Data Ingestion Patterns

Introduction:

In the last few years data platforms are more equipped to handle various types of data (structured, semi-structured, unstructured) and with companies adopting ELT patterns, data ingestion becomes one of the most important workload. This helps with easy access to the data for business without going through complicated data pipelines and enables a direct query layer for end users to explore the raw data and decide what they need for their use cases.

What is Data Ingestion:

For starters, data ingestion refers to the process of collecting raw data from various sources into a system or storage infrastructure for further analysis. It involves bringing data from multiple sources, such as databases, files, APIs, sensors, or streaming platforms, and making it available for use in data analytics, business intelligence, machine learning, or other data-driven applications.

Importance of Data Ingestion in ELT Pattern:

Data ingestion is a critical component of the ELT (Extract, Load, Transform) pattern, which is an alternative approach to traditional data integration processes. In the ELT pattern, data ingestion takes place before transformation, focusing on the efficient extraction and loading of data into a target system or storage infrastructure without immediate transformation. This approach offers several important benefits.

1. Data ingestion in the ELT pattern emphasizes efficiency. By separating the extraction and loading steps from transformation, the focus is on swiftly loading the data into the target system. This is achieved by leveraging high-speed data loading mechanisms and taking advantage of scalable storage solutions, such as data lakes or cloud-based platforms. By prioritizing efficient data ingestion, organizations can significantly reduce the time required to load data, ensuring that it is readily available for subsequent processing.
2. Data ingestion in the ELT pattern enables organizations to maintain the raw or minimally transformed state of the data in the target system. This raw data can then be accessed flexibly for various purposes such as exploratory analysis, ad hoc querying, or different transformation requirements. By postponing transformations, data scientists, analysts, and business users gain the flexibility to work with the data in its original form, supporting agile and iterative data exploration and analysis.
3. Ingesting data efficiently and without immediate transformation allows for better scalability in the ELT pattern. By leveraging scalable ingestion mechanisms, such as distributed streaming frameworks or parallel loading techniques, organizations can handle large volumes of data and accommodate high-velocity data sources. This scalability is crucial when dealing with real-time or streaming data, as it ensures the ingestion process can keep up with the incoming data flow.
4. Data ingestion in the ELT pattern supports future-proofing of the data pipeline. By ingesting data in its raw or minimally transformed state, organizations can adapt to evolving business needs and technological advancements.

Now that we understand Data Ingestion, let’s look into some of the patterns In Snowflake Data Platform which supports a wide variety of workloads without moving the data out of the system.

Data Ingestion Pattern (Snowflake)

Note: I have not listed a few other components that could exist like Snowflake Extractors, Native Apps, and the usage of Iceberg tables but those can also be added to the above diagram. But while writing this, I don’t have full visibility of those features.

Snowflake, a cloud-based data platform, provides a flexible and powerful environment for data ingestion. Snowflake supports various data ingestion patterns, allowing organizations to efficiently load data into their Snowflake account for further processing and analysis. Some common data ingestion patterns in Snowflake include:

1. Bulk Data Loading: Snowflake supports bulk data loading from various sources, such as files (CSV, JSON, Parquet, etc.), databases, or cloud storage platforms like Amazon S3 or Azure Data Lake Storage. Organizations can use Snowflake’s native COPY Utility to load large volumes of data in parallel directly into Snowflake tables. This pattern is ideal for initial data loading or batch processing scenarios.
2. Streaming Data Ingestion: Snowflake provides seamless integration with streaming platforms like Apache Kafka or Amazon Kinesis, enabling real-time data ingestion. Organizations can use Snowflake’s streaming functionality to consume data from streaming sources and load it into Snowflake tables in near-real-time. This pattern is suitable for scenarios where data needs to be processed and analyzed as it arrives. ( While writing this article this feature is in public preview)
3. External Tables: Snowflake supports the concept of external tables, which allows data to be queried directly from external data sources without loading it into Snowflake’s storage. Organizations can define external tables that reference data residing in cloud storage platforms like Amazon S3 or Azure Data Lake Storage. This pattern provides a virtualized view of data, reducing the need for data movement and allowing for on-demand querying and analysis.
4. Snowpipe: Snowpipe is a Snowflake feature designed for continuous data ingestion. It provides an automated and serverless approach to load data into Snowflake. With Snowpipe, organizations can define ingestion pipelines that automatically load data from cloud storage as it arrives, eliminating the need for manual intervention. This pattern is suitable for high-velocity streaming data scenarios.
5. External Function: Although this pattern is not necessarily ingestion it is another way to bring external lightweight data to the Snowflake platform. External Function allows you to invoke code or functions that reside outside of the Snowflake environment, such as in an external system or service, and integrate them seamlessly into your pipeline. This feature provides a way to leverage external computational capabilities and extend the functionality of Snowflake using custom code or services.
6. Snowflake Share: Similar to the previous one although not the typical ingestion pattern, snowflake share can be a powerful feature to bring data securely to your snowflake platform from other snowflake accounts. Snowflake Share is a feature in the Snowflake cloud data platform that allows organizations to securely and selectively share their data with external parties, such as business partners, customers, or vendors. With Snowflake Share, you can extend the benefits of Snowflake’s data platform to collaborate and exchange data seamlessly while maintaining control over data access and security.

What are some of the other patterns you are working with, feel free to leave in the comment section.