Benchling vs. Teradata Vantage

Benchling is especially well suited to groups or contexts where there are many users who do not have a coding background but need a seamless and structured approach to data. Benchling is particularly useful in cases where there are data flows from instruments and other devices where the data can be deposited in an automated fashion. It is likely less appropriate or useful to users who are just looking for a general data warehouse solution.

Incentivized

Teradata Vantage is well suited for large scale ETL pipelines like the ones we developed for anti money laundering risk matrices. It handles heavy joins, aggregations, and transformations on transactional data efficiently. We generate alert variables, adjust for inflation, and monitor establishments monthly with it, all integrated with Python and Control-M for a centralised automation across the company. For less appropriate, I would say that heavy resource demands might slow down experimentation for iterative work.

• Excellent easy to use and beautiful UI
• Great customer support and training
• Has no code ways of retrieving and depositing data

Incentivized

• ETL (Extract - Transfor - Load)
• NOS to send data from Teradata Vantage to S3 and from S3 to Teradata Vantage
• Teradata GeoSpacial feature
• Bulk reading and writing in huge tables
• MPP capacity already mature
• Temporal Capacity more mature that other solutions
• TASM

Incentivized

• Some of the integrations can be a bit spotty so it depends on what kind of data source you are integrating
• Sometimes new users are not always aware of all the various functionality that Benchling has - can do better to provide more user awareness of more complex features

Incentivized

• Teradata can improve by supporting more native AWS cloud features. Currently if a node goes down the EC2 instance must be restarted. It isn't something that happens frequently but more tight integration with cloud providers like AWS and Azure will allow Teradata to offer truly dynamic scaling.
• Some Teradata features are oversold before they are ready for prime-time. Teradata is not unique in this but if something is sold as an integrated product stack it should really be integrated not something that requires an extensive development cycle to be integrated at a customer's expense. If something is supported it should've really be tested and QAed thoroughly before a customer touches it.

Incentivized

Teradata is a mature RDBMS system that expands its functionality towards the current cloud capabilities like object storage and flexible compute scale.

Incentivized

Teradata Vantage allows us to create a scalable infrastructure to support our strategic initiatives. The dedicated compute power ensures reliable performance with isolated workloads and dedicated resources, optimizing workflows for faster, more efficient data transfers. The compute clusters support ETL processes and OSF’s developers and data science team with the flexibility to create self-service analytics, to spin up/down at any time, driving better performance and minimizing costs.

We have meetings at the beginning with the technical team to explain our requirements to them and they were really putting in a lot of effort to come up with a solution which will address all our needs. They implemented the software and also trained a few of our resources on the same too. We can get in touch with them now as well whenever we run into a roadblock but it's very less now.

Incentivized

Benchling was much more of a full stack solution and provide much more features that were relevant to the group. Airtable was more of a generic way to manage large amounts of data, but the complexity was still high for the types of data that would be need to be managed and there would need to be some workarounds. Overall Benchling was selected since it also had an electronic lab notebook feature which was very useful to associates in addition to its data workflows.

Incentivized

Teradata is way ahead of its competitor because of its unique features of ensuring data privacy and data never gets corrupted even in worst case scenario. In most cases, the data corruption is a major issue if left unused and it leads to important data being wiped off which in ideal case should be stored for 3 years

Incentivized

• It had a positive ROI in terms of reducing the amount of time spent on data movement and curation by associates
• It had a positive ROI in terms of increasing the number of insights from structured data
• It reduced the number of data entry and analysis errors by associates which led to a positive ROI in terms of efficiency and reducing time wasted by tracking down errors in data

Incentivized

• Teradata is been absolutely phenomenal for our project because we feed huge chunks of data to it and get back the desired results in no time which earlier used to take hours to process and then also sometimes timeout.
• We don't have to do any manual intervention for resource or task allocation, it is all taken care by Teradata internally and all the AMP's are given equal amount of work and have their own resources to complete them with no sharing with another.

Incentivized