AtScale Blog

How Much Dough is in Your Data?!

Posted by Bruno Aziza on Sep 19, 2016

This morning, O'Reilly Media published the results of its 2016 Data Science Salary Survey.  The report covers a wide set of topics such as salary differences by gender and countries as well as details for the types of skills that can give employees an edge when it comes to earnings.  We tooked a closer look at the Business Intelligence answers and what we found out might surprise you...

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Topics: Hadoop, Tableau, Business Intelligence, spark, Big Data, excel, powerbi

I like [ Big BI ] and I Cannot Lie...

Posted by Bruno Aziza on Sep 7, 2016

There is no shortage of advice if you’re in the market for a “BI on Big Data” solution or as we sometimes like to call it “Big BI”.  Big BI refers to a Business Intelligence (BI) platform that conforms with end-users needs (typically seamlessly integrated with Tableau or Excel), that can scale on any data size to deliver top query performance.  

A resource you might find particularly interesting when sizing up your BI tools, is the latest version of the “G2 Crowd Grid for Business Intelligence Software”. The Winter 2016 version just got published. We reviewed the grid and its report and found some interesting insights worth sharing.

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Topics: Gartner, Business Intelligence, Big Data, surveys

CDOs: They Are Not Who You Think They Are

Posted by Bruno Aziza on Aug 22, 2016

Google the word “CDO” today and your search will mostly results return articles about the “Chief Digital Officer”.  However, if you came to this blog, you’re probably looking for guidance on the other title this acronym refers to: “The Chief Data Officer”...

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Topics: Hadoop, Business Intelligence, Big Data, Chief Data Officer

Hortonworks chooses AtScale as its standard for BI-on-Hadoop

Posted by Dave Mariani on Jun 28, 2016

We started AtScale because we believe that everyone should be able to use all data for all their decisions.  We believe that people should have unencumbered and secured access to information, work with data of all shapes, at lighting speed and in the tools they are already familiar with like Tableau and Microsoft Excel.  

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Topics: Hadoop, Business Intelligence, Big Data, hortonworks

What is Spark and Why You Should Care

Posted by Ashley Huang on Jun 24, 2016

Rumor has it that with the rise of Apache Spark, Spark will replace Hadoop.

Wait What? 

Well, let’s take a look. Apache Spark is an open-source processing engine that supports interactive quieries while Hadoop is an easy to scale, cost effective data storage. The truth is- Spark does not replace Hadoop, in fact, Hadoop and Spark complement one another.

Now you may wonder: how will Spark and Hadoop affect your big data strategy?

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Topics: Hadoop, Business Intelligence, spark, Big Data

What to Watch for at Hadoop Summit San Jose (June 2016)

Posted by Joshua Klahr on Jun 23, 2016

With Hadoop Summit San Jose just around the corner, I thought it might be helpful to preview what to watch out for a the conference. In some ways, not much has changed in the past few months - streaming data is a hot topic, more and more people are adopting adjacent technologies (like Spark), and “in memory” is “in vogue” in the world of big data.  However, a quick tour around the Hadoop Summit website reveals a few more trends that deserve some additional attention.

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

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

What Clique is Qlik Joining? A Few Things to Consider

Posted by Bruno Aziza on Jun 7, 2016
Last week was an interesting one for Private Equity and Enterprise Software.  On Tuesday, Marketo, Inc. (NASDAQ: MKTO) entered into an agreement to be acquired by Vista Equity Partners for $1.79 Billion and on Thursday, Qlik (NASDAQ: QLIK) announced it would be acquired by Thoma Bravo for $30.50 per share - an all-cash transaction valued at approximately $3.0 billion.
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Topics: Hadoop, Business Intelligence, Big Data

How a Unified Semantic Layer Can Help Business Intelligence

Posted by Ashley Huang on Jun 2, 2016

Research shows that the average enterprise has at least 6 to 10 Business Intelligence tools.  Microsoft Excel, the world's most prevalent analysis tool is used by 1 Billion users.

Other companies like Tableau, Qliktech, MicroStrategy or Business Objects have had great success too.   

However, there is one key issue with a heteregeous BI environment: each tool uses a different protocol so data has to be customized to work with each BI tool.    

For instance, Excel uses MDX and Tableau prefers SQL.   What if your company uses new tools like Looker or even open-source tools like Apache Zeppelin? 

How can your deliver one version of the truth to all users, regardless the tool they use? This post helps you get there.  

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Topics: Hadoop, Business Intelligence, Big Data

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