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.

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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.

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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.

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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?

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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?

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

Mythbuster: Hadoop Not Just Cheap Storage

Posted by Ashley Huang on Jun 17, 2016
 
 

In its early days, Hadoop was chosen because it was much cheaper to store large amount of data, compared to Enterprise Data Warehouse . However, Hadoop required users to have strong technical background to be able to query or do anything on Hadoop. Therefore, the assumption was that Hadoop was only good for data storage.

 Today, Hadoop is still the best option for inexpensive data storage. And the reality is as more technologies developed, Hadoop has become more and more user friendly too. In fact, the latest Big Data Maturity surveys indicate that in addition to the traditional data storage warehouse capability, a significant number of companies are using Hadoop for BI.

Companies like Yellow Pages are seeing sub-second BI query response time on Hadoop and have been able to drive increased Hadoop adoption across their organization. If driving Hadoop adoption has been a concern for you in the past, maybe you should reevaluate Hadoop for BI now. 

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Topics: Hadoop, Business Intelligence, Big Data, molap, olap, rolap

OLAP, MOLAP, ROLAP? Why Should BI Care? Which Should You Choose?

Posted by Ashley Huang on Jun 10, 2016

Since the 1980s, the world has been using OLAP technology to provide a business interface to analyze data stored in traditional ERP and CRM systems. As the demand for insights increased, MOLAP and ROLAP became key technologies.

With all of the different OLAP options out there, you may wonder which one can actually help you achieve your big data strategy. Which strategy is most suitable for your Hadoop environment?

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Topics: Hadoop, Business Intelligence, Big Data, molap, olap, rolap

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