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How a Data Ingestion Framework Powers Big Data Usage

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 evolution has created astonishing increases in the volume, velocity, and variety of data. These increases require a new data ingestion framework. If you’re new to data ingestion, 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/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 all data was ingested before the rise of Big Data, and it continues to be commonly used. Batch processing groups data and transports it into the 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-time data, it’s best to ingest data using a streaming process.

Streaming Data Ingestion

Streaming data ingestion transports data into the system as soon as its 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 more translucent. 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 vs. Data Integration

Although data integration is related to the data ingestion framework, it’s not the same thing. Integration usually includes ingestion, but it involves additional processes that ensure the data is compatible with the repository and 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.


Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are data integration methods. The difference between the two lies in the sequence of events in each process.

ETL — ETL collects from data various sources, transforms it (cleanses, merges, and validates), 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, transformation was able to gain speed and become more flexible. ELT was born. ELT allows raw data to be loaded into the data platform or tool. 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

Although ELT is the newest process and is based on modern technology, ETL is still best for many use cases. If you perform multiple analyses on the same set of data, it makes sense to transform it once before loading it into your data platform or tool. If, on the other hand, you work with multiple data sets and frequently combine data in different ways, then it will be more efficient to transform the data as you need it for each ad-hoc query. 

Ideally, your data platform will support both processes and allow you to ingest data and transform it in the most efficient way possible at the opportune time. 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. A big-picture view allows the entire framework to function seamlessly and efficiently. 

Modern Optimized Data Integration

Some data platforms and warehouses (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

With Snowflake’s auto-ingest tool, Snowpipe, an organization 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 file 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:

  • Seamlessly integrate structured and semi-structured data (JSON, XML, and more). 

  • Automate and increase data ingestion speed to provide faster business analytics.

  • Easily scale compute resources up or down to match data demand and handle unplanned high data loads.

  • Use either or both Azure and AWS data ingestion pipelines (multi-cloud).

Amaury Dumoulin, Senior Data Scientist at Qonto and Snowflake customer, sums up the benefits of using Snowpipe to ingest data by saying, “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 Cloud Data Platform

The Snowflake cloud data 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 UI and sample data sets

  • Process semi-structured data with full JSON support

  • Instantly scale compute resources up and down to handle unique concurrency needs

  • Set up and run ETL and connect to leading BI tools

  • Experiment with programmatic access and Spark/Python

  • Process semi-structured data with full JSON support

  • Choose to continue with Snowflake right away with pay-as-you-use billing

To see Snowflake’s potential for improving your data ingestion for better BI, sign up for a free trial