Devops Specialist at a tech vendor with 10,001+ employees
Real User
Top 10
Sep 11, 2026
DataStax Enterprise is currently deployed in production at Amdocs where we provide service to the AT&T account. To store customer data, we manage all customer usage information. Our company is a telecom company that provides telecom services to US clients such as AT&T and T-Mobile. At AT&T, we use DataStax Enterprise in production to save and store all customer data and billing information. The entire process of customer data is stored in DataStax Enterprise and is retrieved to complete customer orders. My use case focuses on backup and restore of DataStax Enterprise because if production goes down, we need to be able to restore from previous successful backups. We recently transitioned from using Elasticsearch as a database in the AT&T production account. We moved to DataStax Enterprise because it is a very scalable and highly optimized database software for production scale, given that our customer base is very large. We wanted a database that is very reliable. Therefore, we chose DataStax Enterprise, but we also needed to always have a backup for disaster recovery. For that use case, I was tasked with learning how to perform backup and restore, so I used Medusa, which is provided by DataStax Enterprise. This was a very extensive activity because I needed to test how Medusa works and I used the YAML backup configuration provided by DataStax in order to perform the backup. The backup is performed via Cron, with a daily schedule. The first backup is a full backup and consecutive backups are incremental backups. Every day we take backups, and for restoration, we maintain 14 days of data that can be restored because we have Kafka which can replay messages from only the last 14 days. There is no point in keeping a backup of 30 days. We configured our backup to hold only the last 14 days of data, so in case of disaster, we could recover from the previous 14 days to the current day. The remaining data can be retrieved from Kafka messages after replaying them with the timestamp when the disaster occurred. This was one of the use cases where we had to test backup and recovery using Medusa of DataStax Enterprise to confirm that when multiple sites go down, we are still able to recover our data, which is very critical to the customer and to the organization. We actually faced a couple of issues when performing backup and restore, and we also encountered certain challenges. This was the major use case where if we use DataStax Enterprise in production, we must be in a position to perform backup and recovery as well.
DevSecOps Engineer (Software Development Engineer II) at a financial services firm with 1,001-5,000 employees
Real User
Top 20
May 13, 2026
My main use cases for DataStax Enterprise are building a scalable and high availability application that needs to handle large amounts of data across multiple locations, and for real-time analytics, such as storing time-series data and supporting applications that require fast read and write performance. I also use advanced features such as search, analytics, and security to support business-critical workloads.Main use cases involve building applications that need to handle large amounts of data with high speed and reliability, and real-time analytics, where fast data processing is important. One of the other use cases is storing time-series data such as logs or sensor data that needs to be available across different locations.
Senior Engineer at a financial services firm with 10,001+ employees
Real User
Top 20
Apr 4, 2026
My main use case for DataStax Enterprise is handling the high-volume transactional data in a distributed system. I work on an application where we need to store and process a large amount of real-time data with low latency and high availability. We mainly use it for storing event-based data and ensuring that the system stays up even if some nodes go down. We also scale the application horizontally. DataStax Enterprise handles our real-time event-based data requirements effectively. In my project, we have a system that logs every action a user takes, such as when the user clicks login, performs a click or transaction, or gets any API call. All of these are treated as events, and these events come in very high volume, thousands of events per second. We performed the data modeling for these, including user ID and event type. This allows for fast writes and efficient queries to get recent events for users.
My main use case for DataStax Enterprise is for search and transactions with a focus on availability. A specific example of how I use DataStax Enterprise for search and transactions with high availability is that we have a multi-node cluster with disaster recovery across two data centers, specifically for our e-commerce website. This ensures that users do not face any downtime. This is the main scenario I have regarding my use case with DataStax Enterprise.
DataStax Enterprise serves as the primary database for all transactional processing in my organization. DataStax Enterprise provides linear scale as well as multi-data center real-time replication of data such that we can maintain uptime even with the loss of multiple data centers. Keeping the system up and the data fresh is of paramount importance for our clients. Performance is also top of mind, and DataStax Enterprise delivers best-in-class performance. A specific example of how I use DataStax Enterprise in my organization is that there is a learning curve and tasks that are simple in traditional RDBMS systems can be complicated with DataStax Enterprise. However, once I get the hang of denormalizing data and getting the data model correct, DataStax Enterprise is very usable. Usability from a developer's standpoint is very simple, but the complication is on the architectural side with the data model. I also love the ability to have our self-services up and running even with a total outage at one of our data centers.
DataStax Enterprise offers a high-performance, scalable database solution designed for modern data requirements, supporting a wide array of use cases that demand real-time analytics and robust security.Focusing on delivering powerful distributed databases, DataStax Enterprise integrates the open-source foundation of Apache Cassandra, delivering enhanced features for enterprises. It supports mission-critical applications at scale, providing real-time query capabilities and fault tolerance....
DataStax Enterprise is currently deployed in production at Amdocs where we provide service to the AT&T account. To store customer data, we manage all customer usage information. Our company is a telecom company that provides telecom services to US clients such as AT&T and T-Mobile. At AT&T, we use DataStax Enterprise in production to save and store all customer data and billing information. The entire process of customer data is stored in DataStax Enterprise and is retrieved to complete customer orders. My use case focuses on backup and restore of DataStax Enterprise because if production goes down, we need to be able to restore from previous successful backups. We recently transitioned from using Elasticsearch as a database in the AT&T production account. We moved to DataStax Enterprise because it is a very scalable and highly optimized database software for production scale, given that our customer base is very large. We wanted a database that is very reliable. Therefore, we chose DataStax Enterprise, but we also needed to always have a backup for disaster recovery. For that use case, I was tasked with learning how to perform backup and restore, so I used Medusa, which is provided by DataStax Enterprise. This was a very extensive activity because I needed to test how Medusa works and I used the YAML backup configuration provided by DataStax in order to perform the backup. The backup is performed via Cron, with a daily schedule. The first backup is a full backup and consecutive backups are incremental backups. Every day we take backups, and for restoration, we maintain 14 days of data that can be restored because we have Kafka which can replay messages from only the last 14 days. There is no point in keeping a backup of 30 days. We configured our backup to hold only the last 14 days of data, so in case of disaster, we could recover from the previous 14 days to the current day. The remaining data can be retrieved from Kafka messages after replaying them with the timestamp when the disaster occurred. This was one of the use cases where we had to test backup and recovery using Medusa of DataStax Enterprise to confirm that when multiple sites go down, we are still able to recover our data, which is very critical to the customer and to the organization. We actually faced a couple of issues when performing backup and restore, and we also encountered certain challenges. This was the major use case where if we use DataStax Enterprise in production, we must be in a position to perform backup and recovery as well.
My main use cases for DataStax Enterprise are building a scalable and high availability application that needs to handle large amounts of data across multiple locations, and for real-time analytics, such as storing time-series data and supporting applications that require fast read and write performance. I also use advanced features such as search, analytics, and security to support business-critical workloads.Main use cases involve building applications that need to handle large amounts of data with high speed and reliability, and real-time analytics, where fast data processing is important. One of the other use cases is storing time-series data such as logs or sensor data that needs to be available across different locations.
My main use case for DataStax Enterprise is handling the high-volume transactional data in a distributed system. I work on an application where we need to store and process a large amount of real-time data with low latency and high availability. We mainly use it for storing event-based data and ensuring that the system stays up even if some nodes go down. We also scale the application horizontally. DataStax Enterprise handles our real-time event-based data requirements effectively. In my project, we have a system that logs every action a user takes, such as when the user clicks login, performs a click or transaction, or gets any API call. All of these are treated as events, and these events come in very high volume, thousands of events per second. We performed the data modeling for these, including user ID and event type. This allows for fast writes and efficient queries to get recent events for users.
My main use case for DataStax Enterprise is for search and transactions with a focus on availability. A specific example of how I use DataStax Enterprise for search and transactions with high availability is that we have a multi-node cluster with disaster recovery across two data centers, specifically for our e-commerce website. This ensures that users do not face any downtime. This is the main scenario I have regarding my use case with DataStax Enterprise.
DataStax Enterprise serves as the primary database for all transactional processing in my organization. DataStax Enterprise provides linear scale as well as multi-data center real-time replication of data such that we can maintain uptime even with the loss of multiple data centers. Keeping the system up and the data fresh is of paramount importance for our clients. Performance is also top of mind, and DataStax Enterprise delivers best-in-class performance. A specific example of how I use DataStax Enterprise in my organization is that there is a learning curve and tasks that are simple in traditional RDBMS systems can be complicated with DataStax Enterprise. However, once I get the hang of denormalizing data and getting the data model correct, DataStax Enterprise is very usable. Usability from a developer's standpoint is very simple, but the complication is on the architectural side with the data model. I also love the ability to have our self-services up and running even with a total outage at one of our data centers.