Alternatives to Navicat

There is no comparison between MySQL and HeidiSQL as MySQL is a database server and HeidiSQL is the client to communicate with the databases. Following are some links to help you understand 😊:

Databases: https://stackshare.io/mysql and https://stackshare.io/postgresql

Database Clients: https://stackshare.io/heidisql and https://stackshare.io/mysql-workbench

Happy coding

I'm learning SQL thru UDEMY and I'm trying to DL My SQL onto my machine, but when I get to the terminal, that's where I encounter my issues- nothing can be found. If I use SQLPro Studio for the course, is it better? I ask because MySQL WorkBench integrates with SQLPro Studio. I just want to get certified and start working again.

We have a 138 row, 1700 column database likely to grow at least a row and a column every week. We are mostly concerned with how user-friendly the graphical management tools are. I understand MySQL has MySQL WorkBench, and Microsoft SQL Server has Microsoft SQL Server Management Studio. We have about 6 months to migrate our Excel database to one of these DBMS, and continue (hopefully manually) importing excel files from then on. Any tips appreciated!

Which tools are preferred if I choose to work on more data side? Which one is good if I decide to work on web development? I'm using DBeaver and am now considering a move to AzureDataStudio to break the monotony while working. I would like to hear your opinion. Which one are you using, and what are the things you are missing in dbeaver or data studio.

When I joined NYT there was already broad dissatisfaction with the LAMP (Linux Apache HTTP Server MySQL PHP) Stack and the front end framework, in particular. So, I wasn't passing judgment on it. I mean, LAMP's fine, you can do good work in LAMP. It's a little dated at this point, but it's not ... I didn't want to rip it out for its own sake, but everyone else was like, "We don't like this, it's really inflexible." And I remember from being outside the company when that was called MIT FIVE when it had launched. And been observing it from the outside, and I was like, you guys took so long to do that and you did it so carefully, and yet you're not happy with your decisions. Why is that? That was more the impetus. If we're going to do this again, how are we going to do it in a way that we're gonna get a better result?

So we're moving quickly away from LAMP, I would say. So, right now, the new front end is React based and using Apollo. And we've been in a long, protracted, gradual rollout of the core experiences.

React is now talking to GraphQL as a primary API. There's a Node.js back end, to the front end, which is mainly for server-side rendering, as well.

Behind there, the main repository for the GraphQL server is a big table repository, that we call Bodega because it's a convenience store. And that reads off of a Kafka pipeline.

We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

I can't recommend it highly enough.