Every once in awhile, the ultimate question comes up: "What is the best analysis tool for BI on Hadoop?!". AtScale is not in the business of favoring one tool versus the other. We are in the business of making all of them work. There are indeed many reasons why business users and I.T. departments choose particular analysis tools. Here are a few things to consider.
Yes, there are actually ways to 'Do Big Data Analytics Right'.
Leaders and innovators in the Big Data space have learned the hard way, and now those of you looking to dip your toe, or jump head first, into the BI on Big Data waters can capitalize on their early experiences. Let go of the fear or ego or whatever may be holding you back and take the chance to learn from those who took the early adopter risk.
Is your Big Data ‘mature’? You may be puzzled by this question, since many in the industry have been saying ‘Big Data is Dead’ for years. But Big Data is far from dead, and instead technologies and solutions that make up the Big Data space are maturing at an ever increasing rate. From traditional players like Teradata, to open-source Hadoop, to new Cloud players like Google Big Query, the Big Data space is doing more to help companies manage and gain insights from their exploding and morphing data than at any other point in history. So what?
Big Data analytics leader previews industry’s first platform to enable unified business intelligence for Teradata, Hadoop, Google Dataproc and BigQuery
San Mateo, CA, November 17, 2016 – AtScale, the first company to provide enterprises with a fast and secure self-service BI platform for Big Data, today announced a significant expansion of its services, from BI on Hadoop to BI on Big Data.
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.
Do you remember those word problems on tests like the SAT or the ACT?
They would go something like this: Train A leaves the Station at 1:15PM, gradually accelerating for 7 minutes until reaching a speed of 52mph for a distance of 76 miles. Train B leaves 12 minutes later, accelerating to a speed of 47mph in 11 minutes. After one hour, how far behind Train A was Train B?
This sort of problem reminds me of the Business Intelligence (BI) business, alternately known as Decision Support, Analytics, Reporting, Data Discovery, etc. It seems that no matter how fast Train B can go (organizations implementing BI), they can only, at best, keep up with Train A (the relentless march of technology).
In biology, this is called the ‘Red Queen Effect’. named after poor Alice in 'Through the Looking Glass', where the faster she runs with the Red Queen, the faster the landscape moves with them so they have to go as fast as they can to merely keep up.
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.
Data. It isn't a process. It's an asset.
Welcome to the 1st in a series of 8 blogs, where I will dive-in to separate and clarify both concepts and relationships across Business Intelligence and its active component OLAP, predecessor technologies, and data. OLAP in particular has suffered from issues of scale and speed, but the need for this type of analysis is greater than ever. And while, the industry of analytics has been overrun with big data and data science, there is a general lack of understanding that previous drawbacks of BI and OLAP have been solved with the new architecture of big data, Hadoop/Spark and Cloud.
Join me as I uncover both the vital need for multi-dimensional analysis and the vastly improved capabilities that exist for big, nae massive, data of today. I believe you will find some rather interesting surprises.
This morning, O'Reilly Media published the results of its 2016 Data Science Salary Survey. The report covers a wide set of topics such as salary differences by gender and countries as well as details for the types of skills that can give employees an edge when it comes to earnings. We tooked a closer look at the Business Intelligence answers and what we found out might surprise you...
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”...