Amazon's Relational Database Service (AWS RDS) is a web-based service launched by Amazon Web Services (AWS) that unifies the setup, operation, and scaling of relational databases in the cloud. By using a dependable and feature-rich ETL (Extract, Transform, Load) tool, you can simplify the process of retrieving, transforming, and sending data from the various systems you already use, like AWS RDS.

DB Viewer is a plugin that provides a UI to browse databases. But it is also a great option to access Database data through REST API, for example, to export data. In this article, we will explain how to navigate the data scheme and find all the needed information to export events and their granular data from Countly. ‍ Let's say you have some other database, and you want to populate it with data from Countly. Or you just want to prepare some kind of report through a third-party application.

When developers build applications, they need to think about how they will connect their products to data sources. Direct database access vs. REST APIs: which should you pick? Both currently stand out as two of the most popular options. Let’s explore the pros and cons of each so you can determine whether API-led connectivity serves your needs. Here are 5 things to know about direct database access and REST APIs: Table of Contents.

Often businesses have a different Database to store transactions (Eg: Amazon Aurora) and another Data Warehouse (Eg. Snowflake) for the company’s Analytical needs. There are 2 prime reasons to move data from your transactional Database to a Warehouse (Eg: Aurora to Snowflake). Table of Contents Firstly, the transaction Database is optimized for fast writes and responses. Running Analytics queries on large data sets with many aggregations and Joins will slow down the Database.

Before we dive deep into MongoDB to Snowflake data migration steps, it is important to understand the unique properties of MongoDB and Snowflake that make a data migration like this both challenging and exciting. Table of Contents NoSQL databases like MongoDB address very specific use cases. Data storage and access patterns will be highly optimized for fast write and retrieval and many other factors like the availability and durability of data.

MariaDB and MySQL are two widely popular relational databases that boast many of the largest enterprises as their clientele. Both MariaDB and MySQL are available in two versions – A community-driven version and an enterprise version. But the distribution of features and development processes in the community and enterprise versions of MySQL and MariaDB differ from each other.