DataStax Enterprise Reviews

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.

DataStax Enterprise is highly scalable. We can continue adding new nodes to provide our customers with fast retrieval of their data, making our system function very smoothly. It is also very reliable because DataStax Enterprise can be expanded to multiple zones and data centers, making it quite reliable. In case of any issues, other nodes in different data centers can provide the data. It is very reliable, scalable, and easy to manage and troubleshoot. It was a great experience to understand DataStax Enterprise because when a company hosts a lot of complicated data, DataStax Enterprise helps us as it is an upgraded version of relational database systems and is based on Cassandra, making it highly efficient for daily purposes. The backup and recovery process is also pretty smooth.

When discussing scalability, I have seen that in certain environments where the volume of data is pretty low, we have only three nodes of DataStax Enterprise because those are sufficient for our use case. In production, DataStax Enterprise had 45 nodes in each site, basically having a blue-green structure. Blue has 45 nodes, and green has 45 nodes as well. We could scale from 3 to 45 nodes, and it was very easy to scale up when customer needs increased over time, such as during month-end cycles or year-end cycles. We saw significant increases in the volume of data on the system. With DataStax Enterprise, we were able to expand the nodes to the required amount, and it was well managed by the cluster and DataStax node. The scalability was quite efficient. When it comes to reliability, when any nodes go down, the other nodes in other data centers step in to maintain reliable communication or extraction of data. Even if any node goes down in any of the data centers, the other nodes are there to cover it up, making it a very reliable use case for us.

When I was taking the backup with a data volume of 15 to 20 GB, the full backup running for the first time did not provide metrics to understand what percent of progress had been made. We had to read the logs, but we did not see any percentage in those logs. This is an improvement item for DataStax Enterprise to provide metrics, a bar, or a percentage so we can grasp how much data has been backed up or restored. There are no graphs available to make us aware of how long the process will take to complete, which is a drawback I noticed during backup and restore.

In addition, I notice a huge time lag during restoration because each node takes a lot of time to be added to the pool and to the cluster node, one by one. The entire time the production is down presents a concerning situation. There should be an alternative for restoring where data can be provided to live traffic so it does not impact customers in the case of a disaster.

I have been using it for the last one year.

Sometimes I notice that the nodes restart on their own, and I am not sure if that is because of the data, related to our organizational configuration, or something intrinsic to DataStax Enterprise.

The experience with the scalability of DataStax Enterprise is quite positive, as we can deploy as many nodes as needed, and those nodes communicate and are managed efficiently by DataStax Enterprise cluster.

Customer support for DataStax Enterprise is exceptional because I have worked closely with their customer service, and they have been very active. We created multiple tickets, and they were very responsive, often joining calls to help us with whatever we faced. They were crucial in fixing all of our issues during our struggles. I would rate the customer support a 10 because I always receive the help I need from them.

We previously used Elasticsearch Enterprise before switching to DataStax Enterprise.

I did not evaluate other options; this was handled by management, who might have evaluated alternatives. I was involved later and focused solely on backup and recovery.

I would advise others looking into using DataStax Enterprise to adopt it and work closely with customer service, as they provide exceptional service. They resolve all doubts, and they will be present with you during any significant issues. Therefore, I definitely recommend adopting DataStax Enterprise for data storage. I give this product an overall rating of 8.

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.

The best features DataStax Enterprise offers include scaling, speed of data access, and ease of use for those familiar with traditional SQL.

The scaling and speed of data access have benefited my team because the scaling and the speeding of data provide linear scale as well as multi-data centers' real-time replication of data such that we can maintain uptime even with the loss of multiple data centers. It enables us to maintain our uptime, which is very crucial for our clients.

DataStax Enterprise has positively impacted my organization by providing the ability to have our services up and running even with a total outage at one of our data centers. There is no need to maintain windows since we can turn off data centers while doing maintenance and then put them back in the rotation and move on.

I can share specific outcomes or metrics that show this positive impact, such as improvements in performance of about 60% and a reduction in downtime of about 40 to 45%, which is very great.

I think DataStax Enterprise can be improved by having a hybrid on-prem and cloud solution with Astra. Better compatibility with prior versions in terms of codebases should also be improved.

More ways to reach out to customer support would be great as additional feedback on the needed improvements.

Better compatibility with prior versions in terms of codebase would be appreciated as another improvement needed for DataStax Enterprise.

I have been using DataStax Enterprise for three to four years.

DataStax Enterprise is very stable.

DataStax Enterprise's scalability is very fast with linear scalability and hence is very scalable.

The support team is best-in-class, navigable and easy-to-customize in UI. The customer support is responsive and proactive, but they should find more ways to be reached.

My advice to others looking into using DataStax Enterprise is that real-time transaction processing, both reads and writes, is where DataStax Enterprise shines the most. It's very fast with linear scalability. Should more resources be needed, additionally, nodes are added very easily. DataStax Enterprise on its own without Solr or Spark enabled is well-suited for long, complicated reports. The data model doesn't support joining multiple tables together, which is common in BI reporting.

I have additional thoughts about DataStax Enterprise, stating that the escalation process is excellent, the best I've seen. The support team is incredibly strong. Outside of emergencies, the team is very helpful with general questions and working through the data model exercise. I believe the subscription still comes with some hours to help get the data model reviewed.

Negative

I previously used Azure Cosmos DB as a different solution. I decided to switch from Azure Cosmos DB to DataStax Enterprise because I believe DataStax Enterprise is the best-in-class. There are some things that are different with the schema-less system, but I found that DataStax Enterprise is the easiest to implement while evaluating. The replication is on par or better than others in practice. We are evaluating Astra in our test environment, and that has additional benefits we are looking forward to using.

I have seen a return on investment. If not keeping current with updates, updating from an older major version to a newer major version can be a bit complicated and time-consuming, but DataStax Enterprise support will help us with this.

My experience with pricing, setup cost, and licensing indicates that the cost is a bit affordable, especially for my organization. However, for smaller organizations working under a tight budget, it might not be very affordable compared to other alternatives.

Before choosing DataStax Enterprise, I evaluated other options including Oracle Java SE, Microsoft Power BI, and MySQL.

I would rate DataStax Enterprise an eight out of ten.

I chose eight out of ten because it provides us with 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, which is very crucial for our clients.

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.

Some of the best features DataStax Enterprise offers are services such as Cassandra, the built-in Cassandra instance that it provides, which has no single point of failure. It has support for an active-active architecture, and if a node goes down, it has a very robust fault tolerance allowing the systems to keep running. The massive scalability that it offers means the database can scale up to terabytes and petabytes easily, and it is also globally present across various regions. Additionally, it has high write performance, easily handling write-heavy workloads and is ideal for logging events, transactions, and user activities.

DataStax Enterprise positively impacts our organization by helping our developers write code that works very well and providing us with multi-region support.

DataStax Enterprise can provide other solutions that are well-suited for other use cases such as an auditing system or writing systems. Additionally, many other details can be provided for monitoring tools.

I gave DataStax Enterprise a rating of 8 out of 10 because there is still some room for improvement. For example, it can implement some cost optimization where the license can be expensive, and compared to open-source Cassandra, cost is a concern. Some other areas for improvement would be simplifying the setup, as the initial cluster setup is complex.

I have used DataStax Enterprise for around two to three months.

DataStax Enterprise is pretty stable.

DataStax Enterprise provides enough stability for our organization, and scaling can be done up to terabytes and petabytes.

The active-active architecture and fault tolerance of DataStax Enterprise helped my team significantly, as we had a multi-region deployment. One of our regions was India, and another was Singapore, meaning both regions handled reads and writes at the same time. Data is replicated, one in the India region and one in the Singapore region, with a replication factor of three. In this case, the active-active architecture helped us really scale and provide data to both Singapore and Indian users.

We find the customer support to be good.

I would rate the customer support nine out of 10.

This is a new implementation, and we did not have a distributed system before this.

My experience with pricing, setup cost, and licensing is that I feel the cost is a bit expensive compared to the open-source implementation of Cassandra, and the initial cluster setup is complex.

Before choosing DataStax Enterprise, we evaluated options from Azure and Google Cloud services, as well as other cloud services. We also considered MongoDB and PostgreSQL.

Some of the positive outcomes I see are that system stability improved a lot, there is no single point of failure, so downtime is reduced significantly, and the system stays up even when we face issues in a node. It handles the high traffic smoothly, with a thousand requests per second being managed well without major performance degradation, and the end users also see low latency.

My advice for others looking into using DataStax Enterprise is to do their metric analysis and cost and licensing analysis to check if DataStax Enterprise is suitable. If you have high-volume data and a write-heavy workload, then definitely go for DataStax Enterprise. I give this solution an overall rating of 8 out of 10.

My main use case for DataStax Enterprise involves supporting a bank, one of the leading banks in the North American region, which at one point had around 1800 plus Cassandra clusters, all of them quite large. They asked us to perform a version upgrade from a very old version of DataStax to a modern version, 6.X at that time, and we used ZDM and CDM as a zero downtime migration strategy for continuous data migration.

A particular challenge we faced with DataStax Enterprise in that bank project was that the previous version was experiencing performance issues due to the legacy systems being old. Many performance improvements and security vulnerability fixes were added in the latest version of DataStax Enterprise Cassandra. The team was experiencing slowness and performance problems, and during an audit, security issues were identified. Another driving factor for our migration was that the previous DataStax Enterprise version was out of support, which motivated the upgrade of those thousand plus database clusters.

The main challenge with the migration was the nature of the banking and insurance field, as their databases were quite active, limiting downtime to hardly 15 minutes a month. Achieving a complete zero downtime migration for those systems was not easy. However, after collaborating with DataStax engineers and utilizing their premier support, we achieved a near zero downtime migration strategy that met the client's downtime expectations. Despite some initial difficulties, the support from DataStax helped us overcome challenges with ZDM and CDM.

Some of the best features that DataStax Enterprise offers are performance improvements, as new features were added compared to the previous legacy system using version 5.X and the target we migrated to was 6.8. This migration helped stabilize performance issues and introduced new tracing facilities and optimizer level changes.

Although it has been some time since we delivered the bank project, the new tracing facilities and optimizer changes in DataStax Enterprise were very useful in identifying slowness in critical SQL statements. This helped us understand issues that were difficult to diagnose in earlier versions.

I am a fan of distributed systems, especially DataStax Cassandra, because of its distributed architecture and ability to scale horizontally while maintaining availability. I experienced this while working on a project for a telecom company. We had a testing scenario to achieve high IOPS requirements for a government bank in Sweden, where the database layer was replaced with DataStax Enterprise Cassandra due to its ability to handle write operations quickly, allowing us to deliver the project on time.

One of the big strengths of DataStax Enterprise for my organization is its multi-data center availability, which allows applications to operate across locations and tolerate infrastructure failures. With the correct replication factor defined for key spaces, even if nodes go down, all read and write operations go smoothly, supported by a strong backend team for immediate replacements.

The positive impact of DataStax Enterprise on my organization is reflected in specific outcomes, such as improved performance. Post-migration, certain critical queries were observed to be 20 percent faster than previous runtimes, significantly enhancing cost efficiency and reducing the impact of performance problems on critical workloads.

In terms of improvements for DataStax Enterprise, I would highlight a comparison with other NoSQL databases, particularly MongoDB, which can be easier initially for developers due to its flexible document model. DataStax Enterprise requires more discipline around data modeling but excels in predictable large-scale distributed workloads.

I believe DataStax Enterprise could be easier in areas such as operational complexity, especially for those coming from a traditional relational database background. There is a learning curve involving consistency levels, token ranges, and distributed system behaviors that could benefit from more automation and intelligent diagnostics.

I have a total of six years of experience working with all different versions of DataStax Enterprise.

DataStax Enterprise is stable in my experience.

The scalability of DataStax Enterprise is amazing, as it allows for horizontal scaling while maintaining availability, which is one of its best features.

The customer support for DataStax Enterprise is amazing. Any requests I have raised receive prompt responses, often within minutes, and the support engineers are knowledgeable and helpful.

We realized significant cost savings after migrating to DataStax Enterprise, as the previously slow critical queries now run significantly faster, improving efficiency and performance metrics across the board.

We conducted a proof of concept between Cassandra and ScyllaDB before standardizing on DataStax Enterprise.

We realized significant cost savings after migrating to DataStax Enterprise, as the previously slow critical queries now run significantly faster, improving efficiency and performance metrics across the board.

I was not involved in the licensing for DataStax Enterprise, which is managed by a different team, but I understand it operates on a core-based licensing model, which is standard. The licensing team handles renewals, and while I do not have direct experience with it, it seems to function regularly.

We conducted a proof of concept between Cassandra and ScyllaDB before standardizing on DataStax Enterprise.

I advise those looking into DataStax Enterprise to stop comparing relational systems with distributed NoSQL systems, as they are designed to solve different problems. Understanding the use case is vital for making informed decisions regarding database choices.

DataStax Enterprise is deployed in a hybrid cloud environment in my organization, utilizing Azure and GCP. I most often use Azure with DataStax Enterprise.

I would rate this product 9.5 out of 10.

My main use case for DataStax Enterprise is that Astra DB is a major feature of it. It is a NoSQL database, so you can store unstructured data in it, and it is very scalable. Initially, the setup is very easy, and they provide the flexibility to use any cloud and any region closer to your users' proximity.

Whenever I need to save data for my users, I call an API and the data gets saved to Astra DB. This is one of my workflows.

The ability to choose regions and cloud providers has positively affected my project because most of my application's users are based in the US. I chose AWS US East-1 for this reason.

DataStax Enterprise offers the best features including the ability to scale and the ability to choose the particular cloud provider. The pricing is very upfront, and you can see what each cloud costs to you. You can choose the region on which you want to host your database. The major factor is that if you choose the region of your database too far from your users, there will be latency, and you need to choose a location where most of your users are based.

DataStax Enterprise has positively impacted my organization because during research for a NoSQL database, developers are very positive about using DataStax Enterprise because of its really easy setup and the querying to the database is very efficient. You can use GraphQL to query, and it is very good.

I can share specific outcomes resulting from using DataStax Enterprise as we have been seeing an uptick in user adoption. Since the introduction of this, our users have jumped from around 20,000 to 50,000.

There is still room for improvement and DataStax Enterprise can definitely improve their UI.

I have been using DataStax Enterprise since 2022.

DataStax Enterprise is stable.

DataStax Enterprise's scalability is pretty scalable. It auto-scales, and as user demands increase, we can gather more compute resources from the cloud and speed up the servers.

I have not directly communicated with customer support, but one of my colleagues contacted them and found it to be pretty efficient.

I did not previously use a different solution. We started using DataStax Enterprise initially from the start of our application.

My experience with pricing, setup cost, and licensing is pretty good. I do not find any difficulty in it.

I have seen a return on investment as we have saved time to market. Earlier it was around 15 months, and we have been able to deploy and scale our application within 10 months. We have saved around five months.

My experience with pricing, setup cost, and licensing is pretty good. I do not find any difficulty in it.

Before choosing DataStax Enterprise, we evaluated other options, specifically MongoDB.

The latency for my US users is very minute, being around 200 milliseconds, so it is good.

DataStax Enterprise is very useful, and I found it very useful overall.

Per my use case, I found DataStax Enterprise pretty efficient and am content with it.

The advice I would give to others looking into using DataStax Enterprise is to choose their cloud provider clearly, as it will impact their cost and their reason.

I rate this product an 8 out of 10.

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.

In my previous project, DataStax Enterprise's high availability and fault tolerance were crucial because we needed our application to be online all the time, even during maintenance and unexpected server issues. With DataStax Enterprise, we could add more nodes easily as our data grew, and we did not have performance problems. There were no failures thanks to the fault tolerance, so there was no downtime or compliance issues. Even when some nodes failed, the system automatically rerouted traffic and kept the service running.

Advanced security features such as encryption and access controls helped us protect sensitive data and meet internal security policies, which made it easier to prepare for compliance audits. I also appreciated that DataStax Enterprise offers strong monitoring and management tools, making it easier to keep track of system health and performance.

DataStax Enterprise has positively impacted my organization by helping us handle large amounts of data more easily and quickly, improving our system speed, and making our application more reliable. We can scale up or down based on our needs, saving us time and money, and the built-in security features have made our data safer and more secure from attackers and threats. Overall, it has made my team more productive and our customers happier.

I would suggest adding more automation features to DataStax Enterprise, which could reduce manual work even further.

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.

The best feature DataStax Enterprise offers, in my opinion, is OpsCenter, which provides a user interface that allows us to manage our instance, take backups, and overall makes everything very much easily manageable.

OpsCenter makes things easier for me as we schedule backup jobs and monitor our cluster from there, which saves us from having to purchase another monitoring tool.

DataStax Enterprise has positively impacted my organization as we are now an e-commerce website that has never faced downtime, which helps our business to boom and significantly enhances our customer experience.

I can confirm that the outcomes of using DataStax Enterprise show that our database uptime has increased drastically to around 99.9%.

I believe that DataStax Enterprise could be improved by working more on making the OpsCenter user interface more user-friendly, particularly regarding the fonts and overall UI.

In addition to the needed improvements on the UI, I think DataStax support could also benefit from enhancements, particularly in terms of online resources.

I would appreciate improvements in customer support.

I have been using DataStax Enterprise for almost three years.

DataStax Enterprise is very stable.

The scalability of DataStax Enterprise is very good, and we are currently using two data centers.

Before DataStax Enterprise, we were using MongoDB, but we faced frequent database issues and a lack of uptime, which led us to switch.

I already mentioned that we evaluated MongoDB as our previous option before deciding on DataStax Enterprise.

We have seen a return on investment with DataStax Enterprise as we saved a lot of money and time, despite investing more on infrastructure; our ongoing business success with a 99.9% uptime helps us earn more.

My advice to others looking into using DataStax Enterprise is that if your budget allows and your use case fits, you can definitely go with it, as it is very flexible and will give you more uptime.

I would like to ask if there are any new updates or features coming for DataStax Enterprise.

I found this interview to be very interactive, and I appreciate it.

I give DataStax Enterprise an overall rating of 10.