In this digital world, people prefer to work remotely. There are numerous tasks that each of us goes through every day. If I talk about an application's usage, then it has been predicted that there will be a 25% increase in global app downloads between 2018 and 2022.
But it can be quite challenging for organizations to maintain and create multiple passwords. Hence, a password management software can be a great choice to manage this effectively. It is like your bank locker, where one can store numerous passwords. The users only need a master password to access the software.
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The Big Data Tools plugin seamlessly integrates HDFS into your IDE and provides access to different cloud storage systems (AWS S3, Minio, Linode, Digital Open Space, GS, Azure). But is this the end? Have we implemented everything and now progress has stopped? Of course not.
In this short digest, we'll take a look at 15 popular distributed file systems available on the market and try to get a sense of their individual advantages.
Almost all of these systems are free or open-source, and you can find the sources on GitHub. The sites of these projects, their documentation, and online reviews provide most of the information we’ll consider here. Other than HDFS, none of these technologies have been implemented yet in Big Data Tools. But who knows? Perhaps someday we'll see them in our plugin.
As you know, we regularly post new content on programming and various interesting events from our company's life on our blog. The range of topics is expanding, and the number of articles is growing, so at some point, we started to attach tags to our posts so that you could quickly find content on topics you're interested in. Just click the appropriate tag and – presto! – a sorted list of related articles appears.
Many of us grew up reading a classic, that was a staple of many home libraries. However, it doesn’t seem to be as prominent as it once was. To understand the ongoing shift in tech coverage, we need to explore its roots.
Each game has data that game-designers work with. In RPG there is a database of items, in match-3 — the cost in the crystals of tools from the store, in action — hit points, for which medical kit heals.
There are many ways to store such data — someone stores it in tables, in XML or JSON files that edit with their own tools. Unity provides its own way — Scriptable Objects (SO), which I like because you don't have to write your own editor to visualize them, it's easy to make links to the game's assets and to each other, and with Addressables this data can be easily and conveniently stored off-game and updated separately.
In this article I would like to talk about my SODatabase library, with which you can conveniently create, edit and use in the game (edit and serialize) scriptable objects.
EAP 11 of the Big Data Tools plugin for IntelliJ IDEA Ultimate, PyCharm, and DataGrip is available starting today. You can install it from the JetBrains Plugin Repository or inside your IDE.
Big Data Tools is a new JetBrains plugin that allows you to connect to Hadoop and Spark clusters and monitor nodes, applications, and jobs. It also brings support for editing and running Zeppelin notebooks inside IntelliJ IDEA and DataGrip, so you can create, edit, and run Zeppelin notebooks without ever having to leave your favorite IDE. The plugin offers smart navigation, code completion, inspections, quick-fixes, and refactoring inside notebooks.
Before we start, I'd like to get on the same page with you. So, could you please answer? How much time will it take to:
It will take longer than you expect. I will explain why.
rotor is a non-intrusive event loop friendly C++ actor micro framework, similar to its elder brothers like caf and sobjectizer. The new release came out under the flag of pluginization, which affects the entire lifetime of an actor.
Zeppelin is a web-based notebook for data engineers that enables data-driven, interactive data analytics with Spark, Scala, and more.
The project recently reached version 0.9.0-preview2 and is being actively developed, but there are still many things to be implemented.
One such thing is an API for getting comprehensive information about what's going on inside the notebook. There is already an API that completely solves the problems of high-level notebook management, but it doesn’t help if you want to do anything more complex.
This post is handling the following situation - how to setup up simple Mysql services with group replication being dockerized. In our case, we’ll take the latest Mysql (version 8.x.x)
FYI: all mentioned code (worked and tested manually) located here.
I will skip not interested steps like ‘what is Mysql, Docker and why we choose them, etc’. We want to set up possibly trouble proof DB. That’s our plan.
