Apache Hadoop Reviews

I use the solution in my company since it makes the analytical processing easy. It takes data into one cluster and then processes it. While working on any GPU whatever the analytics are, and what I get as insight from the data, I can say that processing is very fast.

The main features of the tool are the distribution, how it makes data clusters, and what the data is, which are all very organized in Apache Hadoop.

The product's distributed computing capability is the most effective. It allows us to distribute data processing tasks across multiple nodes, significantly speeding up processing time.

The product's availability of comprehensive training materials could be improved for faster onboarding and skill development among team members.

We use it to store data. Our team then takes this data to create reports on top of that.

We primarily use Kafka for intensive data streaming. For batch-based processing, we use Hadoop. Additionally, we have our own custom batch catalog that likely helps prepare data for further analysis or use.

We have many projects where our main data storage is done in Hadoop only. All projects take data from Hadoop to provide data insights and reports.

Hadoop YARN for resource management is a really good aspect. It is is very good for managing large data volumes. It allows us to monitor data processing effectively. We can see how much data there is, the consumption of RAM or ROM, and how resources are allocated. It's good for managing and previewing the scale of data processing.

We use the Hadoop File System. We usually keep the data for our tables or big data on it. Hadoop has a query engine called Hive. We write SQL queries, and the tool usually processes in a parallel environment and gets us the data on Hive.

Hadoop File System is a perfect choice if we want to use any database systems or file systems because it is open-source. It has no cost. Or else, we’ll have to use Amazon S3 or Azure database, for which we will have to pay a lot. A lot of big data processing needs a proper partition and structure. Hadoop File System is compatible with almost all the query engines. That’s another reason why people would be very comfortable working with the Hadoop ecosystem.

We used the product primarily for data analysis and storage. It helps handle large data sets, performing tasks like filtering, sorting, and joining. The platform is useful for data warehousing and provides distributed coordination and synchronization functionalities.

The solution has effectively supported our operations primarily due to its cost efficiency. It enables us to manage large data sets without incurring excessive subscription costs, resulting in more efficient data handling and operations.

Our use case is for a customer who wants to migrate their data warehouse to Hadoop. It's a request from a customer in Senegal who wants to migrate their Oracle data warehouse to Hadoop. I'm trying to migrate it to Hive or HBase.

They're choosing between upgrading Oracle or moving to Cloudera Hadoop. They seem to prefer Cloudera.

The current data warehouse runs on Oracle DB, but we have to migrate the analytics process to Hadoop.

My customers like the HDFS and the data warehouse capabilities within Hadoop.

They have integrated other tools as well, like Power BI and Oracle BI, both on Azure, for reporting. Oracle BI is difficult to integrate.

It is difficult to integrate them with Hadoop.

I have been using the latest version of Apache Hadoop. It is a file system for data collection. There are nodes in this cluster that contain all the information, directories, and other files. The nodes are based on the MySQL database.

Hadoop isn't so problematic. It deals with file storage and maintenance. It is a network of file operations.

The stability of the solution needs improvement.