AWS Security Hub: Master Cloud Security with a Unified Approach

This has changed the way we interpret information by giving us a look into past insights but also to forecast future events, allowing us to make informed decisions.

Tableau 2021.2. includes ‘ask and explain data’ for viewers, allow connected desktops, Collections, and many more valuable features. Some of the key enhancements are listed below.

Collections provides a new format to organize content across your sites on both Tableau Online and Server into manageable folders. You may group item together from different projects and workbooks and you can reuse content in multiple contexts without additional storage or resources. Collections also makes it easier to share content around a central theme. For example, you can create a “Daily Sales” collection that includes dashboards with daily sales statistics, ETL data flow, data sources, etc.

Collections helps you congregate your data. You are given the leeway to create, explore and save your content privately. Another key feature of ‘Collections” is that users can create customized collections which are by default private. However, you are given the option to share the collection with any authorized users you choose to provide access to.

1) User Management Enhancement

In earlier versions, a subscribed user, deleted by an admin, was not erased from the system entirely. They were then classified as an 'unlicensed user'.

With version 2021.2, the subscribed creator will be deleted automatically when an admin deletes a user either via the UI or the REST API, without the extra step of reassigning the subscription ownership.

2) Ask Data Enhancements

Ask Data Lenses

A new feature introduced with this update is 'Ask Data Lenses'. It allows for easy data curation with defined columns and value synonyms and also provides suggested questions to allow for more inclusive data from a variety of sources.

(The update brings a new content type that is Ask Data Lenses, making it easy to curate data with the definition of column and value synonyms and suggested questions so you can better leverage existing published data sources.)

They are created alongside published data sources Ask Data use case(s) while maintaining the underlying data source as its own entity.

These 'lenses' are comparable to ‘views’. For those of you who are adept with SQL, where you can write selected statements specifically to extract the required columns, give definitions, whilst maintaining the integrity of the data source. Similarly, once created, ‘lenses’ can be accessed by viewers, opening Ask Data to a new class of users that struggle to self-serve their needs today.

Entity Search

Entity Search shows users search results of keywords, like the Google search box. Ask Data gives you word-by-word search results, giving you instant feedback on your data and what Ask Data can do. Ask Data will automatically choose the most relevant interpretation of your search and these search results help you build that input more effectively by selecting the right fields and values in the data set. Ask data learns from your selections to choose smarter defaults for future searches.

MFA allows users to easily add an additional layer of security to their accounts.

This feature unlocks the ability for Tableau Online customers who utilize native Tableau ID authentication to enforce multi-factor authentication (MFA) when their users sign into their sites. End-users can use applications like Salesforce Authenticator or Google Authenticator to perform additional verification of their identity when they log on to Tableau.

MFA makes it much harder for common threats like phishing attacks and account takeovers to succeed. MFA is one of the easiest and most effective ways customers can enhance login security and safeguard their business and data against external threats. Easily rename multiple fields in prep allows creators to transition seamlessly from web authoring to Tableau Desktop with a single click of a button. Creators will now be able to edit any workbook that they have permission to on Desktop. Prior to 2021.2, users had to manually change each header name. For example, if a user wanted to change “Customer” at the start of multiple header names, they would need to click on each field name and individually change/remove “Customer” in the field name. Not a big deal when there are less than 10 columns to update. However, for customers with data sets of 50+ columns, it is more cumbersome to have to individually change each field name. This feature allows a customer to quickly add a prefix, rename or add a suffix to multiple fields collectively. Tableau Prep is expanding its output capabilities to include Google BigQuery, enabling you to add or update data in Google BigQuery with clean, prepped data from your flow each time it is run. TABLEAU DESKTOP 2020.2 – Key Features 1) Maps: Spatial File Support The Marks Layers Control SP1 feature provides a control that allows users to toggle the visibility of layers on a map viz. The control works like a filter and the user is free to choose which layer(s) to view in order to answer their question. In addition, the user can control the interactivity of the map viz by selectively enabling or disabling selection on the layer in question. Toggle button – Our users can now use a button to show/hide any dashboard zone, floating or tiled. This function was previously limited to floating horizontal and vertical containers only. URL support for images – Users can now add images via external URLs, which also provides GIF support for images on the internet and workbooks. Loading these images will be time-efficient.

The cloud-based approach delivers:

1. Better performance
2. More flexibility
3. Enhanced cost savings
4. Improved security
5. Facilitates excellent teamwork opportunities

Specifically, Tableau on AWS lets you process data more quickly and scale up or down resources as needed. Plus, you can access a range of AWS services to optimise your Tableau setup. AWS also provides tools to help you save money and a secure environment to protect against cyber threats and data breaches. Ultimately, Tableau on AWS enables teams to collaborate more efficiently, taking their data analysis and business intelligence to the next level.

There are several other benefits to using Tableau on AWS beyond scalability, cost, and security. Here are some additional insights:

1. Faster Deployment: With Tableau on AWS, you can deploy new instances of Tableau in minutes rather than days or weeks as you would with on-premises infrastructure. This is because AWS has pre-configured templates for Tableau that make it easy to spin up new instances quickly. 2. Better Performance: Tableau on AWS is designed to exploit AWS's high-performance infrastructure. Tableau runs faster and more efficiently on AWS than on traditional on-premises infrastructure. 3. Integration with Other AWS Services: Tableau on AWS integrates seamlessly with other AWS services, such as Amazon S3 for data storage, Amazon Redshift for data warehousing, and Amazon EMR for big data processing. This makes building a complete analytics solution easier by using Tableau and other AWS services. 4. Improved Disaster Recovery: With Tableau on AWS, disaster recovery is built. AWS provides automated backup and recovery services to quickly recover your Tableau environment and data if there is a disaster or outage. 5. Global Reach: AWS has data centres worldwide, meaning you can deploy Tableau in the region closest to your users for better performance. This is especially important for organisations with a global presence.

Overall, Tableau on AWS offers several advantages over on-premises infrastructure. By leveraging AWS's scalability, cost-effectiveness, and security, organisations can run Tableau more efficiently and with better performance. Additionally, AWS's integration with other services and global reach make it an attractive option for organisations looking to build a comprehensive analytics solution.

Tableau Server on AWS deployment options

The following list outlines the available options for deploying Tableau Server on AWS:
1. Self-Deployment on EC2 Instance: This option involves users provisioning and configuring an EC2 instance and deploying Tableau Server. This provides the most significant control over the deployment process and can be customised to specific needs. However, it also requires more expertise and effort from the user.
2. Quick Start Deployment: The Tableau Server on AWS Quick Start provides an automated deployment process using AWS CloudFormation templates. This simplifies the deployment process and ensures that best practices are followed. However, it may be less customisable than self-deployment.
3. AWS Marketplace Deployment: Tableau Server is also available on the AWS Marketplace with pre-built AWS CloudFormation templates. This provides a quick and easy way to deploy Tableau Server, with different pricing and instance options public. However, users may have less control over the deployment process than over self-deployment.

Users should evaluate their specific needs and expertise when selecting a deployment option. Self-deployment provides the most significant control and customisation, while Quick Start and AWS Marketplace deployment offer simplified and quick deployment options.

As a frequent user of Tableau Desktop and Server, check out my top picks from the list. • Improved dynamic parameter • Multiple marks layer support for maps • Block comments in calculations • Web authoring enhancements • Offline map support for Tableau Server • Tableau Server Management (TSM) improvements • Multiple key activation on Tableau Server prior to TSM initialization • Analytics Extension for Tableau Online • Merge duplicate external assets • Tableau Prep Builder in the browser Tableau Desktop Improved Dynamic Parameter The dynamic parameter was one of the features which I used extensively starting from 2020.1. The feature allows us to populate parameter values based on a specified field whenever the workbook is opened. We will be able to select only the field with matching data types. The feature update announced in Tableau 2020.4 gives me more privilege to customize the Dynamic Parameter. I can now set a parameter’s range to a field’s min and max value upon opening the workbook, or when the field’s data source is refreshed. So, whenever I open the workbook or the extract is refreshed, Tableau displays the unique values within the range specified, giving more control over the parameter values displayed. Multiple Marks Layer Support for Maps This is an exceptional feature to bring multiple spatial layers and context together to better understand and analyze geospatial data and map views. I will be able to include multiple marks layers from a data source to map visualizations and enhance the geospatial analysis. I can present more context in a single map view and perform further analysis with this feature. Block Comments in Calculations Block comments is a simple yet one of the most useful features for me, which Tableau has announced in 2020.4. I often used to add comments in complex calculations for easy understanding in future references. Earlier, only single-line comments were possible, limiting the description I could add. But from now on, I can add comments of any length to calculation windows with block comments by simply starting the comment with /* and ending with */. What makes it unique is that the new multi-line block comment feature is consistent with other popular programming languages. This feature is an example of Tableau’s on-going effort to provide its customers with an intuitive user experience. Tableau Server Web Authoring Enhancements Starting from Tableau 2020.4, it is possible to author dashboards from the browser just like how we design it in Tableau Desktop. With this web authoring enhancements, I can include Highlight actions, Format Mark Labels, apply filters to worksheets, create fixed sets, and even create extract in my workbooks from the browser itself. I no longer have to make changes from Tableau Desktop and publish it to the server. I can directly do it from the browser itself. Offline Map Support for Tableau Server Rendering dashboards with maps is now faster compared to previous versions. Now, I can create maps using the offline map style in web authoring, ensuring the performance of map views in Tableau Server. Offline map support is a great deal for organizations with strict internet access restrictions, assuring that map view access to all its users. Tableau Server Management (TSM) Improvements Tableau Server Administration activities like installation, upgrade and backup are now easy like never before. I can retry installation or upgrade from the last checkpoint in case of an unexpected issue or error during installation or upgrade, saving my efforts to obliterate tableau server. Backups can be performed twice as fast as previous versions, and I can monitor the progress with the new progress bar giving visibility into what step the backup is on and how much time is remaining. Backups can also be scheduled using TSM command starting from 2020.4 and that is awesome. I no longer need to prepare batch script and depend on the windows task scheduler to schedule the backups on regular intervals; instead, I can schedule it with just a single command. Multiple Key Activation on Tableau Server Prior to TSM Initialization During Tableau Server installation, it is now possible to activate multiple license keys prior to TSM initialization. I will be able to save a lot of time by eliminating the need to restart after the installation is completed to activate multiple licenses and experience a smoother installation. Analytics Extension for Tableau Online The power of the Analytics extension is now unlocked in Tableau Online too. The feature was already available in Tableau Server, and it helped us dynamically perform advanced analysis with models and functions in R, Python and other platforms. Analytics extension in Tableau Online significantly enhances the scope of using Advanced Analytics by the common users. Merge Duplicate External Assets Earlier, the Database or Table with similar names used to appear as multiple assets within Tableau Catalog. But, the new feature helps me to merge those multiple assets into a single one. I can manage assets easily and keep an organized view of External assets by merging the common ones. Tableau Prep Tableau Prep Builder in the Browser Prepare the data for visualizations from anywhere using a browser! Starting from 2020.4, Tableau is bringing the data prep process into one integrated platform on the web. Now I can easily prepare and manage prep flows from anywhere using a browser. Conclusion Tableau is progressively evolving as a single platform for data preparations, visualization, and collaboration with every update and version release. Author : Firdous Maqbool Images Courtesy : Tableau

1. Connect Data without Migration

Getting data into Data Cloud is straightforward, with batch and real-time ingestion options. Whether you're a financial institution managing sensitive customer data, a retailer analyzing sales trends, or a healthcare provider processing patient information, the ability to bring in data from existing systems like AWS, Snowflake, or Google Big Query without migration or duplication is a game-changer.
With Tableau's seamless integration, all your data is available for rich, interactive analytics. Imagine a retail company analyzing millions of transactions in a blink with Hyper, a SQL database system designed for fast queries. The Hyper engine, running natively on Data Cloud, offers low latency and efficient access to data, allowing businesses to make swift, informed decisions.
Use Case: Tableau Cloud enhances the data fabric approach by enabling healthcare providers to visualize and interact with integrated patient data using dashboards and analytical reports from various departments in real time. This empowers professionals in making quicker, more informed decisions without needing to migrate data, ultimately improving patient care through seamless access to unified records.

2. Ready-to-use Dashboards

Tableau Accelerators are ready-to-use dashboards built using standard data objects in Data Cloud, designed to provide immediate value. Whether you're in marketing, operations, or customer service, these accelerators can help you quickly gain insights without starting from scratch.
For instance, a marketing team in a retail company can use the Segmentation and Customer Insights dashboard to understand customer demographics and monitor changes in segments over time, enabling more targeted marketing campaigns.
Use Case: Using Tableau Accelerators, the retail chain quickly enhances its sales performance monitoring capabilities, enables faster decision-making, and improves business outcomes. The pre-built nature of Tableau Accelerators allows for rapid deployment and customization, making them a powerful tool for businesses looking to accelerate their analytics initiatives.

3. Deeper Customer Insights

In Data Cloud, you can segment your data to better understand your customer base. Marketers and analysts across industries now have a new way to create segments in Data Cloud directly from Tableau. As you explore data, you can make selections within your visualization and publish a new segment in Data Cloud.
This feature is particularly valuable for industries like retail and hospitality, where customer segmentation is key to personalizing experiences and driving loyalty. The ability to share these segments across Salesforce clouds or other platforms ensures that strategic actions, such as targeted marketing campaigns, are data-driven and effective.
Use Case: Tableau Cloud is used by the marketing teams to analyze social media campaign data and enhance user experience with AI personalization. This enables them to continuously refine their understanding of customer preferences as new data comes in, allowing for more agile and responsive marketing strategies. It also helps identify the trends and patterns in customer behavior across different channels and invest in the channel with more ROI.

4. Explore Hidden Insights from Unstructured Data

Unstructured data, such as call transcripts or social media posts, can be challenging to analyze. However, with advancements in AI, Data Cloud makes it easier to search and analyze this data in Tableau.
Industries that deal with large amounts of unstructured data, like insurance or telecommunications, can significantly benefit from this capability. For example, an insurance company can analyze call transcripts to identify common issues and improve customer service processes.
Use Case: The telecom companies use NLP to analyze the text from these interactions, categorizing them by issue type and sentiment. By analyzing the data using Tableau, the companies identify recurring problems, leading to targeted training for customer service agents and quicker resolution times. This results in improved customer satisfaction and reduced service handling time.

5. Custom ML Models

Using Einstein Studio, you can easily apply custom machine learning models to your Data Cloud data. These models can be used to visualize predictions in Tableau, helping organizations across industries make data-driven decisions.
For example, a manufacturing company could proactively use predictive models to foresee equipment failures and schedule maintenance, reducing downtime and saving costs. Similarly, a retail company could predict which products are likely to sell out and adjust inventory levels accordingly.
Use Case: The manufacturing companies incorporate IoT sensor data from the production floor into a Data Cloud. AI algorithms analyze this data, identifying trends and predicting potential machine failures. Subsequently, these predictive models are visualized in Tableau Cloud, affording real-time insights to the operations team. The dashboards showcase real-time equipment status, failure probabilities, and recommended maintenance schedules, enabling the operations team to proactively schedule maintenance, thus reducing downtime and averting costly repairs.

Transforming Industries with Data Cloud and AI

The integration of Data Cloud and AI with Tableau offers unparalleled opportunities for industries to unlock the full potential of their data. From financial services to retail, healthcare to manufacturing, these innovations enable organizations to gain deeper insights, make smarter decisions, and drive business success.
Whether you're looking to streamline data access, jumpstart your analytics, create targeted customer segments, analyze unstructured data, or leverage machine learning models, the combination of Data Cloud and AI in Tableau provides the tools you need to stay ahead in a data-driven world.
Are you ready to transform your business with the power of Data Cloud and AI in Tableau? The future of analytics is here—it's time to embrace it. Connect with us for a free demo: https://www.beinex.com/free-tableau-software

Semi-structured Formats with Snowflake Support

The number of sources that produce semi-structured data has increased exponentially in recent years. The arrival of Snowflake Data Cloud has made it effortless to process complex datasets. Snowflake supports storing and processing semi-structured data. It supports semi-structured formats enlisted below:

• • JSON
• • Avro
• • ORC
• • Parquet
• • XML

This article is about the two features in Snowflake that come in handy for Data Engineers while handling semi-structured data.

• • Variant
• • Flatten

Variant

A variant is a datatype which can hold semi-structured data in a single field. Snowflake stores semi-structured data in the column format when semi-structured data is loaded into a VARIANT column. A single row can contain other underlying data. Let’s demonstrate with the help of examples:

The stored data can be easily retrieved and structured from the above table by a simple query as below:
select
v:time::timestamp as observation_time,
v:city.id::int as city_id,
v:city.name::string as city_name,
v:city.country::string as country,
v:city.coord.lat::float as city_lat,
v:city.coord.lon::float as city_lon,
v:clouds.all::int as clouds,
(v:main.temp::float)-273.15 as temp_avg,
(v:main.temp_min::float)-273.15 as temp_min,
(v:main.temp_max::float)-273.15 as temp_max,
v:weather[0].main::string as weather,
v:weather[0].description::string as weather_desc,
v:weather[0].icon::string as weather_icon,
v:wind.deg::float as wind_dir,
v:wind.speed::float as wind_speed
from json_weather_data
Note: ‘v’ is the Field (Column) name, and the JSON tags are arranged alongside to retrieve datasets.

Flatten

Compound values are flattened into multiple rows with the use of the Snowflake Flatten Command. A tool called Snowflake FLATTEN is used to transform semi-structured data into a relational structure. Relatively complex JSON structures such as nested JSON structures can be structured with FLATTEN function in Snowflake. Here is an example of the same: The above JSON file stores a single set of conversations between two texters in a single row. If the requirement is to get every individual message as a separate row a FLATTEN function can be used to produce the following result:

Here is the query to generate the above table from semi-structured data:

(SELECT
b.value:message_date::TIMESTAMP AS Time_Of_Message,
b.value:conversation_number::STRING AS Conversation_num,
b.value:message:message_text:msg_txt::STRING AS Message,
ROW_NUMBER() OVER ( PARTITION BY b.value:dialogId::STRING ORDER BY
b.value:message_date::TIMESTAMP ASC) as Message_order
FROM customer_messages t,LATERAL FLATTEN(input => t.v) b);

Summing Up

The ability of Snowflake’s assistance is undeniable. It aids you to make better decisions and offers a better overall experience when attempting to get the most out of your data, through features like Flatten and Variant. If you want to be a part of Snowflake’s incredible platform, let us help you. Beinex’s partnership with Snowflake enables us to offer you advanced features like automated tuning and elastic computing, along with analytics modernization services, to help your organisation realise exponential Return on Investment.