2022): PySpark-like dataframes on Snowflake
+- **Daft** (2022): distributed dataframes in Python via Rust
 - **BigQuery DataFrames** (2023): pandas API on Google BigQuery (via [Ibis]{style="color:#7C65A0"}!)
 - **Snowpark pandas API** (2024): pandas API on Snowflake
+- [**SQLFrame**]{style="color:#7C65A0"} (2024): backend-agnostic dataframes in Python (PySpark API)
+- **DataFusion dataframes** (2024): multicore dataframes in Python via Rust
+
+:::
+
+## Obligatory standards xkcd
+
+![standards](https://imgs.xkcd.com/comics/standards.png)
+
+## Standards and composability
+
+All Python dataframe libraries that are not Ibis (or SQLFrame) **lock you into an execution engine**.
+
+::: {.fragment}
+::: {.r-fit-text}
+***Good [standards are composable]{style="color:#7C65A0"} and adopted by competitors.***
+:::
+:::
 
 ## Python dataframe history (aside) {.smaller}
 
@@ -411,6 +565,7 @@ pandas clones:
 ::: {.column width=33%}
 PySpark clones:
 
+- [SQLFrame]{style="color:#7C65A0"}
 - Snowpark Python (sort of)
 - DuckDB Spark API
 - SQLGlot Spark API
@@ -419,14 +574,16 @@ PySpark clones:
 ::: {.column width=33%}
 something else:
 
-- Ibis
+- [Ibis]{style="color:#7C65A0"}
 - Polars
+- Daft
+- DataFusion
 :::
 
 ::::
 :::
 
-## database history
+## Database history
 
 - they got faster
 
@@ -544,7 +701,7 @@ penguins.group_by(["species", "island"]).agg(penguins.count().name("count"))
 
 A distributed SQL query engine.
 
-## and more!
+## ...and more!
 
 :::: {.columns}
 
@@ -576,10 +733,9 @@ New backends are easy to add!^\*^
 ^\*^usually
 :::
 
-
 # how
 
-## try it out now
+## Try it out now!
 
 Install: