What I really like about Starburst is how much better it makes managing our scattered data. And we’ve integrated it with S3 and Snowflake to move data in a way that’s not manually moving data. This is incredibly user friendly, and enables us to run SQL queries with minimal hassle which saves us so much time. And just its broad feature set one in particular is that its real time queries ability also keeps our work flows efficient and responsive. Review collected by and hosted on G2.com.

Starburst’s biggest drawback, for small teams that are scaling, is the cost. It’s also a little more technical to setup and configure than some teams have readily available expertise for. Some of our projects have slowed down due to these challenges as we become more dependent on IT help. While it’s well integrated it can be more complex from a technical perspective so onboarding new team members can take longer and be more difficult. Review collected by and hosted on G2.com.

My all time favorite thing about Starburst is its ability to rapidly query data from multiple sources. Through integration with tools like AWS, Google Cloud and Snowflake we can run complex queries without having to move the data around. Intuitive interface makes it easy for teammates of disparate technical levels to get going. This is fast – perfect for querying a huge dataset that has made our data analysis process very efficient. Review collected by and hosted on G2.com.

Starburst is not without its flaws – it can take quite a while to learn during the setup phase making it a very steep learning curve. Everything needs to be configured correctly and it might be difficult for smaller teams to manage. Also its pricing tends to be difficult for smaller businesses with limited budgets since as the query volume increases, the costs rise further, rendering it less budget friendly for teams that want to scale cheaply. Review collected by and hosted on G2.com.

My favorite thing about Starburst is its awesomeness at querying data from multiple sources without having to move or transform data into a central repository. This saves us huge amounts of time and resources. It works with platforms like Tableau and Apache Kafka thus making our data visualization and real time analysis. We have been able to quick query large datasets with its efficient distributed query engine, increasing our team’s productivity. Review collected by and hosted on G2.com.

A disadvantage of Starburst is that execution often requires resources in some cases it affects the general performance of the system particularly when retrieving data on large scale. This has caused some delay at times that hampers quick analysis primarily during high traffic usage occasions. Also, to set it up and fine tune it for sophisticated searches needed some real heavy work and knowledge, which could sometimes be a problem for teams without applied data scientists. Review collected by and hosted on G2.com.

Starburst Insight to monitor all the admin activities.

BIAC feature which helps us to completely move out from Ranger dependencies for access control mechanism.

Superfast for processing large HDFS datasets compared to all other available mediums like Hive engine, etc.

NoSQL databases can be read in an SQL manner which helps us to derive better reports.

Push-down enable/disable feature which helps to decide if workload should be executed at the Trino engine or host engine based on benchmark results.

Easy setup & upgrade.

Very easy to implement.

Easy to integrate with any THP (Third party tool).

Customer Support is very helpful and easily approachable. Review collected by and hosted on G2.com.

The only thing I can think of regarding ETL-related data pipeline work execution is that it is still not very feasible in Starburst. Review collected by and hosted on G2.com.

I really appreciate how Starburst keeps everything very simple to access and analyze data from all the different data sources and it’s all in real time. Integrating with cloud data warehouses like AWS Redshift and Google BigQuery means that we can work faster and can concentrate on figuring out insights rather than connecting. It enhanced our overall data, from the duplication to moving to data, and overall data workflow and productivity. Review collected by and hosted on G2.com.

Starburst has great functionality and whilst lots of people find it helpful, it has some performance issues when dealing with very large datasets. And setting everything up was more complex a challenge than anticipated, especially so when I considered integrating all the data sources. Our analysis was slowed down by the fact that at times the software lagged when running complex queries. Also, it can sometimes be difficult to resolve performance issues if not someone with knowledge on the system. Review collected by and hosted on G2.com.

Really appreciate the quick turnaround time for resolving any issues. Review collected by and hosted on G2.com.

The pricing is much on the higher side. Plus when it comes to renewal the annual escalation is high. Review collected by and hosted on G2.com.

Super easy to manage.

Good query editor.

Responsive autoscaling with good performance in query execution.

Support for different datasources and federated query execution.

Great customer support.

Easy to implement and start using starburst galaxy. Review collected by and hosted on G2.com.

Catalog features are still basic and lack certain functionalities.

Its a lot of manual effort to maintain access policies as we have to add each schema/table while creating policies.

We are missing AI features to convert text to queries, optimize certain queries etc. Review collected by and hosted on G2.com.

When it comes to what I like most about Starburst, I believe that one of the greatest benefits is the ability to run highly complicated queries on many data sources simultaneously, and the fact that it can do it without having to shuffle the information. This makes our analysis work much easier. Integrating with reporting tools like Tableau improves our reporting by adding rich visualization on top of the provided data. The ability to quickly and effectively process complex questions further enhances the value of detailed data analysis. Review collected by and hosted on G2.com.

The only issue that may be associated with using Starburst is the relatively high cost structure that may not be sustainable for teams and businesses of a moderate size. Due to this, it becomes quite challenging to justify the cost against the benefits that come with using this device. Additionally, although it is robust in many ways, there is always the issue of slow query speed when dealing with very large datasets. This occasional lag can cause havoc for data analysis and decrease the overall efficiency. Review collected by and hosted on G2.com.

In my perspective, probably one of the potent aspects of Starburst is its powerful query engine since it offers broad queries for big streams of data and inquires from many sources. It has been quite useful to compile data from a number of data bases into one and easy to analyze from the same platform. Since it interconnects with data warehouses and BI tools, we can generate vast reports and analyses without moving much information. This has enhanced our decision making as well as operations in general. Review collected by and hosted on G2.com.

Starburst eliminates the issues associated with data discovery and management for large scale analytics and queries since it is a single platform that serves as a powerful data processing tool. It has been possible to create summative reports and analyses that would have otherwise required amalgamation of data across various systems. Consequently, our team can quickly make more effective decisions, thus improving the general team performance and strategy development. Review collected by and hosted on G2.com.

I think the most useful function of Starburst is its capacity to query several sources at once. What makes it stand out is that it excels particularly well with big data, which eventually allows for quicker processing. How it connects to other applications such as Tableau for dashboards and Apache Kafka for real time data feeding improves our total effectiveness. I have found this unified approach to data management as a marked improvement in our analytical functions. Review collected by and hosted on G2.com.

The main downside is that Starburst may become resource intensive, which results in some implications for performance with very large data sets. This can hamper our analysis and the overall productivity of the workflow. Furthermore, system setup and configuration is not very intuitive and this has made initial training time long for new users. Sometimes these issues have led to a decline in our productivity and more effort was needed to handle them. Review collected by and hosted on G2.com.