G2

Nijat I.

Full-stack Developer

Information Technology and Services

Small-Business (50 or fewer emp.)

5/3/2026

"Teradata Vantage Excels at Big Data Processing and Advanced Analytics"

4.5/5

What do you like best about Teradata Autonomous Knowledge Platform?

Speed on large datasets is the main reason we use it. We run analytical queries over tens of millions of rows daily. Before Teradata, those queries would time out or take twenty minutes. Now most finish in a few seconds.

The parallel processing handles volume differently than regular databases. Adding more nodes actually scales performance, so growth doesn't mean slowdowns. We've doubled our data volume in two years and query times stayed the same.

Being able to run Python analytics where the data lives saves us a ton of time. Our data science team used to extract data, move it to separate tools, run models, then bring results back. Now they write scripts that execute inside Teradata. No moving data around, no sampling, no waiting. Review collected by and hosted on G2.com.

What do you dislike about Teradata Autonomous Knowledge Platform?

The learning curve is steep. The platform has so many features that new team members spend weeks getting comfortable. It's not something you hand to a junior analyst on their first week. One new hire told me it felt like learning a new language.

Pricing is high compared to cloud alternatives. We looked at Snowflake and BigQuery recently. Both were cheaper for our usage patterns. But migrating off Teradata would be a massive project, so we're staying for now.

Documentation could be clearer. When we run into issues, finding answers sometimes means digging through support case comments instead of published docs. The community forums aren't as active as I'd like.

Query tuning for complex SQL is non-trivial. The explain plans aren't always easy to interpret, and optimization often requires deep expertise. Our best analyst spends a lot of time on this. Review collected by and hosted on G2.com.

What problems is Teradata Autonomous Knowledge Platform solving and how is that benefiting you?

Before Teradata, our analytical queries on large datasets were painfully slow. Reports that business users needed by morning sometimes arrived in the afternoon. The CFO once waited two hours for a revenue breakdown by region. Teradata solved that speed problem.

It also solved the data silo issue. Different teams kept copies of data in different places. Finance had one total for Q3 sales, marketing had another. Nobody knew which was right. Meetings started with "whose number are we using?" Now one central repository means everyone works from the same source.

For our analytics team, building models directly in the database without extracting data first cut development time significantly. What used to take days of data movement and transformation now happens in hours. We've launched three new predictive models this year that wouldn't have been possible before.

The platform also helped us scale. As our data volume grew year over year from various new data sources, performance didn't collapse. We added more departments and more use cases without rebuilding everything from scratch. That alone has saved us from a major re-architecture project. Review collected by and hosted on G2.com.