We had a data warehouse before all the data. We can process a lot more data structures.

The product has been instrumental for all computing needs. We have a data warehouse and a data lake. We read from S3 and load it into different databases. We compute all the transformations, logic, and code we write in PySpark or Spark Scala. Spark is very valuable for data processing.

CDH has a wide variety of proprietary tools that we use, like Impala. So from that perspective, it's quite useful as opposed to something open-source. We get a lot of value from Cloudera's proprietary tools. 

It has been helpful in allowing data storage in one centralized location with data lakes and all of the surrounding applications.

All of the data processes are being stored into the Big Data Lake.

It gives us the opportunity to offer more options to our clients and create better solution models.

We're now able to store large volumes of data through Cloudera Distribution for Hadoop. We're able to push large volumes of data to the platform, and that used to be a challenge, especially when storing a terabyte of information. This is the area where Cloudera Distribution for Hadoop improved the organization.

We used to collect data in small cases, and with Cloudera Distribution for Hadoop, we used data on large scales. It helps to store and protect the data and is helpful for processing.

That is still in PUC stage, as I have mentioned our analyst used to do the actuarial on a spreadsheet but after Hadoop  implementation they are getting confidence that now analysis is more appropriate and fast. Now exploring cloud implementation as well.

We are working on research for genomic data looking for specific genes and variances. Even Hive was not good enough to process it correctly, only with Impala are we getting results quicker.

We are moving from an standard SQL environment (Oracle DataWarehouse) to a Big Data environment, and the Hadoop cluster will be the key of our new organization. It will allow to scale in an easy namer.

For the clusters using CM, we are able to more tightly control and manage the configuration of all nodes in the clusters. 

We are currently running six production clusters totaling 900+ nodes, and are building three more clusters. Knowing that if someone has some custom configuration on a node that they haven’t communicated out, and that I can ignore that configuration and bring that node into line with where we’ve decided to run the cluster, is very beneficial.

Improved Business Intelligence reporting from daily to every two hours satisfying the business stakeholders who would favour transactional systems to draft reports because it had the latest data. 

The issue that arises using transactional systems with multiple version of truths across the enterprise. With faster turn-around time business stakeholders are now adopting the BI systems designed to give a cohesive view of the performance metrics important to them.

Implementing a Hadoop cluster has become relatively straight-forward using CDH. Administering it is also less complex. As a result, efforts spent in these areas are less than anticipated.

Before the introduction of Cloudera Manager (that actually works), all the orchestration was done with scripts and Chef, and inexperienced team members had difficulties to participate in maintenance. The Cloudera Hadoop manager eased the work.

We used it to build an enterprise data hub.

It's improved our storage and the availability of analytics tools such as Hive, Pig, Impala, and Spark helps us tremendously.

We were able to utilize data which was untapped previously. We've got great use cases now to drive business revenue.

It has enabled us to move BI out of our OLTP database and build a data warehouse.

• Providing robust infrastructure
• Fairly matured tools like Cloudera Navigator, Cloudera Manager, etc. 
• Professional support enabled us to provide great customer service
• Our clients are able to perform proactive maintenance in an efficient manner

It made Hadoop easy to use and made it easy to get started.