TABLEAU 2019.3 – WHAT TO EXPECT?

AWS Storage Solutions

AWS offers a comprehensive array of storage solutions tailored to specific use cases and demands. These include Amazon S3, Amazon EBS, Amazon EFS, Amazon Glacier, AWS Storage Gateway, Amazon FSx, and the AWS Snow Family. Each service is designed to address storage needs, from simple data storage to high-performance file systems and large-scale data transfer.

Here are some of the key storage solutions provided by AWS: Amazon S3 (Simple Storage Service):

Object storage service for storing and retrieving large amounts of data.

Highly scalable, durable, and cost-effective.

Supports various storage classes for different use cases, including S3 Standard, S3 Intelligent-Tiering, S3 One Zone-IA, S3 Glacier, and more.

Suitable for data lakes, backups, static website hosting, and more.

Amazon EBS (Elastic Block Store):

Provides block-level storage volumes that can be attached to Amazon EC2 instances.

Supports different volume types, including General Purpose (SSD), Provisioned IOPS (SSD), Cold HDD, and Throughput Optimized HDD.

Used for running databases, applications, and workloads that require low-latency storage.

Amazon EFS (Elastic File System):

Manages file storage service that can be mounted to multiple EC2 instances.

Supports NFS (Network File System) protocol.

Ideal for shared file storage, content management, and data backup.

Amazon Glacier:

Low-cost archival storage service for data that is rarely accessed.

Suitable for long-term data retention and compliance requirements.

AWS Storage Gateway:

A hybrid cloud storage service that connects on-premises environments with cloud storage.

Supports file, volume, and tape gateways.

Amazon FSx:

Managed file storage for Windows (Amazon FSx for Windows File Server) and Lustre (Amazon FSx for Lustre) file systems.

AWS Snow Family:

A range of physical devices for transferring large amounts of data into and out of AWS when network transfer is impractical.

Latest Developments in AWS Storage Services

Over the past two quarters, AWS has introduced several exciting enhancements to its storage services, offering users greater flexibility and efficiency in managing their data. Let's delve into these notable updates:

AWS Backup's Local Time Zone Support

AWS Backup is a fully managed service that simplifies data protection across AWS services and hybrid workloads. The latest enhancement allows users to select their local time zone when creating or modifying backup plans. This eliminates the need for manual time zone conversions from UTC, making backup scheduling more user-friendly and convenient.

Amazon S3's Multivalue Answer for DNS Queries

Amazon S3 now offers Multivalue Answer (MVA) support for responding to DNS queries for S3 endpoints. With MVA, users can obtain up to eight S3 IP addresses per DNS query. This feature enables the automatic establishment of multiple concurrent connections to S3, potentially boosting throughput significantly. Moreover, MVA enhances retry operation efficiency by allowing applications to automatically attempt an alternative IP address without waiting for another DNS query. These improvements give users more robust and efficient access to Amazon S3 services.

General Availability of Mountpoint for Amazon S3

AWS has announced the general availability of Mountpoint for Amazon S3, an open-source file client that offers high-throughput access to Amazon Simple Storage Service (Amazon S3). This innovation reduces processing times and compute costs for data lake applications. Mountpoint for Amazon S3 acts as a file client that translates local file system API calls into S3 object API calls, including GET and PUT operations.

This feature is particularly valuable for workloads involving reading large datasets, ranging from terabytes to petabytes in size, and requiring the scalability and high throughput offered by Amazon S3. Use cases include machine learning training, reprocessing, autonomous vehicle data processing validation, and more. It supports sequential and random read operations on existing files and sequential write operations for creating new files. These capabilities enhance the efficiency of processing large-scale data stored in Amazon S3, making it a valuable tool for data-intensive applications.

Amazon S3 Glacier Flexible Retrieval Enhancements

Amazon S3 Glacier Flexible Retrieval has significantly improved data restore times, reducing them by up to 85%, and this improvement comes at no additional cost. These faster data restores are automatically applied to the Standard retrieval tier when Amazon S3 Batch Operations are used. Objects begin to be returned within minutes of initiating the restore process, facilitating quicker processing of restored data.

This enhancement benefits various tasks, such as transcoding media, restoring operational backups, training machine learning models, and analysing historical data. Leveraging S3 Batch Operations, archived data can be efficiently restored on a large scale by showing the objects to be retrieved and specifying the retrieval tier. The Standard retrieval tier now initiates restoring things within minutes, a substantial improvement compared to the previous 3–5 hour wait time. Overall restore throughput is also enhanced, thanks to new performance optimisations, enabling faster data workflows and quicker responses to business requirements.

AWS Backup Audit Manager's Delegated Backup Administrator Support

With AWS Organizations, the delegation of backup management has been expanded beyond the management account to dedicated administration accounts. This means that delegated administrators can now assume backup management responsibilities. As part of this launch, delegated backup administrators can centralise report generation and management on a large scale through AWS Backup Audit Manager. This feature is designed for auditing and reporting on data protection posture compliance.

Project Quotas Support in Amazon FSx for Lustre

Amazon FSx for Lustre, a fully managed service for high-performance file systems, now supports project quotas. This update allows users to easily group multiple files or directories within the file system into distinct projects and monitor storage usage per-project basis. Project quotas are particularly valuable for storage administrators responsible for managing file systems serving multiple projects or teams. This feature ensures no project exceeds its designated storage capacity, helping maintain effective resource allocation and control within the file system.

Beinex+ AWS Offerings

Beinex is an AWS consulting partner, and we empower customers to host their BI solutions and much more on the cloud. Our cloud migration experts bring in best-in-class stability and reliability by understanding your business strategy and working closely with you to deploy AWS infrastructure as a service.

AWS's commitment to innovation ensures that its storage solutions will remain at the forefront of the industry, providing users with the cutting-edge tools and capabilities required to thrive in the digital age. Embrace AWS storage solutions' possibilities and stay ahead in your data management endeavours.

But this does not apply to a slicer visual, which only has cross-filtering and no interaction feature. This made me wonder if we can achieve cross-highlight with a slicer visual through some work-around that allows me to compare one category with other easily through cross-highlight. After some research, I may have found a way to do this. Below are the steps in detail to achieve this. Normal filtering of data with slicer: The below example shows normal slicing of data where only the selected value will be reflected in the bar chart. This does not allow me to compare other values easily or focus in on the selected value through highlighting. Dynamic highlighting with Slicer: The below example shows the dynamic highlighting where I can choose the categories in the slicer to highlight for comparison with the other categories. I can easily focus on the selected categories and compare the measure values with other categories. Solution: First, I have created a disconnected table with the categories. This can be easily done with the following dax formula.

Selected Category = VALUES(Orders[Category])

Had made sure there is no relationship in the model view between the source table and new category table. Created a measure which will be added to the conditional formatting in data colour section in the format pane.

Selected Colour bar = 
var selected_category = VALUES ('Selected Category'[Category]) 
var category_to_highlight = SELECTEDVALUE (Orders [Category])
var filtered = ISFILTERED ('Selected Category'[Category])
var result = 
    SWITCH(TRUE(),NOT(filtered),"#0055cc", category_to_highlight in selected_category && filtered,"#0055cc","#9cd0ed")
return result

Explanation: Selected Colour bar =

var selected_category = VALUES ('Selected Category'[Category]) // taking values from category table

var category_to_highlight = SELECTEDVALUE (Orders [Category]) // Using selected value function 

var filtered = ISFILTERED ('Selected Category'[Category]) // checks if the column is filtered and will return true or false

var result = 
SWITCH(TRUE(),NOT(filtered),"#0055cc", category_to_highlight in selected_category && filtered,"#0055cc","#9cd0ed") 

/* The first condition checks if the there is no filtration will return all values, Then will be checking if the selected set of values is contained in the category column. The selected value will be returning a specific dark colour while the unselected value will be giving a lighter colour. */

return result

Created a bar chart with total sales given in the value section and the category column from the source data will be given as axis. Gave the highlight effect by adding a measure in the field value section of the data colour conditional formatting. Added the measure in the field value section. Added category slicer from the Selected Category table Finally arranged them and saw the magic happen. Conclusion: This goes to show the hidden features of Power BI one can explore with a little bit of tinkering with a dash of DAX. This blog is a first in a series of many nifty blogs. Hope you like it and looking forward to your feedback.

Tableau filters allow their users to perform simple filtering using available metrics, as well as perform advanced filtering using complex calculations. While developing dashboards, Tableau offers the flexibility to view different types of filters such as drop-down filters, wildcard filters, list filters and many more. To enhance dashboard interactivity, Tableau has provided “Filter Actions” which allow users to filter an entire dashboard as per the value selected by the user in a particular visualization.
To create a filter that suits the user's requirements, Tableau engages several filtering techniques which also enhance data processing efficiency Each filter is designed for a specific function, and the order in which they are run can significantly impact performance.

6 Types of Filters in Tableau

Tableau employs six different types of filters, which are listed in the sequence of execution:

1. Extract Filters
2. Data Source Filters
3. Context Filters
4. Dimension Filters
5. Measure Filters
6. Table Calculation Filters

1. Extract Filters

Extract filters are utilised during the initial stage of data filtering. When datasets are uploaded to Tableau, we can utilise the extract filter to minimize the number of Tableau queries for the data source. By applying filters to the extract, we can further decrease the size of the data.

2. Data Source Filters

Data Source filters sort out any crucial or sensitive information while putting data into Tableau. It is compatible with both the Live and Extract connections.
We can add the data source filter to any column by selecting the ADD option. After selecting the ADD option, the ADD Filter dialogue box will appear, displaying all the fields. We can then select the field to which we wish to apply the filter and it is possible to change or delete data source filters as needed.

3. Context Filters

A context filter is generally used when we require this filter to be applied before any other filter. It has the potential to enhance the performance of massive data sources. They are made by dragging the dimension to the filter section box and selecting the Add to context option. This will make the dimension pill change its colour to “Grey”, indicating it is a context filter. Similarly, by clicking "Remove from Context", the filter becomes a simple dimension filter. A common use case for this filter would be when a user would like to find the top N products in any category.

4. Dimension Filters

Dimension fields include discrete categorical data, and we can remove or add the values we want to investigate. The process is simple as outlined below:

Step 1: Drag the dimensions from the dimension list to the filter section box.
Step 2: From the Filter Dialog box, select the values that we want to analyze.

The Filter dialogue box contains four tabs:

1. General: To select the members present in the dimension to either include or exclude
2. Wildcard: To filter the result based on a particular pattern
3. Condition: To filter the result based on a particular condition
4. Top: To filter the Top N products belonging to a particular category.

5. Measure Filters

Measure filters are applied to the measure fields, which have quantitative data. It can be used as follows:

1. Drag the measured field from the Measure box to the Filter area
2. Filter dialogue box with several functions will appear.
3. Select the operation that needs to be performed and click Next.

In the subsequent dialog box, there are 4 types of filters:

1. Range: To select the range of values to include in the result.
2. At least: Select the minimum value of a measure to filter the data.
3. At most: To select the maximum value of a measure to filter the data.
4. Special: To select null or non-null values.

6. Table Calculation Filter

Table Calculation Filters, being the last type of filters to be executed in the sequence of execution, do not filter out the underlying data. It is mostly used to filter out contents in the current view. Table Calculations are functions that are utilised for building Calculated Fields like LOOKUP, WINDOW SUM, WINDOW AVG, and so on.

Summing Up

Tableau Filters are the most commonly used feature by many organizations. It allows the clients to filter out confidential data and allows them to provide controlled view access to different internal users so that only data relevant to the user is accessed. Filtering out data means data processing becomes more efficient, which in turn makes the report generation process faster which in turn helps the organization arrive at quick and insightful business decisions.

Use Cases Addendum

Let’s find out how Tableau filters work to solve various tasks :
Use Case 1:

A simple drop-down filter can be used to filter the view of our requirements as shown in the visualization above.
In this example, Dept A has been selected such that the Total Budget is shown only for Dept A. Once we select Dept B, the Total Budget shows the respective value for Dept B

Use Case 2:

In the visualizations above, we can see the customer demographics related to gender, age group and educational status. With the help of Tableau’s Filter Actions, one can click on a particular segment and filter the rest of the visualizations as per the requirement.
For example, if we require to find how many customers in the age group of 35-44 are males, all we have to do is to click on the doughnut chart for “Male”, and the visualizations will show only the data for males. Now, we can find that the number of male customers in the age group of 35-44 is 272.

Use Case 3:

The Filter Action feature in Tableau can be leveraged to develop several creative solutions for customers. One such solution is the “Reset Filter” as shown in the above visualization.
With a combination of filters and filter actions, one can develop a reset filter that, on clicking, would reset all the currently applied filters on the dashboard and bring it back to its original unfiltered view. This comes in handy if the dashboard consists of multiple visualizations and contains heavy data.

What Are Spatial Parameters?

Spatial parameters in Tableau allow you to dynamically interact with geospatial data within your visualizations. These parameters enable you to select spatial objects like points, polygons, multi-polygons, lines, or collections for calculations. Unlike traditional parameters, which work with values like text or numbers, spatial parameters offer the flexibility to work with geospatial data like coordinates and spatial shapes. You can create spatial parameters in two ways: 1. From a data source: If your data contains spatial fields (like latitude and longitude), you can load these as spatial parameters. 2. Using Well-Known Text (WKT) : This method allows you to manually input spatial data in text format to create custom spatial parameters. Spatial parameters can be used in the same way as other parameters in Tableau, including parameter controls, actions, and dynamic values. Note: Spatial parameters can only be created from spatial data fields (such as latitude and longitude). Creating spatial parameters from text string fields, like a "Country" field, is not feasible as it might be assigned as a geographic role but remains a text field.

Top Benefits of Using Spatial Parameters

Spatial parameters have offered immense possibilities for Tableau experts to analyze and visualize geospatial data. Key benefits are listed below: 1. Cross-Data Source Spatial Exploration With spatial parameters, you can explore spatial relationships between data sources that don't support joins. Unlike traditional data sources, which can be limited by join constraints, spatial parameters allow you to compare spatial regions from different datasets. This means you can dynamically analyze relationships across multiple data sources without the need for complex joins. 2. Skip Long Joins Previously, working with large spatial datasets required time-consuming spatial joins. Now, spatial parameters enable you to bypass these long joins and conduct spatial analysis much faster. You can use parameters to compare regions across multiple data sources instantly, even with vast datasets. 3. Interactive and Dynamic Analysis Spatial parameters allow users to visually interact with geospatial data in real time. Like other parameter types, you can use parameter controls to input points, lines, or polygons and adjust your analysis on the fly. This flexibility allows you to dynamically change spatial boundaries and relationships during your analysis. 4. Distance-Based Queries With spatial parameters, you can perform distance-based queries without complex calculations. For example, you can control the size of a buffer and see what lies within that buffer directly from the worksheet. This makes it easy to conduct proximity analysis without the need for complicated formulas. 5. Create Custom Regions Spatial parameters allow you to create custom regions by selecting points, lines, and polygons and combining them into a single parameter. For instance, you can create a sales region by combining multiple states into one spatial parameter. This enables you to tailor regions for specific analysis scenarios.

Spatial Calculations in Tableau 2024.3: What’s New

Tableau 2024.3 also introduces three new spatial calculations to help you evaluate spatial relationships: • SYMDIFFERENCE: Identifies areas that are unique between two regions. • INTERSECTION: Shows the overlap or intersection between two regions. • DIFFERENCE: Displays areas present in one region but not in the other. These new spatial calculations provide powerful tools to analyze and compare geospatial data.

Union Aggregation for Spatial Data

In previous versions of Tableau, the only aggregation available for spatial data was Collect, which groups spatial elements together. Union Aggregation has been introduced in Tableau 2024.3, allowing you to dissolve the boundaries between regions and providing even greater flexibility in working with spatial data.

Validating Spatial Calculations

Tableau 2024.2 introduced the Validate Calculation feature, which can help you detect and correct errors in your spatial data. This tool is handy when working with complex spatial datasets. Tableau's new Spatial Parameters and related features, such as spatial calculations and union aggregation, provide powerful capabilities for geospatial analysis. Whether comparing spatial regions, performing proximity-based queries, or creating custom areas, these features enable dynamic and flexible analysis beyond traditional methods.

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What is Time Series Analysis?

Time series analysis involves examining data that changes over time or where time is a variable in the outcomes. Time isn't just a data point; it's the primary axis on which the data is based.

The main difference with time series analysis is that data is collected at regular time intervals. This helps identify patterns in the data, forming trends, cycles, or seasonal variations. With a consistent time frame of historical data, time series forecasting becomes a valuable tool for predicting future data. Time's crucial role as a variable in data across industries makes time series analysis widely applicable. Explore a few examples outlined below.

6 Real-world Examples of Time Series Analysis in Various Industries

Example 1: Health Authority Enhances Patient Care

Problem: Inefficient resource use and rising costs of care and operations over time.

Solution: Utilizing data analytics, a prominent health authority conducted a comprehensive analysis of patient data. Examining historical data on patient stays, treatments, and conditions, they identified optimal times for administering medication, resulting in reduced average length of stay and cost savings for both patients and the system.
Insight: Time series analysis in healthcare extends beyond patient care to chronic disease research and epidemic-scale studies. Tracking chronic diseases over time and analyzing patient data using time series methods contribute to advancements in the field.

Example 2: Retail Giant Identifies Sales Opportunities

Problem: Slow data analysis affecting decision-making days before events.

Solution: A leading retail giant's front-office team integrated all data sources to gain a comprehensive view. Implementing time series analysis dashboards, they swiftly identified sales opportunities by forecasting against seasonal trends. Proactive measures were taken to increase ticket sales for upcoming events based on real-time insights.
Insight: Time series analysis aids retail giants in making data-driven decisions, predicting consumer trends, and strategizing marketing efforts, ensuring maximum impact.

Example 3: Manufacturing Company Improves Forecasting Accuracy

Problem: Inaccurate and time-consuming operations, manufacturing, and sales forecasting.
Solution: A manufacturing company revamped its forecasting process using time series analysis and modeling. By leveraging better data and faster analysis, they reduced analysis time from one day to one hour. Accurate forecasts in supply chain and manufacturing processes led to significant cost reductions in inventory, supply chain, labor, and capital equipment.
Insight: Time series analysis proves invaluable in optimizing forecasting models, enhancing accuracy, and streamlining operations in manufacturing.

Example 4: Marketing Analytics Scaling for Media Clients
Problem: Evolving marketing technologies making it challenging to quickly analyze information for media clients.
Solution: An analytics firm centralizes over 100 data sources using Tableau, allowing quick data retrieval and the creation of custom dashboards. Time series analysis aids in media forecasting, enabling the firm to develop insightful "what if" analyses. This empowers clients to make informed decisions about marketing investments.
Insight: Time series analysis combined with data centralization is crucial for scaling marketing analytics, providing clients with actionable insights.

Example 5: Streamlining IT Costs Through Self-Service Analytics
Problem: Business decisions based on static reports, consuming time and resources.
Solution: A retail group enables department staff with self-service analytics in Tableau, reducing IT costs by 20%. Time series analysis is applied for accurate forecasting of retail and IT trends, optimizing product orders and resource allocation.
Insight: Time series analysis aids in accurate forecasting for retail and IT trends, optimizing resource allocation, and reducing IT costs.

Example 6: Innovative Use of Data Analytics in Auditing Processes
Problem: Traditional audits were time-consuming and lacked value addition.
Solution: An organization in the energy sector used Tableau to analyze a year's worth of data, uncovering trends in financial processes. This data-driven approach revolutionized their auditing processes, providing deeper insights into the financial health of the organization.
Insight: Time series analysis proves instrumental in data-driven audits, providing a comprehensive view of historical data and uncovering trends for better decision-making.
These real-world examples showcase the versatility and impact of time series analysis across different industries, emphasizing its crucial role in data-driven decision-making and business optimization.