AtScale Blog

TECH TALK: BI Performance Benchmarks with Google BigQuery

Posted by Joshua Klahr on Apr 6, 2017

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 Google’s BigQuery, and how it stacks up in the BI-on-Big Data ecosystem.

Read More

Topics: Business Intelligence, Big Data, olap, BI

Announcing AtScale 5.0: Rapid Innovation for our Customers

Posted by Joshua Klahr on Mar 7, 2017

CONTINUING OUR TRACK RECORD OF RAPID DELIVERY & INNOVATION

Today we announced the general availability of AtScale 5.0 and I couldn’t be more excited about the host of great new features that are included in this release.  As we’ve continued to gain traction in a number of industries - ranging from healthcare to retail to financial services to telco to online- we continue to learn from our customers and use these learnings to feed directly back into our product features.  With the release of 5.0, AtScale customers now have an even richer set of capabilities that they can use to derive business insights and value from their Big Data investments.  I’ve included some of the highlights of the release in the sections below.

Read More

Topics: Business Intelligence, Big Data, olap, BI

TECH TALK: Multi-Level Metric Analysis. Uncover the Hidden Relationships

Posted by Joshua Klahr on Mar 2, 2017

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.

Read More

Topics: Business Intelligence, Big Data, olap, BI

TECH TALK: Solving the Unrelated Dimension Dilemma. A Connect the Dots Story of Sorts.

Posted by Joshua Klahr on Feb 15, 2017

Wouldn’t it be great if you could load all of your data from a single file into an Excel pivot table for easy analysis? 

Unfortunately, this approach isn’t usually viable when dealing with complex business analytics and big data.  Take for example a typical use case found inthe world of healthcare insurance.  A large insurance provider has 10s of millions of members, and processes 100s of millions of claims a year.  As flexible as Excel is, we all know it won’t handle this volume or velocity of data. 

As a result, more and more enterprises store  large data sets in big data platforms like Hadoop.  And while Hadoop provides a low-cost and performant approach to store and process this information, there is still the challenge of supporting the many types of analytics required on claims and member data sets.  But why? Why and how, with all of the advances in technology, can a simple calculation cause so much complexity?

Read More

Topics: Business Intelligence, Big Data, olap, BI

BIG BI:  Business Intelligence in Digital Transformation

Posted by Neil Raden on Dec 8, 2016

Digital transformation is a broad term that has various meanings by application, but in general, it means that more and more of what organizations, people, governments do is happening in computers, mobile devices and networks. As a result, the way things are done is changing, especially in the way things are connected. So in this new world of data flying everywhere, being generated and consumed, where does one stop for a second to take a look at what’s going on?

Read More

Topics: Business Intelligence, Big Data, olap, BI, Analytics

AtScale Delivers the Industry’s First Modern Business Intelligence Platform, enables BI on Hadoop and Big Data, On Premise and Cloud

Posted by AtScale on Nov 17, 2016

Big Data analytics leader previews industry’s first platform to enable unified business intelligence for Teradata, Hadoop, Google Dataproc and BigQuery

San Mateo, CA, November 17, 2016 – AtScale, the first company to provide enterprises with a fast and secure self-service BI platform for Big Data, today announced a significant expansion of its services, from BI on Hadoop to BI on Big Data.

Read More

Topics: Hadoop, About AtScale, Tableau, Business Intelligence, spark, Big Data

The 6 Principles of Modern Data Architecture

Posted by Joshua Klahr on Nov 15, 2016

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.

Read More

Topics: Hadoop, Business Intelligence, Big Data, Hadoop Summit

BIG BI:  Running with the Red Queen: BI is Here to Stay

Posted by Neil Raden on Nov 2, 2016

Do you remember those word problems on tests like the SAT or the ACT?

They would go something like this:  Train A leaves the Station at 1:15PM, gradually accelerating for 7 minutes until reaching a speed of 52mph for a distance of 76 miles. Train B leaves 12 minutes later, accelerating to a speed of 47mph in 11 minutes. After one hour, how far behind Train A was Train B?

This sort of problem reminds me of the Business Intelligence (BI) business, alternately known as Decision Support, Analytics, Reporting, Data Discovery, etc. It seems that no matter how fast Train B can go (organizations implementing BI), they can only, at best, keep up with Train A (the relentless march of technology). 

In biology, this is called the ‘Red Queen Effect’. named after poor Alice in 'Through the Looking Glass', where the faster she runs with the Red Queen, the faster the landscape moves with them so they have to go as fast as they can to merely keep up.

Read More

Topics: Hadoop, Business Intelligence, Big Data

TECH TALK:  BI-on-Hadoop Engine Wars Continue...Everybody Wins

Posted by Joshua Klahr on Oct 18, 2016

Just this week, AtScale published the Q4 Edition of our BI-on-Hadoop Benchmark, and we found 1.5X to 4X performance improvements across SQL engines Hive, Spark, Impala and Presto for Business Intelligence and Analytic workloads on Hadoop.

Bottom line, the benchmark results are great news for any company looking to analyze their big data in Hadoop because you can now do so faster, on more data, for more users than ever before.

While this blog provides a high level summary of our findings, you can access the full Q4 2016 Edition of the BI-on-Hadoop Benchmarks here, and also listen to our webinar replay discussing this in more details here.

Read More

Topics: Hadoop, Business Intelligence, spark, hive, bi-on-hadoop, Big Data, impala, presto

BIG BI: Data isn’t a Process. It’s an Asset.

Posted by Neil Raden on Oct 6, 2016

Data. It isn't a process. It's an asset.

Welcome to the 1st in a series of 8 blogs, where I will dive-in to separate and clarify both concepts and relationships across Business Intelligence and its active component OLAP,  predecessor technologies, and data. OLAP in particular has suffered from issues of scale and speed, but the need for this type of analysis is greater than ever. And while, the industry of analytics has been overrun with big data and data science, there is a general lack of understanding that previous drawbacks of BI and OLAP have been solved with the new architecture of big data, Hadoop/Spark and Cloud.

Join me as I uncover both the vital need for multi-dimensional analysis and the vastly improved capabilities that exist for big, nae massive, data of today. I believe you will find some rather interesting surprises.

Read More

Topics: Hadoop, Business Intelligence, Big Data

Learn about BI & Hadoop

The AtScale Blog is the one-stop shop for cutting edge news and insights about BI on Hadoop and all things AtScale.

Subscribe to Email Updates