Striim Subscription

Primary use cases are mostly in the manufacturing domain. Manufacturers of semiconductors, solar panels, and windmills utilize our solutions. Specifically in the manufacturing domain, we provide preventive, predictive, and prescriptive analytics. We predict if there will be a failure in any of the components or equipment. We send alerts or triggers so that clients can take proactive decisions and make the necessary changes to prevent their manufacturing plant from failing. This provides a unified dashboard or view. For example, if a windmill or solar panel is going to fail in the next two weeks, they receive that trigger or alert immediately so they can take necessary actions to avoid it.

Striim is mostly for real-time change data capture. We integrate it with edge devices and sensors. If there is an anomaly or outlier, we detect it immediately and generate the alert. Databricks is mostly for low volume tickets and is not necessarily real-time processing but rather near-time processing. There can be a delay of two or three days before the report is generated. We use Databricks mostly for healthcare on-premises databases. Striim is mostly for real-time data capture. There is another complementary data platform called FactViewer as well, which operates along the same lines.

Trend detection is beneficial through the three Ps of maintenance: predictive, prescriptive, and preventive. When it comes to predictive analysis, the trend indicators inform our clients when a failure is going to occur in the system. The preventive part helps us suggest certain measures which they can take to avoid it. The prescriptive part provides recommendations or guidelines based on the historical analysis of the data, including patterns and correlations of earlier events or triggers. We identify those and give suggestions as to how they can avoid it in the future. Trend analysis is how we help our customers.

In terms of metrics, one major KPI is the turnaround time for report generation, which provides real-time insights. In relation to network infrastructure, the downtime and how many SLAs we have met are also important. If there is a network failure, there is a service level agreement that specifies acceptable downtime and when servers and infrastructure must be operational. These are some of the KPIs which we use for different industries. Mean time to detect failures or mean time to resolve a certain issue are also tracked. In a service desk situation wherein tickets are flowing in, we classify them automatically based on historical patterns without requiring triaging. We classify the tickets automatically and assign them to respective resources.

The Change Data Capture (CDC) feature is what I find most valuable in Striim. That is the most important feature. Its ability to integrate with all visualization platforms like Power BI and Tableau is also valuable. We can customize the reports based on the client's requirements. It is plug and play where you fetch an API and get the reports generated.

Striim allows both real-time and batch processing. We can decide the source and target and how the data will be ingested. Striim has the medallion architecture that allows us to decide how data will be extracted, transformed, and loaded. This includes both the ETL and ELT parts. Building AIML models on top of it allows a lot of collaborative work for data scientists and ML engineers. They can create their own experiments on top of that. Any organization has an AIML roadmap for five or six years. Based on the use case, we can design models on top of that. Striim provides a unified experience that comes in handy for users across domains.

The interactive dashboards help with failure alerts, such as if there is a pipeline which is failing. We have the new human in the loop feature where we can deploy agents. Not necessarily does there need to be a person who monitors the pipelines or builds them. We can deploy agents, and whenever there is a critical decision which has to be made, a human in the loop can receive a summary report of the situation and based on that, take necessary actions. Interactive dashboards is a very good feature in Striim.

We have integrated structured, unstructured, raw data, and data available on various social media platforms and ingested it into the lakehouse architecture. We cleanse it and build models on top of it. Based on the prospect's requirement, either real-time insight or near-time insight is provided. We have integrated various sorts of data, not necessarily clean or structured data, but data available on various platforms. We have ingested it, cleansed it, and built models on top of it so that they can use it as per their requirement.

The pricing is a bit on the higher end for Striim. If customers have that budget, Striim is a good option. However, if there are budget constraints, people generally tend to go with Databricks or Apache NiFi, or other various open-source tools that provide the same capability at much lower cost. Striim is good for large enterprises, but the pricing plans could be better.

My only major concern at the moment is the pricing part.

I have been using Striim for four plus years.

Striim's technical support is the best and at par with competitors. Since it originated from Oracle GoldenGate, the customer service, support, or whatever help is required is the best. There is no room for improvement there.

I would give Striim's technical support a solid nine.

We went with the partner route for both Databricks and Striim, which were gold partners with our organization. They provided us certifications for in-house resources. Any requirement that came up, we had their support from their solution architects in terms of designing the solution approach and sizing, determining the number of clusters or nodes required for data migration. That support was always there.

My main use case for Striim is mostly for migration projects, where I migrate databases from SQL Server on Azure to Google Cloud SQL.

I can walk you through a quick specific example of how I used Striim during one of these migrations: there is an agent that I install on the source server, which starts the continuous data capture connecting to the source database, replicating all the database objects including tables with data, stored procedures, functions, and other database objects, with the target database specified as Google, but other source and destination combinations such as on-premises, AWS, and some other clouds can also be included.

One thing I would add about my use case of Striim is it is not very seamless; there were some failures encountered that were problematic, especially for very large databases where replication issues arise after a couple of days, making it difficult to backtrack and start the process again, effectively wasting a lot of time.

Striim is not deployed in my organization, as I used it strictly for a client's data migration while working for a consulting company; it was deployed on Azure, which is a public cloud, with replication to Google Cloud Platform.

Striim has impacted my organization positively as a data migration tool by streamlining data capture during migrations, replacing inefficient import and export methods that couldn't provide the latest data and changes, keeping everything synchronized with the CDC feature.

Regarding the time saved with Striim compared to previous manual methods, I believe these migrations are not even possible without it due to the size and complexity involved, and it greatly assisted in accuracy during the cutover phase, allowing for thorough database checks before final migration to Google Cloud Platform.

The best features Striim offers include continuous data capture and a smooth replication process from source to destination, along with a testing environment that allows for checks before cutting over and doing the DNS update.

What stood out to me about how Striim handles continuous data capture compared to other tools or manual methods is there was no other way I could see to migrate these very large SQL Server databases over to Google Cloud Platform over the internet, as Google Cloud Platform recommended; the CDC works nicely by replicating data as changes happen to the source database, running syncs to the target, and applying changes in near-real time.

As for additional features or schema conversion options, I cannot add anything at the moment since I didn't use many of the schema conversion options, focusing primarily on migration to Google Cloud Platform.

Striim can be improved in handling CDC connection or connectivity issues that impact data capture, as well as improving recovery options from errors, because starting from scratch with large databases wastes significant time.

I would add that there was a need for better documentation and support, especially regarding SQL Server, as I found it lacking during my experience while relying on support from Google individuals.

I chose a 7 out of 10 because of the need for better support, documentation, making the process slightly more efficient, and the cost, given that Striim is a licensed product with limitations on the number of databases.

There are no other improvements needed for Striim that I haven't mentioned yet, aside from support, documentation, and better handling of CDC failures.

I have been using Striim for a data migration of large SQL Server databases from Azure to Google Cloud Platform, where Google recommended using Striim for continuous data capture and replicating everything that we have on Azure on Google Cloud Platform.

In my experience, Striim was fairly stable, but there were a few instances where it failed during the CDC process after a couple of days, which complicated recovery.

Customer support was non-existent, with some documentation shared by Google personnel but no access to a customer portal for support tickets.

I did not previously use a different solution; the only data migration solution I employed is AWS Data Migration Service.

I have seen a return on investment that is more qualitative than quantitative, stating we migrated incredibly large databases in about five days, which is a much shorter timeline compared to several weeks if attempted manually.

My experience with pricing, setup cost, and licensing was limited to one instance, which was a factor, as we were working with Google professional services to acquire the necessary license for migration. We had to request additional licenses for the second database we wanted to migrate with Striim, facing restrictions around the schedule that impacted flexibility.

Before choosing Striim, I did not evaluate other options, as it was the recommended tool for SQL Server data migration by Google, and while I knew of AWS's DMS service, there wasn't another option from Google.

I would rate Striim a 7 out of 10, as it did the job but there are aspects that could be improved.

I used Azure as the cloud provider for deploying Striim.

My recommendation to others reading reviews on PeerSpot is to consider Striim's CDC requirement for migrations to Google Cloud Platform, as it might be the tool for you.

My main use cases for Striim day-to-day include maintenance, database performance, ensuring data integrity, managing transactional log overload, and simplifying data replication.

Zero-downtime cloud migration and live replication for offloading heavy reporting workloads are additional use cases that have been very helpful.

I ensure data consistency and integrity when using Striim for replication or migration, as this is my highest priority. I consider myself a proactive DBA in that regard.

Striim has impacted my organization positively by saving time, especially when we undertake many projects and day-to-day DBA activities. It was very helpful to ensure zero downtime during the migration process.

Regarding specific outcomes, there is definitely time savings because fewer resources are used, and it helps run faster while maintaining accuracy in following all scripts to ensure everything delegates smoothly without any manual interaction.

Striim's best features include the optimized log-based CDC and traditional replication methods such as transactional replication or trigger-based tracking.

Out of these features, I rely most on the zero-downtime capability for cloud migration, which is very helpful because we undertake many migration projects.

Striim's intuitive UI for monitoring, the flow designer, and the real-time console are straightforward and helpful.

Regarding Striim's governance and security capabilities, I find these very helpful, especially since I have used them heavily for migration. The security has proven quite robust without any issues.

Striim's accuracy and reliability are strong because, using Azure and Sybase, the process runs very smoothly. I believe it is safe to use during cloud migrations.

Striim typically integrates well with other tools in my environment. Once I start using it independently, it proves very helpful.

When troubleshooting issues with Striim arises, the features help by displaying problems easily, which saves time and provides straightforward navigation.

Striim can be improved by providing tools such as short videos or step-by-step manuals to clearly demonstrate its capabilities and help users learn quickly.

Regarding needed improvements, it is important to advance in the AI world. I am not certain whether this can be applicable for all RDBMS, but definitely more updates and features are needed, especially concerning AI integration.

The learning curve for new users getting started with Striim is manageable for those experienced in SQL and other RDBMS, but it can be challenging for newcomers. I recommend adding more learning resources.

I have been using Striim for more than five years, spanning both my previous company and my current company.

Striim has proven stable in my experience, as I have not encountered any downtime or disruptions.

When it comes to scaling with Striim as workloads increase, it has been quite helpful and could adapt easily, saving considerable time in delegation and follow-up because I only need to roll back scripts to ensure that nothing goes wrong.

In terms of scalability, Striim handles large workloads well, and I personally have not had any issues with that.

I have not needed to reach out to customer support because Striim has been an easy tool to use without any issues so far.

Prior to using Striim, we used other solutions, mostly Redgate Monitor, but Striim specifically addresses migration, making it our go-to tool.

I have seen a return on investment from Striim. Time savings are crucial since it saves considerable time, which positively impacts the return on investment.

We have not evaluated other options before choosing Striim. It was introduced to us, and we started using it and loved it.

Striim is deployed in my organization on-premise and in a private cloud, which has been helpful.

For our private cloud deployment, we heavily use Azure.

For monitoring Striim's performance and health in my environment, I heavily rely on Redgate Monitor, which proves very helpful.

My advice to others looking into using Striim is that if there is a trial period available for database administrators or anyone in the field, I am sure it can be helpful for everyone.

I would rate Striim an 8.5 out of 10 because, while there is room for improvement, its helpfulness and ease of navigation for any database administrator make this an accurate rating.

We use Striim to replicate the warehouse data to Google platform to generate reports.

We have developed a centralized server where we pull all the warehouse data through Oracle GoldenGate. Striim sits on Google Cloud Platform and the Striim agent connects to the compute server, reads the files generated by GoldenGate, and replicates the data. Additionally, we have Striim agent running on a standalone server that connects to OCI, reads the trail files from Oracle GoldenGate, and sends the data over to Google Cloud Platform.

Our aim was to use Striim to get the reports from Google Cloud Platform and it is working effectively, so Striim is useful for that purpose.

Striim is definitely my data migration tool and data reflection syncing tool. It reduces manual intervention because it automatically syncs the data from the warehouse. We can get the reports dynamically in real-time data reports. This saves time and helps to improve the business by analyzing the latest data.

We use Striim for normal BI. We do not use much workflow, though we have configured this workflow.

Striim is very easy to configure. You just install an agent and then configure it to connect to your server. Once the connectivity works fine, it is fairly a simple way to use Striim.

I would say Striim performance needs improvement because we use more GoldenGate, so I tend to compare GoldenGate with Striim. Comparing GoldenGate, I feel Striim will struggle a little bit when a batch process runs for a longer time and generates too much data. Otherwise, Striim is acceptable. With respect to certifications, I feel Striim is more expensive in terms of licensing. However, it is good if it is compatible with the product that we use.

Striim could provide more transparency in terms of the log files. When there is an issue, whenever there is an agent problem, we have to reach out to the Striim development team. If they were more transparent with their logs, we could debug and analyze ourselves. Additionally, they could provide more features with less licensing cost.

Documentation and compatibility are fairly straightforward. However, they could improve the documentation by showing how to configure with different platforms. Sometimes we rely on the development team to provide us with instructions whenever there is an agent issue. If there were more detailed documentation available, we could follow that and try to resolve the issues ourselves.

Striim is stable.

Striim can handle the data volumes effectively, but it can struggle a little bit if the data volume is too high.

If the transactions are not too frequent and if there are no long batch jobs, I would suggest going for Striim. If the data volume is comparatively moderate, I would recommend Striim.

Customer support is fairly good. When you contact them, they give you a response straight away and help you identify the issue and fix it. There is no problem with the customer support.

We used Oracle GoldenGate, and we switched to Striim because we wanted to use it in Google Cloud Platform.

The initial setup was straightforward.

I would say Striim saves time as it syncs the real-time data. Employees do not need to be dedicated to reviewing or monitoring Striim. The data team that you have in your company are the ones needed to see the data. No specific employees are needed to handle Striim.

Licensing was a bit more expensive because Striim has to read from Oracle GoldenGate trail files and also integrate them. However, I have no idea how much it was because I was not involved in that part.

We chose Striim as the first option.

The interview process is fairly good and covers all the questions about the product that the customer wants to know. This review has an overall rating of seven.