Pepperdata Capacity Optimizer Reviews & Product Details - Page 3

Its easy to go through the UI and get the stats of the tasks and see the errors and optimize them accordingly. Review collected by and hosted on G2.com.

Nothing to my knowledge. It helps us decoding the root cause while monitoring the application tasks very well. Review collected by and hosted on G2.com.

easy to navigate for all metrics related to spark job, capture all yarn-related metrics. we can search by application id easily. multiple realm is also useful for EMR spark Review collected by and hosted on G2.com.

while setting up pepper data autoscaling first time without pepper data support was made EMR autoscaling aggressive because we have to found working autoscaling config for our clusters Review collected by and hosted on G2.com.

Being able to see the memory, cpu, io and other cluster metrics help us to appropriately size the clusters and tune our jobs. Review collected by and hosted on G2.com.

The UI has a learning curve when dealing with large-scale ephemeral clusters, but after a while the filtering and drill-down become familiar. Review collected by and hosted on G2.com.

Great enterprise solution for optimizing big data workloads. Review collected by and hosted on G2.com.

I have not had negative experiences with Pepperdata. It's a great solution to meeting my needs. Review collected by and hosted on G2.com.

Pepperdata provides insightful details and helpful metrics to monitor cluster performance. The user-friendly interface helps to navigate several details like CPU, Memory, Storage usage, I/O etc.

Pepperdata also helps to optimize the cluster settings to ensure enhanced resource management.

App spotlight feature helps to figure out slow jobs or any data skew scenarios Review collected by and hosted on G2.com.

Pepperdata can integrate automated alerting in case of any cluster parameter shows alarming behavior. It can also provide features like AI-based detection of resource containment and alert accordingly.

It will be good if Pepperdata can integrate workflow managers like Oozie in the monitored services and map Oozie coordinator/Job Ids to spark Application ids. This will help in easy mapping between interfaces and will result in quicker root cause analysis of issues. Review collected by and hosted on G2.com.

I like that Pepperdata allows me to identify various jobs in my ETL architecture to isolate each job and provide a breakdown of: computational time used, memory required, wasted resources, estimated costs. Review collected by and hosted on G2.com.

While Pepperdata provides a wealth of information which can be used to troubleshoot and improve the efficiency of ETL architectures; I have found that it is difficult to build and create a dashboard specific to my own needs. For example, the default dashboards made available to the Enterprise work well at an Enterprise level from the perspective of the person paying the bill, but there seems to be little to no instruction on how I can create a suite of dashboards to focus on my specific subset of the Enterprise jobs running. Review collected by and hosted on G2.com.

The visualization on the data collected from the server logs. With verities of options to create your own customized dashboards. Also can create customized alerts in case of any threshold breaches. Review collected by and hosted on G2.com.

Multiple metrics are available and it is bit difficult to identify the suitable metric. Also the log filters are not so efficient when it comes to analyzing huge logs with number of metrices involved. Initial configuration of the pepper data is challenging due to technical difficulties, but once the configurations are completed the handling of the tool is much simple. Review collected by and hosted on G2.com.

Amount of metrics provided by PD along with recommendations to tune the job Review collected by and hosted on G2.com.

The recommendations tab should also include ways to implement the suggestions ex. spark submit command Review collected by and hosted on G2.com.

Pepper Data provides quite detailed analysis about your cluster health. It provides visualization of CPU Resources, memory utilization, Storage, RAM and other important details. For each visualization you can see more details for a specific range of time and for a specific user. You can switch between chart and table display modes. In addition to this it provides detailed information about Queues and Application Status. Apart from the cluster overall details, you can see the Resource Usage of a Spark App as well by using the yarn application id, which is very useful for improving performance and scaling the processes. Review collected by and hosted on G2.com.

I wish that they provided more info, when hovered on column names, in tabular format/mode, so that it is more easily readable and understandable. Review collected by and hosted on G2.com.

it has YARN resource visualization, which is pretty intuitive. The UI looks good. Also it has some suggestion on resource usage. Review collected by and hosted on G2.com.

1. Sometimes it has some delay on data collection and I have to wait until the job is done.

2. Too expensive to purchase the service

3. Need some effort to adapt the vendor solution to existed system.

4. It lacks of data security options Review collected by and hosted on G2.com.