What is Data Ingestion? Tools, Types, and Key Concepts

Data Ingestion vs. ETL

Data ingestion and ETL may sound similar, but they are distinct processes. Data ingestion pertains to the act of importing data into a database or storage engine whereas ETL involves the process of extracting, transforming, and loading data.

Their similar names and frequent coincidence often result in confusion about the distinction between the two.

The primary distinction between data ingestion and ETL lies in their respective functions.

Data Ingestion

Data ingestion refers to the transfer of data from an external source, such as a database, to a different storage location, such as another database, typically without altering the data.

Suppose you possess a set of files in an Amazon S3 bucket that you want to incorporate into your database. In that case, you would need to perform data ingestion to transfer those files to your database location.

ETL

Extract transform load, also known as ETL, is a method of extracting data from one system, transforming it, and then loading it into another system for utilization.

In this instance, instead of merely transferring data from one place to another without any modifications.

Data Ingestion vs. Data Integration

The act of transferring data between systems can be described as data ingestion and integration. Specifically, data ingestion pertains to the act of adding data to a database, while data integration involves retrieving that same data from a database and inserting it into a different system.

One may need to integrate data when seeking to utilize a product from a different company in conjunction with one’s own product, or when merging internal procedures with external entities.

The discrepancy between the two expressions is based on their respective definitions.

The process of bringing data into a database or storage location is known as data ingestion. This normally requires the usage of an ETL (extract, transform, load) tool to relocate data from one system (such as Salesforce) into another repository, such as SQL Server or Oracle.

Data integration involves consolidating several datasets into a single dataset or data model, which can be utilized by various applications, specifically those from varying vendors such as Salesforce and Microsoft Dynamics CRM.

Types of Data Ingestion

The process of data ingestion entails the gathering, cleansing, transforming, and integrating of data from diverse sources into one unified system within a data warehouse, in order to enable its analysis.

Data ingestion can be broadly categorized into two main types.

1. Real-time ingestion involves streaming data into a data warehouse in real-time, often using cloud-based systems that can ingest the data quickly, store it in the cloud, and then release it to users almost immediately.
2. Batch ingestion involves collecting large amounts of raw data from various sources into one place and then processing it later. This type of ingestion is used when you need to order a large amount of information before processing it all at once.

Data Ingestion Tools

Any organization heavily relies on data ingestion tools which are software products that collect and transfer data in structured, semi-structured, and unstructured formats from one destination to another. Since they automate the ordinarily challenging and manual ingestion procedures, organizations can devote their time to using the data to enhance business decisions and reduce data movement time.

Data is moved along a data ingestion pipeline, a series of processing steps that take data from one point to another. The pipeline might start with a database or other source for raw information, then pass through an ETL tool that cleanses and formats it before moving it on to a reporting tool or data warehouse for analysis.

In today’s digital economy, it is essential for businesses to possess the capability to rapidly and effectively assimilate data in order to remain ahead of the competition.

Data Ingestion Framework

The components of the data ingestion framework (DIF) comprise a range of services that facilitate the import of data into your database. These components encompass:

1. The data source API enables you to retrieve data from an external source, load it into your database, or store it in an Amazon S3 bucket for later processing.
2. The data source API proxy provides an interface between your application and the data source API. This proxy acts as a gateway between your application and other AWS services, enabling your application to access resources such as Amazon S3 buckets without requiring credentials or further authorization details from you.
3. The data source service contains all of the code required to interact with external data sources through one or more APIs using a method similar to web browsing (for example, GET requests).