Gathr.ai Reviews

Summary: We used to run separate workflows for text-heavy and tabular data. Gathr.ai helped us unify that effort, saving time and enabling richer use cases like customer 360 and support analytics.

Positive: It allows us to work with structured data like ERP tables alongside unstructured sources like PDFs and logs in a single flow. That’s been a major enabler for our analytics, end-to-end.

Negative: Nothing significant. It integrates well with our diverse data landscape.

Summary: Most data processing tools lack the versatility and capabilities for GenAI use cases. But with Gathr.ai, we were able to readily build and manage multiple GenAI solutions. For instance, we’ve seamlessly leveraged data from Snowflake, CRM systems, and internal policy documents (brand guidelines, campaign SOP, and communication best practices), to create AI-driven, contextualized outreach campaigns.

Positive: When we started to prep our data for GenAI use cases, we faced many challenges. We needed a solution that could help us work with our structured and unstructured data at scale. We used Gathr.ai on a pilot basis, and it just clicked. With Gathr.ai’s ready-to-use connectors, GenAI building blocks, and low-code/no-code UI, we have successfully built and scaled multiple GenAI applications.

Negative: On the technology front, Gathr.ai is easily one of the most advanced and versatile products in the market today. So, no major complaints there. The only minor issue they can work on is providing more native connectors so that we don’t have to build new connectors from scratch in certain cases.

Summary: We had data scattered across sources and systems. With Gathr.ai, we orchestrated end-to-end pipelines, extracting structured information, transforming it, and combining with other datasets. This helped us unify fragmented data and accelerate analytics across multiple business functions.

Positive: Made it easy to ingest and process large volumes of structured and unstructured data—from databases like Oracle and HBase to docs and PDFs. It handled diverse data formats efficiently and streamlined transformations with a low-code interface and built-in operators.

Negative: While Gathr.ai offers a solid set of built-in connectors, one of our sources didn’t have native support. The team helped us build a custom connector quickly, so integration remained seamless.

Summary: It’s helped us eliminate fragmented workflows across marketing, sales, and operations. We’re able to ship analytics faster, with clearer definitions and a consistent understanding of data across teams.

Positive: Gathr.ai streamlines real-time ingestion, transformation, and metadata management—helping us maintain fresh, reliable data at all times. This has been essential for time-sensitive use cases like campaign analytics and operational alerts.

Negative: Nothing stands out—it’s been effective across our analytics and AI use cases.

Summary: Given the massive datasets we work with, entering metadata details manually for database objects was very time-consuming. We can now ingest and prepare our data much faster!

Positive: Gathr.ai’s built-in function automatically generates metadata descriptions for all tables and their columns. What’s more, you can easily enter instructions for querying each table. This is super helpful!

Negative: Looking for some more in-depth demos/tutorials, will reach out to the support team and see how it goes.

Summary: Gathr.ai is helping my team save a lot of engineering effort. By using NL to perform complex data transformation tasks, we’ve been able to expedite pipeline building for multiple downstream use cases.

Positive: Love that I can use simple, natural language prompts to search for relevant data assets, transform data, and even build pipelines.

Negative: Not really a lot of negatives so far. Been using Gathr.ai on a daily basis for 2+ years and still going strong.

Summary: With fewer pipeline failures and shorter debugging cycles, we can spend more time innovating and tackling certain use cases we didn’t have time for earlier. Also, reduces manual effort of creating data pipelines.

Positive: Before working with Gathr.ai, we had every kind of data issue—drift, late events. Gathr.ai made our pipelines resilient without overengineering.
Easy to create data engineering pipelines.

Negative: No deal breakers yet. There's could be more learning material available online though.
There must be learning videos or sessions on YouTube or on internal company's learning portal, so it may be easier to learn Gathr.ai for new user in a team.
Session connection can be made more faster without frequent disruption.

Summary: Setting up and maintaining data pipelines used to be time-consuming. With Gathr, I can quickly build, test, and deploy pipelines with minimal effort. GathrIQ (Gathr’s Data+AI Copilot) makes this even easier by allowing data transformation and pipeline creation through simple natural language prompts. Plus, Gathr’s fault-tolerant self-healing capabilities have improved pipeline reliability and reduced downtime significantly.

Positive: Gathr has exceeded my expectations. If I had to point out something, API syncing could be a bit smoother, but it’s a minor issue. Overall, there’s little to dislike.

Negative: Gathr has exceeded my expectations. If I had to point out something, API syncing could be a bit smoother, but it’s a minor issue. Overall, there’s little to dislike.

Summary: Gathr has helped free us from time-consuming, tedious, manual plumbing work across data integration, pipeline orchestration and maintenance. We are now able to focus more on analytics and uncovering insights.

Positive: Gathr’s data integration capabilities are outstanding. Three things that stood out for me and my team: #1 Handles both batch and streaming data at any scale. #2 Tracks data lineage in real-time, with visibility into each transformation step. #3 Offers an easy-to-use, drag-and-drop UI with built-in operators for smooth data preparation.

Negative: Even though the ingestion and integration process are smooth, setting up custom connectors requires a bit of additional effort. Also, if Gathr provided more templates, it would help analysts develop applications faster. Although, both are minor issues and not deal breakers!

Summary: Our team used to spend lot of time building and maintaining data workflows, which was less than ideal. With Gathr.ai we are able to harness our data the way we want to, reduce development time, and spend less cycles on fixing issues and maintaining pipelines. Same team is now focusing more on analytics and new-age AI initiatives.

Positive: Gathr.ai excels in handling large datasets and complex data workflows. Its ability to integrate and prepare data for analytics is second to none - not only it's super simple, but the pipelines have been running in prod, processing TBs of batch and real-time data on a daily basis, without any issues and performance bottlenecks.

Negative: So far, we haven’t found anything to complain about. With Gathr.ai we are able to harness our data the way we want to, reduce development time, and spend less cycles on fixing issues and maintaining pipelines.

Summary: It has streamlined our efforts required for building data pipelines significantly. Tasks that used to take months are now completed in days, boosting the overall productivity and saving us valuable time, resources and opportunity cost.

Positive: The intuitive visual interface is a game-changer for building data pipelines. It simplifies what used to be a tedious process, allowing us to design and deploy workflows much faster without complex coding.

Negative: Haven’t faced any real drawbacks so far. Gathr.ai has helped in streamlining our data pipeline efforts.

Summary: Gathr’s seamless handling of both batch and streaming data is truly unparalleled. Its in-built machine learning capabilities enable us to build regression models effortlessly, delivering deeper insights into customer behavior and enhancing our analytical capabilities.

Positive: Gathr’s ability to handle both batch and streaming data seamlessly is unparalleled. The in-built machine learning capabilities allow us to build regression models quickly, providing deeper insights into customer behavior.

Negative: The UI could be slightly more customizable to suit specific workflows, but it doesn’t detract much from the overall experience.

Summary: Our experience with Gathr has been highly positive. The platform excels in streamlining ETL processes and integrating diverse data sources with minimal latency. Its intuitive interface, real-time processing capabilities, and ability to handle terabytes of data daily have been instrumental in driving faster decision-making and improved business outcomes.

While there are some connectors still missing, the responsive team’s readiness to deliver custom solutions ensures that our unique needs are met. Additionally, Gathr's support for creating triggers and alerts based on ETL activities has enhanced our operational efficiency and responsiveness. Overall, Gathr has proven to be a robust and adaptable data processing solution.

Positive: Gathr excels in integrating data from diverse sources with minimal latency. Its intuitive drag-and-drop interface simplifies designing data pipelines, making it accessible even for non-technical users.

The platform’s capability to process terabytes of streaming data per day in near real-time has been a game-changer for us. The pre-built connectors saved us weeks of development effort.

For example, we had data scattered across multiple sources:

Large datasets in Oracle
Thousands of files in spreadsheets
Numerous CSV files
Gathr streamlined the process, enabling us to gain actionable insights in a single go. It’s a highly efficient and time-saving solution!

Negative: While Gathr excels in its offerings, there are still some connectors that are currently unavailable. However, the team is highly responsive and willing to develop custom solutions based on specific requirements, which is commendable.