Alternatives to DBeaver logo

Alternatives to DBeaver

HeidiSQL, DataGrip, DbVisualizer, TablePlus, and MySQL WorkBench are the most popular alternatives and competitors to DBeaver.
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What is DBeaver and what are its top alternatives?

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.
DBeaver is a tool in the Database Tools category of a tech stack.
DBeaver is an open source tool with 40.7K GitHub stars and 3.5K GitHub forks. Here’s a link to DBeaver's open source repository on GitHub

Top Alternatives to DBeaver

  • HeidiSQL
    HeidiSQL

    HeidiSQL is a useful and reliable tool designed for web developers using the popular MariaDB or MySQL server, Microsoft SQL databases or PostgreSQL. It enables you to browse and edit data, create and edit tables, views, procedures, triggers and scheduled events. Also, you can export structure and data, either to SQL file, clipboard or to other servers. Read about features or see some screenshots. ...

  • DataGrip
    DataGrip

    A cross-platform IDE that is aimed at DBAs and developers working with SQL databases. ...

  • DbVisualizer
    DbVisualizer

    It is the universal database tool for developers, DBAs and analysts. It is the ultimate solution since the same tool can be used on all major operating systems accessing a wide range of databases. ...

  • TablePlus
    TablePlus

    TablePlus is a native app which helps you easily edit database data and structure. TablePlus includes many security features to protect your database, including native libssh and TLS to encrypt your connection. ...

  • MySQL WorkBench
    MySQL WorkBench

    It enables a DBA, developer, or data architect to visually design, model, generate, and manage databases. It includes everything a data modeler needs for creating complex ER models, forward and reverse engineering, and also delivers key features for performing difficult change management and documentation tasks that normally require much time and effort. ...

  • MySQL
    MySQL

    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software. ...

  • PostgreSQL
    PostgreSQL

    PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions. ...

  • MongoDB
    MongoDB

    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding. ...

DBeaver alternatives & related posts

HeidiSQL logo

HeidiSQL

152
6
GUI client for MariaDB, MySQL, Microsoft SQL Server and PostgreSQL
152
6
PROS OF HEIDISQL
  • 1
    Client application which is lightweight
  • 1
    Easy configuration
  • 1
    Keep queries after execution
  • 1
    Connect to multiple servers on same client
  • 1
    Run multiple queries simultaneously
  • 1
    Multiple query tabulations
CONS OF HEIDISQL
  • 1
    Mac OS/ Linux incompatible

related HeidiSQL posts

Muhammad Waleed
Community and Content Operations at StackShare · | 3 upvotes · 491.6K views
Shared insights
on
HeidiSQLHeidiSQLMySQLMySQL

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

See more
DataGrip logo

DataGrip

564
17
A database IDE for professional SQL developers
564
17
PROS OF DATAGRIP
  • 4
    Works on Linux, Windows and MacOS
  • 3
    Code analysis
  • 2
    Diff viewer
  • 2
    Wide range of DBMS support
  • 1
    Generate ERD
  • 1
    Quick-fixes using keyboard shortcuts
  • 1
    Database introspection on 21 different dbms
  • 1
    Export data using a variety of formats using open api
  • 1
    Import data
  • 1
    Code completion
CONS OF DATAGRIP
    Be the first to leave a con

    related DataGrip posts

    DbVisualizer logo

    DbVisualizer

    27
    0
    Universal database tool for developers, DBAs and analysts
    27
    0
    PROS OF DBVISUALIZER
      Be the first to leave a pro
      CONS OF DBVISUALIZER
        Be the first to leave a con

        related DbVisualizer posts

        TablePlus logo

        TablePlus

        174
        11
        Easily edit database data and structure
        174
        11
        PROS OF TABLEPLUS
        • 5
          Great tool, sleek UI, run fast and secure connections
        • 3
          Free
        • 2
          Perfect for develop use
        • 1
          Security
        CONS OF TABLEPLUS
          Be the first to leave a con

          related TablePlus posts

          MySQL WorkBench logo

          MySQL WorkBench

          380
          28
          A unified visual tool for database architects, developers, and DBAs
          380
          28
          PROS OF MYSQL WORKBENCH
          • 7
            Free
          • 7
            Simple
          • 6
            Easy to use
          • 5
            Clean UI
          • 3
            Administration and monitoring module
          CONS OF MYSQL WORKBENCH
            Be the first to leave a con

            related MySQL WorkBench posts

            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.

            See more
            Kelsey Doolittle

            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!

            See more
            MySQL logo

            MySQL

            125.4K
            3.8K
            The world's most popular open source database
            125.4K
            3.8K
            PROS OF MYSQL
            • 800
              Sql
            • 679
              Free
            • 562
              Easy
            • 528
              Widely used
            • 490
              Open source
            • 180
              High availability
            • 160
              Cross-platform support
            • 104
              Great community
            • 79
              Secure
            • 75
              Full-text indexing and searching
            • 26
              Fast, open, available
            • 16
              Reliable
            • 16
              SSL support
            • 15
              Robust
            • 9
              Enterprise Version
            • 7
              Easy to set up on all platforms
            • 3
              NoSQL access to JSON data type
            • 1
              Relational database
            • 1
              Easy, light, scalable
            • 1
              Sequel Pro (best SQL GUI)
            • 1
              Replica Support
            CONS OF MYSQL
            • 16
              Owned by a company with their own agenda
            • 3
              Can't roll back schema changes

            related MySQL posts

            Nick Rockwell
            SVP, Engineering at Fastly · | 46 upvotes · 4.1M views

            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.

            See more
            Tim Abbott

            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.

            See more
            PostgreSQL logo

            PostgreSQL

            98.3K
            3.5K
            A powerful, open source object-relational database system
            98.3K
            3.5K
            PROS OF POSTGRESQL
            • 764
              Relational database
            • 510
              High availability
            • 439
              Enterprise class database
            • 383
              Sql
            • 304
              Sql + nosql
            • 173
              Great community
            • 147
              Easy to setup
            • 131
              Heroku
            • 130
              Secure by default
            • 113
              Postgis
            • 50
              Supports Key-Value
            • 48
              Great JSON support
            • 34
              Cross platform
            • 33
              Extensible
            • 28
              Replication
            • 26
              Triggers
            • 23
              Multiversion concurrency control
            • 23
              Rollback
            • 21
              Open source
            • 18
              Heroku Add-on
            • 17
              Stable, Simple and Good Performance
            • 15
              Powerful
            • 13
              Lets be serious, what other SQL DB would you go for?
            • 11
              Good documentation
            • 9
              Scalable
            • 8
              Free
            • 8
              Reliable
            • 8
              Intelligent optimizer
            • 7
              Transactional DDL
            • 7
              Modern
            • 6
              One stop solution for all things sql no matter the os
            • 5
              Relational database with MVCC
            • 5
              Faster Development
            • 4
              Full-Text Search
            • 4
              Developer friendly
            • 3
              Excellent source code
            • 3
              Free version
            • 3
              Great DB for Transactional system or Application
            • 3
              Relational datanbase
            • 3
              search
            • 3
              Open-source
            • 2
              Text
            • 2
              Full-text
            • 1
              Can handle up to petabytes worth of size
            • 1
              Composability
            • 1
              Multiple procedural languages supported
            • 0
              Native
            CONS OF POSTGRESQL
            • 10
              Table/index bloatings

            related PostgreSQL posts

            Simon Reymann
            Senior Fullstack Developer at QUANTUSflow Software GmbH · | 30 upvotes · 11.6M views

            Our whole DevOps stack consists of the following tools:

            • GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
            • Respectively Git as revision control system
            • SourceTree as Git GUI
            • Visual Studio Code as IDE
            • CircleCI for continuous integration (automatize development process)
            • Prettier / TSLint / ESLint as code linter
            • SonarQube as quality gate
            • Docker as container management (incl. Docker Compose for multi-container application management)
            • VirtualBox for operating system simulation tests
            • Kubernetes as cluster management for docker containers
            • Heroku for deploying in test environments
            • nginx as web server (preferably used as facade server in production environment)
            • SSLMate (using OpenSSL) for certificate management
            • Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
            • PostgreSQL as preferred database system
            • Redis as preferred in-memory database/store (great for caching)

            The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

            • Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
            • Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
            • Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
            • Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
            • Scalability: All-in-one framework for distributed systems.
            • Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
            See more
            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            MongoDB logo

            MongoDB

            93.6K
            4.1K
            The database for giant ideas
            93.6K
            4.1K
            PROS OF MONGODB
            • 828
              Document-oriented storage
            • 593
              No sql
            • 553
              Ease of use
            • 464
              Fast
            • 410
              High performance
            • 255
              Free
            • 218
              Open source
            • 180
              Flexible
            • 145
              Replication & high availability
            • 112
              Easy to maintain
            • 42
              Querying
            • 39
              Easy scalability
            • 38
              Auto-sharding
            • 37
              High availability
            • 31
              Map/reduce
            • 27
              Document database
            • 25
              Easy setup
            • 25
              Full index support
            • 16
              Reliable
            • 15
              Fast in-place updates
            • 14
              Agile programming, flexible, fast
            • 12
              No database migrations
            • 8
              Easy integration with Node.Js
            • 8
              Enterprise
            • 6
              Enterprise Support
            • 5
              Great NoSQL DB
            • 4
              Support for many languages through different drivers
            • 3
              Schemaless
            • 3
              Aggregation Framework
            • 3
              Drivers support is good
            • 2
              Fast
            • 2
              Managed service
            • 2
              Easy to Scale
            • 2
              Awesome
            • 2
              Consistent
            • 1
              Good GUI
            • 1
              Acid Compliant
            CONS OF MONGODB
            • 6
              Very slowly for connected models that require joins
            • 3
              Not acid compliant
            • 2
              Proprietary query language

            related MongoDB posts

            Jeyabalaji Subramanian

            Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

            We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

            Based on the above criteria, we selected the following tools to perform the end to end data replication:

            We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

            We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

            In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

            Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

            In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

            See more
            Robert Zuber

            We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

            As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

            When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

            See more