In the ever-evolving world of data architecture, decision-makers are often faced with a foundational choice: ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform)? For years, ETL was the gold standard—especially when on-prem systems and batch processing dominated the landscape. But as cloud computing, real-time analytics, and modern data stacks surged, so did the practical advantages of ELT.
This post explores real-world scenarios where ELT didn’t just work—it outperformed traditional ETL by a wide margin. These insights are for teams stuck at the crossroads of modernizing their stack, scaling operations, or simply tired of overcomplicating their data pipelines.
Use Case 1: Real-Time Data Visibility for Marketing Dashboards
A global marketing firm approached our team with a common problem: delays in reporting. Their ETL process took over 8 hours to run, rendering “daily” dashboards outdated before stakeholders even opened them.
By shifting to ELT, we pushed raw data into a cloud warehouse as it was created—no waiting. From there, lightweight transformations inside the warehouse made it possible to update dashboards in near-real-time.
This switch drastically improved executive visibility and marketing agility. Visualizing this shift was only made possible through a smarter data foundation, powered by our data engineering consulting services in Austin, Texas. The decision to transform data after loading it gave teams the flexibility to run multiple transformation versions and improve queries without touching upstream logic.
Use Case 2: Enabling Advanced Analytics in Healthcare
Healthcare providers are under immense pressure to turn data into actionable insights, fast. In one scenario, a client with strict HIPAA compliance rules needed to merge EHR data from various sources to identify trends in patient outcomes.
Previously, their ETL toolset struggled with data volume, versioning issues, and schema changes. Our team moved them to an ELT architecture, which loaded all raw data into a secure cloud environment and executed transformations using SQL-based logic—directly within the warehouse.
The result? Analytics teams were empowered to iterate faster, adapt to regulatory changes, and produce more accurate models using services like our advanced analytics consulting services in Texas. Because the raw data was always available, models could be retrained or compared against historical versions instantly—something traditional ETL couldn’t support without redesign.
Use Case 3: Agile Product Analytics with Tableau
An e-commerce client needed to understand how product features impacted user engagement, but their ETL processes were rigid and hardcoded. Every schema change required days of rework, blocking fast experimentation.
We introduced a cloud-native ELT approach that funneled all user interaction logs into their warehouse continuously. With the data already accessible, business analysts could use advanced Tableau consulting services in Texas to explore metrics in real time, apply custom calculations, and even test hypotheses without involving engineering.
This dramatically improved how fast teams could respond to product performance questions, iterate on UX experiments, and deliver reports that aligned with rapidly changing business priorities. It wasn’t just faster—it was finally scalable.
Why ELT Wins in the Cloud Era
The shift to ELT is not about replacing ETL everywhere—it’s about knowing when to use the right tool for the job. ELT thrives when:
Data volume is high
Schema evolution is frequent
Real-time insights are critical
Multiple teams need access to raw or semi-processed data
You want analytics to evolve without changing core logic upstream
These advantages are amplified when paired with robust warehouse technologies like Snowflake, BigQuery, or Redshift. ELT enables data engineers to build scalable pipelines, analysts to iterate quickly, and business leaders to make informed decisions faster.
It’s More Than a Trend—It’s a Strategy
Many organizations hear “ELT” and assume it’s just another buzzword. But as the above use cases show, it’s a strategic advantage when deployed correctly. ELT doesn’t just streamline the data journey—it creates room for innovation.
If your team is still stuck debating whether to move to ELT, it might be time to explore your current bottlenecks. Are your reports always delayed? Are schema changes dragging down your entire dev cycle? Is your warehouse underutilized? These are signs that an ELT-centric approach may unlock the performance you’ve been chasing.
Our team at Dev3lop has helped companies across industries migrate to modern data stacks with ELT at the center. Whether it’s integrating with Tableau, Power BI, or MySQL consulting services and other backend systems, our software innovation approach is built to scale with your growth.
In the age of data overload and attention scarcity, ELT isn’t just faster—it’s smarter.
If you’re ready to rethink how your business handles data transformation, now’s the time to explore solutions that scale with you—not against you.
Building dashboards in PowerBI quickly is important because decision makers are eager to start using these rocket ships we are creating. However, if you’re new to PowerBI that may be asking a lot! Tracing is helpful because it empowers us to quickly create a solution and design from scratch.
What is tracing? Drawing over lines on a superimposed piece of transparent paper, and with figma, you will be able to do this digitally speaking. Allowing you to trace over any designs to abstract your own.
Tracing dashboards is a good way to recreate something net new and offers a fast path for getting people talking about your dashboard designs.
In this article, you will learn to become a master of making powerful designs from scratch, and this will empower you to Create dashboards in PowerBI quickly. Here’s a lot of screenshots to show you what you’re going to be building and potentially a template you can copy and paste into your next PowerBI Design.
Create visual documentation for PowerBI Design
Here at DEV3LOPCOM, LLC, we passionately believe visual documentation improves project deadlines. Plus, allows for fast data product creation and we want to show you how we would create a dashboard from scratch without any direction on the style or design.
Figma works, but any app that allows tracing over an image will work, and in this blog we will show you how to create this template.
We show the steps to tracing the design, and adding it to PowerBI. This can help you operationalize your templates and improve your dashboarding speed across any dashboarding product.
About this PowerBI dashboard Data and our Goals
First, lets learn about the data and establish goals about our workload to keep us focused on an objective.
All data should have a brief description, otherwise it’s hard for others to collaborate with your data sources.
Using the following data about Social Media and Mental Health, was recently released by University of Maryland in July 2024.
Our goal is to quickly generate a dashboard to help others learn PowerBI. However we have thin requirements, it’s fun to pretend this is a real world software consulting engagement, and similar to a real world use case in a busy business environment, perhaps people are too busy to give us insights.We must research and learn on our own.
About data: The dataset encompasses demographic, health, and mental health information of students from 48 different states in the USA, born between 1971 and 2003.
How do I get my CSV Data into PowerBI?
Open your PowerBI software. You don’t need to buy anything, just go grab the software and get started with me.
In the Home tab, click Get Data. Then select Text/CSV.
Once we have our CSV data open, you may notice we have weird Column headers that aren’t sensible to anyone on the dashboarding end.
This is typical in a lot of APIs, Data Warehouses, and Data Engineering in general is ripe of columns not being named correctly for each team. Luckily for us, PowerBI can change column names with great ease.
Finding Artwork to Trace Your Dashboard
First, we need to start with learning about “artwork.” When learning to draw, an art teach will ask you to trace something 100 times, and then by the 100th time you’ll be drawing it better.
Same with the internet, we often are reverse engineering each others design to improve our design. In this process we will find some artists we enjoy, choose one, and trace our dashboard on this design.
I like using Dribbble to find ideas and learn about modern approaches. It has a lot of really stylish content, and it’s easy to start here as a dashboarding guru.
I search for ‘modern dashboard style…
If working with a client, I will find 3 designs and then ask them to choose one. Then I’ll build everything around this template. I like using figma because it’s easy enough for people to dig into the weeds, and see they can access the design elements.
Pretend our client suggest the following design.
Okay, paste the dashboard we are asked to mimic into figma and lets start tracing.
You’ll notice as you do this you’ll start to create your own unique design to your dashboarding solution.
Start tracing design for PowerBI Dashboard
Cover the surface with a square.
Once hidden completely, lets edit transparency hitting 5 on keyboard. This should adjust the transparency.
Okay, keep flowing. Next same thing for side menu. Trace it. But before we go, adjust the edges to be rounded.
Easy enough in figma, grab little white ball and pull it down until it hits the line we are tracing. adjusting one side adjust all 4 sides.
Okay, hit the side menu.
Next, TEXT overlays. And button overlay with squares.
I prefer starting with a highlighted button so i know the sizing, then replicate that size across. Alt drag and drop for similar copy paste of previous object.
Working through buttons should be easy, let the software guide you to make it perfect too. Notice this has a 7 pixel gap.
Skipping ahead…
Now that we have this style, lets see what it looks like in PowerBI.
Adding your Figma design to PowerBI is simple. It’s a file.
Export the file to your computer.
Add image to PowerBI.
Resize dashboard so it fits cleanly.
Remove padding, this is my least favorite thing to have to do in Tableau and PowerBI. These apps automatically pad everything for some reason, haha.
Making decisions about our new Figma Style for PowerBI
In the beginning stages it’s about speed and repeatability. In more advanced dashboard development Figma saves a lot of time.
Next, lets duplicate our work area, and move the sub button navigation for today to the right side.
This is good enough for PowerBI. But before we leave just yet, lets dive into how we can improve the color pallet. I’m using coolors for an easy one.
Now, start to style your dashboard so that it’s appealing. Don’t spend too much time here because chances are the design will change, and you’re just trying to make it look decent. Use corporate colors so you’re following the “designers” pattern. They can send your a pdf file with the correct style guide, which improves this process, but today we are tracing and coming up with our own style guide.
As you’re applying color, start to focus on subtle details…
Improving PowerBI Dashboard with Logos, Style, and Strategy
Logos make dashboards pop. You know you can easily grab one, so grab the most recent logo. Don’t edit peoples logos, use what they supply online.
I’m choosing the source data logo, to help represent the source information because putting my logo here would not be a factual representation of the effort.
Now, notice what happens when I size it next to the buttons, depending on your screenshot and size of dashboard to be traced, in Figma, it’s subtle… Notice my sizing is subtly off and I can’t seem to make it exact, I generate black guide bars… aim to sync up for “perfect”… people will use your dashboard more often if it’s synced up.
In this example/screenshot I’m demonstrating how lining up this logo is a little more tedious than allowing figma to define things by snapping edges, I created black guide lines to help me follow a consistent flow from top to bottom. This is a kind of “hawk eye” or “pixel perfect” strategy I need you to deploy to create powerful dashboards in any reporting software or designed front-end!
Before we part, a few more subtle wins to consider as you perfect your traced design for PowerBI.
This will give a very nice clean view. In figma, click the red square, paste the image. Very easy process if you created the space for the icon. As you do this selection of icons, realize nothing is perfect, we are prototyping, get something in there because that’s the key, fast/repetitive!
Notice how we made some decisions that moved us away from the original design, this is called “making it your own.”
One more layer of decisions to clean it up.
The strategy here is making things clean and lined up, using LINES to guide ourselves. Delete these guide lines once you’ve mastered this technique and keep duplicating to avoid having to do this process again…
Here’s my work station, notice I’m starting to document what goes inside of buttons, and the documentation is in the same screen. This helps with identifying where our hard work belongs.
The header looks a little close to the first square, however a good starting point, we can optimize that later. The point of using guides/lines is the important part of this training.
Choosing cool icons for PowerBI Navigation
Since we are prototyping and not rushing to production, we need a simply PNG file for icons. Google search will bring up a lot of options you can trace, “black icon mental health heart.”
Simply click a square in figma, and ctrl+v paste.
This is why we created that square in tracing section, it outlines my sizing requirements.
Now, we have two buttons, logo. Things are cooking. Plus, custom icons. Always tell people it’s easy to change icons, this is just a prototype.
Many tracing apps can be found in the figma community. Great for icon tracing. This creates a vector trace of the heart/brain icon.
Once you trace the svg, you can color the file and it’s a vector rendering. I like changing the color to match the pallete.
Now, to finalize the visual. I use more guides but in the shape of a square this time. Find what works best for you.
Insert image into PowerBI
Woot, you’re here! You’re doing you’re own design based on a tracing.
I hope you’re proud of your drawing. If not, simply grab more ideas and trace until you’re satisfied.
Open Insert Tab, then click image. Navigate to the image you created in Figma. Group it and export it.
Start to play with dashboard sizing based on your image size.
Adding your first charts on new style in PowerBI
Okay, so you’re adding your new traced design to PowerBI as an image. You fixed the canvas.
And you’re beginning to add charts.
I’ve started with the easier charts, that feel very global. Like the amounts of states accounted for in the overall survey. The differences between gender, and the general health column popped to mind considering our button says General Health too. Even though it’s a place holder, perhaps we can go into detail about general health as a button too. Also, I like making actionable KPI to flow with buttons, so end users know if they click that bar chart, perhaps they will learn more about General health, and also the button General health will take them there too.
Scaling up on your new Traced PowerBI Design Template
Okay, people are going to ask you to change your PowerBI Design, for example pixels aren’t perfect, maybe 2 pixel boarder around charts isn’t great.
This is why I love having my dashboard design in Figma. Easy to edit. Copy and paste and start new styles.
In powerbi, similar process, right click dashboard tab, and click duplicate to duplicate your dashboard.
Now, delete the background image, and add a new image. Should look like this if you’re still adding charts. As long as you don’t move boxes, you’re safe to simply add back the new image and it will fit perfectly.
This is a good sign, you’re not depending on a reporting platform to manage your design elements. You can slap this background into any reporting software.
Now, you have a duplicate tab in PowerBI, I went with nuerophism, a cool design technique that makes it feel like it’s popping off the screen because of the light and dark shadows. Do you notice the differences in the shadows?
Conclusion to Tracing designs with with Figma for PowerBI Desktop
While working with designers, often we are given screenshots of artwork, and tracing allows us to gain what we need to be successful.
I hope you enjoyed this tutorial on creating quick PowerBI products using Figma to trace.
Keep perfecting your craft and let us know if you need help with any dashboard designing services!
We will add more training like this in our articles here on dev3lop, stay tuned.
Although, we started as a Tableau Consulting Company, we have been navigating into more and more PowerBI the past few years.
Ensuring that even the next data expert won’t find it too daunting.
To excel in quick calculations, it’s essential to identify and address slower ones.
#1 Problem with Slow Tableau Workbooks
Solving slow Tableau workbooks is often a calculation optimization game.
Then the migration of transformations, Boolean style calculations for example are easily pushed to SQL because SQL does Boolean logic with ease, so why make Tableau do this for you? This is a subtle win and as you continue you’ll find bigger wins in our blog below.
Think of Tableau as a tool you don’t need to over complicate. You can protype, transform, build a data product, and then stress about the “improvements” we discuss below in the near future.
Stressing these tiny details now will slow down your project, and stress out business users. Do it when no one is looking or when someone asks “why is this slow?”
During Tableau Consulting engagements, we see it’s easy to move your slow moving calculations into your database after the prototyping phase, and consider pushing heavily updated calculations to your SQL end the hardening phase that you do at the end. Anything being changed often is best to keep in your Tableau Workbook until everyone has completed their apples to apples.
Optimizing Calculations in Tableau Desktop for Better Performance
When it comes to Tableau Desktop, writing fast and efficient calculations isn’t just a nice-to-have—it’s a must for performance and scalability. A calculation that works is great, but one that works fast is better, especially as data grows. Let’s break down why certain choices in your calculations can have a massive impact on performance, focusing on the example provided.
The Problem: Slow String-Based Calculations
Here’s the first example:
if month(date) >= 5 then "blue"
else "orange"
end
Why is this slow? Strings.
Strings Are Heavy: Every time Tableau processes this, it’s comparing strings instead of lighter, numeric values. Strings take up more space and are slower to process than integers.
The else Isn’t Necessary: If your logic doesn’t need an else, don’t add one just to fill in. else assigns a default value—if that value isn’t relevant, you’re doing extra work.
The Improvement: Simplifying the Logic
Here’s a slightly improved version:
if month(date) >= 5 then "blue"
end
This avoids unnecessary processing by dropping the else. If the condition isn’t met, Tableau will simply return NULL. However, this still relies on strings, which slows things down.
The Better Option: Switch to Numbers
if month(date) >= 5 then 1 // blue
elseif month(date) <= 4 then 2 // orange
else 0 // filter out
end
This is a solid step forward. Why?
Databases Love Numbers: Integer-based logic is much faster because databases and Tableau’s data engine process integers far more efficiently than strings.
Strings have thousands of possible values.
Integers have only 10 basic values (0-9) in a single digit, making calculations simpler and faster.
Future-Proof Logic: By using integers, you’re not just optimizing today; you’re setting yourself (and your team) up for easier scaling and maintenance tomorrow. Want to add another category? It’s just another number.
Ease of Filtering: Returning 0 for filtering out irrelevant data reduces additional logic elsewhere, streamlining workflows.
Why Does This Matter?
When you write calculations that rely on strings, Tableau (and the underlying database) has to:
Compare values character by character.
Manage much larger datasets because strings require more storage.
Perform extra lookups if you’re working with case-sensitive text.
Switching to numeric logic tells Tableau to focus on lightweight, easy-to-process values. Over time, this can lead to noticeable performance improvements, especially with large datasets or frequent dashboard updates.
Pro Tip: Comment for Clarity
This isn’t just about optimizing calculations; it’s about teaching better practices. Add comments like this:
if month(date) >= 5 then 1 // blue
elseif month(date) <= 4 then 2 // orange
else 0 // filter out irrelevant months
end
By documenting your choices:
You make your logic easier for others to understand.
You reduce the need for future troubleshooting.
You create a shared knowledge base, improving team productivity.
The Bottom Line: Calcs need to be faster!
When building calculations in Tableau, think beyond “does this work?” to “how efficiently does this work?” Opt for integer-based logic over strings whenever possible. It’s a small change with a big payoff, especially as your dashboards grow more complex. Less work for Tableau = faster insights for you.
Got other optimization tips? Let me know in the comments!
A Faster Tableau Calculation
The simplest and fastest approach? Stick with numbers and Booleans:
if month(date) >= 5 then 1 // blue
else 0 // orange
end
Why It Works: You’re just typing numbers. The comments explain the logic for human readers without bogging down Tableau with unnecessary strings.
Scalable: This approach is ideal for larger datasets and complex workbooks. As your project grows, you’ll appreciate the simplicity and speed of integer-based logic.
For an even lighter touch:
month(date) >= 5
Boolean Flag: This returns TRUE or FALSE directly, which is incredibly efficient. Boolean logic is the leanest and fastest calculation type Tableau can process.
Why Writing Fast Tableau Calculations Matters
Writing fast calculations isn’t just a power move for your own dashboards—it’s a cornerstone for building a thriving Tableau community. Here’s why it matters:
User Adoption: Fast calculations mean responsive dashboards. That translates to better user experiences and higher adoption rates for your work.
Community Growth: When you optimize your calculations, you share best practices that help others master Tableau’s native features.
Future-Proofing: Hundreds of slow calculations will drag your workbook down over time. Optimized logic ensures your dashboards remain scalable and maintainable.
Let’s keep the momentum going: Write fast Tableau calculations, build amazing dashboards, and grow the community together. Pretty dang fast, right? 🚀
Today, we would like to highlight the functionality of Date Buckets, which is how we like to think of it mentally, and others call it Period-over-Period Analysis within Tableau Desktop. Both periods are buckets of dates and work great with min(1) kpi dashboards and often used in our Tableau Consulting engagements.
This blog delves into a method for date calculations to be used as trailing periods of time, to gain access to quick change between two periods in Tableau. In other words; We are focusing on identifying the last two periods in your data source, and the end user supplies a value to increase those buckets based on a date part you pick.
This approach enhances the efficiency and clarity of your analytical processes with Tableau and is easy to re-use. There are many ways to write this calculation and this is one way to write the calculation.
between dates filter
In Tableau this between date filter will create two calendar inputs, most executives don’t want to click anything.
It only takes 3 steps to build self generating, automated (not static set filters), date buckets in tableau desktop that trail with your max date in the date column [w].
lol, type this stuff or paste the code coming from this tutorial.
Below please find my quick win tutorial as a means of quickly winning… on any Tableau workbook with a date and a parameter.
We will be using the SuperStore Subset of data.
Which comes with every license of Tableau Desktop. In your data, you probably have a date. Use that date and follow along with these next two steps.
To begin, you need a date, and a parameter.
Step 1, make a date variable named W.
Create a new calculated field in tableau desktop, call it W.
make a simple variable W in place of your date. your date goes in this calculated field.
Now make the parameter.
Step 2, make a parameter variable named X. It’s an integer.
This will be the number of ‘X’ per period of analysis.
make a simple variable X in place of your parameter.
Paste the calculation below in any workbook with a Date and Parameter.
Above, if you followed along, you will not need to make any major changes to the calculation.
if DATETRUNC('month', [W])> DATEADD('month', -([X]+ datediff('month',{MAX([W])},today())) , TODAY()) then "Current Period" //make this 0 elseif DATETRUNC('month', [W])> DATEADD('month', -([X]*2+ datediff('month',{MAX([W])},today())) , TODAY()) then "Previous Period" //make this a 1 else "Filter" //make this a 2 END //[W] = date //[X] = parameter
Drag drop this on to the view, right click filter, filter filter…
Now, only two buckets of time are available. You’re welcome!
Automated period over period analysis in Tableau
You’ve just implemented automated date buckets in Tableau, allowing end-users to control visualizations using the bucket generator. Personally, I find the tool most effective when using it in a daily context rather than a monthly one. However, the monthly option provides a convenient way to encapsulate dates within distinct periods, while the daily granularity offers a simpler and more immediate view.
Having a rapid date divider or bucket automation at your disposal is highly advantageous. It empowers you to visually highlight disparities between two date periods or employ the calculations for logical flagging, subtracting values, and determining differences, all without relying on the software to construct these operations through window calculations.
Optimization date buckets or period over period in Tableau
Optimization #1: remove LOD calculations
Nothing against LOD calcs, except they are slow and built to help users who don’t know SQL.
{max(W)} seeks to find the max date, you can find it easier using a subquery in your select statement. If you don’t know what that means, ask your data architect supporting your environment to add the max(date) as a column, and have it be repeated per row too. They will know what to do or you need a new data architect.
Optimization #2: stop using % difference or difference table calculations
Nothing against table calculations, except they are slow and built to help users who don’t know SQL.
Optimization #3: change strings to integers.
Nothing against strings, except they are slow.
It’s likely not your fault that you’re using strings in 2018 with if statements, it’s probably because someone taught you who also did not know how to write optimized Tableau calculations.
Optimization #4: ‘month’ date part… add a swapper.
The Datetrunc is used to round the dates to the nearest relative date part, that’s just how I explain it easily.
Date part can be a parameter.
DATEPART(date_part, date, [start_of_week])
NO I Don’t mean the Function Datepart.
DATETRUNC(date_part, date, [start_of_week])
YES I Mean Date_part, which is scattered in the calculation and easy enough to replace with a parameter full of date_parts. Now end user can play a bit more.
Optimization #5: remove max(date), add an end date parameter…
Remove {max(date)} or the subquery of max(date) explained above because you can give your end user the opportunity to change the end date using parameter.
Tableau Pricing has simplified to 3 different user purchases and Tableau aka Salesforce increased their price. The Tableau pricing does change, so be aware of what they are offering!
The Tableau price is broken into the users ability to access the Tableau Desktop product, exploring dashboards built by Tableau desktop users, or viewing reports.
Tableau Creator: Creators within your organization encompass individuals who establish connections with data sources and transform that data into valuable insights for both themselves and their colleagues. They typically engage in the creation of reports and disseminate them through methods such as email or shared drives. These data power users frequently engage in ad hoc analyses to address deeper inquiries arising from their data.
Tableau Explorer: Explorers represent the contemporary business users who act as a driving force for organizational change. While their job titles may not explicitly label them as analysts, they possess a comfortable familiarity with data. They are motivated to advance their respective businesses and require the ability to delve deeply into data to discover answers to their unique questions, which often extend beyond the confines of pre-built reports.
Tableau Viewer: Viewers leverage data to enhance their decision-making processes. Tableau Viewers encompass a wide spectrum of individuals, ranging from team members who rely on data for their day-to-day tasks, to department heads who need insights into the progress of critical projects, and even to the CEO, who depends on high-level metrics to gauge the overall health of the organization.
About these various prices in Tableau
Tableau Creator caters to a diverse range of professionals, including business analysts, vice presidents, data architects, office administrators, and even SQL experts. It empowers them with the flexibility they require to craft on-the-fly data discovery visualizations and engage in self-service analytics.
Furthermore, the latest Tableau pricing, which includes Tableau Creator, introduces a new lightweight ETL (Extract, Transform, Load) tool. This tool adds a layer of efficiency to data management, making it easier to extract, transform, and load data for analysis.
The Explorer and Viewer roles extend this accessibility to a broader audience, granting everyone the capability to access and view work published online. This inclusivity fosters an environment where individuals can freely pose questions and provide answers, all without the typical delays associated with a conventional centralized Business Intelligence model.
As a result, Tableau Creator not only streamlines the development of reports and dashboards but also eliminates the need for extensive back-and-forth email exchanges during the report/dashboard creation process. This enhances collaboration and expedites the decision-making process across your organization.
Tableau Pricing is now bundled into 3 simple plans.
We recommend you work with your Tableau Software sales manager if you are looking to implement Tableau Server. You have 15 days to get your work completed, let them know about your installation, and if it will take longer than 15 days, tell them upfront.
The cost of Tableau is an annual purchase, not a month to month purchase, and they bundle in a Tableau Server user with every purchase too.
Using Tableau Creator or Tableau Desktop is a minimum cost of $900+.
Tableau Creator:
Price: $75 per user per month (billed annually).
What You Get: Tableau Desktop, Tableau Prep Builder, and one Creator license on Tableau Cloud.
Description: Unleash your analytics potential with our Creator package, essential for every deployment. This suite empowers you to discover insights and manage your end-to-end analytics workflow.
Tableau Explorer:
Price: $42 per user per month (billed annually).
What You Get: One Explorer license for Tableau Cloud.
Description: With Tableau Explorer, you can explore trusted data and find answers to your questions quickly, enjoying full self-service analytics capabilities.
Tableau Viewer:
Price: $15 per user per month (billed annually).
What You Get: Access to view and interact with dashboards and visualizations on a secure and user-friendly platform.
Description: The Tableau Viewer allows you to view and engage with dashboards and visualizations easily, making data insights accessible to everyone.
Here’s the information presented in a simple table format:
Tableau Plan
Price per User/Month (Billed Annually)
Included Features
Tableau Creator
$75
– Tableau Desktop – Tableau Prep Builder – One Creator license on Tableau Cloud
Tableau Explorer
$42
– One Explorer license on Tableau Cloud
Tableau Viewer
$15
– View and interact with dashboards and visualizations on a secure platform
Having a solid understanding of the executive overview of Tableau is paramount. Tableau is not just another software solution; it’s a top player in the realm of business intelligence and data analytics. Many organizations, regardless of their size or industry, rely heavily on Tableau to gain insights from their data, make informed decisions, and stay competitive in the market.
The recent shift to monthly licensing in Tableau’s pricing structure signifies a broader industry trend toward greater flexibility and adaptability. This shift makes it easier for organizations to align their analytics capabilities with their evolving needs and financial constraints, opening the door to more accessible, scalable, and cost-effective data analytics solutions.
Being well-versed in Tableau’s executive overview is a strategic advantage for businesses and professionals. It allows them to harness the power of Tableau to visualize and explore data, create interactive dashboards, and generate valuable reports. With its user-friendly interface and robust capabilities, Tableau has the potential to transform the way businesses leverage data for decision-making.
Whether you’re an analyst, executive, or any professional seeking to unlock the potential of data analytics, understanding Tableau’s significance as a widely adopted reporting tool is a critical step toward staying ahead in the data-driven world. By choosing the right Tableau plan, you can tap into its capabilities and drive your organization’s success through data-driven insights.
Tableau Desktop looks great on this MSI laptop.
Our experience with tableau pricing.
We have experience talking to new users, clients, classrooms, and random people in elevators about Tableau pricing, and we are happy to help you learn more. However it’s also good to start the conversation with Tableau about pricing with their software, and learn who your sales representative or account manager will be.
We have experience with Tableau Consulting; installing tableau server, building data products end to end, helping companies monetize their data, building APIs for webhooks to talk to on your internal servers, setting up tableau online aka tableau cloud for your customers, & even building first time KPI dashboards, including design services!
If you have any questions regarding the price of Tableau or if it makes sense for your environment, contact us.
