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AtScale Blog

Joshua Klahr

Recent Posts

Announcing AtScale 6.5

Posted by Joshua Klahr on Mar 6, 2018

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!

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Topics: Business Intelligence, bi-on-hadoop, Big Data, Cloud, BI, Analytics, BI on Big Data, Data Strategy, data driven

The 6 Principles of Modern Data Architecture

Posted by Joshua Klahr on Jan 19, 2018

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.

 

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

AtScale and HDInsight on Microsoft Azure

Posted by Joshua Klahr on Nov 2, 2017

The Forecast Calls for Cloudy Weather

You don’t have to be a clairvoyant to know that there is an ever-increasing trend in cloud adoption among start-ups and enterprises alike.  In the world of Big Data, the past few years have shown a significant increase in cloud adoption.  While Amazon initially led the way with cloud data products - with Amazon Elastic MapReduce (EMR) for Hadoop and Redshift for data warehousing - the past 12 months have seen new entrants on the scene.

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Topics: Hadoop, bi-on-hadoop, Analytics, BI on Big Data, 6.0, azure, HDInsight, Microsoft

TECH TALK: AtScale 6.0 brings Universal Semantic Layer Benefits to Google Cloud

Posted by Joshua Klahr on Oct 10, 2017

Is it October already?

It’s hard to believe that October is here. It feels like only a few days ago that we released AtScale 5.0 and AtScale 5.5.  Both releases contained a number of great new features that I was excited to share with the Big Data and Business Intelligence communities. Although I may be a little biased, I really do believe that the release of AtScale 6.0 marks one of the biggest releases in the history of the company. 

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Topics: Hadoop, Tableau, bi-on-hadoop, Analytics, BI on Big Data, Google BigQuery, BigQuery, 6.0

TECH TALK: Making Enterprise BI work on Big Data with Atscale 5.5

Posted by Joshua Klahr on Jun 6, 2017

Production deployments continue to drive rapid pace of innovation
When we announced AtScale 5.0 you may recall that I was quite excited about the rich set of analytical capabilities included in the release, including multi-fact support and an improved design experience for complex models. These capabilities have been well received by our customers, and have helped them to put more use cases into production using the AtScale Intelligence Platform.

With the 5.5 release we’ve continued to provide new capabilities in working towards a modern BI platform for big data

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Topics: Hadoop, bi-on-hadoop, Analytics, BI on Big Data, High Availability, 5.5

TECH TALK: AtScale, Hive, Druid: A Match Made In Heaven

Posted by Joshua Klahr on May 11, 2017

The rapidly exploding demand for business intelligence on big data is nothing new - this trend is clearly indicated in the latest Big Data Maturity surveys (2015 and 2016).  As shown in the graphic below, 75% of respondents are planning on deploying BI workloads on their big data platforms (with 73% of respondents already with some BI use cases deployed).

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Topics: Hadoop, hive, bi-on-hadoop, Analytics, BI on Big Data, druid

TECH TALK: BI Performance Benchmarks with BigQuery from Google

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

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Topics: Business Intelligence, Big Data, olap, BI, Google BigQuery

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

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