I am a data scientist. I use the solution to fetch and update data
The easiest route - we'll conduct a 15 minute phone interview and write up the review for you.
Use our online form to submit your review. It's quick and you can post anonymously.
I am a data scientist. I use the solution to fetch and update data
It is easy to handle unstructured data with the solution. The execution is fast. The tool is easy to learn.
The product must be added to a cloud platform. The link to Oracle must be provided on the cloud platform. It will help people to integrate the tool easily. We need the solution mainly for updating or manipulating data. All DBs need a query. We need to see the processing speed and whether we can fetch more data. When we have data, we can push it in batches or push all the data simultaneously. Ultimately, it must be fast to complete the project faster. These things must be improved in Oracle.
I have been using the solution for the last four years.
The vendor upgrades the database often. I am using Python 3.8.0. I am not able to install some Oracle tools with that version.
There are 15 members on my team.
I tried contacting the support team but did not get much response. So, I contacted my IT team, and the IT personnel helped me sort out the issue. I did not get any updates from Oracle, though.
The solution is deployed on the cloud. The installation is difficult. I tried with cx_Oracle. It is too difficult. I work with a finance company. They have many privacy and governance policies. The main issue I faced was with the installation.
I also use Oracle Database. MongoDB is more difficult to learn than Oracle.
If someone is working on a project that requires JSON, they might prefer using MongoDB. The choice depends on the project and the data we use. The product is good overall. There is an issue with installation, but the processing is fast, and we can update large amounts of data. If we need more analytics, we can choose MongoDB. Overall, I rate the product a seven out of ten.
The tool is used by our company's different customers who have requirements for big data management. When our company's customers want to build a platform for big data management, they choose Cloudera as their tool and as a big data management platform even though there are different options in the market since it is best suited if they consider having an on-premises solution. If a customer wants a cloud-based solution for big data management, then there are other tools in the market that better suit their requirements. For an on-premises big data management platform, Cloudera is the best choice.
The best part of the tool is that it is able to expand horizontally and vertically when its customer wants to grow the business. The tool can be deployed using different container technologies, which makes it very scalable.
The tool's ability to be deployed on a cloud model is an area of concern where improvements are required. The tool works very well when deployed on an on-premises model. The deployment on a cloud platform is where Cloudera needs to work more. There are competitors who are way ahead of Cloudera.
I have been using Cloudera Distribution for Hadoop for five years. My company has a partnership with Cloudera.
It is a very stable solution. Stability-wise, I rate the solution a nine out of ten.
It is a scalable solution. Scalability-wise, I rate the solution a nine out of ten. Scalability depends on the environment, but it can scale up in an on-premises environment. There are challenges with its scalability on the cloud.
My company deals with around seven customers who use the product.
The technical team in my company deals with the product support team.
The ease or difficulty in setting up the product depends on the environment of the customer where the tool is deployed. If a banking, industrial, or retail sector firm is taken into concentration, depending on how big of a database is maintained, including the applications that are to be hosted, the deployment process can range from a simple to a very complex phase, depending on the architecture.
For Cloudera Distribution for Hadoop, one has to go through the usual deployment process, like for any software product. You have to have different environments before going into production, like pre-production environments, test and dev environments. You install and configure all the components in the test environment and then test them on the pre-production environment. Once UAT is done, you move them to the production environment. In general, it's a critical product deployed in a company.
The tool is expensive. Overall, it's not a cheap software tool, and that is why only large enterprises who are mature enough and have an architecture that is complex enough opt for Cloudera, as its ROI would make sense to such businesses. For the SMB market or customers whose environments are not that complex and do not have multiple systems running, Cloudera might not be a good option.
Speaking about the security features of the tool, I feel that it is a very secure system, but I cannot comment more on it since I don't have a technical background. The product follows international security guidelines to comply with the PII data and other kinds of regulated data for its end customers.
I recommend that those planning to use the solution examine their environment and its complexities. There are cheaper tools in the market since everybody is not well-suited to using Cloudera Distribution for Hadoop. All the large enterprises' on-premise architecture definitely needs to have the tool. As most of our company's customers are now moving to the cloud, Cloudera's role in their environments has been reduced.
The benefits of the solution stem from the fact that it is a tool for big data management that can host multiple technologies. The other benefit of Cloudera is that you can use it to support your AI or artificial intelligence initiatives since the tool can host different data warehouses or data lakes, which provides you with the flexibility of hosting an AI solution on top of it. Customers can leverage Cloudera platform for their AI initiatives. There has been an increase in hardware utilization over the years, so the servers, hardware, memory, IOPS, and CPU required need to be much more efficient than in the past.
I rate the tool a nine out of ten.
