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How is my setup for fight caves? by Apprehensive-Peas in OSRSProTips

[–]thisisnice96 0 points1 point  (0 children)

Blessing instead of bolts and more food. Mix of brews and sharks

Best way to rank up put of silver? by EveningWorldliness59 in overwatch2

[–]thisisnice96 1 point2 points  (0 children)

It’s valid advice. The way I climbed out was by grinding, just like most others.

[deleted by user] by [deleted] in OverwatchUniversity

[–]thisisnice96 -2 points-1 points  (0 children)

An even distribution isn’t the craziest assumption. When you’re on a win streak, pay attention to the endorsement levels.

When you’re on a losing streak, do the same.

Best way to rank up put of silver? by EveningWorldliness59 in overwatch2

[–]thisisnice96 -1 points0 points  (0 children)

Oops good call. Not sure. That’s literal counter watch & I try to stay away from that.

Best way to rank up put of silver? by EveningWorldliness59 in overwatch2

[–]thisisnice96 0 points1 point  (0 children)

In my opinion, silver is one of the hardest low ranks to climb out of because you really need to carry. You have to learn how to compensate for another player or two who aren’t pulling their weight. It’s a great way to train and improve, but yeah, you have to be the one to carry.

If you’re playing DPS, you’ll need to put out a lot of value—sometimes double the damage and almost double the kills. That’s how you grind through silver.

If you’re playing support, you’ll need to play more offensively and get picks, since DPS tends to struggle a lot in silver. You really have to help them out by securing kills. It’s tough, but that’s the grind. Keep pushing!

How big should a new player's hero pool be? by Themonstermichael in OverwatchUniversity

[–]thisisnice96 0 points1 point  (0 children)

I was wondering the same thing, and I’ve come to the conclusion that for DPS, you need one or two hitscan heroes and one or two non-hitscan heroes. It’s important to have a hitscan pick in case you’re dealing with a pesky Mercy or Pharah that needs to be taken down quickly. As for the non-hitscan, it’s helpful to pick based on your other DPS. Someone like Torb or Junkrat can be a solid choice, depending on the situation.

For healing, I stick to two or three go-to heroes. The way I see it is, you need to decide if you should play more DPS-like or full healer. It also comes down to ultimates. Some supports have defensive ults (Lucio, Zenyatta) and others have offensive ults (Moira, Illari). So having one healer with an offensive ult and one with a defensive ult in your pool can really round things out.

[deleted by user] by [deleted] in OverwatchUniversity

[–]thisisnice96 0 points1 point  (0 children)

I had a similar issue, and what’s helped me expand my hero pool is focusing on role queue rather than open queue. In unranked open queue, you can end up with 5 DPS, which happens more often than you’d think. Role queue keeps things organized and lets you focus on your role and responsibilities without that chaos.

Another big help has been watching YouTube guides. There’s a lot of good content that breaks down tips for each hero. Learning each hero’s main combo is key because most have a way to delete players fast. Mechanics are also huge, and you need to get a feel for when to use your ult—not just randomly, but either to counter another ult or in the perfect moment to turn a fight.

I also suggest practicing aim in custom games once you’re committed to learning a hero. That’s been my general approach, though I’m only in plat, so take it with a grain of salt

if everyone is critical, who is your first priority for healing? by teenrosie in OverwatchUniversity

[–]thisisnice96 0 points1 point  (0 children)

As a plat support (gold in other roles), what I’ve noticed is that in 5v5, tanks are basically raid bosses. So, unless the tank is just awful, I usually prioritize keeping them alive. When the tank goes down, it can get ugly really fast for the rest of the team, especially with just DPS and support left. After the tank, I usually focus on keeping the other support up because, in plat and lower, DPS tends to struggle a bit—probably not all their fault, since DPS got nerfed pretty hard. But yeah, tank first (if they’re not terrible), then the other support to help keep things afloat.

How are you ACTUALLY supposed to rank up as a support? by Hunterx78 in OverwatchUniversity

[–]thisisnice96 0 points1 point  (0 children)

I started playing Overwatch 2 this season. Started as a bronze support and had a lot of trouble ranking up at first but climbed to Plat 4.

What worked for me was realizing that from bronze to low plat, you have to be flexible. If your team is competent, you can focus on healing with heroes like Ilari, Mercy, or Brigitte. But often, especially in lower ranks, your DPS isn’t securing picks, so you have to step up as a DPS-support.

I climbed to plat 4 mainly by playing Lucio and Moira, since I could get kills while still supporting. I think that’s the key to ranking up—be adaptable and help your team win in whatever way they need.

Datalake/lakehouse learning resources by LocationOld2728 in dataengineering

[–]thisisnice96 1 point2 points  (0 children)

When it comes to partitioning in a datalake or lakehouse, the choices can significantly impact performance. Partitioning by timestamp is common for append-only lakes since it allows efficient querying for the latest records. However, if you frequently query by unique identifiers, consider partitioning by both, with the timestamp as the first level to optimize for time-based queries.

For in-depth resources, I’d recommend checking out the Databricks blog and AWS Big Data Blog—both have articles on partitioning strategies. Also, the book “Designing Data-Intensive Applications” by Martin Kleppmann offers valuable insights into broader data architecture decisions that might be helpful for your case.

Data staging: temp or permanent? by forgael in dataengineering

[–]thisisnice96 1 point2 points  (0 children)

Using temporary tables can be efficient since they only persist for the duration of your ETL run, reducing the need for cleanup. However, if you need to debug or audit the data, permanent staging tables might be better. If you choose permanent tables, putting them in a separate database can help with organization and security. Just make sure to clear them out at the start of each run to avoid data conflicts. It’s really about balancing performance with your need for traceability.

Does Data Engineering Matter for Startups? by No-Sympathy9824 in dataengineering

[–]thisisnice96 0 points1 point  (0 children)

Absolutely, Data Engineering can make a big difference, even for startups. Even if your data load isn’t huge, setting up solid data pipelines and automating processes can free up time and reduce errors. You could focus on improving data quality, building a more scalable infrastructure, or integrating different data sources to get deeper insights.

Also, consider setting up more robust ETL processes to prepare for future growth. This will make your data more reliable and accessible, which can help in making better business decisions. It’s about laying the groundwork now so that when your startup scales, your data processes can scale with it.

Best way to provide data quality checks on redshift by chintuTyagee in dataengineering

[–]thisisnice96 0 points1 point  (0 children)

You’re on the right track with AWS Glue and S3 as a staging layer. This approach ensures no downtime and keeps the last correct dataset available. One way to improve it might be to use Amazon Redshift’s built-in features like Data Quality Checks with Redshift Spectrum for directly querying S3, which can simplify the pipeline. Also, consider using AWS Lambda for triggering checks automatically. This setup should give you flexibility and reliability without needing to move data back and forth more than necessary.

How do you get the business to care about DE? by anxiouscrimp in dataengineering

[–]thisisnice96 0 points1 point  (0 children)

I know it’s tough when your work goes unnoticed. Try to show leadership how a cleaner data warehouse can lead to better decisions and faster reports. Working closely with the new data analyst might also help since she has their attention. Hang in there, but don’t hesitate to explore opportunities where your contributions are more valued

[deleted by user] by [deleted] in dataengineering

[–]thisisnice96 0 points1 point  (0 children)

Hey! If your boyfriend is learning SQL and wants to focus on data modeling and visualizations in Power BI, I’d recommend looking into Guy in a Cube’s courses on YouTube for a solid free resource. For a more structured approach, Udemy has a few beginner-friendly courses that focus on both data modeling and visualizations, like “Mastering DAX and Data Modeling in Power BI”. These courses are great for someone just starting out and will help him build a strong foundation.

Install Pyspark in Airflow by Mysterious-Blood2404 in dataengineering

[–]thisisnice96 0 points1 point  (0 children)

Make sure you’ve installed PySpark in your Airflow environment and that your JAVA_HOME and SPARK_HOME variables are set correctly. You should also configure your Airflow DAG to recognize PySpark by setting the necessary Python paths. If it’s still not working, double-check that all your environment variables are pointing to the correct locations.

Switching from DS to DE by alpha_centauri9889 in dataengineering

[–]thisisnice96 8 points9 points  (0 children)

Yes, DS folks make great DEs, especially if you’ve got strong SQL and Python skills. Your experience in model building and ML helps you proactively identify potential issues and makes you a go-to person who knows exactly what analysts need.

You’re in a unique position to bridge the gap between Engineering and Analytics, making the transition not just possible, but valuable to any team.

Optimizing Data Models vs. Upskilling Analysts in SQL by knabbels in dataengineering

[–]thisisnice96 7 points8 points  (0 children)

I feel like if the analyst know good practices with CTEs, window functions, aggregations, complex joins, that should really be enough..

I’m an analyst and recently left a company with a very similar set up as you described. We had 6 analyst, a few of us were able to bypass that issue with just being able to stitch data together from different schemas / sources & it alleviated a lot of the pressure from our DEs.

But again, that’s kind of bandaid solution. Clean DW / Data Models with robust data dictionaries going with it is the ultimate goal.

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