It is a mainstream destination to have organizations produce data on multiple platforms, also fulfilling the need to consolidate all this data. To bring all this data into a single entity for analysis, it is important to have the means to carry out this integration. To gain resourceful insights from data for decision-making for the help of an organization, Data Integration plays an integral part.
To convert unstructured data into the required format for the efficient output of work, one should take into consideration the Data Pipelines to connect the data transfer from the pipeline to the Data Warehouse, where all the data will get stored. Snowflake is one of the most used and trusted data warehouses and it also has a feature known as Snowflake Integration for carrying out effective data integration.
In this article, we will get to know about Snowflake Integration, how it works, and also how Hevo is making the use of Snowflake Integration.
What is Snowflake Integration?
It helps in getting rid of extended and lengthened Electronic Data Interchange (EDI), FTP, ETL, and integration cycles that are usually required by traditional data marts. To consolidate and process the Semi-Structured Data with full Support of JSON, Snowflake Data Integration helps to address this issue to keep in check your organization’s needs and with very prompt resource scaling and it also provides you with programmable access to Spark/Python, connect to BI tools, and run ETL operations.
Snowflake Integration generally requires some other additional processes of transformation to make sure that your data is accepted and correspondent with the location to be loaded and is compatible with the data that is already present there.
It generally includes Data Ingestion also, which is defined as the process in which you add data to a Data Warehouse or any other data repository but without transformation. Here is a list of some Snowflake Integration tools.
Syntax of Create Snowflake Integration
It is used to replace an integration that is already in place to start a new integration. Integration can be considered as an object of Snowflake that acts as a bridge between the external services and Snowflake.
CREATE [ OR REPLACE ] <integration_type> INTEGRATION [ IF NOT EXISTS ] <object_name> [ <integration_type_params> ] [ COMMENT = '<string_literal>' ]
Here, integration and integration type_ are the integration type parameters.
Syntax of Alter Snowflake Integration
It helps in changing the properties of an integration that is already installed and here each object has its own set of actions.
Syntax of Describe Snowflake Integration
The properties of integration are outlined here.
DESC is a short form of DESCRIBE.
name: To explain the integration, the identifier is stated. If the identifier has spaces or special characters, the whole string should be contained in double-quotes. The case should also be taken into consideration when using the identifiers enclosed in double quotes
How Snowflake Integration Works?
Snowflake is a cloud data platform that also helps with data transformation throughout the loading process. Some of the features that Snowflake has to offer are as follows:
Data can be loaded in bulk from local files or cloud storage outside of Snowflake using the COPY command. Other file formats supported include CSV, JSON, Avro, Parquet, XML, and more. Snowflake provides numerous data conversion capabilities when utilizing the COPY commands.
Numeric, logical, date-time, string, semi-structured data types like array object, geo-spatial data types, and variant as well as unsupported data types like blobs and clobs, are all supported by the Snowflake Integration Platform.
>Error management is possible.
>SQL statements can be generated and executed in real-time.
>Execution of a procedure based on roles.
Streams — This object keeps track of table changes like deletes, inserts, and updates as well as the metadata that goes along with them. Change Data Capture (CDC) is an important part of the data warehouse installation process. An updated table containing metadata fields identifying the type of DML action is delivered to the user.
Once the code is delivered to Production, CI/CD pipelines are required to automate the data ingestion process and schedule it on a regular basis. Tasks and dependencies can be set for Snowflake Data Integration so that when the master task is triggered, all downstream tasks are executed in a chain reaction.
However, as a developer, we've discovered that Snowflake lacks the following capabilities:
Connectors to other data sources — In Snowflake, there are no connectors for apps such as Salesforce. To ingest data from various applications, API calls must be made, and data must be obtained in the form of files into external stages before being put into Snowflake.
Email notification - Intimate users in the Snowflake Integration Platform do not receive email notifications of task failures or accomplishments.
Snowflake Integration using HEVO
With Hevo Data's strong integration with 100+ sources and BI tools, you can not only export data from sources and load it into destinations like Snowflake, but also transform and enrich your data and make it analysis-ready, allowing you to focus only on your most important business needs and perform insightful analysis with BI tools. Hevo Data makes Snowflake integration simple.
Snowflake has established itself as a prominent participant in the Cloud Data Warehousing landscape, thus knowing how to incorporate data into it has become critical. By going to Snowflake's tools page and selecting the platform you require, you can see a list of tools that can be incorporated into it. This article gave you an overview of Snowflake and covered the most important components of Snowflake Integration.