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.

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.

I'm from the data governance team, and this is how my team uses Apache Hadoop: there's a GUI called Apache Atlas, then there's an option called the "business glossary". My team uses the business glossary from Apache Atlas and also uses Apache Ranger. Apache Ranger is another GUI where you can check who is using which data source through the Apache Hadoop platform. My team also uses the Apache Hadoop platform for AI-related use cases and relevant data, so the data required from any kind of AI use case, that data is processed with ETL, specifically with the Talend tool. My team then loads the data in Apache Hadoop, uses that data by making some clusters, and uses the data for AI/ML cases.

We work on Apache Hadoop for various customers. 

I use the solution in my company for security purposes.

In my company, we have intranet portals that we need to ensure are not accessible by outsiders. All the data that are within the internal applications is only accessible with valid credentials within the domain. In general, my company uses Apache Hadoop to secure our internal applications.

We use Apache Hadoop for analytics purposes.

Its main use case is to create a data warehouse or data lake, which is a collection of data from multiple product processors used by a banking organization. They have core banking, which has savings accounts or deposits as one system, and they have a CRM or customer information system. They also have a credit card system. All of them are separate systems in most cases, but there is a linkage between the data. So, the main motivation is to consolidate all that data in one place and link it wherever required so that it acts as a single version of the truth, which is used for management reporting, regulatory reporting, and various forms of analyses.

We have done two or three projects with Hadoop, and we have taken the latest version available at that time. So far, it was deployed on-premises.

We use the Apache Hadoop environment for use cases involving big data engineering. We have many applications, such as collecting, transforming, loading, and storing lag event data for big organizations.

This solution is used for a variety of purposes, including managing enterprise data hubs, monitoring network quality, implementing an AntiFraud system, and establishing a conveyor system.

We use it as a data lake for streaming analytical dashboards.

The primary use case of this solution is data engineering and data files.

The deployment model we are using is private, on-premises.

Big Data analytics, customer incubation. 

We host our Big Data analytics "lab" on Amazon EC2. Customers are new to Big Data analytics so we do proofs of concept for them in this lab. Customers bring historical, structured data, or IoT data, or a blend of both. We ingest data from these sources into the Hadoop environment, build the analytics solution on top, and prove the value and define the roadmap for customers.

We use the solution as a data link for our customer payment and SaaS information. We get data from various sources and then utilize and leverage that data.

As an example of a use case, when I was a contractor for Cisco, we were processing mobile network data and the volume was too big. RDBMS was not supporting anything. We started using the Hadoop framework to improve the process and get the results faster.

We use this solution for our Enterprise Data Lake.

There are several use cases for Hadoop. Sometimes it's used for data warehousing. Other times, it's analytics. And In some cases, it's used to do transformation. For example, I have one client using it to decompress, compress, or encrypt data on ingestion. So, he used it like an ETL engine.

We used Apache Hadoop mainly for ETL and data analysis.

We primarily use the solution for the enterprise data hub and big data warehouse extension.

We primarily use this product to integrate legacy systems.

• Content management solution
• Unified Data solution
• Apache Hadoop running on Linux

We mainly use Apache Hadoop for real-time streaming. Real-time streaming and integration using Spark streaming and the ecosystem of Spark technologies inside Hadoop.

We are primarily dumping all the prior payment transaction data into a loop system and then we use some of the plug and play analytics tools to translate it.

The primary use is as a data lake. 

Data aggregation for KPIs. The sources of data come in all forms so the data is unstructured. We needed high storage and aggregation of data, in the background.