Snowflake is one of the most popular data warehouse platforms on the market. DataOps leaders choose Snowflake for its cloud-native architecture, scalability, data-sharing capabilities, security features, integration ecosystem, and SQL-based processing. Snowflake also aids in the orchestration of data pipelines built in other tools that create efficient, scalable data workflows.
Managing your data pipelines in Snowflake enables the movement, transformation, management, and processing of data in a structured way. Data pipelines are a top business priority in 2023—they’re essential to unifying all your data sources in your cloud data warehouse. This short guide explains how to build data pipelines, use Snowflake to orchestrate them, and get more business value out of your data.
What is a data pipeline?
A data pipeline is a workflow or a series of interconnected steps that enables the movement, transformation, and processing of data. There are many kinds of data pipelines that allow you to bring all your data sources together into a central cloud data warehouse like Snowflake.
Benefits of managing data pipelines with Snowflake
- Centralized efficiency: Snowflake provides a single platform for data storage, processing, and analysis. This simplifies management and eliminates the need for separate ETL tools and environments.
- Elastic scalability: Orchestrating with Snowflake enables dynamic resource allocation for varying workloads—enhancing performance and reducing processing time.
- Real-time insights: Snowflake’s feature, Snowpipe, comes with real-time data ingestion that allows immediate processing and loading of newly available data. This is vital for time-sensitive analytics and streaming data pipelines.
- Automated workflows: Automated workflows in Snowflake trigger transformations and loads based on schedules or events to reduce errors and manual intervention.
- Integrated transformations: Snowflake's SQL capabilities for transformations streamline data cleansing and manipulation within the platform.
- Data security: Snowflake ensures data security, compliance, and auditability. It maintains integrity throughout data pipelines and transformations.
- Cost optimization: Snowflake's pay-as-you-go model and elasticity let you allocate resources as needed to optimize costs and scale.
What are the main components of a data pipeline?
Data pipeline architecture varies by the type of pipeline you’re building but there are some common components you need to account for. The next data pipeline you connect to Snowflake might be an extract, transform, load (ETL) process or a streaming pipeline for real-time updates.
Whatever form it takes, here are the steps you need to build a data pipeline.
- Data extraction: The pipeline starts by extracting data from various sources, such as databases, files, or cloud storage. This can involve querying databases, ingesting streaming data, or retrieving files from sources like S3 or Azure Blob Storage.
- Data transformation: Once the data is extracted, it undergoes transformations to prepare it for further processing or analysis. Transformations involve cleaning, filtering, aggregating, joining, or enriching the data to ensure it’s in a suitable format for downstream consumption.
- Data loading: After the data is transformed, it’s loaded into Snowflake tables. Snowflake provides efficient loading mechanisms, including bulk loading, COPY command, or streaming ingestion to move the transformed data into the appropriate tables.
- Processing and analytics: Once the data is loaded, it can be processed or analyzed inside Snowflake. This can involve running SQL queries, performing advanced analytics, applying machine learning models, or generating reports and visualizations.
- Workflow orchestration: Snowflake helps you coordinate and sequence the steps above. It ensures that the data moves through the pipeline in a controlled and organized manner. Workflow orchestration manages dependencies, schedules, and triggers for each step.
- Monitoring and error handling: Snowflake provides monitoring and data observability capabilities to track the progress, status, and performance of your pipelines. Error handling mechanisms are in place to handle exceptions, errors, or data quality issues that may arise during the data processing stages.
Snowflake features for orchestrating data pipelines
Snowflake stands out as a data pipeline orchestration platform thanks to its scalability, separation of storage and compute, SQL-first approach, unified platform, security and governance features, and compatibility with a diverse integration ecosystem.
Whether you’re building data pipelines in Shipyard or Apache NiFi, Snowflake gives you a set of critical features for orchestrating them and moving data through your cloud data warehouse.
- Snowpipe: Snowpipe is a real-time data ingestion service provided by Snowflake. It automates the process of loading data from various sources into Snowflake tables as soon as new data arrives. It supports various data formats and can be integrated with cloud storage services like Amazon S3.
- External stages: Snowflake allows you to define external stages, which are pointers to cloud storage locations where your data files are stored. These stages can be used as sources or destinations for data during ETL processes.
- Transformations: Snowflake supports both transformation during (ETL) or after loading (ELT). Snowflake works with a wide range of data integration tools, including Shipyard, Informatica, Talend, Fivetran, Matillion and others.
- Integration with ETL+ Tools: Snowflake can be integrated with various ETL+ tools and data integration platforms. This allows users to design complex ETL workflows, ELT workflows, orchestrate data pipelines, and schedule data movement and transformations.
- Streams and tasks: Snowflake supports streams, which are changelogs capturing changes in a table. Tasks can be defined to automatically perform actions based on changes detected in streams. This can be useful for maintaining near-real-time data synchronization.
- Third-party integrations: Snowflake can be integrated with third-party data pipeline tools and workflow management services, allowing you to build sophisticated data pipelines that involve multiple stages and transformations.
How do I start managing data pipelines with Snowflake?
Snowflake helps you manage your data pipelines, improve data quality, ensure consistency, and enable data-driven decision-making. We built Shipyard’s data pipeline tools and integrations to work with your existing Snowflake-based data stack.
If you want to see for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start building data pipelines in 10 minutes or less, automate them, and see if Shipyard is where you want to start building your Snowflake data pipelines.
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