Airbyte

My main use case for Airbyte Cloud is that in my previous job, we used it to integrate two data sources into our database. A specific example of how I used Airbyte Cloud to integrate those data sources is that we set up a connector between the sources and the database, which would run once daily to retrieve any fresh data from the source and store it in our database.

The best features Airbyte Cloud offers are that in our limited use case, it was fairly easy to set up, and the web portal was intuitive to navigate, to monitor the jobs, to trigger them if anything failed, and to set up the connectors, making it user-friendly.

The navigation was intuitive overall, and there was no particular part of the setup or monitoring that stood out as especially helpful; it was just an overall experience where the navigation was pretty intuitive.

Airbyte Cloud has positively impacted my organization as it was a fairly easy and low-cost way of integrating those external data sources with our overall data ecosystem. I noticed that the first solution we selected was Airbyte Cloud because it was the lowest cost and it offered us everything we needed, so I cannot really compare it to anything else.

Everything worked fine from my perspective and from my limited experience, so I don't think there are many improvements needed for Airbyte Cloud that I haven't mentioned yet. I have nothing to add regarding improvements needed for Airbyte Cloud that I haven't mentioned yet.

I have been using Airbyte Cloud for roughly two years.

Airbyte Cloud is very stable; there were some glitches from time to time, but overall, it was pretty stable.

I have no idea how Airbyte Cloud's scalability is because we use it in very limited scenarios, so we never plan to scale it to big numbers.

We did not use any solution previously.

My experience with pricing, setup cost, and licensing is that I did not deal with the billing side of Airbyte Cloud, but I think the overall cost was relatively low, so I don't think we had any issues with billing or costs.

I have no idea if we evaluated other options before choosing Airbyte Cloud, because when I started my previous job, it was already part of our data ecosystem.

Regarding Airbyte Cloud's AI capabilities, I did not have a chance to use any of the AI features, so I cannot say anything about its governance and security.

Regarding Airbyte Cloud's AI capabilities, I did not get a chance to use any of the AI features, so I have nothing to say about its accuracy and reliability of output.

My advice to others looking into using Airbyte Cloud is that I think they should give it a try; it is fairly easy to use in the scenarios we use it for, and the costs are reasonable. I gave this review a rating of ten out of ten.

My main use case for Airbyte Cloud is as an integration tool for connecting to different sources, and for the client I was working with, we used Airbyte Cloud's established Amazon connector to sync data from Amazon, with some data scheduled for a daily run and others for hourly runs.

I mostly use Airbyte Cloud for connection and integration from the source, as it is an integration and orchestration tool that allows you to avoid using Airflow to orchestrate the runs or job runs since Airbyte Cloud has those capabilities.

Airbyte Cloud's best features include scheduled runs, many pre-built connectors, and an easy no-code setup. I am looking at my next project where I plan to leverage the Agentic AI workflows within Airbyte Cloud, and one thing I have noticed is that with the managed platform, it is easier to do other tasks and just build the connectors while ensuring downstream apps or users do not miss out on any data.

Airbyte Cloud has positively impacted my organization by cutting down the number of deployments, as it handles orchestration monitoring that would typically require an engineer. It is also easier to integrate with DBT for transformation, enabling pipeline management, and I appreciate its flexibility in deployment, preferring the managed option due to the lack of engineering overhead and its ability to handle large data volumes through parallel extraction.

I think Airbyte Cloud can be improved in terms of its subscription rigidity, particularly for small business owners who want a managed platform connecting their data sources with DBT runs while keeping costs to a bare minimum. I suggest Airbyte Cloud consider creating cost tiers for small businesses with fewer syncs, which would be advantageous for them. Overall, I think Airbyte Cloud's capabilities are strong.

I have been using Airbyte Cloud for a few months while working on a freelance project for a client who is building a data warehouse for his Amazon data to BigQuery, so I helped him integrate Airbyte Cloud and also with DBT Cloud.

On a scale of one to ten, I rate Airbyte Cloud an eight.

I chose that number because there are numerous connectors available, and with Airbyte Cloud's more than 600 connectors, the products and solutions are improving. I believe with the adoption of Agentic AI, it might even move to a nine once I start using the Agentic AI workflows.

I have not used Airbyte Cloud's AI capabilities yet, including its governance and security features, so I am not sure about them at this moment, but that is something I plan to explore in the future.

I have not leveraged Agentic AI in Airbyte Cloud so far, but I believe that if you consult AI, it could assist in building a connector, troubleshooting errors, and providing a deep dive on how to address connection errors you might encounter while setting up a connector in the cloud.

In the organization I worked for, Airbyte Cloud is deployed in a private cloud setup on AWS.

I did not purchase Airbyte Cloud through the AWS Marketplace, as I was already aware of Airbyte Cloud's capabilities and suggested using Airbyte Cloud for integration.

My advice for others considering Airbyte Cloud is that if you are a small business needing to integrate your data sources, such as running an e-commerce business, Airbyte Cloud is the tool to use due to its easy connectivity. If you are strategic about it, you can save on costs related to syncs while avoiding excessive engineering overhead.

My experience with Airbyte Cloud is that I recommended it to a client, and they are currently using it as a customer.

Airbyte Cloud is a strong tool for extracting and loading data from various sources to a data warehouse, which I am eager to leverage since I do not have to build my own custom extractors. It is a no-code platform, and you only need to ensure correct source syncing and orchestrate the runs. It is also easy to integrate with DBT Cloud for transformation and connect with data warehouses like Snowflake.

My main use case for Airbyte Cloud is managing an OLTP database. We had our OLTP database in Oracle, and we were moving daily data from Oracle to Redshift, which is our OLAP database, for our dashboarding and analytics requirement.

The best features Airbyte Cloud offers are the pipelines we are building, which are seamless and just a drag and drop, no-code, low-code platform. This is the most liked feature I have seen in Airbyte.

This definitely saves time and it is also easy for anyone to pick up the skill and develop the pipelines, impacting my daily workflow positively.

Airbyte Cloud has positively impacted our organization by making our daily tasks much easier. It helped with faster reporting and it saved the team's time to develop and build the ETL pipelines, which are specific outcomes and metrics I can share.

Currently, I do not have much feedback on which things Airbyte can improve. I do not have any needed improvements to add.

I have been using Airbyte Cloud for the past three years.

Airbyte Cloud is stable.

Airbyte Cloud is highly scalable, and we can scale it up whenever required on demand.

Customer support is decent.

Previously, I did not use any other solution; I was using Airbyte Cloud as the first thing.

My experience with pricing, setup cost, and licensing was a hassle-free experience.

Definitely, time is saved, indicating I have seen a return on investment.

We did not purchase Airbyte Cloud through AWS Marketplace.

I have explored Fivetran before choosing Airbyte Cloud.

I recommend Airbyte Cloud's self-hosted solution if needed, and only if you require the features of Airbyte Cloud, you can opt in for Airbyte Cloud. I gave this review a rating of eight.

My main use case for Airbyte Cloud is ELT data ingestion into Snowflake. I use Airbyte Cloud to extract data from Postgres and a few SaaS sources, then load it into Snowflake for analytics and reporting. The Postgres to Snowflake sync is the core pipeline, mostly for operational data replication. For SaaS sources, it is more for consolidating business data into a single warehouse so downstream data dashboards and queries can run consistently.

A typical example is our Postgres to Snowflake sync. We configure Airbyte Cloud to run scheduled syncs for specific tables that contain transactional data. Once set up, it runs automatically in batch mode, and I mainly monitor sync status and handle occasional schema changes or failed sync retries. On a day-to-day basis, it is not heavy manual work. Most of the time it involves checking that syncs are healthy, reviewing logs when something fails, and adjusting sync configurations if there are changes in source schema.

One significant benefit for us is reducing the amount of custom pipeline maintenance. Once the sync is configured in Airbyte Cloud, we do not have to maintain scripts or connectors ourselves, simplifying the process of keeping Postgres and Snowflake in sync reliably. It also helps with onboarding new data sources faster; instead of writing custom ingestion logic, we can set up a connector and validate the schema in Snowflake, which speeds up getting data into analytics.

The best features I found most useful were the large number of pre-built connectors, the managed scheduling for syncs, and the ability to monitor sync status and failures through the UI without needing to maintain infrastructure. The connector ecosystem is probably the biggest advantage, especially for Postgres and common SaaS tools. The scheduling and retry handling reduce the operational work, and the UI makes it easy to see failed syncs and debug issues without digging into manual logs.

The connector ecosystem has helped mainly by reducing engineering time when adding new sources. For example, when we needed to bring in additional Postgres tables and a couple of SaaS sources, we did not have to write custom ingestion logic. We could just configure existing connectors and focus on schema mapping in Snowflake. The UI made it easier to quickly see sync status and failures without digging into logs or infrastructure. If a sync fails, we can immediately see which connector has failed and roughly why, such as authentication issues or schema changes, and then decide whether to retry or adjust configuration.

My suggestion for improvement would mainly be around areas that could be enhanced: error debugging depth in the UI, more granular visibility into why a sync failed, and better handling or guidance around schema changes when they happen frequently in source systems. In some cases, having more proactive alerts or clearer recommendations when syncs fail would reduce the time spent manually checking logs. For teams with many connectors, better organization or filtering in the UI would help manage at scale.

I have been using Airbyte Cloud for around twelve to eighteen months, mainly in production workflows for moving data from Postgres into Snowflake, along with a few other SaaS and database sources.

In my experience, using it for scheduled Postgres to Snowflake syncs and a few SaaS sources, it has been generally stable for day-to-day use. There are occasional sync failures or schema-related issues.

Airbyte Cloud has handled our workloads well for scheduled syncs between Postgres and a few SaaS sources and Snowflake. We did not hit scaling limits in our usage pattern.

Most of the issues we encountered were handled internally through configuration adjustments, so we did not escalate cases to the support team.

Previously, we did not formally migrate from another dedicated ELT tool. Before Airbyte Cloud, the ingestion workflows were custom script-based. Airbyte Cloud replaced a set of in-house or script-driven pipelines instead of a commercial tool.

From my perspective as a user, the main benefit was reducing engineering time spent maintaining custom ingestion pipelines and lowering operational overhead around data syncs, which indirectly contributes to efficiency. I cannot quantify this in terms of financials, but the time saved on pipeline maintenance and troubleshooting was the most noticeable practical benefit.

I was not directly involved in the structured evaluation of multiple ELT tools. The move towards Airbyte Cloud was mainly driven by the need to reduce maintenance on custom ingestion pipelines and move to a managed solution.

My main advice would be to start small with well-defined use cases, such as a single Postgres to Snowflake pipeline, and validate reliability and schema handling before expanding to more sources. I would rate this solution an eight out of ten.