Snowflake Integration: An Easy Guide

It is commonplace to have entities produce data on diverse platforms and hence, the need to merge all of these data. It, therefore, becomes imperative that a means has to be developed through which all this data can be brought together into a single unit for further analysis. Data, when utilized in the right way, can give you creditable insights to help in the decision-making of the company or organization, and that is where Data Integration comes in. 

The Data Warehouse where the data will be stored, and the Data Pipelines to connect the data should also be put into consideration as you will need one which can transform your Unstructured Data into desired formats to enable you to manipulate and work with them effectively. It’s not possible to talk about Data Warehouses without mentioning Snowflake, and with Data Integration in the picture, you have Snowflake Integration.

This article talks about Snowflake Integration. It also gives an overview of Snowflake, Data Integration, and ETL Tools.

What is Snowflake?

Snowflake is a Cloud Data Warehouse service that is built on Amazon Web Service or Microsoft Azure infrastructure that provides an unbounded platform for storing and retrieving data. The architecture of Snowflake separates its “Compute” and “Storage” units, thereby scaling differently allowing customers to use and pay for both independently. 

With Snowflake, there is no hardware or software to select, install, configure, or manage, therefore, making it ideal for organizations that do not want to have dedicated resources for setup, maintenance, and support for in-house servers. 

The sharing functionality of Snowflake makes it easy for organizations to quickly share and secure data in real-time using any available ETL solution. Snowflake’s architecture allows flexibility with Big Data.

Since Snowflake decouples the Storage and Compute units, it means organizations that have high storage demands but less need for CPU cycles, or vice versa, do not have to pay for an integrated bundle that requires payment for both, making it very attractive to companies.

What are the key features of Snowflake?

Here are some of the benefits of using Snowflake as a Software as a Service (SaaS) solution:

• Snowflake enables you to enhance your Analytics Pipeline by transitioning from nightly Batch Loads to Real-time Data Streams, allowing you to improve the quality and speed of your analytics. By enabling secure, concurrent, and monitoring access to your Data Warehouse across your organization, you can improve the quality of analytics at your company.
• Snowflake uses the Caching Paradigm to swiftly deliver the results from the cache. To avoid re-generation of the report when nothing has changed, Snowflake employs Persistent (within the session) Query results.
• Snowflake allows you to get rid of silos and ensure access to meaningful insights across the enterprise, resulting in better Data-driven Decision-Making. This is a crucial first step toward bettering partner relationships, optimizing pricing, lowering operational expenses, increasing sales effectiveness, and more.

What is Data Integration?

Data Integration is the process of combining data from multiple sources into a unified single view allowing users to obtain valuable and actionable information from the integrated data. The escalation in the production of data and the rapid advancement in technology has made Data Integration from diverse sources essential for businesses who constantly seek to improve their ways of doing business by trying to analyze the data they produce.

Therefore, the major purpose of Data Integration is to enable businesses to manage large amounts of generated data from various sources by combining them to form a chunk that is structured to allow an analysis to be performed on it to uncover hidden truths.

The important part of integrating data coming from various sources is having efficient Extract, Transform, Load (ETL) tools to carry out the operations seamlessly. So, making the right decision when it comes to choosing from a wide array of ETL platforms available today can have a long-lasting effect on your success. 

Data Integration includes a wide range of operations that involves Data Preparation, Migration or Movement and Management of Data, and Data Warehousing Automation. But it comprises three primary functions namely Extract, Transform, and Load commonly referred to as ETL processes. The Extract portion can be defined as the process of exporting data from specific data sources, Transformation means modifying the source data using rules, merges, lookup tables, or other conversion methods to match the target where the data will be stored, and Load is the importation of the transformed data into the Database Storage.

The benefits of Data Integration are enormous as it gives users access to data within an organization, reduction in errors and rework, efficient Data Preparation for analysis, and makes for unified, clean, and consistent data across your company.

What is the Importance of Data Integration?

Big data, with all of its benefits and challenges, is being embraced by businesses that want to stay competitive and relevant. Data integration enables queries in these massive datasets, with benefits ranging from business intelligence and customer data analytics to data enrichment and real-time data delivery.

The management of business and customer data is one of the most common use cases for data integration services and solutions. To support enterprise reporting, business intelligence (BI data integration), and advanced analytics, enterprise data integration feeds integrated data into data warehouses or virtual data integration architecture.

Customer data integration provides a complete picture of key performance indicators (KPIs), financial risks, customers, manufacturing and supply chain operations, regulatory compliance efforts, and other aspects of business processes to business managers and data analysts.

In the healthcare industry, data integration is also important. By organising data from various systems into a unified view of useful information from which useful insights can be gained, integrated data from various patient records and clinics aids doctors in diagnosing medical conditions and diseases. Medical insurers benefit from effective data acquisition and integration because it ensures a consistent and accurate record of patient names and contact information. Interoperability is the term used to describe the exchange of data between different systems.

What are the Benefits of Data Integration?

Data integration benefits businesses in several ways as a necessary prerequisite for consolidating data and making it accessible to users. Here are a few examples:

• Data that is unified, clean, and consistent throughout the organization (single source of truth)
• Access to cross-company data has been improved for users.
• Data preparation and analysis are completed more quickly.
• Errors and rework are reduced.

What is Snowflake Integration?

Snowflake Integration allows you to integrate data using various ETL tools as it works with a wide range of leading Data Integration tools which include Hevo Data, Informatica, Talend, Tableau, Matillion, and others. 

Snowflake Integration eliminates prolonged ETL, FTP, and Electronic Data Interchange (EDI) integration cycles often required by traditional data marts. Snowflake Data Integration helps you to integrate and process your Semi-Structured Data with full JSON support, address the issue of your organization’s concurrency needs with instant compute resource scaling, and allows programmable access to Spark/Python, run ETL operations, and connect to BI tools.

Snowflake Integration usually involves additional processes of transformation to ensure that your data is accepted, compatible with the location to be loaded, and matches with the existing data.

Snowflake Integration normally includes Data Ingestion too which is defined as the process of adding data to a data repository, such as a Data Warehouse but without transformation. You can check out the full list of Snowflake Integration tools.

Syntax of Create Snowflake Integration

Replaces an existing integration or create a new one in the system. Integration is a Snowflake object that acts as a link between Snowflake and external services.

CREATE [ OR REPLACE ] <integration_type> INTEGRATION [ IF NOT EXISTS ] <object_name>
  [ <integration_type_params> ]
  [ COMMENT = '<string_literal>' ]

Where integration type params are integration type_

Syntax of Alter Snowflake Integration

Changes the properties of an already installed integration.

ALTER <integration_type> INTEGRATION <object_name> <actions>

Where each object type has its own set of actions.

Syntax of Drop Snowflake Integration

Removes a link from the account’s integrations.

DROP [ { API | NOTIFICATION | SECURITY | STORAGE } ] INTEGRATION [ IF EXISTS ] <name>

name: The identifier for the integration to be dropped. The entire string must be enclosed in double-quotes if the identifier contains spaces, special characters, or mixed-case characters. The case is also taken into account when using identifiers enclosed in double quotes (e.g. “My Object”).

API | NOTIFICATION | SECURITY | STORAGE:

The integration type is specified here.

Syntax of Show Snowflake Integration

The integrations to which you have access privileges in your account are listed here.

Integration metadata and properties are returned in the output.

SHOW [ { API | NOTIFICATION | SECURITY | STORAGE } ] INTEGRATIONS [ LIKE '<pattern>' ]

API | NOTIFICATION | SECURITY | STORAGE: Only integrations of the specified type are returned.

LIKE ‘pattern‘:

Filters the output of the command by object name. With support for SQL wildcard characters (percent and _), the filter uses case-insensitive pattern matching.

The following patterns, for example, produce the same results:

… LIKE ‘%testing%’ …
… LIKE ‘%TESTING%’ …

Table properties and metadata are provided in the following columns in the command output:

| name | type | category | enabled | created_on |

Column	Description
name	Name of the integration
type	Type of the integration
category	Category of the integration
enabled	The current status of the integration, either TRUE (enabled) or FALSE (disabled)
created_on	Date and time when the integration was created

Syntax of Describe Snowflake Integration

The properties of integration are described here.

DESCRIBE is a short form of DESC.

DESC[RIBE] [ { API | NOTIFICATION | SECURITY | STORAGE } ] INTEGRATION <name>

name: The identifier for the integration to describe is specified. If the identifier contains spaces or special characters, double quotes must be used to enclose the entire string. The case is also taken into account when using identifiers enclosed in double-quotes.

How does Snowflake Data Integration work?

Snowflake not only serves as a cloud data platform, but it also helps transform data as it is loaded. The following are some of the features that Snowflake offers:

• Data can be loaded in bulk using the COPY command from local files or cloud storage outside of Snowflake. CSV, JSON, Avro, Parquet, XML, and other file formats are supported. While using the COPY commands, Snowflake also provides various data conversion functions.
• Data types – The Snowflake Integration Platform supports a wide range of data types, including numeric, string, date-time, logical, semi-structured data types like variant, array object, and geo-spatial data types, as well as unsupported data types like blobs and clobs.
• Stored Procedures – To write stored procedures, Snowflake SQL can be extended with Javascript. Combining SQL and Javascript has a number of advantages.

However, as a developer, we have noticed that Snowflake does not provide the following features –

• Connectors to other data sources – There are no connectors for applications like Salesforce in Snowflake. API calls must be made to ingest data from these applications, and data must be procured in the form of files into external stages before being loaded into Snowflake.
• Email notification – Email notification of job failures and successes in the Snowflake Integration Platform is not available to intimate users.

Conclusion

Snowflake has become a major player in the Cloud Data Warehousing structure and understanding how to integrate data into it has become important. Snowflake has a list of tools that can be integrated into it by simply accessing its tools page and selecting the platform you need.

This article introduced you to Snowflake and discussed the main aspects of Snowflake Integration. Hevo Data is a good data tool to integrate with Snowflake as it helps you to create efficient datasets and transforms your data into insightful actionable leads making your Snowflake Integration seamless.

visit our website to explore hevo

Hevo Data, with its strong integration with 150+ Sources & BI tools, allows you to not only export data from sources & load data in the destinations such as Snowflake, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools. Make your Snowflake Integration easy with Hevo Data.

FAQ

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