Data Ingestion vs. ETL: Key Differences for Data Management

Both data ingestion and ETL are two essential components of a data management strategy. This guide highlights the importance of data ingestion in handling big data. It also provides insights into how data ingestion differs from ETL.

Understanding Data Ingestion and ETL

Most modern data stacks require managing large volumes of data. Therefore, data ingestion and ETL are two essential components of such a data stack.

Data ingestion involves the collection and import of raw data from various sources.
ETL is the process of extracting, transforming, and loading data into a data storage system.

In this section, we will explore the differences between data ingestion and ETL and their roles in a data management strategy.

Data ingestion explained (vs. data integration)

Data ingestion is a subset of data integration.

It focuses on getting raw data into the target system as quickly and efficiently as possible. Other characteristics of data ingestion include the following points.

Data ingestion focuses on efficiently getting raw data into the target system. data integration involves more complex data transformations. It merges data from multiple sources.
Data ingestion is often done in real-time or near real-time. data integration is typically done on a scheduled basis (e.g., daily or weekly).
Data ingestion is a simpler process compared to data integration.
Data ingestion can handle both structured and unstructured data,

ETL explained (extract, transform, load, automate, aggregate)

Data migration is moving data from one system to another. You can use ETL for data migration. In data migration, you only need to move data from one source to another. However, ETL is specifically suitable when you need to transform data.

ETL is a process with 3 steps. Those are extract, transform, and load.

Extract: Data is extracted or pulled from one or more sources, such as databases or files.
Transform: The data is transformed to fit the target system's schema and requirements. This includes tasks like data cleaning, data enrichment, and data standardization.
Load: The transformed data is loaded or pushed into the target system. This target system can be a data warehouse.

ETL can also involve automation and aggregation of data. these processes enable organizations to efficiently process and analyze large volumes of data. The goal of ETL is to ensure that data is accurate, complete, and consistent in the target system. It allows organizations to make informed decisions based on high-quality data.

Comparison Table: ETL vs. Data Ingestion

Aspect	Data Ingestion	ETL
Definition	The initial step in data integration is to move raw data from its source to a central destination for storage.	ETL is s process to organize ingested data in a specified format and store it in a repository like a warehouse.
What is it	Data ingestion is a process. You can use various methods to ingest data into a staging area.	ETL works on the data after it comes to the staging area. ETL standardizes data.
Purpose	Its goal is to establish a centralized repository for all data. Then the repository is made available for the relevant parties.	Increase the accessibility of your data by standardizing them. This helps to derive insights from data.
Tools	Apache Kafka, Matillion, Apache NiFi, Wavefront, Stitch data, Funnel.	Portable, Xplenty, Informatica, AWS Glue

The role of data ingestion in a data management strategy

The data management strategy of an organization involves several steps.

Data Governance
Data Collection
Data Ingestion
Data Storage
Data Transformation
Data Loading
Data Analysis
Data Visualization
Data Reporting
Data Maintenance

Data Ingestion sits middle of the data management strategy. Data ingestion is typically the first step in a data processing pipeline. ETL, data processing and analytics tools will follow it.

Data ingestion plays a critical role in enabling streaming ETL and real-time data processing.

Undeniably, it's a key component of the modern data stack.

3 Types of Data Ingestion

There are three main types of data ingestion techniques. Those are

Batch data ingestion
Real-time streaming data ingestion
Source data ingestion

Batch data ingestion collects and loads data in the form of large chunks at regular intervals. This technique is useful for non-time-sensitive tasks and can handle high data volumes.

Real-time streaming data ingestion transfers data from the source to the destination in near real-time. This method offers low data latency.

Source data ingestion collects data directly from the source without staging the data. This technique is suitable for data sources that do not require significant transformation.

Data Ingestion Process

Data ingestion is the process of extracting raw data from various sources. This data is loaded into a destination for further analysis.
A data ingestion pipeline can help efficiently scale up and down the process to meet an organization's needs.
Disparate sources of data require appropriate data transformation and cleansing.
Data can be ingested from various sources. Some examples are databases, cloud services, and third-party applications.
Following the best practices for data ingestion ensures standardized, cleaned, and high-quality data. This leads to more accurate and effective data analysis and decision-making.

Types of Connectors for Data Processing

Connectors are tools that integrate different data sources. These sources include SaaS apps, business intelligence platforms, Microsoft SQL Server, and Snowflake.

Connectors for SaaS apps integrate data from cloud-based applications with other sources.
Connectors for business intelligence platforms facilitate data analysis and reporting.
SQL Server and Snowflake connectors make it easy to transfer data between different data storage platforms.

Need an enterprise data connector? (Portable has 300+ hard-to-find ETL connectors and offers lightning-fast custom development)

We understand the need for a seamless flow of data between different systems. That's why we offer over 300 hard-to-find ETL connectors. You can easily integrate them into your data workflows. And, if you're in need of a custom connector, we offer lightning-fast custom development.

Data Ingestion Tools

1. Apache Kafka

Apache Kafka is a tool that captures data in real-time. Kafka is being used by 80% of Fortune 100 companies. However, managing Apache Kafka can be a complicated task, as stated by Nasdaq. To address this, the co-founders of Apache Kafka established Confluent, which is a data platform for managing Kafka.

Notable Customers

Adidas, The New York Times, Grab, Cisco, Intuit, Goldman Sachs, Spotify, LinkedIn.

2. Apache NiFi

NiFi is an open-source platform to automate data flow across systems. Initially, it was built by Onyara and was first used by the US NSA to understand sensor data.

In 2014, the software was released under an open-source license. In 2015, Onyara was acquired by Hortonworks.

Today, Hortonworks has merged with Cloudera and provides commercial support for NiFi.

Notable Customers

Micron, GoDataDriven, Hastings Group, Looker, Ona, Slovak Telecom.

3. Informatica

Informatica can handle large volumes of data for both on-premises and cloud-based repositories. The company was founded by Gaurav Dhillon and Diaz Nesamoney in 1993. It is well known for creating the Intelligent Data Management Cloud (IDMC).

Notable Customers

NYC Health+ Hospitals, Databricks, HelloFresh, CVS Health, TELUS, KPMG.

4. Matillion

Matillion Data Loader is a product offered by Matillion Ltd, which was founded in 2011. It is an ETL tool that is supported by various investors. Some examples are Scale Venture Partners, Sapphire Ventures, Lightspeed Venture Partners, General Atlantic.

Notable Customers

Western Union, Cisco, Duo, Knak, Western Union, LiveRamp, Slack, Pacific Life, Cimpress.

5. Stitch Data

Stitch data offers a set of tools to move and integrate data from different sources into a data warehouse. The company was founded by Bob Moore and Jake Stein in 2016 and later acquired by Talend in 2018.

Notable Customers

Envoy, Heap, Calm, EZCater, Third Love, Invision, Indiegogo, Peloton, and Wistia.

6. Fivetran

Fivetran is a data ingestion tool. It was launched by George Fraser and Taylor Brown in 2013. It was backed by several investors including Andreessen Horowitz and General Catalyst. Fivertran claims that they have automated most of the complex processes in ETL.

Notable Customers

Docusign, Asos, Lufthansa, Canva, Databricks, Intercom, Square.

How ETL Tools Simplify Data Workflows

ETL tools simplify this whole process of extracting data from one source, transforming data, and loading data to another source. This enables organizations to process and analyze large volumes of data quickly and efficiently.

How ETL Connectors work (a high-level list of steps, extract, transform, and load)

ETL connectors are used to connect different data sources and targets in an ETL workflow. The process involves the following steps.

One of the first steps to getting started with ETL is to identify the data sources and define the data extraction process.
Extract: The connector pulls data from one or more sources. These sources can be databases, files, or APIs.
Transform: The connector then transforms the data to fit the target system's requirements. This may include tasks like data cleansing, data enrichment, and data standardization.
Load: The transformed data is then loaded into the target system. This target system can be a data warehouse.
The data can be loaded into tables, views, or other structures within the target system.

Benefits of ETL Tools (compare/contrast with ELT, data quality, unstructured data, data warehouses)

ETL tools are more suitable for high-volume data processing compared to ELT tools.
ETL tools provide more reliable data quality due to data transformations and cleaning.
ETL tools can easily handle unstructured data compared to traditional data warehousing solutions.
ETL tools can automate the process of loading data into data warehouses. This automation makes the process quicker and more efficient.

ETL Process Best Practices (schema, data lake, data pipeline, low latency)

Data within the ETL process should have a well-defined schema.
Using a data lake to store large volumes of data before it is processed in an ETL pipeline. This is especially good practice for unstructured data.
Having a well-designed data pipeline. It allows organizations to process large volumes of data in real time.
Reducing data latency in an ETL pipeline to ensure that data is up-to-date.

Data Integrity Best Practices

Following data integrity best practices is important to ensure that the data is

Consistent.
Accurate.
Reliable.

Maintain same formats (data ingestion patterns, data sources, validation)

You need to maintain the same data format. It ensures that data is consistent and can be analyzed effectively. To do that, you need to follow the below steps.

Adopting consistent data ingestion patterns
Ensuring that all data sources adhere to the same format
Data validation

Transform disparate sources of data (cleanse, standardize, and alter the data to the desired format)

Transforming disparate sources of data ensure that data is consistent and in the desired format.

Data cleanse removes any errors or duplicates and
Standardizes data to make it adheres to the same format across all sources
Altering data in a way they can be effectively analyzed.

For further reading, Amazon offers advice on ingesting big data in AWS. It includes using scalable solutions that can handle large volumes of data. It also uses machine learning to cleanse and standardize data and ensure data privacy and security.

Practice proper data governance (consistent schema, data engineers, mitigate issues with data transformation)

Practicing proper data governance is essential to ensure that data is accurate and can be used effectively. To do that, you need to be aware of the following things.
Adopt a consistent schema to ensure that data is in the desired format. It also allows us to compare data across different sources.
Data engineers can help to identify and mitigate issues with data transformation. It ensures that data is transformed accurately and efficiently.

Transform data after ingestion (maintain historical context, cloud data warehouse, raw data, high quality, further processing)

You need to transform data after ingestion. It is crucial for maintaining data quality and improving data usability. Usually, this process involves the following steps.

Preserving historical context,
Storing data in a cloud data warehouse
Preserving raw data for further analysis.

By transforming data after ingestion, organizations can improve their data-driven decision-making and gain a competitive advantage in their market.

Portable Streamlines Your Data Pipeline For Better Decision-Making

Portable will build the long-tail ETL connectors you won't find available with Fivetran.

Portable comes as the first ETL tool that offers connectors on-demand for data teams. It helps you to have connectors that you can't find anywhere else. Thanks to the massive connector support portable help streamline your data pipeline.

Connect 300+ Data Sources (SaaS, data lake, data warehousing, business needs)

Portable supports more than 300 connectors.
Unlike most ETL tools, Portable specializes in long-tail connectors.
You can find connectors for SaaS applications, data lake applications, and data warehousing applications.

No-Code Experience (use-cases such as data analytics, e-commerce, marketing, people, and embedding in SaaS products)

Portable is a no-code tool that provides a user-friendly interface.
You won't need data engineering experts to work with Portable.
Because of that, you can easily and quickly set up and customize data pipelines.
This is ideal for various use cases such as data analytics, e-commerce, and SaaS products.

Unlimited amount of data (optional scheduled data sync, API, real-time)

Portable can handle an unlimited amount of data. It supports optional scheduled data sync, API, and real-time ingestion capabilities.
Especially, Scheduled data synchronization ensures up-to-date and accurate data.
As a result, organizations are able to analyze large volumes of data quickly and efficiently.

Ingest real-time data sets with less engineering burden (business intelligence, data science, data-driven)

Portable has real-time data ingestion capabilities. It enables organizations to ingest and process data sets in real time. This in return reduces the engineering burden for the following things.

Business Intelligence
Data Science
Data-driven decision-making

Overall, with these features, users have timely access to accurate and high-quality data. You really can't go wrong with Portable.