Alternatives to Microsoft Access logo

Alternatives to Microsoft Access

Oracle, MySQL, Power BI, FileMaker, and Google Sheets are the most popular alternatives and competitors to Microsoft Access.
79
87
+ 1
0

What is Microsoft Access and what are its top alternatives?

It is an easy-to-use tool for creating business applications, from templates or from scratch. With its rich and intuitive design tools, it can help you create appealing and highly functional applications in a minimal amount of time.
Microsoft Access is a tool in the Databases category of a tech stack.

Top Alternatives to Microsoft Access

  • Oracle
    Oracle

    Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database. ...

  • 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. ...

  • Power BI
    Power BI

    It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...

  • FileMaker
    FileMaker

    It is a Platform to create innovative custom apps for your workplace.

  • Google Sheets
    Google Sheets

    Access, create, and edit your spreadsheets wherever you go—from your phone, tablet, or computer. ...

  • Airtable
    Airtable

    Working with Airtable is as fast and easy as editing a spreadsheet. But only Airtable is backed by the power of a full database, giving you rich features far beyond what a spreadsheet can offer. ...

  • 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. ...

Microsoft Access alternatives & related posts

Oracle logo

Oracle

2.3K
113
An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism
2.3K
113
PROS OF ORACLE
  • 44
    Reliable
  • 33
    Enterprise
  • 15
    High Availability
  • 5
    Hard to maintain
  • 5
    Expensive
  • 4
    Maintainable
  • 4
    Hard to use
  • 3
    High complexity
CONS OF ORACLE
  • 14
    Expensive

related Oracle posts

Hi. We are planning to develop web, desktop, and mobile app for procurement, logistics, and contracts. Procure to Pay and Source to pay, spend management, supplier management, catalog management. ( similar to SAP Ariba, gap.com, coupa.com, ivalua.com vroozi.com, procurify.com

We got stuck when deciding which technology stack is good for the future. We look forward to your kind guidance that will help us.

We want to integrate with multiple databases with seamless bidirectional integration. What APIs and middleware available are best to achieve this? SAP HANA, Oracle, MySQL, MongoDB...

ASP.NET / Node.js / Laravel. ......?

Please guide us

See more

I recently started a new position as a data scientist at an E-commerce company. The company is founded about 4-5 years ago and is new to many data-related areas. Specifically, I'm their first data science employee. So I have to take care of both data analysis tasks as well as bringing new technologies to the company.

  1. They have used Elasticsearch (and Kibana) to have reporting dashboards on their daily purchases and users interactions on their e-commerce website.

  2. They also use the Oracle database system to keep records of their daily turnovers and lists of their current products, clients, and sellers lists.

  3. They use Data-Warehouse with cockpit 10 for generating reports on different aspects of their business including number 2 in this list.

At the moment, I grab batches of data from their system to perform predictive analytics from data science perspectives. In some cases, I use a static form of data such as monthly turnover, client values, and high-demand products, and run my predictive analysis using Python (VS code). Also, I use Google Datastudio or Google Sheets to present my findings. In other cases, I try to do time-series analysis using offline batches of data extracted from Elastic Search to do user recommendations and user personalization.

I really want to use modern data science tools such as Apache Spark, Google BigQuery, AWS, Azure, or others where they really fit. I think these tools can improve my performance as a data scientist and can provide more continuous analytics of their business interactions. But honestly, I'm not sure where each tool is needed and what part of their system should be replaced by or combined with the current state of technology to improve productivity from the above perspectives.

See more
MySQL logo

MySQL

126.2K
3.8K
The world's most popular open source database
126.2K
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 · 4.3M 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
Power BI logo

Power BI

925
27
Empower team members to discover insights hidden in your data
925
27
PROS OF POWER BI
  • 18
    Cross-filtering
  • 2
    Database visualisation
  • 2
    Powerful Calculation Engine
  • 2
    Access from anywhere
  • 2
    Intuitive and complete internal ETL
  • 1
    Azure Based Service
CONS OF POWER BI
    Be the first to leave a con

    related Power BI posts

    Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.

    See more

    Which among the two, Kyvos and Azure Analysis Services, should be used to build a Semantic Layer?

    I have to build a Semantic Layer for the data warehouse platform and use Power BI for visualisation and the data lies in the Azure Managed Instance. I need to analyse the two platforms and find which suits best for the same.

    See more
    FileMaker logo

    FileMaker

    37
    8
    Platform to easily create custom apps that manage user contact
    37
    8
    PROS OF FILEMAKER
    • 2
      Rapid development
    • 2
      REST API
    • 1
      API
    • 1
      Permissions
    • 1
      All included
    • 1
      Easy to learn
    CONS OF FILEMAKER
    • 1
      Expensive

    related FileMaker posts

    Google Sheets logo

    Google Sheets

    1.1K
    15
    Create and edit spreadsheets online, for free
    1.1K
    15
    PROS OF GOOGLE SHEETS
    • 10
      Simultaneous shared editing
    • 5
      Online alternative to MS Excel
    CONS OF GOOGLE SHEETS
      Be the first to leave a con

      related Google Sheets posts

      Jason Barry
      Cofounder at FeaturePeek · | 10 upvotes · 352.4K views

      If you're a developer using Google Docs or Google Sheets... just stop. There are much better alternatives these days that provide a better user and developer experience.

      At FeaturePeek, we use slite for our internal documents and knowledge tracking. Slite's look and feel is similar to Slack's, so if you use Slack, you'll feel right at home. Slite is great for keeping tabs on meeting notes, internal documentation, drafting marketing content, writing pitches... any long-form text writing that we do as a company happens in Slite. I'm able to be up-to-date with everyone on my team by viewing our team activity. I feel more organized using Slite as opposed to GDocs or GDrive.

      Airtable is also absolutely killer – you'll never want to use Google Sheets again. Have you noticed that with most spreadsheet apps, if you have a tall or wide cell, your screen jumps all over the place when you scroll? With Airtable, you can scroll by screen pixels instead of by spreadsheet cells – this makes a huge difference! It's one of those things that you don't really notice at first, but once you do, you can't go back. This is just one example of the UX improvements that Airtable has to the previous generation of spreadsheet apps – there are plenty more.

      Also, their API is a breeze to use. If you're logged in, the docs fill in values from your tables and account, so it feels personalized to you.

      See more

      Hey everyone, My users love Microsoft Excel, and so do I. I've been making tools for them in the form of workbooks for years, these tools usually have databases included in the spreadsheets or communicate to free APIs around the web, but now I want to distribute these tools in the form of Excel Add-ins for several reasons.

      I want these Add-ins to communicate to a personal server to authorize users, read from my databases, and write to them while they're using their Excel environment. I have never built a website, so what would be a good solution for this, considering I'm new to all of these technologies? I know about the existence of Microsoft Azure, Microsoft SharePoint, and Google Sheets, but I don't know how to feel about those.

      See more
      Airtable logo

      Airtable

      1K
      40
      Real-time spreadsheet-database hybrid
      1K
      40
      PROS OF AIRTABLE
      • 19
        Powerful and easy to use
      • 8
        Robust and dynamic
      • 6
        Quick UI Layer
      • 4
        Practical built in views
      • 3
        Robust API documentation
      • 0
        Great flexibility
      CONS OF AIRTABLE
        Be the first to leave a con

        related Airtable posts

        Jason Barry
        Cofounder at FeaturePeek · | 10 upvotes · 352.4K views

        If you're a developer using Google Docs or Google Sheets... just stop. There are much better alternatives these days that provide a better user and developer experience.

        At FeaturePeek, we use slite for our internal documents and knowledge tracking. Slite's look and feel is similar to Slack's, so if you use Slack, you'll feel right at home. Slite is great for keeping tabs on meeting notes, internal documentation, drafting marketing content, writing pitches... any long-form text writing that we do as a company happens in Slite. I'm able to be up-to-date with everyone on my team by viewing our team activity. I feel more organized using Slite as opposed to GDocs or GDrive.

        Airtable is also absolutely killer – you'll never want to use Google Sheets again. Have you noticed that with most spreadsheet apps, if you have a tall or wide cell, your screen jumps all over the place when you scroll? With Airtable, you can scroll by screen pixels instead of by spreadsheet cells – this makes a huge difference! It's one of those things that you don't really notice at first, but once you do, you can't go back. This is just one example of the UX improvements that Airtable has to the previous generation of spreadsheet apps – there are plenty more.

        Also, their API is a breeze to use. If you're logged in, the docs fill in values from your tables and account, so it feels personalized to you.

        See more

        I would like to build a community-based customer review platform for a niche industry where users can sign up for a forum, as well as post detailed reviews of their experience with a company/product, including a rating system for pre-selected features. Something like niche.com or areavibes.com with curated information/data, ratings, reviews, and comparison functionalities.

        Is this possible to build using no-code tools? I have read about the possibility of using Webflow with Memberstack, Airtable, and Elfsight through Zapier / Integromat, which may allow for good design and functionality. Is it possible with Bubble or Bildr?

        I have no problems with a bit of a learning curve as long as what I want is possible. Since I have 0 coding experience, I am not sure how to go about it.

        Any advice would be greatly appreciated!

        See more
        PostgreSQL logo

        PostgreSQL

        98.9K
        3.5K
        A powerful, open source object-relational database system
        98.9K
        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 · 11.9M 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

        94K
        4.1K
        The database for giant ideas
        94K
        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