There was a local third-party vendor involved in the installation process.

We have been working through the implementation with our own team.

We implemented Cloudera Distribution for Hadoop through the vendor. Deployment was done by an integrator. It usually doesn't take a lot of time. It usually takes just a day to deploy the solution.

Our implementation strategy for Cloudera Distribution for Hadoop was more into outsourcing. For example, the hardware, including its management, was outsourced, so the admin, data management, support, etc., were also outsourced. We were looking into having the application done in-house, with the team. We were looking at a one-year implementation plan to move more and more governance and data sets into Cloudera Distribution for Hadoop. Every quarter, we planned to have other features reintroduced into the platform.

Two people did the installation and two people did the deployment. It was deployed in a single location, and we initially had ten users of Cloudera Distribution for Hadoop.

We are very well-versed on these tools, so we implemented it ourselves. 

In-house.

We did the implementation only using our team and resources. It was a hard start, but an easy landing.

We implemented with an in-house team. Our initial idea was to stop the cluster during the weekends and when there was no usage. However, we found strong difficulties and we were not able to start programmatically the whole cluster, so finally we left the cluster working all the time.

This issues were mainly related with the cloud provider and how this provider manages the resources for the cluster machines.

In house via chef automation. Chef, or similar systems, makes it much simpler to stand up large scale clusters. That said, I have not used or evaluated vendor team implementation methods.

We have our own team of consultants who are proficient in implementing it. The high level steps about the implementation remain the same; however, it is the environment specific issues which are challenging.

For a POC deployment, we required only one DevOps. On larger-scale implementation, we also require a data engineer. 

In our case it was an in-house team including data scientists and data engineers (management & QA as well). With the appropriate training and the support offered by the vendor, it is not that hard to implement a small to medium scale project solution. However, complexity and size varies significantly between projects; therefore, it really depends.

We did it in-house.

We used a vendor team to implement the solution.

Both vendor team and in-house depending on the cluster size and use cases. Some customers may require certain number of certified personnel, something to think about when choosing a partner.

It was done by the vendor team, who were great.

We used a local system integrator for the implementation. We had approximately five people for the implementation.

We have not had to do maintenance of the solution because we are still in the testing phase.

In-house.

I have an internal team that does maintenance for the solution.

We are Big Data consultants, so we implement it.

It was implemented in-house.

We did it in-house. They have good technical support to help with implementation.