TetraScience R&D Data Cloud.

We were using TetraScience  for data lake purposes. We also utilized the ingestion engine and employed it for some data transformation.

The ingestion engines were pretty good. The data lake storage paradigm was also efficient, though not necessarily the search capabilities.

While functional during ingestion workflows, the automation toolkit required manual processes. Challenges included identifying how to get users to store data appropriately and segregating files for long-term storage. Attaching the appropriate metadata to files was not straightforward since it wasn't part of a traditional user workflow. Some connectors for specific instruments were difficult to work with and weren't available out of the box.

I used TetraScience  for two and a half years.

There were no stability issues with TetraScience.

We were a relatively small company, so we did not encounter any scalability issues. However, this doesn't imply that TetraScience is free of scalability issues; we just didn't experience any at our scale.

The support team was responsive and a good company to work with overall. I would rate their support as seven out of ten. There have been some communication difficulties, mostly concerning the business side of our implementation.

Neutral

We did not use a different solution before TetraScience; it was our first data lake.

The initial setup was like a large project typical of large enterprise systems. These issues were mainly during the initial deployment or initial setup.

Implementation services are provided, and you need to bring in implementation engineers to support it. It is not a plug-and-play solution. Additionally, a project manager may be needed due to the organizational impact of such a large project.

TetraScience is moderately expensive but not the most expensive enterprise software. It is cheaper than Veeva or BenchLink. They were willing to work with us regarding costs.

I would recommend TetraScience conditionally. It depends on the user's needs and their ability to understand some of the limitations of what can be done out of the box.

I'd rate the solution seven out of ten.

TetraScience  is a platform that integrates instruments into a laboratory environment into other software applications that can help leverage the data. In most pharma companies, the application is used to automate large-scale projects within labs that use either GXP or GMP. We have used it mainly to create projects to categorize data, extract metadata from instruments like LCMSs, HPLC, ILITs, and genomic sequencing, and link that to a variety of applications like ELN, LIMS, and archival applications.

It helps the company get data off of instruments without having to really touch anything. It provides more FAIR capabilities: findable, accessible, interoperable, and reusable. This results in less hands-on and more automated features that really help in a lab setting.

TetraScience  has connectors that allow for data to be moved from server to TetraScience's AWS  backend, which has been extremely helpful. The crawler agents they provide, as well as TetraScience exclusive parsers, allow for specific instruments that we use in our labs with proprietary formats to extract data and put it into more standard formats for various purposes.

The application has a difficult-to-use parsing capability, which requires a lot of reengineering when the use case isn't specifically met. The application also lacks capabilities within its terminal commands that are not available in their GUI. It requires a lot of configurability, which could be streamlined for an enterprise application user.

The company has been using TetraScience for about five years, and I have been using it in my role for about two years. In a previous company, I used it for an evaluation for about four or five months.

We haven't had too many issues with stability. We are within the scope of the application usage.

It is very scalable with a lot of capabilities for scaling to other instruments and labs. However, there is a huge learning curve, which limits the timelines for scaling.

We work hand in hand with their support and customer service. They are good, responsive, and help us get things done.

Neutral

The initial setup was a little difficult to do with AWS infrastructure, but it was mainly longer than expected due to our organization's slower timelines.

I would approach with caution. The platform has a high knowledge gap and the proprietary nature of its parsers and crawling agents. Before approaching TetraScience, have your use case in hand and understand the scope of the lab, instruments, data importation, and connectivity. Go to them with a solid project plan before implementation, as they are not a one-stop shop but rather a niche type of company with both benefits and challenges in automation.

I'd rate the solution six out of ten.