Are Dashboards Dead And Great Data Engineering Project Ideas

Newsletter #8

Are dashboards dying?

Recently I read Taylor Brownlow’s article on Towards Data Science where they proclaimed that dashboards are dead.

Instead, they suggested that much of analytics would switch over to more notebook-style presentations.

Although there were a lot of good points in the article in terms of why many companies struggle with dashboards. It was a far cry from needing to throw the baby out with the bathwater.

Also, Taylor is the Head of Data at a company focused on notebooking vs. dashboards. So perhaps there is a little bias in the article…just a little.

The truth is, dashboards can and are valuable tools when done well.

Dashboards can help us synthesize and summarize millions, if not billions of rows into concise and digestible bits of information.

And this is not limited to internal users.

It is becoming more and more common for low-code solutions to provide the ability to share your dashboards externally.

May it be to provide our clients extra value, or maybe your entire product is a dashboard that you sell to other external users.

Some tools range from Tableau to Yellowfin, from custom code to drag and drop solutions. All with different price tags and features that could make or break your companies final decisions.

In this newsletter, we will outline some of these products.

5 Dashboard Tools - From Tableau To Yellowfin

Tableau 

Tableau is an analyst-first tool.

It is easy to use in terms of developing reporting. This is something I hear over and over again from analysts.

Recently I was working with a client who was trying a few different tools and they found some tools like Trevor.io clunky and Looker difficult to manage.

However, as soon as they tried Tableau, they were instantly converted. Tableau’s drag and drop focus can make it a great solution for analysts who don’t have as much SQL experience.

Tableau also plays a role when it comes to data exploration. Its drag and drop nature makes it easy to quickly create scatter plots and other charts that can be used by analysts to understand complex data sets.

In addition, Tableau also provides developers the ability to embed their reporting for external customers.

Pros

  • Analyst centric user interface

  • Offers “Viewer” user types that can help reduce costs

  • A lot of supporting documentation

  • Large user base

Cons

  • Lack data modeling and data dictionary capabilities

  • No real version control

  • Tableau can get expensive depending on how many Creator licenses you need. One creator license costs about $1,000 per user(but one viewer license only cost about $150)

Looker

Figure 2. Looker Report Example

Looker is often described as the developers data visualization tool. Many users find it a little more difficult to use compared to Tableau.

However, Looker is 100% cloud based and doesn’t require you to download an application(Like Tableau). In turn making it easier for developers to share their work and collaborate.

This also leads to improved performance because of the cloud native architecture.

Looker’s architecture takes advantage of modern cloud databases’ native scalability and performance. Unlike most competitors, Looker doesn’t rely on stale data extracts or a proprietary in-memory architecture that force you to predict the questions your users will ask. - Looker

Overall, Looker has become popular as a cloud first data visualization tool.

Pros

  • Developers may prefer in terms of integration

  • 100% Cloud based and easy to collaborate on

  • Data modeling layer that can help predefine and manage complex data models

Cons

  • More developer-centric

  • Can be very expensive (Looker’s License is around $50,000 and then $30 per user per month)

Mode

Mode is a BI application that not only has tools for you to perform data analyses and build dashboards, it also offers tools for you to perform more complicated data analytics using machine learning with Python/R in one single cloud-based application. And Mode promises to give their users an experience of “for analysts by analyst”.

Pricing, like many solutions, is highly dependent on your needs. Mode offers both a business and Enterprise pricing tier which forces interested parties to request more information.

However, with our research, we would say it slightly cheaper than Looker for the yearly licenses but about the same cost per user after.

Pros 

  • Version Control is automatically integrated into Mode’s process (unlike some of the other solutions on this list)

  • Cloud-First Architecture - Meaning its easy to collaborate and share your charts

  • A good mix of dev and analyst friendly features

Cons

  • Mode is still fighting for market share which means there are often more analysts familiar with Looker and Tableau

  • Mode is not as friendly as Tableau and is not as well documented

Holistic.io

Established in 2015, Holistic provides an end-to-end data analytics and business intelligence platform. Like most tools developed after the early 2000s, Holistic is cloud-based. meaning it is easy to collaborate and share reporting.

Holistic also boasts that you can ingest, transform and visualize data within a single tool.

Truth be told, I have rarely found it a good idea for a tool to try to take care of the entire data workflow. Although Holistic focus is to improve the DataOps process. I do often find it concerning when a tool doesn’t specialize as it often leads to problems either with the flexibility of usage or usefulness of features.

But Holistic is still relatively room so there is a lot of time for it to grow and find its best fit.

Pros

  • Analyst friendly development environment (drag and drop)

  • Notification systems developed for Email and Slack so you can set scheduled reporting, alerts, and shareable links.

  • Centralized data management that prevents data knowledge from being locked to a few individuals

Cons

  • Holistic.io is still a newer product and in turn, has a lot of room for improving the features the product offers

  • Holistic.io is a start-up which means it could be bought out by a larger company to reduce competition

  • Limited documentation

YellowFin

Figure 3. YellowFin Report Example

YellowfinBI is a lesser-known BI option when it comes to embedding reports. However, it can provide an alternative to some of the popular solutions. It doesn’t have an intuitive UI compared to Tableau and in turn, does have a little bit of a learning curve. 

Some benefits it offers include the ability to enforce versioning of reports and dashboards so you can always roll back to the previous version.

Pros 

  • Allows you to white label dashboards and their reporting server

  • A good mix of developer and and analyst friendly

Cons

  • A flat fee of 50,000 USD for a yearly licenses for 4 cores (Fine if you’re a large company but not if you’re a small company)

  • Yellowfin is not as friendly as Tableau

Dashboards Aren’t Dead

Dashboards are far from dead.

They still provide a lot of value when they are developed correctly. A well-developed dashboard helps condense complex information in a world that is blowing up with data.

Using dashboards to find the signal in the noise helps business executives and owners make more confident decisions.

In addition, dashboards can be developed to easily share both internally and externally with clients and vendors who may also benefit from the knowledge.

So for now, dashboards are here to stay.

Ask A Data Consultant - Office Hours

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Career Corner - 5 Data Engineering Project Ideas

Merely graduating with a background in Computer Science or Information Systems is rarely enough to get a data engineering position.

You'll need related real-world experience to fine-tune your hard skills. Concerning your future job search, one of the best ways to develop and convey these skills is through a data engineering portfolio projects.

In this video, we will review five potential project ideas with data sources.

Watch 5 Data Engineering Project Ideas

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Articles Worth Reading

There are 20,000 new articles posted on Medium daily and that’s just Medium! I have spent a lot of time sifting through some of these articles as well as TechCrunch and companies tech blog and wanted to share some of my favorites!

Achieving Insights and Savings with Cost Data

Business profitability and sustainability are powerful reasons to invest in infrastructure efficiency, but it is easy to feel lost about how to actually reduce costs. A foundation of robust and actionable data is essential for a successful efficiency program. At Airbnb, building this foundation made it possible to prioritize savings opportunities and ushered in a wave of cost reductions.

More importantly, cost data has become a lever for long-term control. The team can react quickly before a spike wreaks havoc on the monthly bill and plan ahead when a big new project could become expensive. At the company scale, visibility into cost and usage has sparked a cultural shift. When savings can be measured, they can be recognized, and cost efficiency projects become exciting opportunities. As of early 2021, the most viewed dashboard at Airbnb is a dashboard of AWS costs.

Read The Full Article Here

How Pinterest Fights Spam Using Machine Learning

Hundreds of millions of people regularly visit Pinterest to visually discover inspiring ideas among billions of Pins. Inspiration is a high bar and we must be vigilant in ensuring that Pinners don’t see spam, harmful content or misinformation. To enforce our community policies and maintain an inspiring environment, we use the latest in machine learning technology to build automated systems that swiftly detect and act against both spammy content and spammers.

Read The Full Article Here

Streamlining Data Monitoring With WhyLogs And MLflow

Everyone who has worked with machine learning models in production is familiar with their complexity of deployment and lifecycle management. The task becomes particularly grueling given the current fragmented tooling ecosystem.

There is a smörgåsbord of tools supporting each individual stage of this cycle, which forces data science teams to build custom pipelines for the various ML frameworks (or even individual algorithms) that they use. Something as inconsequential as training and deploying a simple Scikit Learn linear regression model to SageMaker can take weeks or months to productionize, if your team doesn’t have a pipeline already built for these specific frameworks.

Read The Full Article Here

End Of Day Eight

Dashboards aren’t dead and using dashboards to display your data engineering project might be a good plan.

Whether you are looking into the classic Tableau or into the modern data stack options like Mode, these tools can make displaying data easy.

Whether you know code or not.

So what is your thought, notebooks or dashboards?