Morph provides an environment where users can manage and analyse data centrally by working with a variety of data sources. This section presents the main data sources supported by Morph.

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SQL databases

Morph allows users to work with a variety of external data sources. This allows users to query directly from the following databases and data warehouses using SQL.

  1. MySQL: Open source relational database management system, widely used in many applications worldwide.
  2. PostgreSQL: A powerful open source object-relational database system, characterised by reliability, feature richness and performance.
  3. BigQuery: Google Cloud’s serverless, highly scalable data warehouse supports rapid SQL queries on large volumes of data.
  4. Snowflake: Cloud-based data warehousing service with excellent data sharing and scalability to support diverse data workloads.
  5. Redshift: Cloud data warehousing with Amazon Web Services, for fast analysis of large data sets.

By connecting these data sources to Morph, data can be integrated across different platforms and tools to efficiently perform data analysis and business intelligence tasks. Morph’s interface allows you to easily set up connections and query data directly.

You can start analysing the data in the database immediately by creating an SQL Connection from the Connections tab, as shown below. The connection name can be obtained from the list, and it can be changed if necessary.

Use the connection name created in SQL to retrieve data.

{{
  config(
    connection="connection_name"
  )
}}

select * from table_name;

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SaaS

Morph provides integration with many SaaS. This allows users to concentrate on implementing the necessary business logic, leaving the setup to Morph.

You can start analysing the data in SaaS immediately by creating an Integration from the Connections tab, as shown below:

Get the access_token from the Integration created in Python.

import pandas as pd

import morph
from morph import MorphGlobalContext
from morph_lib.api import get_auth_token # ← access_tokenを取得する関数

@morph.func
def freee_get_balance(context: MorphGlobalContext) -> pd.DataFrame:
    access_token = get_auth_token("Freee")

    # ↓↓↓ call API with access_token ↓↓↓

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Non-SQL databases

Morph can also work with NoSQL databases and other non-SQL databases, including MongoDB and Cassandra. Data can be retrieved and analysed against these databases using Python, which utilises powerful libraries to facilitate data manipulation and transformation — effectively extracting insights from non-SQL databases.

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Interfacing via API

Morph can also be integrated with various external services and applications via API. This allows real-time data to be retrieved from web services or integrated with other cloud services, for example.

Users can use Python scripts to retrieve data from the API and process and analyse it directly within Morph. This allows for flexible and customisable data integration.

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File upload

Morph also supports data upload from local files. Users can easily upload CSV, JSON, or Excel files and import them into your Workspace. Uploaded data is immediately available and can be analysed and visualised using Morph’s tools.

Built-in PostgreSQLMorph AI Features