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AtScale Blog

5 BI-on-Big Data Blogs You May Have Missed, But Shouldn't

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 As 2016 draws to a close, and the AtScale Blog continues to grow, it is easy for a few fantastic posts to have been overlooked over the year. With this in mind, we present to you … Five BI on Big Data Blogs You May Have Missed This Year, But Shouldn't … that offer unique insights to expand your thoughts and ability to drive success in your BI and Big Data journey. 

 We encourage you to take a few moments to not only read the five posts we’ve highlighted from our 2016 blogs, but also weigh in and share your perspectives and opinions as well. We’d love to learn from your insights as we jointly contribute to the Big Data and BI community of knowledge.

1)    Mythbuster: Hadoop Not Just Cheap Storage

This blog post explores where Hadoop can take you today, compared to its origins; when it was often chosen because it was much cheaper to store large amount of data, compared to Enterprise Data Warehouse .  Click here to delve into why Hadoop is gaining value for so much more than just a cost effective option for data storage.

"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."

2)    BIG BI: Data isn’t a Process. It’s an Asset

The first blog post in an eight part series, by Neil Raden, taking readers into a depths of clarifying both the concepts and relationships across Business Intelligence, OLAP, predecessor technologies and data.   This kick-off post in Neil’s series uncovers the vital need for multi-dimensional analysis and the vastly improved capabilities that exist for today’s massive data.

 “When the triple threat of Big Data/Cloud/Hadoop hit the world like a tsunami, attention to ‘how’ data informs decision making was inundated with options. All eyes focused in on ‘data scientists’ who with their programming, statistical and subject matter skills combined were the new data alchemists. The problem was that the domain of data scientists’ investigations was very different from what everyone else in the organization was doing.”

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

In technology we all look towards benchmarks. How do we know how fast something really is, or stable something is, without a known benchmark we’re measuring against? As such AtScale conducted a series of performance benchmarking tests during 2016, culminating in the Q4 Edition of our BI-on-Hadoop Benchmark. This blog post offers you a high level summary of our findings.
“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.”

4)   The 6 Principles of Modern Data Architecture

Have you ever found yourself pondering what the Principles of Modern Data Architecture were? If you work with big data, then of course you have.  Join Josh Klahr, AtScale’s Head of Product Management, as he helps you navigate through the principles that will guide you through the modern world of data and decisions.   The 6 Principles of Modern Data Architecture laid out by Josh will allow you to build a foundation allowing your business to run at an optimized level today and well into the future.

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.”

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

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. 

“The traditional Multi-dimensional BI (aka OLAP) approach provides support for very common and necessary analytical use cases, including hierarchical data representations and time-relative (First and Last Child) measurements."

Thank you for spending part of your year with us and we look forward to bringing you new, interesting and informative topics throughout 2017.

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Topics: Big Data, BI

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