Snowflake Integration: An Easy Guide

on Data Automation, Data Integration, Data Warehouse, ETL • September 28th, 2021 • Write for Hevo

Snowflake Integration | Hevo Data

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.

Table of Contents

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.
  • Snowflake allows you to better analyze Customer Behaviour and Product Usage. You can also use the whole scope of data to ensure customer satisfaction, drastically improve product offers, and foster Data Science innovation.
  • Snowflake allows you to create your own Data Exchange, which allows you to securely communicate live, controlled data. It also encourages you to improve data relationships throughout your business units, as well as with your partners and customers.

Simplify Snowflake ETL and Data Integration using Hevo’s No-code Data Pipeline

Hevo Data helps you directly transfer data from 100+ data sources (including 30+ free sources) to Snowflake, Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Hevo takes care of all your data preprocessing needs required to set up the integration and lets you focus on key business activities and draw a much more powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. 

Get started with hevo for free

Let’s look at some of the salient features of Hevo:

  • Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
  • Scalable Infrastructure: Hevo has in-built integrations for 100’s of sources that can help you scale your data infrastructure as required.
  • Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Sign up here for a 14-day free trial!

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 Components of Data Integration?

The primary operations of data integration, also known as “extract, load, transform,” or ETL, are as follows.

  • Data extraction and export from specified data sources
  • To match the destination data, transform the source data as needed, using rules, merges, lookup tables, and other conversion methods.
  • Import the transformed data into a target database during loading.

Extract, load, and then transform, or ELT, is another common method of data integration, as sophisticated data systems perform transformation after loading, rather than before.

Data integration can cover a wide range of tasks, such as:

  • Preparation of data
  • Data migration, or data movement and management, is a term used to describe the process of
  • Automation of data warehouses

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 ETL Tool?

Snowflake Integration: ETL Tools | Hevo Data

To be able to perform a good Data Integration process, you must look out for ETL tools that are easy to use, maintain, and are highly secured. The tools must also be able to connect with various data sources to retrieve relevant files, and they must work seamlessly with other components in the data platform such as Data Lakes

Considering the ETL tool to use is important as data can be fetched from disparate sources and are often heterogeneous. So having the right tool can help you to schedule or automate the ETL process to ensure a smooth operation from the point when data is being extracted from the primary source, transformed to become structured, and to the stage where it is analyzed by the Data Analyst or Data Scientist.

ETL tools can also be used to identify updates or changes made to data streams when collecting, reading, or migrating data from various sources and this will avoid constant movement of the whole dataset. ETL tools also Filter, Join, Merge, Reformat, and Aggregate data. They also integrate with Business Intelligence (BI) applications to ensure you make the most out of your produced data.

How to choose an ETL Tool?

In data integration projects, picking the right ETL tool is crucial. Snowflake’s data integration supports both ETL and post-load transformations (ELT). Snowflake can handle a wide range of data types, including structured, semi-structured, and unstructured data. There are a number of plug-and-play tools that work with Snowflake. The following are some of the factors to consider:

  • Paid or Open-Source: The price of the integration tool is an important factor to consider. Paid tools will be more structured and come at a cost, but they will also be more reliant on ongoing support. Open-source tools, on the other hand, are free but evolve over time.
  • Ease of Use: Some ETL tools are simple to use and have a drag-and-drop interface. Others advise developers to write SQL or Python scripts to facilitate complex ETL transformations.
  • Adding/Modifying data sources: Data integration tools should be able to support a wide range of data sources, including applications, text-based, unstructured, blob, and so on. Multiple data sources should be able to be added to the integration tool.
  • Ability to Transform the Data: Before loading data into a data warehouse, transformations are required to add business logic to the data. Some data integration tools offer a wide range of transformations, such as expression, aggregation, and joiner, while others offer only a few or none at all. Depending on the number of transformations required in the data warehouse, the tool of choice may vary.
  • Product Documentation: Understanding the various properties and checkpoints while using any data integration tool relies heavily on documentation. It’s a good idea to have detailed documentation for the ETL/ELT tool.
  • Customer Support: Many cloud-based ETL tools offer customer support 24 hours a day, 7 days a week. Data issues are resolved with the assistance of their support desk. Customer service can be contacted via phone or email to resolve issues.

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 creates 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 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 |
ColumnDescription
nameName of the integration
typeType of the integration
categoryCategory of the integration
enabledThe current status of the integration, either TRUE (enabled) or FALSE (disabled)
created_onDate 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.
  • Stored Procedures – The native Snowflake SQL can be extended with Javascript to write stored procedures. Combining both SQL and Javascript has some benefits like procedural logic (like branching, looping)
    • Error handling can be done
    • SQL statements can be dynamically created and executed
    • Role-based execution of a procedure
  • Streams – This object keeps track of table changes such as inserts, updates, and deletes, as well as the metadata associated with each change. Change Data Capture (CDC) is a critical component of data warehouse implementation. The user is given a modified table with metadata columns indicating the type of DML operation.
  • CI/CD pipelines are needed to automate the data ingestion process and schedule it on a regular basis once the code is deployed to Production. Tasks and dependencies can be created for Data Integration with Snowflake so that once the master task is triggered, all downstream tasks are executed in a chain reaction.

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.

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Hevo Data, with its strong integration with 100+ 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.

Give Hevo Data a try and sign up for a 14-day free trial today. Hevo offers plans & pricing for different use cases and business needs!

Share your experience of working with Snowflake Integration in the comments section below.

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