Building Data Pipelines in Snowflake

Table of Contents

Businesses rely heavily on efficient data pipelines to extract, transform and load (ETL) data for analysis and decision-making. Snowflake, a cloud-based data platform, offers a powerful ecosystem for building robust data pipelines. In this blog post, we will explore how to leverage Snowflake’s streams, tasks and Snowpipe to construct a reliable and scalable data pipeline.

Why Businesses Need Data Pipelines

Data pipelines are crucial for organisations to efficiently manage, integrate, transform and analyse their data. They provide the foundation for data-driven decision-making, improved operational efficiency and unlocking the full potential of data assets. Some ways that data pipelines can improve business outcomes include:

Data Integration: Organisations often deal with data from various sources, such as databases, applications, external systems and third-party APIs. A data pipeline enables the seamless integration of data from multiple sources into a centralised location, providing a holistic view of the data.

Data Transformation: Raw data often needs to be transformed, cleaned and standardised before it can be used for analysis or other purposes. Data pipelines allow you to apply transformations and enrichments to the data, ensuring its quality and consistency.

Data Quality and Governance: Data pipelines can incorporate data validation and cleansing mechanisms to ensure data quality. They help enforce data governance rules, such as data security, privacy and compliance regulations, throughout the data flow.

Real-time and Near Real-time Analytics: Data pipelines can enable real-time or near real-time data processing, allowing organisations to gain immediate insights and make data-driven decisions faster. By continuously ingesting and processing data as it arrives, organisations can stay up to date with the latest information.

Scalability and Performance: Data pipelines are designed to handle large volumes of data efficiently. They leverage distributed processing and parallelism to scale with growing data volumes, ensuring optimal performance and reducing processing time.

Automation and Efficiency: Data pipelines automate the data extraction, transformation and loading (ETL) process, reducing manual effort and potential errors. Automation enables faster data processing and frees up resources for more value-added tasks.

Data Replication and Disaster Recovery: Data pipelines can replicate data across different environments or regions, ensuring data redundancy and disaster recovery capabilities. This redundancy helps organisations protect their data against system failures, data corruption or natural disasters.

Advanced Analytics and Machine Learning: Data pipelines provide a foundation for advanced analytics and machine learning workflows. They enable the seamless integration of data into analytics platforms and machine learning models, empowering organisations to derive insights and build predictive models based on comprehensive and up-to-date data.

Snowflake & Data Pipelines Explained

Snowflake is a cloud-based data platform that enables organisations to store, process and analyse their data using a scalable and flexible architecture. Its architecture consists of three layers: storage, compute and services. The storage layer is responsible for storing data, the compute layer processes queries and analyses and the services layer handles tasks such as query optimisation, security and metadata management.

Some of the key features of Snowflake for enabling continuous data pipelines are Snowpipe, Stream, Task and Kafka connectors. These features help to address the challenges of collecting real-time data, data change capture, scheduling and orchestration.

Understanding Snowflake Streams

A stream object records Data Manipulation Language (DML) changes made to tables, including inserts, updates and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as Change Data Capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a ‘stream’) makes a ‘change table’ available of what changed, at the row level, between two transactional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.

Let’s take a closer look at the code for working with Snowflake streams:

In the above example, we create a stream called my_stream on the source_table. Then, we insert two rows into the source_table. By querying the stream, you can see the captured changes, including the inserted rows.

Working with Snowflake Tasks

Snowflake tasks are units of work scheduled to be executed automatically based on specified conditions. They enable the automation and orchestration of data pipeline activities.

A task can execute any one of the following types of SQL code:

  • Single SQL statement
  • Call to a stored procedure
  • Procedural Logic using Snowflake Scripting

Tasks can be combined with table streams for continuous ELT workflows to process recently changed table rows. Streams ensure exactly one semantics for new or changed data in a table.

Tasks can also be used independently to generate periodic reports by inserting or merging rows into a report table or performing other periodic work.

The image below depicts a Directed Acyclic Graph (DAG), this is when there is no continuous loop.

The following practical example shows how it could be used to update dimension tables in a sales database before aggregating fact data.

Let’s explore a sample code snippet to understand how tasks work:

In the above example, we create a task called my_task. It uses the my_warehouse warehouse to execute the SQL statement inside it. The task is scheduled to run daily at midnight in the America/Los_Angeles time zone. The SQL statement inside the task inserts the data from the my_stream into the target_table.

Finally, we enable the task to start the automated execution.

Automating Data Ingestion with Snowpipe

Snowpipe is a feature in Snowflake that automates data ingestion into Snowflake from various sources, such as Amazon S3, Azure Blob Storage or Google Cloud Storage.

Here’s an example of setting up Snowpipe for data ingestion:

In the above example, we create an external stage called my_stage that points to an Amazon S3 bucket.

The stage credentials are specified using AWS access key ID and secret key. We then create a pipe called my_pipe to load data from the stage into the target_table, using a specified file format called my_format.

Finally, we create a Snowpipe named my_snowpipe with the AUTO_INGEST option set to TRUE, enabling automatic ingestion of data from the external stage into the pipe and subsequently into the target table.

Integrating Kafka Connector into the Data Pipeline

Snowflake provides a Kafka Connector that allows you to integrate Snowflake with Apache Kafka, a popular distributed streaming platform. The Kafka Connector enables real-time data ingestion from Kafka topics into Snowflake streams, providing seamless integration between the two systems.

Here’s an example of using the Kafka Connector:

In the above example, we create a Kafka integration called my_kafka_integration that connects Snowflake to the Kafka broker. We specify the Kafka broker URL, Kafka topic and Avro schema registry URL. Next, we create a stream called my_kafka_stream on a target table. We start consuming data from Kafka into the stream using the STREAM_ON_KAFKA function, which is provided by the Kafka Connector.

Finally, we can query the stream to see the captured changes from the Kafka topic.

Applying a Real-world Use Case

Now, let’s put it all together and build a data pipeline using Snowflake streams, tasks, Snowpipe and the Kafka Connector for a real-world problem.

An insurance company aims to leverage real-time data analytics to enhance customer experience, optimise risk assessment and improve operational efficiency. The company receives a constant stream of policy updates, claims data, customer interactions and external data sources such as weather and market data. They want to build a data pipeline that captures and processes this data in real-time to drive actionable insights and enable proactive decision-making

Data Pipeline Steps 

Data Ingestion from Kafka

The data pipeline begins by ingesting data from various sources, including policy updates, claims data, customer interactions and external data feeds, using the Kafka Connector for Snowflake. The Kafka Connector facilitates seamless integration between Snowflake and Kafka, enabling real-time data streaming. It captures the data from Kafka topics and pushes it into Snowflake’s streams.

Capture Data Changes with Streams

Snowflake streams capture the changes from the incoming data streams. For example, streams can be created to capture policy updates, claims submissions and customer interactions. These streams continuously track and store the changes, ensuring real-time data synchronisation. 

Transformation and Enrichment

Once the data changes are captured in the streams, users can apply transformations and enrichment operations to the data. This includes data cleansing, normalisation and enrichment with additional attributes or lookups. For instance, you can enrich customer interactions with demographic data or claims data with geolocation information.

Data Processing with Tasks

Snowflake tasks automate the data processing activities based on predefined schedules or conditions. In the insurance context, tasks can be created to process the data in the streams. These tasks can include SQL statements that perform real-time analytics, risk assessment calculations, fraud detection algorithms or customer segmentation based on recent interactions.

Real-time Data Loading with Snowpipe

Snowpipe automates the loading process, continuously and automatically ingesting transformed data from the streams into target tables within Snowflake. In this pipeline, Snowpipe can be used to load the processed data into tables for further analysis, reporting and integration with other systems. Snowpipe ensures near real-time availability of the data for immediate insights and decision-making.

Analytics and Visualisation

With the data loaded into Snowflake, businesses can leverage Snowflake’s analytics capabilities to perform real-time analysis on the data. This includes running complex queries, applying machine learning algorithms, building predictive models and generating visualisations using tools like Snowflake’s native UI, Tableau or other BI platforms. Visualisation dashboards and reports provide actionable insights to business users and internal stakeholders.

Monitoring and Alerts

Continuous monitoring and alerting are crucial to ensure the performance and reliability of the data pipeline. Snowflake provides monitoring and alerting capabilities to proactively detect anomalies, delays or errors in the pipeline. This helps maintain the integrity of the data and ensures that critical information is delivered in a timely manner. By combining streams, tasks, Snowpipe and the Kafka Connector, you can create an efficient and automated data pipeline in Snowflake that seamlessly integrates with Kafka.

Benefits of Data Pipelines

Easier access to information and insights

  • Business users need easy access to data to make informed decisions. For example, they may need to analyse sales data from a specific customer segment for a certain time. The raw data resides in a database. Creating an automated process that extracts the data from the database, performs the necessary adjustments and algorithms, and feeds it into the sales application enables these analytics. 
  • With legacy data pipelines, businesses often waste time waiting for IT to set up tasks which could translate into a missed opportunity. But, a modern data pipeline is simple to build with a standard set of tools, such as SQL, that are easy to learn. With this capability, users have the power to gain greater insights into data. 

Faster decision-making

  • In today’s fast-paced world, even just a few hours of lag time can mean lost business. For example, if a sales division doesn’t know about a forecasted storm, it can’t offer the right products to customers who may need help with disaster preparedness. If the service managers at a logistics company don’t know about mechanical issues on a truck, they won’t be able to service it in time to avoid a disruption.
  • Traditional data pipelines feed information in batches, often once nightly. In modern data pipelines, the data can be extracted and processed in real-time. This gives more up-to-date information and decreases the time from data collection to insight. As a result, organisations can make better decisions and leverage more opportunities.

Agility to meet peaks in demand

  • Data should be available to everyone who needs it – when they need it. With traditional pipelines, if several departments in a company need access to data at the same time, there could be slowdowns and delays. In the cloud, businesses should be able to add data storage and processing capacity within minutes instead of days or weeks, without straining budgets. 
  • Legacy data pipelines tend to be rigid and slow, inaccurate, hard to debug and unable to scale easily. Their creation and management involve significant amounts of time, money and resources. They also typically can’t run multiple processes concurrently, hamstringing the business during peak times.

Modern data pipelines offer the instant elasticity of the cloud at a fraction of the cost of traditional solutions. They provide immediate, agile provisioning when data sets and workloads grow, simplify access to common shared data and enable businesses to quickly deploy their entire pipeline, without the limits of a hardware setup. 

With the help of Snowflake and Billigence, businesses can gain a competitive edge through better data insights and faster decision-making.


Snowflake provides a robust ecosystem for building scalable data pipelines. By leveraging Snowflake streams, tasks, Kafka Connector and Snowpipe, organisations can achieve real-time data synchronisation, automate pipeline activities and seamlessly ingest data from various sources. The examples and code snippets provided in this blog post serve as a starting point for harnessing the power of Snowflake in constructing reliable and efficient data pipelines.

If you’re not sure where to start, or aren’t yet a Snowflake customer, get in contact using the form below – we’re here to help!

Leave a Comment

Scroll to Top