AtScale Blog

Modeling Advanced Business Logic in AtScale

Improve BI Performance Through Efficient Time-Specific Calculations

Puddles On The Data Lake

IBM and AtScale Agree: There are 34 Billion Reasons why Enterprises Should Move Data to the Cloud

3 Ways an Intelligent Data Fabric Improves Tableau Performance on an Enterprise Data Warehouse

How AtScale Helps Enterprises Improve Performance and Cost on Snowflake

Three Important Questions to ask about Chief Data Officers

Two Ways that AtScale Delivers Faster Time to Insight: Dynamic BI Modeling and Improving Query Concurrency

AtScale Enables Enterprises to Optimize Usage and Save on Costs with AWS Cloud Storage

How AtScale uses Machine Learning to help Enterprises efficiently leverage Google BigQuery

The Ultimate Big Data Architecture Checklist

What You Might Have Missed in June 2018

Three Trends From Dataworks Summit 2018

It's Always "Day 1": AtScale's New CEO

Data Lake Intelligence with Amazon Redshift Spectrum and AtScale

Make Big Data Work on Any Cloud

What You Might Have Missed in May 2018

The Future of Tech: Cloud, AI and your budget...

2018 Dataworks Sessions You Should Check Out

Big Data: Hope or Hype?! What the research says…

What You Might Have Missed in April 2018

Looking for the Perfect Date using AtScale and Tableau

10 AtScale Scientific Observations

Selecting The Right Use Case For Your Big Data Implementation

API - Using AtScale’s Webhooks

What You Might Have Missed in March 2018

Through the Eyes of a BI Author: Why Tableau and AtScale make a great solution together

Is The Cloud Right For You?!

Conceiving Valuable Data Driven Insights

Happy Data = Happy Users? Maximizing AtScale and Tableau

Announcing AtScale 6.5

Gartner Magic Quadrant for Analytics and Business Intelligence Platforms 2018: What's New?!

Supercharge Your Percentile Calculations for Big Data (Part III)

Supercharge Your Percentile Calculations for Big Data (Part II)

Gartner Magic Quadrant for Business Intelligence (BI) 2018: The Good, The Bad, The Ugly...

Supercharge Your Percentile Calculations for Big Data (Part I)

How to Become Data Driven in Four Easy Steps

The Hidden Costs of Self-Service BI Initiatives 

The 6 Principles of Modern Data Architecture

What is a “Semantic Layer” and Why Would You Want One?

Revolutionizing The Cloud with AtScale and Amazon RedShift

AtScale and HDInsight on Microsoft Azure

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

The True Cost of doing Big Data...the old fashioned way...

Big Data: The Unknown Unknowns

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

What's the best BI tool for Hadoop?

What are the Costs of Building a Team of Data Scientists?

TECH TALK: BI Performance Benchmarks with BigQuery from Google

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

Gartner Magic Quadrant for Business Intelligence (BI) 2017: The Good, The Bad and The Wookie?!

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

BIG BI:  Business Intelligence in Digital Transformation

BI-'in'-Hadoop is Dead

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

CDOs: They Are Not Who You Think They Are

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

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

How a Unified Semantic Layer Can Help Business Intelligence

How To Load Data Into Hadoop

TECH TALK: Scale-Out Business Intelligence with Hadoop

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

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

Gartner Magic Quadrant for Business Intelligence (BI) 2016: The Good, The Bad, The Ugly...

Hadoop: What has changed?!

Performance on Hadoop, Now!

Unprecedented Concurrency with AtScale and Cloudera Impala

BI Tools: The Do’s and Don’t’s of Integrating Hadoop

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