One of the highlights of Alteryx is Data Connections. It can be relied upon to generate and handle your data connections from a central location. The user can access it using their username and password.

The main advantages of Alteryx that set it apart :

• • data connectivity is high
• • direct access to the data sources/ bases

With the help of the Manage Data Connections window, it is possible to:

• • view connections you have already created
• • view connections shared with you
• • add new connections.

3 Steps to Create and Manage Your Data Connections with Alteryx

1. Adding
2. Testing
3. Sharing

Let’s get to know the steps in detail:

1. To Add a Data Connection

If the user wants to add a data connection, select Add New Data Connection on the Data Connections page. Then from the Connection drop-down select the connection type.

2. Testing Data Connections

The second step is to test the data connections. A controller and two or more worker machines make up a multi-node setup of the Server. With this arrangement, the test functionality examines the connection on the controller machine rather than the individual worker computers. One should verify the identical database drivers and driver versions are installed on every system to make sure the connection will function on any of them. Connection Test Failures

The connection tests can fail due to multiple reasons. The user can save data connections even though they have failed the connection test.

These are the most typical causes of connection tests failing:

• • The server and database can be inaccessible to you in some circumstances. For instance, only the connection end user can access the server or database in certain cases.
• • Your ability to connect to the server or database may also be blocked by network security.
• • Sometimes the server is unable to connect to the host of the database server. At that time ping the database server host while logged into the server where Server is installed to check for network connectivity. The credentials for the database are incorrect or do not have the necessary access permissions. Get in touch with the database manager.
• • The database is unavailable. To ensure that the database is operational and performing as expected, get in touch with the database administrator.
• • The Server configuration you are using has several nodes.

3. To Share a Data Connection

After creating a Data Connection, go back to the Data Connections page to share it with users or custom groups for use in Designer.

Note: Keep in mind that you must connect with a Curator or the gallery administrator to make sure that they have access to the necessary data connections if you want to allow the workflow to be used by particular users or groups.

Follow the steps mentioned below to share a Data Connection:

• • Choose the shareable data connection by clicking the pencil icon on the Data Connections screen.
• • Select Users or Custom Groups on the Modify Data Connections screen.
• • Enter a user's or a group's name here.
• • Choose the user or the group.

The appropriate tab lists the individual, group, or studio that you are connected to. For more information on how users can access and manage shared connections in Designer, go to Input Data Tool and Manage Data Connections in the help section of Designer.

Note: Verify that the user's machine is installed with the same or a more recent version of the Microsoft SQL Server Native Client before initiating a Microsoft SQL Server connection. Go over to Troubleshooting.

a. Cancelling Access to a Data Connection
Choose the "x" icon next to a user's name to remove their access to a connection.

b. Modify a Data Connection

• 1. Choose the pencil icon on the Data Connections page.
• 2. Alter the Name or Connection String fields on the Modify Data Connections screen.
• 3. You can share the connection with people and groups using the Users or Custom Groups pages.
• 4. Choose Save.

4. Delete a Data Connection
1. Choose the trash can icon next to the connection name to remove the connection.

Tools in Alteryx Designer

Let’s have a look at the input tools available in Alteryx Designer:

1. Input Data Tool

By connecting the Input Data tool to a file or database, you can utilise it to add data to your workflow. This tool has a wide range of configuration possibilities.

2. Directory Tool

The Directory tool can be used to return a list of all the files present in the given directory. The tool returns file names along with additional details about each file, including file size, creation, and modification dates, and more.

3. Dynamic Input Tool

Using the Dynamic Input tool, it is possible to change file names, amend database queries, or alter input pathways that were created using data from your workflow are all possible If you're reading from a database, Alteryx will do it at runtime and will dynamically select which entries are read.

4. Connect In-DB Tool

When compared to conventional analysis techniques, the Connect In-Database tool can significantly enhance performance by allowing blending and analysis on large data sets without removing the data from the database.

Benefits of Snowflake Time Travel

With Snowflake Time Travel, you can access historical data, including data that has been altered or deleted, at any given point. This feature is helpful for various tasks, such as:

• • Querying data that has been modified or erased in the past
• • Duplicating entire tables, schemas, or databases at or before specific dates
• • Restoring deleted tables, schemas, and databases

How to activate Snowflake Time Travel?

Activating Snowflake Time Travel is a simple process that requires no additional effort. It is automatically activated with a retention period of one day. Nonetheless, upgrading to the Snowflake Enterprise Edition is necessary to customise the Data Retention Period and extend it to 90 days for Databases, Schemas, and Tables. It's important to note that increasing the Data Retention Period results in additional storage usage, reflected in your monthly Storage Fees.

Data Retention Period in Snowflake

In Snowflake, Data Retention Period determine how long historical data is retained to support Time Travel functionality. When data in a table is altered, such as through deletions or updates, Snowflake maintains the previous state of the data so that Time Travel operations (like SELECT, CREATE...CLONE, UNDROP) can be performed on it. By default, all Snowflake accounts have a standard retention period of one day (24 hours).

However, the Retention Period can be adjusted at the account and object level in the Snowflake Standard Edition to 0 (or unset to the default of 1 day) for databases, schemas, and tables.

In the Snowflake Enterprise Edition or higher, the Retention Period can be set to 0 for temporary databases, schemas, tables, and temporary tables. For permanent databases, schemas, and tables, the Retention Time can be configured to any duration between 0 and 90 days.

Functions of Snowflake Time Travel SQL Extensions

Snowflake Time Travel SQL Extensions are special SQL commands that allow users to query historical data from a specific point in time using the Time Travel feature. These extensions enable users to perform various Time Travel operations, including:

• a. CLONE: This command creates a copy of a table, schema, or database at a specific point in time using Time Travel.
• b. UNDROP: This command restores a dropped table, schema, or database to a specific point in time using Time Travel.
• c. HISTORY: This command retrieves the history of changes made to a table, schema, or database over time using Time Travel.
• d. AS OF: This command retrieves data from a table as it appeared at a specific point in time using Time Travel.

Specifying a Custom Data Retention Period for Snowflake Time Travel

To specify a custom Data Retention Period for Snowflake Time Travel, you can use the DATA_RETENTION_TIME IN_DAYS argument in the command when creating a table, schema, or database. By default, the maximum Retention Time in Standard Edition is set to 1 day (i.e. 24 hours), while in Snowflake Enterprise Edition (and higher), it can be set to any value up to 90 days.

The Data Retention Time can be set in the way it has been placed in the example below.

To create a schema with a custom Data Retention Period of 60 days, you can use the following SQL command:

create table mytable(col1 number, col2 date) data_retention_time_in_days=60;

Modify the Data Retention Period for Snowflake Objects

To modify the Data Retention Period of a Snowflake object, any change made to the Retention Period affects both active data and data in Time Travel. Depending on whether the period is increased or decreased, the following impacts occur:

• a. Increasing Retention
• b. Decreasing Retention

Let’s dive deep into more details:

a. Increasing Retention
Snowflake Time Travel preserves the data for a more extended period. For instance, if a Table’s Retention Time is increased from 10 to 20 days, the data set to be deleted after ten days will be retained for an additional ten days before being moved to Fail-Safe. However, data over ten days old and already transferred to Fail-Safe mode is unaffected.

b. Decreasing Retention
The duration of data stored in Time Travel is reduced. The shorter Retention Period applies only to active data updated after the Retention Period is shortened. If the data is still within the new Retention Period, it stays in Time Travel; otherwise, it is placed in Fail-Safe Mode. For instance, if a table with a 10-day Retention Period is reduced to 1 day, data from day 2 through day ten will be transferred to Fail-Safe, and only data from day one will be accessible through Time Travel.

Since the background process moves the data from Snowflake Time Travel to Fail-Safe, it may take some time to see the changes. Although Snowflake guarantees that the data will be transferred, it does not specify when the process will be finished. The data remains accessible via Time Travel until the background process is completed. To change an object's Retention Period, use ALTER object command, such as the following command for modifying a table's Retention Period:

alter table mytable set data_retention_time_in_days=30;

Snowflake Time Travel Data Query

To query previous versions of data in Snowflake Time Travel, you can use the AT | BEFORE Clause after making any DML actions on a table. This clause allows you to query data at or before a certain point in the table's history throughout the retention period. The specified threshold can be either time-based (e.g., a timestamp or time offset from the present) or a statement ID (e.g., SELECT or INSERT).

For example, to select historical data from a table as of a specific date and time, you can use a query like:

sql
SELECT * FROM my table AT (TIMESTAMP => 'Fri, 05 May 2023 16:20:00 -

If you want to pull data from a table that was last updated a certain number of minutes ago, you can use a query like:

sql
SELECT * FROM my_table AT(OFFSET => -60*5);

And to collect historical data from a table up to a specified statement's modifications, but not including them, you can use a query like:

Sql
SELECT * FROM my_table BEFORE(STATEMENT => '8e5d0ca9-005e-44e6-b858-a8f5b37c57

How to Restore Deleted Objects by Utilising the UNDROP Command?

To restore a deleted object that hasn't been permanently removed from the system (meaning it can still be seen in the "SHOW object type> HISTORY" output), you can use the UNDROP command in conjunction with Snowflake Time Travel. This command can be applied to various objects, such as tables, schemas, and databases. It effectively reverts the thing to its previous state before it was deleted with the DROP command. For example, the UNDROP command can also restore a dropped database.

Summing Up

Snowflake Time Travel’s features can enhance your decision-making process and overall data experience. If you're looking for a Snowflake service provider, Beinex is an excellent option. Our partnership with Snowflake enables us to offer advanced features like automated tuning, elastic compute, and analytics modernisation services to help your organisation realise exponential Returns on Investment.

AWS and Pay-as-you-go Model

AWS adopts a pay-as-you-go model for the majority of its cloud services. This means you pay only for the specific services you use, for the duration of your usage, and without the need for lengthy contracts or intricate licensing agreements. The pricing structure is akin to paying for utilities such as water and electricity – you are charged solely for the services consumed, and once you cease using them, no additional costs or termination fees apply.
The pay-as-you-go system with AWS ensures that you pay only for the resources your organisation utilises, promoting agility, responsiveness, and scalability. This approach allows you to effortlessly adjust to evolving business requirements without committing to fixed budgets, ultimately enhancing your ability to respond to changes promptly.
In essence, paying for services on a needed basis empowers your organisation to focus on growth and efficiency rather than getting bogged down by unnecessary expenses.
Let’s understand the key benefits AWS customers can reap from embracing AWS Cloud Infrastructure services, unlocking a world of possibilities for organisations seeking to transform their operations and drive unparalleled value.

AWS Economic Overview: Realizing Significant Savings

An economic analysis by the Enterprise Strategy Group highlights that migrating on-premises workloads to AWS results in substantial savings and benefits across various categories:

1. Cost Optimization and Improved Operational Efficiency
2. Faster Time to Value and Improved Business Agility
3. Reduced Risk to the Organization

Let's explore how these benefits unfold in the AWS Cloud Infrastructure journey:

1. Cost Optimization and Improved Operational Efficiency

By transitioning to AWS Cloud Infrastructure, organisations shed the complexity and burden associated with on-premises operations. This shift allows IT infrastructure teams to focus on business innovation rather than managing hardware, creating a ripple effect that enhances the efficiency of application, development, and data service teams. The move to AWS results in simplified administration, automated tasks, and substantial time savings, with reported efficiencies ranging from 60% to 70%.

a. Designed for Best Price-Performance: Employing Customized Solutions

AWS's commitment to delivering the best price-performance for many applications and workloads is evident in its continuous innovation. From the Nitro System and AWS Graviton processors to AWS Trainium and Inferentia accelerators, AWS has engineered custom silicon that optimises performance and efficiency, allowing organisations to achieve cost savings while enjoying top-notch performance.

b. Financial Flexibility, Predictability, and Visibility: Enabling Decision-Making

AWS offers a transparent monthly billing model, shifting organisations from up-front capital investments to a more flexible and predictable cost structure. With tools like AWS Cost Explorer, customers gain visibility into resource utilisation and identify cost-saving opportunities. Flexible purchase models, including On-Demand Instances, Spot Instances, and Savings Plans, provide financial flexibility, enabling organisations to meet infrastructure needs while staying within budget constraints.

c. Managed Services: Enabling Business Transformation

AWS-managed services not only free up internal teams from infrastructure maintenance but also allow them to scale operations using fully managed services. By reducing administrative burdens through offerings for monitoring, incident detection, security functions, and more, organisations can focus on delivering superior data services and business logic. The AWS Partner Network (APN) further enhances this capability, offering a global community of partners to collaborate with and derive greater business value.

d. Improved Environmental Sustainability: A Commitment to a Greener Future

As environmental, social, and governance goals take centre stage, organisations benefit from AWS's global infrastructure to lower their carbon footprint. Moving from on-premises to AWS Cloud Infrastructure can lead to a significant reduction in electricity consumption and associated costs. AWS's commitment to powering operations with renewable energy aligns with the sustainability goals of organisations, contributing to a greener and more responsible future.

2. Faster Time to Value and Enhanced Business Agility

Embracing change can be challenging for any IT organisation, particularly for large enterprises deeply invested in on-premises technology. Overcoming hurdles such as standardising organisational structures and adopting best practices adds complexity to the transition. However, AWS offers a compelling solution, guiding organisations seamlessly through the shift from on-premises environments to the cloud. This transition not only occurs swiftly and securely but also brings about a significant improvement in business agility, unlocking outcomes that were previously unattainable.

a. Faster time to migration

Transitioning on-premises workloads to AWS Cloud Infrastructure enables organisations to swiftly leverage modern end-to-end data architectures. This facilitates immediate benefits in storing, protecting, analysing, visualising, and extracting valuable insights from data. Remarkably, a customer successfully migrated their business applications to AWS in a single weekend, involving over 200,000 business customers and 400 internal users across three global locations.
AWS, as a cloud provider, offers a comprehensive range of services and features within those services, making the process faster, easier, safer, and more cost-effective. Whether rehosting workloads or relocating entire data centres, AWS empowers organisations to build virtually anything, providing confidence through proven phased methods, assessment tools, and mobilisation support.

b. Accelerated path to modernisation and continuous innovation

AWS stands out by delivering unique capabilities and technologies that enable customers to experiment and innovate rapidly. The cloud infrastructure allows customers to benefit from continuous innovation in modern computing, networking, and storage technologies without needing on-premises forklift refreshes.
Technologies like the AWS Nitro System, Amazon FSx for Lustre, and various deployment options empower organisations to modernise applications, accelerate development, and explore edge computing solutions seamlessly. AWS connects containers and microservices with application-level networking, secure API gateways, and advanced tools for modernising or re-platforming applications.

c. Expedited data delivery and insights

The AWS Global Infrastructure facilitates secure, extensive, and reliable global cloud operations. Customers can seamlessly deliver data, applications, and services to regions worldwide while meeting SLAs. Leveraging AWS Direct Connect, customers can run applications using on-premises and cloud resources without compromising performance, ensuring private, secure connections bypassing the internet.
AWS tools and services enable the transformation, enrichment, and accessibility of data for diverse workloads such as AI, ML, HPC, and BI. Customers experience improved data-driven services, efficient access for remote workers through AWS Client VPN, and high-performance applications at the edge using CDN services like Amazon CloudFront.

d. Improved scalability and business agility

AWS empowers organisations to scale computing, networking, and storage resources swiftly in response to changing business demands. With minimal planning, customers can provide additional resources in minutes, starkly contrasting the weeks or months required on-premises. Leveraging AWS Auto Scaling and intelligent optimisation tools, customers ensure predictable, steady performance while optimising costs.
Enhanced scalability allows for dynamic adjustments to resource capacity, saving developers significant time in manual infrastructure maintenance and scaling. Customers can precisely provision resources, proactively reducing infrastructure costs, and flexibly choose from 600 compute instances to meet workload requirements. This flexibility extends to running applications on VMs, containers, or serverless services, enabling deployment options across geographic regions, data centres, or at the edge.

Reduced Risk to the Organization

In prioritising security, AWS is a trusted partner for organisations, addressing concerns related to data protection, service interruptions, data corruption, compliance, and malicious intent. Security holds paramount importance in the design of the AWS Global Infrastructure, custom-built for the cloud and continuously monitored to ensure the confidentiality, integrity, and availability of customer data.

a. Comprehensive Security Measures:

Protection at all levels, including physical security, infrastructure security, network backbone security, and data security. Rigorous access controls, encryption, retention, and auditing to meet compliance requirements. AWS’s commitment to ongoing investments in security technologies and operational best practices.

b. Shared Responsibility Model:

Security and compliance shared responsibility between AWS and the customer. AWS manages components from the host operating system to physical security, while customers handle the guest operating system, application software, and AWS security group firewall configuration.

c. Data Protection and Security:

AWS offers built-in security features at the chip level through the Nitro System, ensuring continuous monitoring and verification. Virtualisation resources are offloaded to dedicated hardware and software, minimising the attack surface. The security model of the Nitro System is locked down to prevent administrative access, reducing the risk of human error and tampering. AWS provides tools for data resiliency, including snapshots, versioning, and full backup and recovery solutions.

d. Secure Access Control and Operations:

AWS customers have tools for securing access, including a centralised firewall, AWS Identity and Access Management, encryption features, and more. Features like Amazon S3 Object Lock, checksums, replication, and versioning ensure data integrity. Protection against exploits and DDoS events with AWS Web Application Firewall (WAF) and AWS Shield.

e. Improved Compliance:

AWS customers receive tools and visibility to demonstrate compliance locally and regionally. AWS CloudTrail helps organisations avoid penalties for regulatory non-compliance.

f. Data Sovereignty and Privacy:

Organisations retain control over data storage, security, and access. AWS ensures data remains within chosen AWS Regions and is committed to confidential computing. Specialised hardware and firmware protect customer code and data from external access.
Summing Up
Embracing AWS Cloud Infrastructure services is not just a technological upgrade; it's a strategic move that unleashes innovation, enhances efficiency, and aligns businesses with the future of digital transformation. The transformative journey with AWS goes beyond cost savings; it's about realizing the full potential of technology to drive growth, agility, and sustainability in an ever-changing landscape. As organisations navigate the complexities of the digital age, AWS stands as a trusted partner, offering a robust foundation for a future-ready enterprise.

[sc name="quote" quote="“The functions, even as they may look simple, are pretty useful for any Tableau Consultant especially if they are working on finance-related projects,”" author="Joseph John, Senior Consultant, Analytics, Beinex"][/sc]

What is Tableau Sum and Running Sum?

Sum

SUM is one of the commonly performed functions in Tableau. The Tableau Sum function seeks out the Sum of records under consideration. It is the total of the values present in a field. The screenshot provided below exhibits the total sum of sales for each of the three categories as given in Sheet 1.

In the sample dataset shown below, the sum of sales is shown corresponding to each of the corresponding values in the dimension "Category". For example, "Furniture" has a total sale of 754,748, which could be comprising of furniture related products such as Tables, Chairs etc.

Running Sum

A RUNNING SUM is a cumulative total in a row or column from the first value to the final value in the respective row or column. For instance, in the example given below, the cumulative values of Furniture and Office Supplies stand at 1,486,641 and that value when added to Technology’s value of 839,893 gives 2,326,534.

You can summarise or modify the granularity of your data using aggregate functions. An aggregate part combines the values of multiple lines to provide a single value. Examples of aggregate functions apart from sum are measurements based on Count, Count Distinct, Fixed Calculations, and other standard integration functions.

Every time you include a measure in your view, an aggregate is automatically applied to that measure. Depending on the context of the view, different aggregation techniques are used. Analysts can well utilise these features to simplify the whole complex process of data analysis, and organisations can harness them for insightful decisions.

The Primary Means to Narrate Stories in Tableau

We can represent the visuals using the following three formats on Tableau:

1. Sheets: Spaces where we can build individual visuals are called sheets. A worksheet has a single view in its sidebar, as well as shelves, cards, legends, and the Data and Analytics panes. A workbook is a sheet file structure along the lines of Microsoft Excel. It includes sheets that can function as a worksheet, a dashboard, or even a story

Source: https://help.tableau.com/current/pro/desktop/en-us/inspectdata_describe.htm

2. Dashboards: Most commonly used reporting format, a dashboard is a layout where sheets are arranged in a meaningful manner. It is a collection of views that helps you to compare a variety of data at the same time. If you have a set of views that should be reviewed every day, then you can create a dashboard that displays all of the views at once rather than navigating to separate worksheets. Think of the efficiency gains that this can bring about.

Source: https://www.tableau.com/about/blog/2020/5/6-dashboards-tableau-partners-help-you-mitigate-covid-19

3. Story:Sheets or dashboards arranged in a sequence to convey information. A story is a collection of visuals that work together to convey information. Stories can be created to tell a data narrative, provide context, show how decisions affect outcomes, or simply make a compelling case.

Source: https://help.tableau.com/current/pro/desktop/en-us/stories

Default Charts in Tableau

Through a set of default charts created with Tableau, data sets can be displayed in a comprehensible way. Let's have a look at a few types of charts:

1. Area Chart:An area chart is a line chart with a colour shaded area between the line and the axis. These charts constitute the most common approach to illustrate stacked lines and are often used to represent accumulated totals over time.

Source: https://help.tableau.com/current/pro/desktop/en-us/qs_area_charts.htm

2. Bar Chart:Place a dimension on the Rows shelf and a measure on the Columns shelf to make a bar chart or vice versa. We may compare numerical data such as integers and percentages using bar charts. Each variable's value is represented by the length of each bar. Bar charts, for example, might demonstrate how much money a small business spends on various expenses.

Source: https://www.tableau.com/data-insights/reference-library/visual-analytics/charts/bar-charts

3. Box-and-whisker Plots:When demonstrating the distribution of data points across a specified metric, box-and-whisker plots, also known as box plots, are an excellent chart to employ. The ranges within the variables measured are represented in these graphs. These graphics are useful for comparing the distributions of multiple variables.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_boxplot.htm

4. Bubble cloud: In bubble clouds, data is displayed in a cluster of circles. Individual bubbles are defined by dimensions, while individual circles are defined by measures. A bubble chart's design allows it to display multiple variables. Individual bubbles represent dimension field values, while measure field values define the size and colour of the bubble. As a result, we can examine a plot with at least three variables, one dimension and two measure fields.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_bubbles.htm

5. Bullet Graph:Bullet graphs are a type of bar graph that was created to replace dashboard gauges and metres. When comparing the performance of a major metric to one or more other measures, a bullet graph is beneficial. A bullet graph can help you visualise your objective, the current data set, and past data sets; all in one visualisation if you have a target goal that you need to meet on a regular basis

Source: https://www.tableau.com/data-insights/reference-library/visual-analytics/charts/bullet-graph

6. Cartogram:Choropleth Maps, also known as Filled Maps, are a powerful tool for studying geographic data, especially for maps with a lot of detail (e.g., US by counties or ZIP codes). They make it simple to detect geographical hotspots and then drill down into these areas using several visualisation options.

Source: https://www.pluralsight.com/guides/build-filled-maps-in-tableau

7. Click View:The circle view is a useful representation for comparative analysis. It's the same as using the circle marker on a scatter plot. Every mark is in the shape of a circle and can be used for subsequent actions.

Source:https://interworks.com/blog/ccapitula/2014/10/17/tableau-essentials-chart-types-circle-view/

8. Gantt Chart:Gantt charts are used in project management to depict the length of time between events or activities. As a project management tool, it highlights the interdependencies between activities and illuminates the workflow timeline.

Source:https://help.tableau.com/current/pro/desktop/en-us/buildexamples_gantt.htm

9. Heat Map:In a heat map, data is displayed along with colours. Using one or more Dimensions members and the Measure value, a heat map can be created. Heat Map helps to compare data by colour. For example, how many products have failed to meet the company's expectations, and how many products have exceeded expectations, and so on.

Source:https://help.tableau.com/current/pro/desktop/en-us/buildexamples_highlight.htm

10. Histogram:A histogram is a graph that depicts a distribution's form. It divides values for a continuous metric into bins and segregates a set of data points into user-specified ranges. The histogram, which resembles a bar graph in appearance, condenses a data series into an easily interpreted visual by grouping many data points into logical ranges or bins.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_histogram.htm

11. Scatter Plot (2D or 3D):Scatter plots are a type of graph that is used to show the correlations between numerical data. They are used to depict the link between three variables by plotting data points on three axes. Each column on the X, Y, and Z axes is represented by a marker, whose position is determined by the values in the columns.

Source: https://www.dataplusscience.com/TabCharts/scatterplotsize.html

12. Streamgraph:Streamgraph shows how a number value (Y-axis) changes in response to another numeric value (X-axis). It is a sort of stacked area chart. The relative proportions of the entire can be studied using a stream chart.

Source: https://greatified.com/2018/09/17/how-to-build-a-stream-graph-in-tableau-software/

13. Text Tables:Text tables (also called cross-tabs or pivot tables) are created by placing one dimension on the Rows shelf and another dimension on the Columns shelf. Then, on the Marks card, slide one or more measures to Text to complete the view.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_text.htm#:~:text

14. Treemap:Treemaps are used to show data in the form of nested rectangles. Dimensions define the structure of the treemap, while measures define the size or colour of the individual rectangles. It is a simple data visualisation that can provide information in a visually appealing format.

Source: https://help.tableau.com/current/pro/desktop/en-us/buildexamples_treemap.htm

15. Word Cloud:The word cloud is an excellent visual for representing the frequency of words in a given volume of Text. In a word cloud, the most important or unique words from the data are arranged in groups. The main goal of making a word cloud is to provide the viewer with a quick understanding of the important and unique words in the data.

Source: https://www.edupristine.com/blog/creating-word-cloud-tableau

Custom Visuals in Tableau

Tableau also provides a range of custom visuals. Creating them is just a question of one's expertise in Tableau.

1. Dot Distribution Map: Maps that help to spot visual clusters are known as point or dot distribution maps. Dot distribution maps are excellent for displaying how data points are dispersed.

Source: https://help.tableau.com/current/pro/desktop/en-us/maps_howto_pointdistribution.htm

2. Network:Nodes and edges make up a network graph. By connecting nodes with similar features, network visualisations show relationships between items. A network graph is a type of data visualisation that allows consumers to quickly grasp data relationships. Nodes are single data points with edges connecting them to other nodes. The relationship between two or more nodes is represented by edges. This enables the user to easily visualise clusters and establish linkages.

Source: https://ladataviz.com/2019/12/15/build-a-network-graph-in-tableau-in-three-steps/

3. Polar Area:The Polar Area Chart, also known as the Coxcomb chart, resembles a pie chart except that all of the slices have the same angle and the length of the slice that extends spirally from the centre represents quantity.

Source: https://tableau.toanhoang.com/creating-a-polar-chart-in-tableau/

4. Radial Tree:A radial bar chart is a type of pie chart. Like a pie chart, it depicts the relationship of parts to the whole, but it can also include subcategories for each part of the total. Each category in the data series plotted in a radial bar chart is assigned a different colour, whereas all subcategories are assigned the same colour.

Source:https://boraberan.wordpress.com/2014/12/31/radial-treemaps-bar-charts-in-tableau/

5. Timeline:The timeline chart, as the name implies, depicts the significant events that occur in the month, year, or even day. The timeline can also be used as a calendar to display forthcoming events.

Source: https://playfairdata.com/how-to-make-a-timeline-in-tableau/