One the big data side is Google's cloud-based BigQuery analytics platform, introduced in May and one of the biggest announcements of the year in the category. It's not just that it's from Google, an association that gave BigQuery instant credibility; it's that this big data service combines scalability and real-time querying with SQL-like query statements.
Under the hood of BigQuery there's the Google File System and Google MapReduce, the core components of Google's big data platform that inspired Hadoop. There's also Dremel, a real-time, distributed query and analysis technology that Google has been working on for five years. Dremel lets users interactively query massive, disparate data sets on Google's platform with the kind of performance they're used to seeing from relational databases. That's also the kind of fast query capability that Cloudera is now hoping to help deliver on the Hadoop platform through its recently announced beta-stage Cloudera Impala project.
[ Want more on Google's cloud-based big data platform? Read Google Launches BigQuery Analytics Service. ]
There's a vast pool of data professionals out there that knows just what to do with the SQL-like select, aggregation, sum and string-matching options that BigQuery provides. But then there's the far larger universe of business analysts and professionals who aren't SQL savvy. That's where Tableau Software comes in.
Tableau is known for intuitive, business-user-friendly data visualization software that has helped make it one the fastest-growing vendors in business intelligence. Through a partnership the two companies have been working on together for five months, Tableau has added a connection to BigQuery in Tableau 8, a next release also announced last week and due out early next year.
"Somebody on the marketing team or the operations team of a company may not know SQL, but they sure know the business and they know the data," said Ju-kay Kwek, product manager of Google BigQuery, in an interview with InformationWeek. "With Tableau in the hands of these business users, they can now connect to an enormous data source and interact with that information in a visual and intuitive way."
Users of Tableau's desktop or server-based software won't have to know a thing about data integration or data mapping, according to Tableau, as BigQuery will show up on the data-source menu like any database, spreadsheet or flat file. With Google's servers doing all the big data heavy lifting, Tableau users will be able to fly through vast data sets with all the visualization options they're accustomed to in the software.
"We love data sources that offer fast query response times, and that's been a big challenge for people who have large amounts of data," Dan Jewett, Tableau's VP of product management, told InformationWeek. "BigQuery just makes it go fast, and you don't have to worry about things like materialized views, pre-aggregations and cubes that become lengthy and expensive IT projects."
With the BigQuery announcement, many speculated that most users of the service would be Google Analytics customers with vast stores of Web analytics data already residing in Google's cloud.
But Google also has landed BigQuery customers in the automotive, beverage, fleet rental, hospitality and pharmaceutical industries that aren't focusing on Web analytics, Kwek said. In fact, he said, they're uploading vast amounts of data from their enterprises, and they're not looking at it as temporary development sandbox for prototyping applications that are ultimately brought into production on premises. That's why end-user accessible BI tools are so important.
"We're building a complementary ecosystem of like-minded companies that are focused on enabling self-service and empowering line-of-business users to get more out of big data," Kwek said.
Thus far that ecosystem also includes BI partners Bime, Jaspersoft and QlikTech, but Tableau is the standout among these vendors where data visualization is concerned. And that's a form of self-service that lots of BI vendors are now trying to bolster in order to democratize data analysis.