Hevo Data Pipelines

My main use case for Hevo involves using it for different purposes with SQL Server, which stores customers' transaction information; Salesforce, which stores sales customers' information; and Shopify, which stores orders, along with an application database that stores operational data. I mainly use it for extract purposes, as Hevo connects me to the source database and reads data. It also helps me transfer data to the destinations for load purposes.

Additionally, transformation is part of its capability, as it can clean, filter, and restructure the data. Lastly, I also use it for monitoring purposes, as it tracks pipeline activity, errors, and progress.

The monitoring and error-tracking capabilities help me keep the Shopify, Salesforce, and SQL Server data flows reliable. Hevo is commonly used for ETL and ELT purposes, which cover extract, transform, and load. Hevo helps me configure a connection pipeline that includes a source, transformation schema mapping, and destination.

While I've listed my main use of Hevo, I also use it for other purposes, such as verifying that SQL Server is running and checking TCP/IP connectivity, including confirming SQL Server ports. Hevo is also useful for server documentation, and commonly used ports such as 1433 require database permissions. I've been using it for this sort of thing.

Hevo has positively impacted my organization, primarily by helping me identify improvements, mainly in time consumption and accuracy. It's easy to use and navigate, which are the main positive aspects.

The rough estimate I can provide for time saved since using Hevo is about 50%, which has allowed me to accomplish a lot of projects.

The best features Hevo offers serve a range of different purposes, but I think the best one is the extract, transform, and load process.

What stands out most for me regarding Hevo's ETL and ELT capabilities is mainly its loading purposes, as it transfers data to the destination, which is quite handy and helpful. Also, for extract purposes, Hevo connects me to the source data and reads the data.

The main things about Hevo's features include activities that can help me as well, particularly functionalities such as Change Data Capture. It captures changes in database change tables, including information about the change data. For example, if a customer updates their phone number, where the customer's ID is 101, the name is John, and the phone number is 555-1000, through Alter, Hevo can change the tracking mechanism to identify record changes and duplicate the upload information.

I believe Hevo can be improved by preparing some guidelines or a manual that any database engineer or related professional can utilize for day-to-day activity. If the support is connected or easily accessible, that could be an improvement.

In addition to the points I mentioned, I think AI compatibility is crucial, as everything is becoming more advanced these days, so if that feature has been advanced or upgraded, it would be beneficial.

I find the accuracy of Hevo's output to be better, though it can still be improved.

I don't have any other improvements needed for Hevo beyond the documentation, support access, and advanced AI capabilities I've mentioned.

I've been using Hevo for the past five-plus years, and I use it for different purposes in my day-to-day practice and also for project-based work.

I believe Hevo is stable, as I haven't had any real concerns or issues in the past.

The scalability of Hevo is pretty good and positive.

I haven't tested Hevo's customer support yet, but it seems quite handy and accessible through email, so I hope it is positive.

I haven't used customer support, so I would rate it a seven.

We haven't previously used a different solution; that's why we were introduced to Hevo, and once we started learning about it as a team, it was quite helpful.

I would explain time saved as one of the metrics, such as 50% of our overall activity without Hevo, as time costs us money in our environment.

We haven't evaluated other options; there were no alternatives considered before choosing Hevo.

On a scale of one to ten, I would rate Hevo overall as an 8.5.

I would give it a rating of eight.

I rate it an eight because there's still a possibility to grow, and I reserve the other two for upgrades or advancements.

Regarding Hevo's AI capabilities, I haven't dived into the details, but it seems quite safe and well-designed in terms of governance and security.

My advice for anyone in database roles, such as administrators, data engineers, or analysts, is to start using Hevo to save time.

Hevo is mainly used for data integration purposes, and I use it to form a pipeline or a gateway where data is collected from various data collection platforms or sources.

Hevo is a data integration platform that I use as a gateway and connector when data is collected from various sources and platforms such as CRM or sales and marketing platforms, and it helps transfer my data into various storage platforms such as Google BigQuery. It ensures that the integration of my data is efficient, allowing various users to utilize it effectively in an automated manner.

A specific example I can share is when we hold events to map customer support or marketing trends, where we meet potential clients or participate in exhibitions, collecting their data and onboarding them with their preferences. With this information, we store it for future reference, enabling background checks when clients return or when engaging with them.

Another key use case for Hevo is the ETL process, which simplifies how my teams handle data by providing prebuilt connectors and an easy interface for creating pipelines, allowing engineers or analysts to move data faster and efficiently execute data recovery processes.

Hevo has positively impacted my organization by ensuring excellent usage of collected data, which is utilized in various workflows, including sales and marketing and customer relationship management, ultimately helping me serve clients better. It has also simplified my data management and improved data security by creating a clear pathway from collection to usage.

I have observed various unique outcomes attributed to Hevo, including reduced costs due to the efficient data integration process that avoids manual methods and automation, which leads to time savings and better data consumption for informed decision-making.

Hevo offers several unique features, such as simplicity of usage with an easy-to-access interface, which reduces the need for specialized expertise in small organizations. It also decreases the workload involved in data engineering, allowing technical teams to focus on other tasks. Moreover, it is a scalable platform that can easily adapt as workloads grow.

The simplicity of Hevo's interface significantly impacts my processes, enabling less technical employees to easily navigate the training manual and quickly adopt the platform, thus reducing training and onboarding time.

Hevo's recovery abilities and monitoring capabilities are noteworthy, as the recovery process is instant and uncomplicated, with efficient monitoring of data integration.

There are areas where Hevo could improve, particularly regarding its cost structure, as the pricing is based on usage and the advanced features can become costly for startups that are not well-established. Additionally, the lack of flexibility in building gateways and pipelines limits customization options.

Furthermore, despite the simplicity of Hevo, technical knowledge is still needed for monitoring and ensuring the pipelines and connectors function efficiently, particularly in implementing the ETL process and managing data recovery accurately.

I have been using Hevo for the last three and a half years.

I would rate this product highly based on my experience with it.

I can give you a specific example of how I use Hevo to move data between databases and Snowflake. We had one software application that had over 500 customers; however, that application, even though it was a SaaS app, had one Azure SQL Server database per customer, and in those databases, there were four different schemas. I needed to get that data from that application, as well as data from other applications using different databases, different public clouds, and different infrastructure into Snowflake. Using Hevo, I tested that it worked well to move data, made four pipelines for one database manually, and then scripted that using the Hevo API to create effectively 2,000 pipelines for this one application. We had all the data from 500 Azure SQL Server databases with a similar structure into a single Snowflake data warehouse, transforming some of the data to prevent it from overwriting itself from all the different customers.

This specific example illustrates how I effectively used Hevo for that application with 500 Azure SQL Server databases, four different schemas in each database, making 2,000 Hevo pipelines, turning on change tracking in the source database, specifying the tables and fields needed, mapping it out to our destination data warehouse in Snowflake, and letting those pipelines run.

The simplicity and fast setup of Hevo significantly benefited my team and organization because we didn't run into challenges while getting started, although we did hit some limits later. We managed to get started very quickly without any challenges. That simplicity and fast setup were crucial as we struggled with other tools that were costly and complicated. We tried to work out how to use AWS tools to get data from some databases, but many of their options weren't available via the GUI, requiring extensive scripting knowledge. Hevo enabled us to sign up and seamlessly go through the steps of connecting our source database and setting up our destination. While other products had complexity and high costs, Hevo made it super easy to get started and moving.

The documentation of Hevo was good, providing clear step-by-step guidance on what to do for specific source or destination systems to be ready for moving data around.

Hevo positively impacted my organization by allowing us to move data quickly and easily, enabling us to focus on our real challenge: performing analytics and reporting out of Snowflake with all customer data centralized despite using various applications. Hevo allowed us to prioritize our analytical work and minimized my time spent on data movement, which typically is a complicated and lengthy process.

I don't have specific metrics or numbers, but I can say we saved time because we were struggling with other products; we had spent weeks trying to work out AWS Glue and other tools. With Hevo, we got it done quickly, saving a massive amount of time in setup, testing, and going live. It let us focus on analyzing data instead of moving it. Although I don't have quantifiable metrics, it's clear that it saved us considerable time, and while it didn't impact employee needs, it definitely freed up significant resources.

Additionally, the API was slower to release compared to the user interface and was not feature-complete, which was frustrating. That said, Hevo actively worked on bridging many gaps. Overall, these were significant pain points I encountered.

I don't have precise metrics, but I can say we saved time with Hevo, getting started quickly. We spent several weeks trying to make alternate products work, but with Hevo, we were up and productive within an hour, which saved us a substantial amount of time. The ongoing maintenance was minimal, so we could focus on analytical and reporting tasks without needing to consider the data movement logistics. Hevo facilitated that process, but I don't have any quantifiable numbers.

Hevo didn't have any AI capabilities, so I cannot comment on its accuracy and reliability of output.

Hevo is deployed in my organization as a SaaS application on whatever infrastructure Hevo operates, which I assume is the public cloud.

My advice for others looking into using Hevo is to use it to test out your concept. Hevo is a great tool for that purpose.

I would rate this product a 7 out of 10.