Introduction:
Azure synapse analytics provides standard database templates for various industries to use and create DB model as per their company needs. These are readymade templates which can be created with rich metadata for a clear understanding that can be implemented anytime with fewer steps. Database templates are in simple terms, business and technical data definitions that are pre-designed to meet the needs of any particular industry. They can be utilized as a blue print that will provide plans derived out of best practices, compliance and government regulations.
“Business area templates” provides deep and granular view of the data for a particular business or domain area. “Enterprise templates” contains only a division or subset of tables within a specific area. It provides an high-level view and displays the connectivity between the related business areas.
Lake Database
Basically, it is very hard to understand how the Data Lakes are structured and how they store data. The Lake database in synapse analytics helps customers to view the database design, metadata information and the data inside which can also identify a possibility to explain how and where the data can be or should be stored. Each table defined in the lake database will have a predefined schema based on the data that is coming inside your data. The database templates main objective is to provide your database with rich information thereby allowing end users of the data to understand easily the type of data available and how useful it can be.
Following are the three main concepts for Lake Database in Synapse.
Database Designer: The database designer has capability to create new data model for the lake database and add information to it. We can describe entities and attributes to provide greater details towards our model. Previously modelling the relationships was a challenge when interacting with data lake but that has been addressed now with Integrated designer which can provide the features that are available in the database but not on the lake.
Data Storage: The Lake Databases utilize the data lake from azure storage account to store the database’s data. The data storage format can be parquet or CSV, you can tweak in the settings to optimize the storage. All the Lake Databases uses linked services to define the location of the root folder and for each branch or entity new folders will be created by default in the database folder of the data lake. Usually all the tables within the lake database use the same format but that can be customized and changed based on the entity business model on requirement.
Database Compute: The Lake Database In Synapse Serverless SQL Pool and Spark pool is exposed and provides users with the capacity to separate storage from the compute engine. Different compute engines provide integrated experience and use the additional information which was not supported in the data lake. A lake database can be simply created using lake database template in azure synapse workspace in few steps.
Summary:
This is a very basic explanation about Lake databases in azure synapse analytics. This discussed about the basic concepts that Lake databases uses and methods through which Lake database can be implemented.