For the last few months, we have been working very hard on the BigBoards project. We think that the time has come to take it to the next level. Big Data really hyped in 2012 when more and more companies started experimenting with the technologies and the first succesful projects went live, proving their ROI. The promise of engineering data solutions without limits really attracted a lot of people, organizations and vendors inclusive. They all started learning about the ideas and principles behind Big Data. But learning Big Data comes with some major hurdles. The technologies are built on clusters of computers to handle scalability and fault tolerance. The solution become intrinsically much more complex to learn and use. Right now, you basically have 3 options to learn Big Data:
The first option is obviously the most common to try out new technologies and start developing. However, it doesn’t learn you to deal with the distributed nature of the solution. When deploying to a grid, most issues you will encounter will stem from misconfigurations, network issues, hardware problems and coordination hickups. While you can learn about configurations on that single pc, all the others issues are very difficult to simulate and experience. If you truly want to immerse yourself in the intrinsics of Big Data, you will have to go with the second option. It will confront you with the raw power of Big Data but you are required to install and configure everything yourself. There are great Big Data distributions out there helping you to get bootstrapped quickly, but they will only take you so far and still leave a lot of questions unanswered. It might be the most powerfull option of the three, but it is also the most intensive and expensive one, due to the upfront and recurring costs. Cloud services can provide you with an alternative, bringing the added value to scale easilly. It is a very good option if you have some relatively small datasets or want to learn the technologies. The pay-as-you-go billing model becomes an advantage because you can save money by spinning down your cluster when idle. On the other hand, a cloud model becomes very expensive when you run your workloads continuously .
Each of the three options described above has some of the advantages you are looking for, but not all:
With BigBoards, we try to balance these advantages. We combine 3 solutions:
While this might seem a bold offering, we count on the ideas and insights of our community members to support us and co-create a great product from which everyone can benefit. We can only accomplish this with true openness and transparency.
We are truly convinced of the power of BigBoards, but we really want to know what you think about our vision, to hear how we can improve BigBoards or to learn what you are expecting. If you need more information, do not hesitate to get in touch!