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Live vs Extract Connection: A Road to Tableau Desktop Specialist Certification
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Live vs Extract Connection: A Road to Tableau Desktop Specialist Certification

Towards AI Team
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March 28, 2022
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Last Updated on March 28, 2022 by Editorial Team

Author(s): Daksh Trehan

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

Chapter 4: An extensive guide to Live & Extract data connection, Logical Table & Physical Table in Tableau

Welcome to the fourth chapter, In this piece, we are going to learn about data connections in Tableau and which one should you go for.

If you want to navigate through other chapters, visit: Tableau: What it is? Why it is the best?; A road to Tableau Desktop Specialist Certification.

If you want to directly go on Tableau Desktop Specialist notes, access them here → https://dakshtrehan.notion.site/Tableau-Notes-c13fceda97b94bda940edbf6751cf30

Use the link to get access to free Tableau certification dumps (Valid till 13 Apr 2022):

https://www.udemy.com/course/tableau-desktop-specialist-certification-dumps-2022/?couponCode=1FA58837A74561DC1EFB

When we connect our data, Tableau let us define two types of connections: Live & Extract.

Table of Content

  • Live Connection
  • Extract Connection
  • Selecting a better connection type for your data
  • Sample Exam Questions from this topic

Live Connection

As the name suggests, a Live connection is made when we want our data to keep flowing in our Tableau Data source from a dynamic database.

This offers flexibility when we want real-time updates and instantaneous insights.

Live data process queries in the source database.

By default, when we create a connection, it is a Live connection.

Single Cylinder represents Live Connection →

With Live connection, the data in our worksheet would be updated every time we re-open a worksheet or manually refreshes the data.

To refresh the live data →

Extract Connection

“Extracts” are one of the most underrated features of Tableau.

An Extract file have .hyper extension(previously referred as .tde)

An extract connection is made over tde. Tableau Data Extract(.tde/also referred to as hyper file) is a compressed snapshot of live data that is stored locally and loaded into memory. To ease off our connection and minimize the load due to complex data, we can apply filters to our data, save it as a .hyper file and later use them for our extract connection. This tends to be much faster than Live Connections and hence provides an edge when used for complex visualizations.

An extract process queries using Tableau Data Engine because a hyper file is so much optimized and efficient that Tableau in-memory Data engine can process queries easily.

An extract is only an extract for that particular workbook, and when it will be shared with others it will act as live data.

Double Cylinder represents Extract connection →

Extracts can’t be updated automatically, but can only be refreshed either manually or periodically.

Extracting Data in Tableau

Method 1

Step1 → Chose “Extract” Connection

Step2 → When we chose “Extract” Data, Tableau ask for the location of .hyper file.

Method 2

Step1 → Go to the Data pane and right-click on Data Connection and click on “Extract Data”

Step2 → Add filters and modify the extract according to your needs.

Refreshing an Extract in Tableau

Once an extract is made and loaded in our Workbook, there are two ways we can refresh our data:

The first option will refresh the connection, but since the connection is static, there won’t be any changes reflected in your data. This option is also present in Live connection and is more specifically to be used when we are working with Live data.

The second option will create a new snapshot of the data i.e. new extract and hence the data in our workbook would be updated.

To periodically refresh the data automatically, we can publish our data to Tableau Server and can choose the time interval for refreshing.

Tableau offers to refresh the data in two ways, we can either go for a full refresh, which replaces all the content in the extract or can use an incremental refresh, which only adds new rows.

(Logical)Single Table vs Multiple(Physical) Table Extraction

Logical Tables are extracted by default.

Only use Physical tables, when the extract constitutes multiple tables with equality joins and meet the following conditions:

  • No RAWSQL.
  • No Incremental refreshes.
  • No Sampling.
  • No Extract Filters.
  • Data Types of Columns used for relationship/joins are identical.
  • All joins between physical tables are equality joins.

Physical Tables should only be used when the size of our extract is larger than expected while Logical Tables must be used when we want to limit the amount of data in our extract using Filters or aggregations.

Selecting a better connection type for your data

Benefits of Live Connection

  • Allows working with real-time data and provides instantaneous insights.
  • This connection is suggested if you’re working with a rapid database.

Benefits of Extract Connection

  • This can be used if you want a portable dataset.
  • It helps us to limit the amount of data we need.
  • It improves performance and efficiency.
  • These are fast to create and work with.
  • These support additional functionalities such as the COUNTD function.

Disadvantages of Extract Connection

  • This connection is not suggested while working with sensitive data, as the extract is saved locally and hence can be shared with anyone.
  • This requires a manual refresh.

Sample Exam Questions from this Topic

Can we automatically refresh our Extract data?

  1. True
  2. False

Solution: True

Why should we not use Extract Data?

  1. They are slow.
  2. They might expose confidential data.
  3. They are difficult to use.
  4. They can’t be refreshed.

Solution: They might expose confidential data.

What is the extension for extracted data?

  1. .hyper
  2. .twb
  3. .tds
  4. .tbm

Solution: .hyper

By default, Tableau starts ____ connection.

  1. Live
  2. Extract

Solution: Live

Which connection should we use for real-time data?

  1. Live
  2. Extract

Solution: Live

Use the link to get access to free Tableau certification dumps (Valid till 13 Apr 2022):

https://www.udemy.com/course/tableau-desktop-specialist-certification-dumps-2022/?couponCode=1FA58837A74561DC1EFB

References:

[1] Tableau Help | Tableau Software

[2] Personal Notes

[3]Tableau Desktop Specialist Exam (New Pattern — 2021) — Apisero

Thanks for Reading!

Feel free to give claps so I know how helpful this post was for you, and share it on your social networks, this would be very helpful for me.

If you like this article and want to learn more about Machine Learning, Data Science, Python, BI. Please consider subscribing to my newsletter:

Daksh Trehan’s Newsletter.

Find me on Web: www.dakshtrehan.com

Connect with me at LinkedIn: www.linkedin.com/in/dakshtrehan

Read my Tech blogs: www.dakshtrehan.medium.com

Connect with me at Instagram: www.instagram.com/_daksh_trehan_

Want to learn more?

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Detecting COVID-19 Using Deep Learning
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GPT-3 Explained to a 5-year old.
Tinder+AI: A perfect Matchmaking?
An insider’s guide to Cartoonization using Machine Learning
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One-line Magical code to perform EDA!
Give me 5-minutes, I’ll give you a DeepFake!

Cheers

Live vs Extract Connection: A Road to Tableau Desktop Specialist Certification was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Join thousands of data leaders on the AI newsletter. It’s free, we don’t spam, and we never share your email address. Keep up to date with the latest work in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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