AtScale Blog

As the World Cup is kicking off, some of us might be spending a lot of sleepless nights (or waking up earlier) to watch the games. Here is a fun fact: the most anticipated match is from Group H,  Japan Vs Belgium (with 4.9B page views out of 9B page views). Interesting.

We definitely want to make sure you don’t miss out on your big data news while you’re enjoying soccer games, here is what you might have missed in June.

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.

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.

Data Lake Intelligence with AtScale

In my recent Data Lake 2.0 article I described how the worlds of big data and cloud are coming together to reshape the concept of the data lake. The data lake is an important element of any modern data architecture, and the data lake footprint will continue to expand. However, the data lake investment is only one part of delivering a modern data architecture. At Yahoo!, in addition to building a Hadoop-based data lake, we also needed to solve the problem of connecting traditional business intelligence workloads to this Hadoop data. Although the term “Data Lake” didn’t exist back then, we were solving the problem of: “How can you deliver an interactive BI experience on top of a scale-out Data Lake” - it turns out we were pioneers in delivering Data Lake Intelligence.

Our experiences and learnings from those initial efforts led to the architecture that sits at the core of the AtScale Intelligence Platform. Because AtScale has been built from the ground up to deliver business-friendly insights from the vast amounts of information in data lakes, AtScale has experienced tremendous success and adoption in enterprises ranging from financial services, to retail to digital media. With the release of AtScale 6.5, we’ve continued to build on and expand AtScale’s ability to uniquely deliver on the promise of Data Lake Intelligence. If this sounds like something you might be interested in knowing more about… keep reading!

Yesterday, Gartner published the 2018 Magic Quadrant for Business Intelligence.  The MQ research for BI has been in existence for close to a decade. It is THE document of reference for buyers of Business Intelligence technology.

At face value, not much seems to have changed...BUT, if you take a closer look, you'll notice that some of the biggest changes in the history of the Magic Quadrant just occurred....

While it may be tempting to focus our efforts only on self-service BI in terms of security and access control mechanisms, it is important to also place emphasis on economies to achieve success.  When an enterprise develops a self-service BI environment, it undoubtedly means that their IT team adopts the role of a service provider. Data and services become available to internal business users for a price. What are these hidden costs?

A version of this article originally appeared on the Cloudera VISION blog.

One of my favorite parts of my role is that I get to spend time with customers and prospects, learning what’s important to them as they move to a modern data architecture. Lately, a consistent set of six themes has emerged during these discussions. The themes span industries, use cases and geographies, and I’ve come to think of them as the key principles underlying an enterprise data architecture.

Whether you’re responsible for data, systems, analysis, strategy or results, you can use these principles to help you navigate the fast-paced modern world of data and decisions. Think of them as the foundation for data architecture that will allow your business to run at an optimized level today, and into the future.

In the world of Business Intelligence and Big Data there continue to be a number of exciting innovations as new and improved options for processing large data sets appear on the market.  You may be familiar with AtScale’s BI-on-Hadoop Benchmarks - where we focus on evaluating the top SQL-on-Hadoop engines and their fitness to support traditional BI-style queries.  As we continue to work with customers who are navigating their journey to BI on Big Data, we are increasingly getting questions about the emerging cloud-based data processing engines.

 In this blog post, we will take a deeper look at BigQuery from Google, and how it stacks up in the BI-on-Big Data ecosystem.

I’ve asked it before and I’ll ask it again. Wouldn’t it be great if you could easily analyze ALL your data from a Excel single file? We all know this isn’t feasible; especially when dealing with big data and complex business analytics needs.

In working at the intersection of Big Data and traditional Business Intelligence, the AtScale team has encountered a number of complex business analytics use cases that are difficult, if not near-impossible, to solve using typical table-based data models and SQL. Today, I’m going to share why and how complex analysis, like for multi-level metrics, is no longer as ‘difficult’ nor ‘near-impossible’ as it once was.