AtScale Blog

It may seem like only yesterday that we said goodbye to 2017, but we are almost half-way through 2018. Big things in Big Data happened in the month of April. Many of us watched Mark Zuckerberg testify in front of Congress about data use and security, and still await the final outcome of Cambridge Analytica’s data abuse. If April seemed like it slipped under your fingers, check out what you might have missed in the world of big data.

My previous blog highlighted some best practices to gain immediate value from all the capabilities that AtScale offers when using Tableau as your BI tool of choice. One of those best practices is to use AtScale-created date dimensions to improve query performance, which is particularly helpful when using date dimensions as filters.  

Five years ago we had a hypothesis that Business Intelligence (BI) needed a reboot. We planned to take the best parts of original BI ideas and merge them with modern engineering and data analytics to build a platform for delivering self-serve, secure, curated and fast analysis to the entire business. Our strategy, we believed, would build a bridge from the old world to the new world while giving us a stage to present new concepts that made Business Intelligence truly intelligent.

Organizations have come to the realization that data is a core part of their strategy and a scalable distributed computing platform central to their technology investment. However, a challenge that big data practitioners face is what use case they should first implement in their journey towards realizing their big data strategy. The reality is, multiple items need to be addressed: choosing the right technology, requisite funding, and the right technical talent. However, identifying the right use case with defined success outcome is the most crucial point of starting a big data project.

Often times, customers approach me with questions around AtScale’s ability to integrate into the customer’s operational stack.  Today, I want to highlight a component of AtScale’s Development Extensions called Webhooks.

 A webhook (also called a web callback or HTTP push API) is a way for an application to provide other applications with real-time information. A webhook delivers data to other applications as certain events occur, meaning you get data immediately as opposed to a REST API which you would need to poll for data very frequently in order to get it close to real-time. This makes webhooks much more efficient for both provider and consumer.

March is gone and Spring has arrived, at least for many of us. A lot happened in March, and we certainly don't want you to miss out on what’s big on big data. Without further ado, here is what you might have missed in March.

If you are like me, a Tableau fan, you’ve probably used Tableau for many years, attended numerous Tableau Conferences, and cheered with great enthusiasm when the engineers at Tableau demonstrated the latest and greatest enhancements to the software.  You may also be very accustomed to creating your own calculations based on the row level data you are connected to.  You enjoy the freedoms that Tableau offers.   

“Cloud computing” is that magnificent umbrella technology term that is broadly used to describe everything from ordering groceries online to keeping track of asset logistics across the globe.

If you are one of our avid followers, you might remember that, when we refer to “The Cloud”, we talk about the infrastructure that lets your people do data analytics at a wide scale.  We’ve written pieces like “the 6 principles of modern data architecture” to provide a guide for how to ‘treat data’ through your modernization journey.  And we’ve provided tools like the Data Maturity Survey to assess where you might be, along that journey.

This week, we’re sharing a piece from 451 Research that we think will help you survive through the inevitable ups and downs of your digital and cloud transformation.

How valuable is an insight if you don’t know what’s driving it?

The investment in big data made in recent years by companies has been significant. Many are now looking to capitalize on the insights to be discovered in their expansive data lakes. Developing an analytic solution is a difficult and laborious process. More than a few projects have been abandoned long before any conclusive benefit is realized. Some efforts end due to constraints on time and money, others as a result of bad design or poor end user adoption. That last point is significant. You’re going to spend considerable resources to empower your decision makers. If you build it, and they don’t come, then what?

Congratulations!  Your data is controlled, aggregated and turbocharged in your AtScale virtual cube.  You have Tableau to create remarkable visualizations. Your data is happy! But are your cube designers and business users too? For instance, did you know that centralizing calculations in your AtScale Virtual Cube eliminates TDE perpetuation, 3rd party ETL processes and version control headaches?  For an enhanced AtScale experience, here are 5 Best Practices you should be implementing in order to maximize AtScale on Tableau.