Join Alan Simon for an in-depth discussion in this video Exploring vendor alternatives, part of Transitioning from Data Warehousing to Big Data.
- Once you've decided that Big Data and Hadoop are the right solutions for your next generation data warehousing and analytical needs, it's time to find the right vendors to help you implement your new solutions. Looking at the marketplace, we find many different software and solution companies out there to help us, and we'll see some very familiar names as well as some newer ones. There's three major categories of vendors that you'll want to take a look at. Those who bring Hadoop to the marketplace by taking the core open source code and then adding their specific enhancements.
Those vendors who provide the tools through which your users will directly interface with all of that Hadoop-based data. And then finally, other vendors who provide specialized solutions for specific industries and business processes. Let's look at some of the names in each of those. In the world of Hadoop distributions, we'll find some very familiar names from the technology world that we've worked with for many years. We see IBM and their BigInsights platform. Most people think of Amazon as a retailer but they actually have a very robust Hadoop environment through their Web Services platform.
And then Teradata, one of the earlier names in managing Big Data for data warehousing has their SQL-H Hadoop environment. Beyond these familiar names, though, are others that have come to the marketplace more recently and are very closely aligned with the world of Hadoop and Big Data. We see Cloudera, Pivotal, Hortonworks, and MapR. Each of these has some variation of a data warehousing on top of a Hadoop platform that you can use to build your next generation data warehousing environments.
We also see an old-meets-new partnership with Microsoft and Hortonworks teaming up to provide the HDInsight platform through the Windows Azure cloud. Our second category of analytics vendors finds many of those names that we've worked with for years in business intelligence. Tableau, MicroStrategy, Business Objects, and IBM's Cognos all access data through their SQL interfaces into the underlying Hadoop environment.
All of them have a very important role in Big Data and analytics going forward. Familiar names from the world of statistics also apply in this new world of Big Data and Hadoop. SAS and SPSS are commonly used for their very robust analytical models where they bring data in from Hadoop just as they traditionally have done from data warehouses and then, can perform our predictive and discovery analytics. Just as with the Hadoop distributions, we find newer names that have come to the market fairly recently and are very closely aligned with the world of analytics on top of Hadoop.
Revolution Analytics, Splunk, Kognitio and the Hadoop distributions themselves usually have native analytic capabilities. So we would also look at Cloudera, Pivotal, Hortonworks and some of the other names from that space. Our third category includes specialized solutions that are closely aligned with a specific industry and usually a specific business process. For example, we would look to Ayata for prescriptive analytics solutions for energy exploration.
Explorys, a company that was recently purchased by IBM, provides Hadoop-based big data and analytics for healthcare. Argyle Data provides fraud detection capabilities. There are many other companies in this space, as well. In fact, we'll find new solutions daily, from old familiar names as well as brand new startups. What you need to do is follow the industry analysts and blogs, read press releases, and then many other sources for the latest information about all these different solutions from different companies and see how they fit into your particular needs.
Be careful, though. You want to make sure that you separate hype from reality. Talk to references, bring vendors in for demos, have them build test systems that make sure, as you explore new technology and as you make the commitments to these new products and platforms, that you're making educated, well-informed decisions.
- Exploring big data, Hadoop, and analytics
- Examining the shortcomings of traditional data warehousing
- Comparing big data architectures for next-generation data warehousing
- Understanding alternatives
- Building a roadmap
- Managing big data-driven projects
- Monitoring and measuring success