AtScale Blog

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.

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

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

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

How a Unified Semantic Layer Can Help Business Intelligence

Posted by Ashley Huang on Jun 3, 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

How To Load Data Into Hadoop

Posted by Ashley Huang on May 20, 2016
 
  
In our last segment, we learned about Hadoop's various components and how they work together as a Big Data Management platform.  The next key step to understand is how your team can load data into Hadoop.
 
Many are under the impression that loading data into Hadoop is complicated and that it may take a lot of resources to get started.  That's actually a myth!
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Topics: Hadoop, Business Intelligence, Big Data

TECH TALK: Scale-Out Business Intelligence with Hadoop

Posted by Joshua Klahr on Apr 21, 2016

The growing popularity of big data analytics coupled with the adoption of technologies like Spark and Hadoop have allowed enterprises to collect an ever increasing amount of data - in terms of breadth and volume.  At the same time, the need for traditional business analysis of these data sets using widely adopted tools like Microsoft Excel, Tableau, and Qlik still remains.  Historically data is provided to these visualization front ends using OLAP interfaces and data structures. OLAP makes the data easy for business users to consume, and offers interactive performance for the types of queries that the business intelligence (BI) tools generate. 

However, as data volumes explode, reaching hundreds of terabytes or even petabytes of data, traditional OLAP servers have a hard time scaling.  To surmount this modern data challenge, many leading enterprises are now in search of the next generation of business intelligence capabilities, falling into the category of scale-out BI.  In this blog I'll  share how you can leverage the familiar interface and performance of an OLAP server while scaling out to the largest of data sets. 

And if you don't have time to read the whole thing, don't miss the 10-minute 'cliff-note' video of scale-out BI on Hadoop near the end.

 

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

TECH TALK:  First-Child & Last-Child Measures in Hadoop

Posted by Joshua Klahr on Mar 24, 2016

As more and more enterprises adopt Hadoop as their next generation data platform, the demands of traditional enterprise workloads, including support for Business Intelligence use cases, are creating challenges.  While Hadoop excels at low-cost distributed storage and parallel data processing, interactive support for BI-style queries remains a challenge.  Additionally, multi-dimensional queries often demand complex OLAP-style calculations and functions.  In this post we will share how AtScale helps to bridge the gap between Business Intelligence users and data that resides in Hadoop.

In many typical business analyses or applications it is important to be able to directly query the first or last value of a particular metric across a hierarchy.  For example:

  • What was the starting or ending price of a security during a particular day
  • What were inventory levels for a SKU at the beginning and end of the month
  • What was the first and last payment amount for a loan agreement

Not Always as Easy as it Sounds

Executing such a query using SQL may involve complex queries consisting of unions, sub-queries, and/or temporary tables.  In MDX (Multidimensional Expression Language) such a query is easier to support, given MDX’s rich support for analytical queries and hierarchical representation.  AtScale has implemented support for First Child and Last Child measures in a way that supports BOTH SQL and MDX clients, which means that virtually any data visualization client can take advantage of this advanced functionality.

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

TECH TALK:  SQL-on-Hadoop Benchmark: A Bit of a Tortoise and Hare Story

Posted by Trystan Leftwich on Feb 24, 2016

Trystan here, Software Engineer and doer of all things technical at AtScale.  Which SQL-on-Hadoop engine performs best?  We get this question all the time!

We looked around and found that no one had done a complete and impartial benchmark test of real-life workloads across multiple SQL-on-Hadoop engines (Impala, Spark, Hive...etc).

So, we decided to put our enterprise experience to work and deliver the world's first BI-on-Hadoop performance benchmark.  

What did we find out?  Well, turns out that the right question to ask is: "Which engine performs best for Which query type?".  We looked across three of the most common types of BI queries and found that each engine had a particular niche.  Bottom line: One Engine does NOT fit all.

Read on to find out the details of our environment and configuration, the types of queries we tested... (or download the full whitepaper here)

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

Hadoop: What has changed?!

Posted by Bruno Aziza on Dec 9, 2015

Every once in awhile, our team gets questions about the validity of Hadoop.  Why it exists, why people should consider using it, etc.   In the below video, I provide a few examples of cases across common industries like financial services or retail that provide a clear answer to this question.  

As usual, the video lasts less than 2 minutes so don't expect to have to sit through long, esoteric explanations.  We get to the point and very quickly at AtScale!  

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Topics: Hadoop, Tableau, Business Intelligence

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