In the first step you are asked to supply the file name of the Microsoft Access Database to convert.
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If your Access database requires you to log in you can specify a user name, password and a system database. Specify Access database. The destination database and MySQL server connection settings are specified as shown below. You will have the choice of placing the result in a dump file instead of transferring it directly to another MySQL database. MySQL connection and destination settings.
You can specify exactly which tables you want to transfer to the destination database.
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Just check the box in front of each table name you want to transfer and click next. Selecting the tables to transfer. Additional transfer options can be set in the dialog shown below.
Basic MySQL Tutorial
You can also select not to transfer any records. This will only create the tables in the destination table and leave them empty. When creating the tables in MySQL the program can preserve the default values specified in the Access field definition. Also auto number properties of a field can be transferred if you check the Auto number properties box.
MongoDB and MySQL Compared
Setting advanced transfer options. After answering all the questions in the wizard you can press Finish. The transfer will now run until all the selected tables are transferred. When the transfer is done you will see a small report telling you how much data was transferred. You can download the latest version here. After downloading the setup program you should simply run it to install the program.
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What is MySQL? In More Detail
MongoDB 4. Through snapshot isolation, transactions provide a consistent view of data, and enforce all-or-nothing execution to maintain data integrity. They are multi-statement, with similar syntax e. Unlike MySQL and other relational databases, MongoDB is built on a distributed systems architecture, rather than a monolithic, single node design. As a result, MongoDB offers out-of-the-box scale-out and data localization with automatic sharding, and replica sets to maintain always-on availability.
MongoDB has a rich query language, highly-functional secondary indexes including text search and geospatial , a powerful aggregation framework for data analysis, faceted search, graph processing and more. With MongoDB you can also make use of these features across more diverse data types than a relational database, and you can do it at scale. A comprehensive list of statements can be found in the MongoDB documentation.
Organizations of all sizes are adopting MongoDB because it enables them to build applications faster, handle highly diverse data types, and manage applications more efficiently at scale. Development is simplified as MongoDB documents map naturally to modern, object-oriented programming languages. Using MongoDB removes the complex object-relational mapping ORM layer that translates objects in code to relational tables. MongoDB can also be scaled within and across multiple distributed data centers, providing new levels of availability and scalability previously unachievable with relational databases like MySQL.
As your deployments grow in terms of data volume and throughput, MongoDB scales easily with no downtime, and without changing your application. In contrast, to achieve scale with MySQL often requires significant, custom engineering work.
Reasons of popularity
Working with data as flexible JSON documents, rather than as rigid rows and columns, is proven to help developers move faster. Why is this? Documents are natural. Documents represent data in the same way that applications do. This makes it much simpler and faster for developers to model how data in the application will map to data stored in the database. Documents are flexible. Each document can store data with different attributes from other documents. With JSON documents, we can add new attributes when we need to, without having to alter a centralized database schema.
At worst, this causes downtime, at best, significant performance overhead in a relational database. The flexibility documents bring allows the developer to much more easily handle the semi and unstructured data generated by modern mobile, web, and IoT applications.
Documents make applications fast. With data for an entity stored in a single document, rather than spread across multiple relational tables, the database only needs to read and write to a single place. Having all the data for an object in one place also makes it easier for developers to understand and optimize query performance. Consider the following:. To experienced MySQL developers, these layers maybe familiar, but to many other developers who want to interact with documents and data through APIs that are natural and idiomatic to their programming language, these layers impose high learning overhead.
ORMs are also generally recognized as hard to optimize for performance and query efficiency — even for experienced relational developers. In addition, query optimization statistics for JSON data are more limited than those maintained for regular relational data types. How is MongoDB different: MongoDB drivers are implemented in the methods and functions that are idiomatic and natural to the programming languages used by developers.
This includes different types of numeric values e. Instead developers have to manually convert text-based JSON in their application, losing the ability to have fields that can take on multiple data types in different documents polymorphism and making the computation, sorting and comparison of values difficult and error-prone. No Data Governance: MySQL offers no native mechanism to validate the schema of JSON inserted or updated in the database, so developers need to add either application or database-side functionality to apply governance controls against the data.
If the schema is then modified to accommodate new application requirements, the table is locked for some operations until existing data is copied into the new schema, requiring applications to be quiesced during schema migration. How is MongoDB different: Developers and DBAs can combine the flexibility of a fully dynamic schema with the governance controls needed for some applications across all data stored in the database, not just subsets of it.
The MongoDB Stitch serverless platform is the best way to work with MongoDB, cutting development time in half by taking care of mundane backend jobs such as service integrations, and getting data safely to your application frontend. Stitch Triggers let your app respond in real time to data changes, wherever the changes originated. The trigger code is written and executed within Stitch, giving them far more flexibility and making them easier to maintain than stored procedures and triggers in MySQL — it also means they don't consume valuable database resources.
MySQL offers no equivalent way of working with data or services, forcing you to waste months writing thousands of lines of undifferentiated, boilerplate code, and then provisioning application servers to run it on. With local access to your data and the full MongoDB query language, your apps run faster, and keep on running — even when disconnected from the network. There is no native mobile MySQL database, so developers are forced to use another database technology such as SQLite and write bespoke, complex code, or integrate third party solutions to sync with the backend database.
Baidu started out with MongoDB in , initially migrating its user address book service from MySQL after hitting performance and scalability limits.
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