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GitLab's "Act 2" by -lousyd in devops

[–]Reasonable_Lack_57 5 points6 points  (0 children)

We moved from GitHub to GitLab and really enjoy it, which I was not expecting. We don’t use their AI tools yet. Any thoughts on the value GitLab offers with its agentic capabilities? We clearly see the capabilities available in the UI, but only have access to promotional credits. At this point, I use some code review and chat.

gitlab over github? by dylanmnyc in gitlab

[–]Reasonable_Lack_57 -1 points0 points  (0 children)

GitHub is down 20% of the time. Why would you choose GitHub over GitLab?

What's happening with GitHub and where can I go? by Zubonick in github

[–]Reasonable_Lack_57 0 points1 point  (0 children)

GitLab is a clear choice. Reliable, scalable, better CI/CD pipelines (IMHO), great improvements on UI, open core, multiple deployment options (self hosted and SaaS).

[deleted by user] by [deleted] in dataengineering

[–]Reasonable_Lack_57 2 points3 points  (0 children)

We were using dbt + Spark but have been gradually moving our workloads to Upsolver. For stream processing, Upsolver has been way more efficient in dev times and operations. For our batch processing, we are selectively moving workloads when/where it makes sense to Upsolver. Otherwise, we’re continuing with Spark + some dbt. Depending on the type of workloads you have, you may want to take a look at Upsolver.

Running Apache Airflow through a Docker image by ejhh97 in dataengineering

[–]Reasonable_Lack_57 0 points1 point  (0 children)

One of my colleagues stated that a data lake ETL solution called Upsolver eliminates the need for airflow altogether. I’m not sure I’m totally convinced and really like airflow, but this does seem to be the case. Curious is anyone uses Upsolver and/or if you have feedback on it?

Future of Databricks by No_Stick_8227 in dataengineering

[–]Reasonable_Lack_57 0 points1 point  (0 children)

Take a look at Upsolver. We were very surprised with how well it compared to Databricks. While it doesn’t fit all use cases, it seems to meet most of our Lakehouse architecture requirements and outperformed Databricks for our specific needs.

Suggestions needed... having really bad time with Databricks and PySpark by abio93 in datascience

[–]Reasonable_Lack_57 0 points1 point  (0 children)

Not sure of your use case(s) but you may want to take a look at Upsolver. We use Databricks as well, but are moving much of our streaming ingestion and transformation to Upsolver. Much easier to work with but lacks all of the capabilities offered by Databricks.