Enables us to build data pipelines without knowing how to code and helped us break down data silos within our organizationUsing StreamSets to create pipelines for batch streaming or ETL is easy and straightforward. However, if one is new to StreamSets, it may not be so simple and may require a lot of documentation for assistance. We utilize StreamSets' ability to connect to enterprise data stores, making it easy to begin trading instantly without needing to be technically skilled. We use StreamSets to move data into analytics platforms. In my experience, it is initially quite easy to move data back if we have a clear understanding of data transit, importation, and exporting from external sources. This solution enables us to build data pipelines without knowing how to code. The solution includes templates that guide us and help us customize our data easily. It is essential that StreamSets does not necessitate coding, as this saves a considerable amount of time that would otherwise be spent writing code, as well as resources that would be required to hire experts. Transformer for Snowflake can help with both simple and complex transformation logic. For example, creating a plan to perform EPL and machine learning operations is easy and fast. However, if the same operations are performed on-site, it can be difficult to troubleshoot events due to limited visibility into the results. StreamSets' Transformer for Snowflake is important to us because it saves us a lot of time and enables us to complete a task remotely with only two or three people. It is important that Transformer for Snowflake is a serverless engine embedded within the platform. We have the capability of creating a data operations platform, so we don't have to worry or even be aware of what we are doing at the moment. We can simply create a device and use it in the pipeline we want it to be in. The solution improved the way we work, benefiting both our customers and our development and retainer teams. StreamSets helps us develop a platform manually, with a lot of teamwork, either remotely or on-site, depending on which option we use. This has had a significant impact on our organization in terms of how we process and transform data. I would say that it is very easy for us to update the template so that we can have real, actual data in APL claims and in the supply chain. StreamSets' data drift resilience is very effective and can run in the data grid. The data drift resilience has reduced the time it takes us to fix data drift breakages by approximately 25 percent. StreamSets helped us break down data silos within our organization. The ability to break down data silos helps StreamSets to gain quick insights. In general, it is a great feature that ensures we have activities or processes in place. We know precisely what to prevent and what to implement. StreamSets saved us around 30 percent of our time, meaning that a task that would take five hours to complete manually can now be done in around three and a half hours. The reusable assets are reducing workload by 35 percent by allowing different people to use a single platform or resource, regardless of whether they have a similar SKU or a different SKU. This feature can help an organization simplify, implement, and transmit more easily. It is not only the cost of one packet that we paid for, but now we are implementing a strategy using different people within the company. It would be very expensive if we had to hire a new person to manage that task and it would also take a lot of time. StreamSets is not only saving us money, but it is also ensuring that we complete strategies on time. StreamSets as well helped us scale our operations, which has had a significant impact on our business. We now have a better understanding of how to secure data and provide reliable security for the transmission of data from internal servers to external services, as well as meeting our client's application needs.
