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.
Rumor has it that with the rise of Apache Spark, Spark will replace Hadoop.
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?
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.
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.
You might not have noticed the photoshop work on the featured photo for this blog. This isn’t a shot of Steph Curry, the legendary point guard for the Golden State Warriors.
We’ve superimposed Josh Klahr’s face, our VP of Product, on Curry’s body. Why?
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?
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.