Snowflake Data Pipeline : Move Data Efficiently

Integrating a database where you store all your in-house information into a cloud warehousing platform like Snowflake can be a beneficial step to consider. Data integration enables you to back up your data and use it for analysis when necessary, positively impacting your business. Snowflake data pipelines can help you streamline data flow into your warehouse easily.

This article explores the Snowflake data pipeline and mentions how it can enable you to move data efficiently.

What is a Snowflake Data Pipeline?

Snowflake data pipelines move data from a source to a destination while simultaneously performing transformations. A temporary staging table stores the raw data in this process, acting as an intermediate location between source and destination. 

You can transform data using SQL commands into a format compatible with the destination schema at this staging table. Data pipelines involve aggregation, organization, and movement of data. They can help you transfer data from your database to a data warehousing platform like Snowflake, where you can analyze and generate insights from the data.

Characteristics of a Data Pipeline

Data extracted from different sources is often messy, but a data pipeline can enable you to clean, transform, and prepare it for further analysis. But what characteristics make a data pipeline perfect for use? This section will discuss a few important characteristics of a data pipeline for seamlessly integrating data from source to destination.

• Continuous data processing is one of the most essential characteristics of a data pipeline. The pipeline must handle data constantly, not just in smaller batches.
• The data must be accessible to all the individuals with the user privileges in your organization.
• The data pipeline must leverage cloud scalability and flexibility.
• Each step performed under the data pipeline must have independent allocated resources to avoid bottlenecks.
• The data pipeline must have high availability and established methods for data recovery during failures.

What Are the Steps Involved in the Snowflake Data Pipeline?

This section highlights the steps that describe the Snowflake data pipeline. To create an efficient data pipeline Snowflake offers multiple features you can utilize in the following steps. Here are the six major steps that combine to create efficient Snowflake streaming data pipelines.

Data Ingestion

The first step of any data pipeline involves ingesting data from multiple sources, including customer interactions, online surveys, claims data, or any other external source. If your business data is in another database environment, you can transfer it into a data warehouse or analytical platform like Snowflake to generate valuable insights.

This transfer can happen in two ways: batch loading or streaming the data from source to destination. Snowflake provides Apache Kafka connectors that can enable you to create Snowflake continuous data pipelines.

Change Data Capture

After data ingestion, if the process used to integrate data is streaming data pipelines, then changing data capture is the second step. In this step, the changes that occur at the source end are reflected in the destination, enabling real-time data analytics.

Data Transformation

In this step, you can perform data transformation steps. These steps involve data cleaning, standardization, and enrichment with additional information. Data cleaning removes discrepancies from the data. Standardization converts the data into a specific range of values such that no field will have more effect due to its value. The enrichment process involves adding additional information fields to the dataset.

Real-time Data Loading

Snowflake has a feature called Snowpipe that enables near real-time transformed data ingestion from streams into Snowflake tables. You can use it for further analysis purposes. In addition to this method, there are SaaS-based platforms that you can choose to perform this step without requiring technical assistance.

Real-time Data Ingestion into Snowflake Using Hevo

To load data in real-time, you can use a no-code, real-time ELT data pipeline platform like Hevo. It cost-effectively automates data pipelines that are flexible to your requirements. With 150+ data source connectors, Hevo lets you connect data from multiple sources and load it into a destination. Here are a few features that you can leverage by using Hevo.

• Data Transformation: Hevo provides analyst-friendly data transformation features to streamline analytics tasks. You can utilize its Python-based and drag-and-drop transformation features to clean and prepare data for further analysis.
• Automated Schema Mapping: Hevo automates the schema management procedure by detecting the format of incoming data and replicating it to the destination schema. You can choose between Full & Incremental Mappings according to your data replication requirements.
• Incremental Data Load: Hevo allows real-time modified data transfer, ensuring effective bandwidth utilization on both the source and the destination ends.

Data Analysis

After loading data into the Snowflake environment, you can perform analytics and generate insights to drive your business needs. This step involves procedures such as executing complex queries, applying machine learning algorithms, and generating compelling dashboards from the recognized data pattern. The visualizations can help business professionals derive actionable measures to scale your business.

Data Monitoring

The final stage is a continuous process throughout all the stages of the data pipeline, which ensures its reliability. Snowflake provides monitoring capabilities to help you detect anomalies, errors, or delays in the data pipeline, helping maintain data integrity.

Why is Snowflake Data Pipeline Required?

Data pipelines are important elements enabling you to integrate, organize, transform, and analyze data, enabling you to employ better data-driven decisions. Here are some of the key aspects you must consider to understand how they can help you:

• As data is collected from multiple sources, transferring data from source to destination by importing/exporting datasets can become cumbersome. Snowflake data pipelines make it easier to perform the data integration task by automating some of the steps involved.
• Data pipelines can enforce governance regulations to maintain data quality, security, and operational efficiency throughout integration. They can also enable you to perform data validation and cleaning methods to maintain consistency.
• Snowflake data pipelines can efficiently handle big data utilizing distributed processing and parallelism procedures. This feature can reduce the time consumed in moving data, enhancing the performance of data pipelines.
• Data pipelines offer real-time data integration, allowing you to derive insights from the latest data updated in the source.
• Automating the data integration tasks allows optimal use of resources, saving you additional time to focus on analytics and insights generation.

What Are Snowflake Streams?

Snowflake stream objects can perform change data capture (CDC) tasks by tracking the changes made to the table. These objects enable recording the data manipulation language (DML) tasks, including inserts, deletes, updates, and additional information about the change. As a result, streams allow you to track the changes made at the source end in real-time.

With this feature, you can perform analytics on the latest available data. The SQL commands below enable you to create a stream and analyze the changes made to the dataset by querying the stream.

CREATE STREAM new_stream ON TABLE source_table;

INSERT INTO source_table (id, name) VALUES (1, ‘ABC’);

INSERT INTO source_table (id, name) VALUES (2, ‘XYZ’);

SELECT * FROM new_stream;

How to Use Snowflake Tasks?

Snowflake tasks provide work units scheduled to be completed based on certain conditions. It includes performing single SQL statements, procedural logic using Snowflake scripting, and stored procedure calls. Snowflake tasks help automate the data pipeline activities. You can combine it with Snowflake streams to automate the data pipeline workflow and process the latest data.

Tasks efficiently perform repeated work automatically, helping you save time and focus on the business aspect of analysis. The following Snowflake data pipeline example highlights how to use Snowflake tasks:

CREATE TASK new_task

WAREHOUSE = my_warehouse

SCHEDULE = ‘USING CRON 0 0 * * * America/Los_Angeles’

AS

INSERT INTO target_table

SELECT * FROM new_stream

ALTER TASK new_task ENABLE

The SQL command above creates a task named new_task and configures the data warehouse you can use for scheduling purposes. The task is scheduled according to the CRON scheduling expression and gets triggered at midnight based on the LA timezone, copying the data from new_stream to target_table.

Benefits of Snowflake Data Pipelines

Data is generated from multiple sources, so your organization might have to deal with large amounts of data every day. The availability of data on different platforms hinders the truth that the data conveys and makes it a hassle for analysts to generate insights.

A unified view can resolve this issue by enabling you to analyze all this data and perform advanced queries.The data flow from the source to the destination cannot be relied on, as issues can occur, including bottlenecks, data inconsistency, and corruption.

This is where Snowflake data pipelines can help you perform data integration, eliminating manual steps and automating the data flow from one place to another. Here are some of the prominent benefits of data pipelines:

• As data pipelines automate data integration tasks, they can help you create a data-driven environment across your organization.
• Real-time data access is another benefit that can help you extract the latest information from your source database and put it into Snowflake.
• Data pipelines can store your in-house data in a cloud environment, where you can leverage the advantages of multiple data platforms.

There are multiple benefits to using data pipelines, but developing a Snowflake data pipeline can become a complex task. Creating it from scratch requires technical expertise and is not considered the most intuitive way of building data pipelines. That is why you can consider alternative ways of creating a data pipeline using third-party, SaaS-based applications.

Conclusion

This article discusses the benefits of using data pipelines to integrate data between multiple sources. These include real-time data analytics, maintaining data consistency, reducing errors, and more.

Snowflake provides features to create a data pipeline manually using Apache Kafka, Snowpipe, streams, and tasks. Although this method creates efficient data pipelines, it might become a hassle.

Want to understand Snowflake Change Data Capture? Explore our guide to see how it can help you track and manage data changes effectively.

To overcome the limitations of creating a data pipeline manually, you can use Hevo to integrate data from multiple sources to a destination.

Frequently Asked Questions (FAQs)

Q1. How to resolve the error “Pipe Notification Bind Failure” while creating a pipeline using auto_ingest = True?

This error highlights that the security token used by Snowflake’s Snowpipe auto-ingest feature is invalid. Security tokens have a limited validity period, after which they expire. To solve this issue, you can generate a new security token and use it with the auto-ingest feature. To learn more, refer to automated continuous data loading.

Q2. How do you run multiple stored procedures in parallel?

Here are three solutions that you can follow to run procedures parallelly.

1. You can run concurrent tasks.
2. You can also use the collect_nowait method.
3. Additionally, external workflow management tools like Apache Airflow can enable you to execute stored procedures concurrently.