Hevo Reviews

I used Hevo to pipeline data from Facebook into Big Query. The service worked as expected and properly transferred my data. It was one of several services I evaluated for our data pipeline needs.

I had some integration needs that HEVO did not include, so I had to go with another provider who included those. Otherwise, HEVO was amongst my top choices.

Hevo Data saved our company in more ways than one. For the bi-directional Hubspot features alone, this tool is worth the cost for us, hands-down. We were able to pull all of our Hubspot data into our data warehouse, annotate, and push back to custom properties and objects.

The tool worked so well, we began using it as the core ETL connector for our data lake, and are testing as our scheduled task manager for the core transformations for our data warehouse.

The pricing model has worked out well for us. The free trial to prototype how the thing works, and how we could be efficient with our usage of connectors, has also been wonderful.

THe list of connectors is really good, but not 100% where we wish. In addition, migrations from one tool to another (e.g. Intercom -> Hubspot) require us to do a bunch of manual mapping that is hard; other tools have these mappings canned and are therefore easier to use for that use-case.

I really like the ease with which you can deploy the tool

The tool can get better in documentation and video tutorials.

Simple and effective hassle free table syncing between Cloud VM SQL Server and Snowflake DWH.

Getting Hevo white listed with other vendors such as FTP sites is the first big hurdle.
Sometimes I'd like to have it sync a view from my source system but it struggles with this. Instead I make it sync the source tables of the view.

Hevo is very easy to set up (the whole process is self explanatory) and has a lot of integrations. It also allows for a lot of flexibility with Rest API and Webhook integrations.
The UI is very well tought out and the whole product feels professional.
I should also point out the Consumer Service, which is great and very helpfull. I get answers every time I talk to them, and I feel my problems are being taken seriously.

Hevo doesn't allow precise control of the Ingestion and Loading frequency and times. You can only choose a frequency or specify certain times, but even this one is limited to a certain number of runs.
This is our only limitation with Hevo right now.

The customer service is great, they were always ready to help, very kind. Great sources opinions, to integrate with and to use as destination.

It can be complicated to make a pipeline with some specific source and it doesn’t seem to have an easy solution to fix the issue.

HIPAA compliance, data integrity, documentation, and support

features I miss from Stitch like column level ingestion. Ingestion and loading logs are also quite thread-bare and hard to get much information on this.

We decided to shop around for a new solution because our current provider decided to raise their prices significantly. Hevodata checked all the boxes.

When we first started out, our BigQuery costs went up, but we were able to mitigate that by changing the frequency.

As an ETL, you couldn't ask an easier tool to set up and test than Hevo. This is great for quickly checking if it'll work for your goals. All processes are transparent and if something seems amiss you can get a handle of a very helpful customer support quickly.

There are some advance features for data handling that seem to be still in development. Overall, it might not be the tool for you if you have a very strict or complicated workflow.

Ease of use, UI is easy to grasp and understand.

Nothing major comes to mind. Faced am issue recently but the customer support helped with the resolution in a satisfactory manner

I like how easy - almost trivially so - that it is to get started with Hevo. It literally just works. The team have made it really simple to start, and include a free trial. Also the support team are excellent. It has great documentation as well so you can synchronise easily from a wide range of sources and destinations.

Some things aren't as polished as you'd like. For instance, you can't categorise pipelines or sort them into folders. You simply have a huge list. Also, there's no easy way - without programming it yourself using the Hevo API - to manage pipelines in bulk. Let's say you have pipelines for similar databases with the same structures; you have to manage them by-hand, individually, or you have to make your own tools.

It has suited all our needs.
- The most critical part was reading Oplog from production MongoDB replica.
- Cleaning PII and sensitive data on the fly before loading to DWH
- Wide range of sources and even Webhooks (any arbitrary source)

I might only add Hevo is constantly evolving.
I participate in proposing new useful features from my PoV.

Ease of use: Hevo's UI is very intuitive and easy to learn. With couple of clicks we can easily build scalable pipelines.
Data Transformations: We can write our transformations in python. Hevo also provides out of the box flattening of complex nested json which is very handy while building Mongo pipelines.
Quick and efficient Support: Hevo Support team are quite reachable and always ready to help us solving critical bugs and errors.

Alerting could be much more precise and intelligent.

The things which I most like about Hevo is the automation level in the product. Hevo will take care of everything from source mapping to destination mapping with just a few clicks. It has many useful features, be it table/column auto-mapping , resizing and basic transformations for complex data sources like Mongo where you want to flatten the nested data.

Alarm feature can be little more intuitive.

Ease of setup
Cost
Quick ingestion times
Ability to connect to multiple sources
Transformations

High CPU usage on our Amazon Aurora PostgreSQL server.

Hevo offers a fair pricing model with competitive features, along with a seamless data integration experience that complies with many data privacy groups. The ability to transform data using python code, auto map object data, and do this all in a user-friendly interface is wonderful. Hevo also ensures pure privacy with data which is vital when using cloud storage integrations.

There was not much to dislike about the software.

Hevo's interface is simple to use, while powerful at the same time. From our tests so far the pipelines work reliably and performantly, and the transformation tools (offered both in a visual manner or through custom Python code for more advanced workflows) are quite useful.
The customer experience and support provided is great.

I seem to be unable to apply new transformation rules retroactively. That would be an important feature to us as sometimes new fields need to be masked or hidden but were already ingested into the destination.

1) Self serve product with super UX - we were able to start our pipelines by ourselves within a day

2) Detailed documentation for every source / destination with appropriate up-to-date references

3) Was impressed by the overall speed - after submitting a job to queue, it picks up the job almost immediately

4) Transformation supports Python which is great, data can be manipulated by custom scripts

5) Full visibility and control over the entire pipeline

6) Schema mapper is great, auto-mapping works for most of the use cases. Manual mapping is also possible. It shows and asks user to confirm what query it is going to execute at the destination, which is good

7) Workbench available to run queries at the destination, custom data models available to create "views" from destination tables

1) Didn't quite like the limit on number of sources in plans

2) Schema mapper doesn't have an option to change column types at the destination

The best thing I like about Hevo is the ease of use and the integrations that are avaialble for multiple sources, which makes the ETL seamless.

In Schema Mapper/Overview tab in each pipeline, an option to skip muliple collections at a time using a check box might be a good feature which will be very helpful for us, otherwise its now taking lot of manaul effort to skip them if needed in a pipeline.

Previously, we were using Fivetran. With Hevo, we get better features for a fraction of the price.

Honestly, I don't have any complaints. Their support is awesome and any issues that I have had, have been resolved within hours.

Hevo's interface is well designed, so that it is straight-forward to set up many pipelines. Unlike other tools, they do not force us into their own standards, for example for naming tables. The service is reliable and performant, including at scale. The team also consists of nice people who have been very friendly and helpful.

We have no complaints. Hevo's MySQL binlog replication works well for us.

We have 10+ pipelines streaming a few million rows from multiple databases to a warehouse. It's very easy to use and configure. It supports all popular database types as sources and we can put custom transformation logic in the pipelines. Hevo's support engineering team has also been very pro-active and helpful throughout our engagement to date.

I think the documentation could be structured in a format of Source (e.g. MongoDB) to Destination (e.g. BigQuery) with the exact steps required to configure this pipeline. Most users would come to Hevo with this type of requirements and it would aid with discoverability.

Hevo is extremely user friendly and very seamless. Data pipelines can be set in a matter of a few clicks. Our business users are extremely happy with all the data at their disposal for any analysis that they need to do. Hevo's support is very prompt and always available to answer your queries.

When we had started using Hevo 2 years ago, there were only a few sources available. However, I see more and more new sources getting added every month.

Extremely Budget friendly, many connector s and destinations, easy to use and good support

Sometimes, the transformations from the raw connectors can be a bit confusing and it requires heavy understanding of the source. But that's expected.

Smooth OpLog/Change Stream based syncing is one of the strongest reasons I stick to this platform.

Transformations UI can be a bit better and more user friendly