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database_slides.qmd
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---
title: "An Overview of Databases and Data Storage"
title-slide-attributes:
data-background-image: img/slides/datalab-logo-full-color-rgb.png
data-background-size: 35%
data-background-position: 5% 2%
author: "Elise Hellwig"
format:
revealjs:
sc-sb-title: true
theme: simple
template-partials:
- title-slide.html
filters:
- reveal-header
slide-level: 3
editor: source
bibliography: data/references.bib
nocite: '@*'
---
```{r format_cars, echo=FALSE}
makers = c('Mazda', 'Merc', 'Toyota', 'Ferarri', 'Lincoln', 'Chrysler', 'Dodge')
origins = c(rep('Japan', 2), rep('Europe', 7), rep('Japan', 2), rep('USA', 3))
maker_ids = sapply(makers, grep, rownames(mtcars)) |>
unlist(use.names=FALSE) |>
unique()
mpg = mtcars[maker_ids, c('mpg', 'cyl', 'hp', 'wt', 'am')]
rownames(mpg) = gsub('Merc', 'Mercedes-Benz', rownames(mpg))
mpg$origin = origins
names(mpg) = c('mpg', 'cylinders', 'horsepower', 'weight', 'manual', 'origin')
```
# Introduction {background-image="img/slides/trail.png"
background-size="60%"
background-opacity="0.5"}
::: {.notes}
Before we dive in...some info. Not required but helpful for me
to know. Raise your hand if:
- Have data you are working with or are in the process of collecting data
- Analyze data using a programming language like R or Python
- Know what a database is
- Have used a database before
:::
## Research Computing Workshops {background-image="img/slides/trail.png"
background-size="8%" background-opacity="0.9"
background-position="97% 97%"}
<br>
::: {style="font-size: 130%;"}
- Intro to SQL for Querying Databases, 4/23
([[event]{.underline}][sql_event], [[reader]{.underline}][dl_sql])
- Spatial SQL, 5/3 ([[event]{.underline}][spat_event],
[[reader]{.underline}][dl_ssql])
:::
<br>
{width="70%" fig-align="center"}
[dl_sql]: https://ucdavisdatalab.github.io/workshop_intro_to_sql/
[dl_ssql]: https://ucdavisdatalab.github.io/Spatial_SQL/
[sql_event]: https://datalab.ucdavis.edu/eventscalendar/workshop-introduction-to-sql-for-querying-databases/
[spat_event]: https://datalab.ucdavis.edu/eventscalendar/workshop-spatial-sql-2/
:::{.notes}
If you decide you want to store your data in database, this would be a good
next step.
:::
## Data Storage Considerations {background-image="img/slides/trail.png"
background-size="8%" background-opacity="0.9"
background-position="97% 97%"}
{width="70%" fig-align="center"}
:::{.notes}
Data store -> method of storing your data.
databases are a type data store but not the only type
- **Structure:**
- Determined by research question
- stores impose structure so match structure to store
- **Interaction:**
- how you format/reformat your data
- based on what analysis you do
- one way or many ways
- **Management:**
- logistics around your data
- who collects it, and how frequently
- how many people access it?
- how long does it need to be available
- **Computing Resources:**
- size of your data
- software you have access to, features you need
:::
## Road Map{background-image="img/slides/trail.png" background-size="40%"
background-opacity="0.9" background-position="80% 30%"}
::: {style="font-size: 90%;"}
- Data
- What is data?
- Data Types
- Data Structures
- Data Stores
- Flat File vs. Database
- Spreadsheet Software
- Relational Databases
- NoSQL Databases
- Picking a Data Store
:::
### Credits {background-image="img/slides/trail.png"
background-size="8%" background-opacity="0.9"
background-position="97% 97%"}
::: {style="font-size: 70%;"}
All images were created by Elise Hellwig unless otherwise noted. Movie
information courtesy of IMDb ([https://www.imdb.com][imdb]). Used with
permission for non-commercial purposes only.
[imdb]: https://www.imdb.com
:::
#### Icons - color modified
::: {style="font-size: 45%;"}
Introduction: [By SkepticalScience - Own work][rm], CC BY-SA 4.0
Data: [By priyanka, "data", an icon from Noun Project][fr], CC BY 4.0
Tabular Data: [By Adam Mullin - Noun Project][tad], CC0
Tree Data: [By HitomiAkane, Tree icon-arzwiki.svg][td], CC BY-SA 4.0
Graph Data: [By Adam Beasley - The Noun Project][gd], CC BY 3.0
Relational Data: [categories by Nira Inds, Noun Project][rd], CC BY 3.0
Data Stores: [By Gaelen Pinnock][ds] - Designer commissioned by the UCT Research
office, CC BY-SA 4.0
Spreadsheets: [By Icons8 - Noun Project][ss], CC0
Relational Databases: [By Aybige - The Noun Project][rd], CC BY 4.0
NoSQL Databases: [By Five by Five - Noun Project][nd], CC0
How To Decide: [By SimpleIcon http://www.simpleicon.com/][htd], CC BY 3.0,
Further Reading: [By Icon Lauk from the Noun Project][fr], CC BY 3.0
:::
[rm]: https://commons.wikimedia.org/w/index.php?curid=99619583
[tad]: https://commons.wikimedia.org/w/index.php?curid=67122938
[td]: https://commons.wikimedia.org/w/index.php?curid=130967171
[gd]: https://commons.wikimedia.org/w/index.php?curid=105266675
[rd]: https://thenounproject.com/icon/categories-6069462/
[ds]: https://commons.wikimedia.org/w/index.php?curid=78355715
[ss]: https://commons.wikimedia.org/w/index.php?curid=67122942
[rd]: https://commons.wikimedia.org/w/index.php?curid=64472365
[nd]: https://commons.wikimedia.org/w/index.php?curid=64905590
[htd]: https://commons.wikimedia.org/w/index.php?curid=47382021
[fr]: https://commons.wikimedia.org/w/index.php?curid=109499378
# Data{background-image="img/slides/Noun_Data.png" background-size="95%"
background-opacity="0.1"}
<br>
> Data (noun): Quantities, characters, or symbols...considered collectively.
>
> \- Oxford English Dictionary
:::{.notes}
- Oxford focuses on digital data, but we know data is not just digital
:::
## What is data?{background-image="img/slides/Noun_Data.png"
background-size="8%" background-position="97% 97%"}
<br>
<center>
**Data is information translated into a form for storage and later use. **
</center>
::: columns
::: {.column width="60%"}
That form could be:
- Rows and columns of a spreadsheet
- A visual diagram
- Written human language
- And many others...
:::
::: {.column width="40%"}
{width="65%"}
:::
:::
:::{.notes}
- paper is still data, and can be a problem
- Clay cuniform tablet, Neo-Sumerian 2041 BCE, cattle receipt
- form is the data's structure
- what form our data takes can help guide our choice of data store
- spreadsheets are very familiar
- people now think of language as data
So how to we categorize our data?
:::
## Data Types{background-image="img/slides/Noun_Data.png"
background-size="8%" background-position="97% 97%"}
Data type tells your computer what types of operations make sense.
- integer
- decimal/floating point numbers
- categorical (small, medium, large)
- characters (text)
- Boolean values (TRUE or FALSE)
- dates and times
- spatial data
:::{.notes}
- Side effects of working on a computer
- May be different than computer science and informatics data types
- tells the computer what to do with a piece of information
- Single point can have data type
- 1 as int, decimal, text and date
:::
## Data Structures{background-image="img/slides/Noun_Data.png"
background-size="8%" background-position="97% 97%"}
<br>
<br>
::: {style="font-size: 130%;"}
Data Structure: a way of organizing data that makes it easier to store and use
:::
<br>
<br>
::: {style="font-size: 200%; text-align: center;"}
Question → Data structure
:::
<br>
:::{.notes}
- capture info about relationship between data pts
- Need multiple points for structure
- structure makes some Qs easier and others harder
- structure choice just as important as analysis choice
:::
## Data Structures{background-image="img/slides/Noun_Data.png"
background-size="8%" background-position="97% 97%"}
::: {style="font-size: 180%;"}
- Tabular/columnar {width="7%"}
- Tree {width="10%"}
- Graph {width="10%"}
- Relational {width="10%"}
:::
:::{.notes}
- may see yours already but don't worry if not
- not an exhaustive list
- if you don't see yours feel free to ask about it
:::
## Tabular Data{background-image="img/slides/tabular.png"
background-size="6%" background-opacity="0.8"
background-position="39% 9%"}
::: {style="font-size: 60%;"}
```{r, echo=FALSE}
library(kableExtra)
mpg_cap = paste("A subset of the mtcars data set, containing information",
"about 32 cars from the 1974 Motor Trend Magazine",
"(Henderson and Velleman 1981).")
kbl(mpg, caption = mpg_cap) |>
scroll_box(width = "100%", height = "550px")
```
:::
:::{.notes}
- rows + columns is organizing principle, mention convention
- entities can be summarized with same set of chars
- focus on questions over entire set of obs
- sorting is easy, relationships not explicit
:::
### Tabular Data Research Questions{background-image="img/slides/tabular.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
::: {style="font-size: 130%;"}
- What is the average value of a particular measurement across a data set?
- What is the range or spread of values in a data set?
- Are there trends in the values of a variable?
- How to two columns in a data set relate to each other?
:::
:::{.notes}
- avg almond yield
- range normal values for vitamin A
- bay area temp increases
- test predict likelihood of complications from diabetes
:::
### `mtcars` Tabular Questions{background-image="img/slides/tabular.png"
background-size="6%"
background-opacity="0.8"
background-position="58% 9%"}
::: {style="font-size: 70%;"}
```{r}
kbl(mpg) |>
scroll_box(width = "100%", height = "600px")
```
:::
:::{.notes}
- avg, range of mpg
- relationship between mpg and other vars
- relationship between cylinders and hp
:::
## Tree Data{background-image="img/slides/tree.svg"
background-size="7%" background-opacity="0.8"
background-position="31% 7%"}
<br>
:::{.notes}
- organized around relationships (nested)
- relationships between entities is explicit
- structure can be more important than entities
:::
### Tree Structure{background-image="img/slides/tree.svg"
background-size="8%" background-opacity="0.8"
background-position="97% 97%"}
{width="130%" fig-align="center"}
:::{.notes}
- root is the center, distance defines place
- subsetting and searching is easy
- this is where text data lives (doc structure)
:::
### Tree Data Research Questions{background-image="img/slides/tree.svg"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
<br>
::: {style="font-size: 140%;"}
- How many nodes separate two entities?
- Which elements fall under a particular subtree?
- Text or directory searching
:::
:::{.notes}
- Elephants closer to rhinos or sloths
- how many dinosaurs are there
- which journals have most articles with AI this year
:::
### `mtcars` Tree Questions {background-image="img/slides/tree.svg"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
:::{.notes}
- needed to parse manufacturers from names
- how many models did japan produce
- which manufacturer did most models
:::
## Graph Data{background-image="img/slides/network.png"
background-size="6%" background-opacity="0.8"
background-position="36% 7%"}
{width="120%" fig-align="center"}
:::{.notes}
- organized around relationships
- structure more important than obs
- no inherent center or hierarchy
:::
### Graph Data {background-image="img/slides/network.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
<br>
[](img/graph_data.png){width="75%" fig-align="center"}
:::{.notes}
- relationships can directional but don't have to be
- structure is contained in the data itself
- very flexible -> high computational intensity
:::
### Graph Data Research Questions {background-image="img/slides/network.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
::: {style="font-size: 130%;"}
- How many edges separate two entities?
- How many paths are there between two nodes?
- Which parts of the graph are most or least connected?
- What is the direction or volume of flow between entities?
:::
:::{.notes}
- number of connections from honolulu to cairo
- how many different ways to get from empire state building to brooklyn bridge
- hospital jobs with the highest exposure risk
- would a transit line to the north bay improve traffic
:::
### Abraham Lincoln Graph Data {background-image="img/slides/network.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
{fig-align="center"}
:::{.notes}
- mtcars is not graph structured, won't even try to reformat it
- as many links to things lincoln was opposed to as he supported
- wiki hyperlinks aren't the same as links in our head
- expect 4 clicks (lincoln movie, Daniel Day Lewis, wild river, kevin bacon)
- but do not directly link each movie in the database, only do notable work
:::
## Relational Data{background-image="img/slides/categories.png"
background-size="7%" background-opacity="0.8"
background-position="44% 6%"}
<br>
{width="140%" fig-align="center"}
<br>
:::{.notes}
- organized by group membership
- similarities
- trees: entities are nested within groups
- graphs: links between entities are non-hierarchical
- difference: entities can show up in multiple groups
- often used with other types of data (ex tabular)
:::
### Relational Data Research Questions {background-image="img/slides/categories.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
::: {style="font-size: 140%;"}
- Which groups are the largest or smallest?
- Do certain memberships tend to co-occur?
- How many groups does an entity tend to belong to?
:::
:::{.notes}
- which state has most gvt agencies related to health and medicine
- do students majoring in Env sci and mgmnt join outdoor rec groups?
- How many diff roles does the average student play on campus
:::
### `mtcars` Relational Questions {background-image="img/slides/categories.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
{width="100%" fig-align="center"}
:::{.notes}
- not really any questions this structure is useful to use to ask
- can't even represent all the data (no numbers)
- this is why choosing the correct structure is important
:::
# Data Stores {background-image="img/slides/data-store.png"
background-size="60%"
background-opacity="0.1"}
> A data store is repository for persistently storing and managing collections
> of data which include not just repositories like databases, but also simpler
> store types such as simple files, emails etc.
>
> Wikipedia
:::{.notes}
- Now we will dig deeper on data stores themselves.
- Note: I make a lot of generalizations about how different software works
- You could fine counter examples to each
- ex. google sheets and concurrent editing without conflicts
- What I'm just saying is rule of thumb
- Ask if you have questions
:::
## Data Store Definitions {background-image="img/slides/data-store.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
<br>
::: {style="font-size: 120%;"}
**Data store:** where your data is saved + how you interact with your data
<br>
**GUI:** Graphical User Interface, a software application that allows you to
interact with the computer via icons, buttons, and windows.
:::
:::{.notes}
- Data store storage+interaction
- these can be independent or together in one software package
- can be very simple or complex
- They also may impose a structure
- Gui - graphical user interface
- as opposed to command line interface
:::
## Database Definitions {background-image="img/slides/data-store.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
<br>
::: {style="font-size: 120%;"}
**Query:** database commands that create, read, update, delete (CRUD)
<br>
**Transaction:** the unit of work in a database, succeeds or fails as a
unit. Allows database to prevent conflicts.
:::
:::{.notes}
- Query - how you interact with a database, submit through the command line
interface
- Transaction - database unit of work
- tracking transactions allows database to prevent conflicts
- transaction succeeds or fails as a unit
- if an update affects multiple parts of a database, the update only
succeeds if every individual part of it is allowed.
:::
## Types of Data Stores {background-image="img/slides/data-store.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
{width="100%" fig-align="center"}
:::{.notes}
- flat file + gui
- storage separate from interaction
- stand alone files, not linked elsewhere
- ex spreadsheet software with csv or xlsx
- dbms
- storage and interaction together
- data linked in some way
- Secret third type is to use a programming language like R or python
- we will talk about spreadsheets, RDBMS, and NoSQL MS
:::
# Spreadsheet Software{background-image="img/slides/spreadsheet.png"
background-size="60%"
background-opacity="0.2"}
## Spreadsheet Software{background-image="img/slides/spreadsheet.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
::: {style="font-size: 90%;"}
1. **Structure:** tabular
2. **Interaction:** Graphical User Interface (GUI), reformatting is hard
3. **Management:**
- Managing multiple users is challenging
- Easy to introduce typos or erros when adding data
4. **Computing Resources:**
- Software is easy to use and widely available
- GUI limits data size
- Lots of data duplication
:::
:::{.notes}
- Tabular structure
- Stand alone (not generally linked to other files)
- Supports relatively low complexity in data
- Interact via GUI or programming language
- One person can edit at a time
- Data storage determined by GUI, is generally low
- Software is very widely available and UC Davis provides licenses
- Very easy to use
:::
<!--
- Imposes tabular data structure
- Widely available and easy to use via a Graphical User Interface (GUI)
- Generally limited to 1 million rows or 10 million cells if using a GUI
- Setting permissions is cumbersome
- Easy to introduce typos or errors
- May cause a lot of data duplication
-->
## Spreadsheet Data{background-image="img/slides/spreadsheet.png"
background-size="6%"
background-opacity="0.8"
background-position="48% 6%"}
::: {style="font-size: 55%;"}
```{r, echo=FALSE}
library(data.table)
library(kableExtra)
dh = fread('data/die_hard.csv', header=FALSE)
setnames(dh, names(dh),
c('title_id', 'person_id', 'title', 'premiered',
'runtime', 'genres', 'rating', 'votes', 'name', 'birth',
'death', 'role', 'characters'))
dh_cap = paste("Information from IMDB about the Die Hard movie franchise and",
"the people who acted in those films.")
dh = dh[,.(title_id, person_id, title, name, characters, premiered,
runtime, genres, rating, votes, birth, death, role)]
kbl(dh, caption=dh_cap) |>
scroll_box(width = "100%", height = "600px")
```
:::
:::{.notes}
- lots of duplication (260)
- if there is a typo it may be hard to find
- may need to correct typos multiple times, in various places
- then redistribute the data
:::
## Example Spreadsheet Software{background-image="img/slides/spreadsheet.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
- Microsoft Excel
- Google Sheets
- Numbers
- LibreOffice Calc\*
- OpenRefine\*
- Open Science Framework\*
\* Open Source
:::{.notes}
- Excel also has analysis tools (especially financial)
- Google sheets has capability for simultaneous editing and some vc
- Open refine provides some data cleaning
- open science framework has project management tools as well
:::
# Relational Database Management Systems (RDBMS) {background-image="img/slides/database.png"
background-size="60%"
background-opacity="0.2"}
:::{.notes}
- This is what people generally mean when they say database
- Both the data storage mechanism and interaction engine
- Related to relational data but this is a data store not structure
:::
## RDBMSs{background-image="img/slides/database.png"
background-size="8%"
background-opacity="0.9"
background-position="97% 97%"}
::: {style="font-size: 77%;"}
1. **Structure:** Tables connected by shared columns called keys
2. **Interaction:**
- SQL (Structured Query Language)
- Data manipulation is easy and reproducible
3. **Management**
- Built in data quality control and multi-user management
- Standardizes data updates
- Provides definitive version of your data
4. **Computing Resources**
- Few built-in size restrictions
- Requires some specialized setup
- thousands of transactions per day
:::
:::{.notes}
- tabular and relational
- instead of GUI, use SQL on command line, much easier than PL
- interacting is reproducible
- standardizes process for updating data
- automatically deals with conflicts via transactions
- multiple people can edit at a time
- built in version control and admin
- easy to set permissions
- larger storage capacity, reduced data duplication
- free options and options through UC Davis Licensing
:::
## Relational Keys{background-image="img/slides/database.png"
background-size="8%"
background-opacity="0.9"
background-position="97% 97%"}
Pair of columns with the same values
:::{.notes}
- databases store connected data
- tables connected by shared columns = keys
- keys implement relational part of relational database
- Primary key = column in table where key uniquely identifies observations
- Foreign key = primary key in another table, isn't necessarily unique
- Allows us to look up phone number without storing phone number with sales
:::
## IMDb Entity Relationship Diagram {background-image="img/slides/database.png"
background-size="8%"
background-opacity="0.9"
background-position="97% 97%"}
:::{.notes}
- visual representation of entire rdb with links drawn between key pairs
- list columns in each table and data types
- 3 types of relationships
:::
## Why use an RDBMS? {background-image="img/slides/database.png"
background-size="8%"
background-opacity="0.9"
background-position="97% 97%"}
::: {style="font-size: 120%;"}
- Reduce data duplication
- Speed up and standardize accessing and updating the data
- Ease of reformatting data
- Ensure data integrity
- Manage multiple users
:::
:::{.notes}
- duplication = 260 -> 156 is 40% reduction in size
- SQL makes everything standardized and reproducible
- update database once and everyone runs new query and gets new data
- very fast = 60M observations, query took 0.5 seconds
- relational nature makes it easy to recombine and reformat data
- data type rules mean QC is easier
- only change typos once in source table
- set permissions and transactions take care of conflicts
:::
## Example RDBMS Software {background-image="img/slides/database.png"
background-size="8%"
background-opacity="0.9"
background-position="97% 97%"}
::: {style="font-size: 60%;"}
```{r, echo=FALSE}
sql = read.csv('data/sql_software.csv')
sql_cols = c("Software", "Support", "Cost", "License", "Spatial Data")
kbl(sql, col.names=sql_cols)
```
:::
# NoSQL Management Systems{background-image="img/slides/nosql.png"
background-size="60%"
background-opacity="0.3"}
:::{.notes}
- Not only sql
- If you like the features of a relational database but you don't have
tabular data
:::
## NoSQL Databases{background-image="img/slides/nosql.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
::: {style="font-size: 77%;"}
1. **Structure:** documents, trees, graphs, key-value pairs, wide column tables
2. **Interaction:**
- Via various query languages
- Data manipulation is easy and reproducible
3. **Management**
- Multi-user management
- Standardizes data updates
- Provides somewhat definitive version of your data
4. **Computing Resources**
- No built-in size restrictions
- Requires some specialized setup
- millions of transactions per day
:::
:::{.notes}
- many data structures, but not all software serves all structures
- like sql data manipulation is easy and standardized
- Also has ways of dealing with multiple users/conflicts
- not as strict as nosql databases, vis a vis transactions
- even faster than SQL, because data isn't all linked
:::
## Why Use NoSQL Databases?{background-image="img/slides/nosql.png"
background-size="8%"
background-opacity="0.8"
background-position="97% 97%"}
::: {style="font-size: 140%;"}
- Compatible with multiple data structures
- Data transactions are much faster than other data stores
- Adding storage capacity is easier than for other data stores
:::
:::{.notes}
- many data structures, but not all software serves all structures
- like sql data manipulation is easy and standardized
- Also has ways of dealing with
- not as strict as nosql databases, vis a vis transactions
- even faster than SQL, because data isn't all linked
:::