Today, data is streaming into businesses from a variety of sources: applications, SaaS solutions, social channels, mobile devices, IoT devices, and more. The big data revolution has created astonishing increases in the volume, velocity, and variety of data. Organizations can increase the value of all this data by making better data-driven decisions when the data is easily accessible to analytics and data science teams. To handle these increases and make data accessible, organizations require a modern data ingestion framework. If you’re new to the topic, read on to learn the types of ingestion and how data ingestion relates to data integration.
What Is a Data Ingestion Framework?
A data ingestion framework is a process for transporting data from various sources to a storage repository or data processing tool. While there are several ways to design a framework based on different models and architectures, data ingestion is done in one of two ways: batch or streaming. How you ingest data will depend on your data source(s) and how quickly you need the data for analysis.
Batch data ingestion
A batch data ingestion framework was the method used for all data ingested before the rise of big data, and it continues to be commonly used. Batch processing groups data and transports it into a data platform or tool periodically, in batches. While batch processing is usually cheaper since it uses fewer computing resources, it can be slow if you’re working with a lot of data. If you need real-time or near real-time access to data, it’s best to ingest data using a streaming process.
Streaming data ingestion
Streaming data ingestion continuously transports data into a data platform as soon as data is created (or identified by the system). It’s ideal for business intelligence that requires up-to-the-minute data to ensure the best accuracy and quick problem-solving.
The lines between batch processing and streaming are becoming blurry in some instances. Some tools marketed as streaming are actually using batch processing. Because they use small data groups and ingest data at short intervals, the process is incredibly fast. This approach is sometimes called micro-batching.
Data Ingestion Versus Data Integration
Although data integration is related to the data ingestion framework, it’s not the same thing as data ingestion. Integration usually includes ingestion, but it involves additional processes that ensure the data is compatible with the repository and with existent data. Another way of thinking about it is that data ingestion is focused on transporting data into a repository or tool, while data integration works further with the data sets to combine them into an accurate single source of truth.
There are two data integration methods: extract, transform, and load (ETL) and extract, load, and transform (ELT). The difference between the two lies in the sequence of events in each process.
ETL: ETL collects data from various sources, transforms it (cleanses, merges, and validates), and then loads it into a data platform or tool. All data is transformed before it enters the destination.
ELT: As compute and storage technology developed, the transformation process gained speed and became more flexible, and ELT was born. ELT allows raw data to be loaded into a database or data platform. The transformation process then happens ad hoc when a user is ready to conduct an analysis. This approach allows organizations to efficiently collect substantial data sets from many different sources for use in daily decision-making.
How to know which integration process to use
A holistic approach to data ingestion and data integration considers not just how data is moved into the data platform, but also how it’s integrated and analyzed. While ETL is fine for some use cases, ELT ensures you have all the data ready when you need it. With a strong platform that supports ELT, you can transform data with all the performance, scalability, and concurrency you need, right where your data lives.
Modern optimized data integration
Some data warehouses and data platforms (including Snowflake) have designed proprietary tools to ingest data. These capabilities take ingestion and integration to the next level, streamlining the process while optimizing resource usage.
Build a Better Data Ingestion Framework with Snowflake
There are several ways and best practices to load data into Snowflake.
For batch data loading, you can use the COPY INTO command to load data in bulk. For continuous data pipelines, you can use Snowflake’s Auto-Ingest feature, Snowpipe. You can simply set up a pipe with blob store notifications and leave the ongoing management of ingestion to Snowflake. On an ongoing basis, the Snowpipe infrastructure processes the new notifications to ingest data.
Snowflake simplifies data ingestion to solve the common problems that organizations face when transporting data. Snowflake’s capabilities allow your organization to:
- Batch load data easily using COPY INTO to keep the data in raw form
- Continuously load data with Snowpipe for auto-ingestion
- Work and connect to streaming sources easily with Snowflake’s Kafka connector
- Seamlessly integrate structured and semi-structured data (JSON, XML, and more)
- Automate and increase data ingestion speed to provide faster business analytics
- Perform change data capture
- Easily scale compute resources up or down to match data demand and handle unplanned high data loads
- Deploy a multi-cloud strategy, including Azure, AWS, and Google Cloud Platform
Amaury Dumoulin, Senior Data Scientist at Qonto and Snowflake customer, summed up the benefits of using Snowpipe to ingest data: “The best thing for our team is that we can ‘fire and forget’ with Snowpipe with no maintenance, no management, and hassle-free. The performance gain and cost-saving are across the board.”
Test Drive the Snowflake Data Cloud
The Snowflake platform supports both ETL and ELT processes, providing the flexibility you need for your use cases. Snowflake’s Snowpipe tool makes working with real-time data quick and efficient. Spin up a Snowflake free trial to:
- Explore the Snowflake UI and sample data sets
- Process semi-structured data with full JSON support
- Instantly scale compute resources up and down to handle unexpected workloads and unique concurrency needs
- Set up and test-run data pipelines and connect to leading BI tools
- Experiment with programmatic access