ETL Data Integration – Definition, Comparison, Quickstart

What are the 4 types of data integration?

ETL (extract, transform, load) is one of the 4 main types of data integration.

Data integration tools fall into four main categories:

Data Integration Type	Use Case	Example Vendors
ETL	Pipelines that centralize data into an analytics environment	Fivetran, Portable
iPaaS & Automation	Point-to-point system integrations to automate workflows	Zapier, Tray, Workato
Data Collection	Technology that can be deployed on a website or mobile app to create event data	Snowplow, Segment
Reverse ETL	Tooling to extract data from warehouses and activate it back into business applications	Hightouch, Census

What are the benefits of data integration?

There are several benefits of data integration, including: 1. Improved data quality 2. Enhanced data access 3. Simplified data extraction 4. Facilitated data migration

Improved data quality

Data integration can help to improve the quality of data by ensuring that it is consistent and accurate. This can be particularly important when working with big data or complex data sets.

Enhanced data access

Data integration can make it easier to access and analyze data from a variety of sources, including legacy systems, on-premises systems, cloud-based systems, and custom APIs.

Simplified data extraction

Data integration solutions such as Portable, Talend, Zapier, and Informatica can help to automate and streamline the process of extracting data from various sources, making it easier to manage and analyze data.

Facilitated data migration

Data integration can also be useful for data migration, allowing organizations to easily move data between systems and platforms.

Overall, data integration can help organizations to better manage, access, and analyze data to support various business processes and drive better outcomes.

Let's now walk through the 4 types of data integration solutions, starting with ETL.

1. ETL tools

ETL tools are used to help business intelligence teams with data analytics by enabling them to extract, transform, and load data from various sources into a target system. There are two approaches to the ETL process: ETL (extract, transform, and load) and ELT (extract, load, and transform). The ETL approach involves aggregating data before loading it into the destination, while the ELT approach focuses on data replication and relies on the downstream data repository to process the data.

ETL tools can be cloud-based or open-source and are designed to scale to handle large volumes of data. They are used to pull data from various data sources, transform it into a usable format, and then load it into a cloud data warehouse, data lake, or target database. By using ETL tools, business intelligence teams can more effectively manage and analyze data to support various business processes and drive better outcomes.

2. iPaaS and automation solutions

iPaaS (Integration Platform as a Service) and automation solutions are used to sync data between business applications. These solutions are typically useful for smaller data sets and may not have the scalability to support the same volumes of data as a warehouse-centric architecture. iPaaS and automation solutions can be used to extract data from source systems via APIs, files, webhooks, and other methods. They can also be used to create real-time data pipelines and push information back into business applications such as CRM systems, ERP platforms, and other SaaS applications.

One of the main benefits of iPaaS and automation solutions is their ability to automate workflows for business users. This can help streamline business processes and improve efficiency, allowing organizations to better meet the changing needs of the business. Overall, iPaaS and automation solutions are useful tools for managing and automating data flow between systems and applications.

3. Reverse ETL and activation platforms

Reverse ETL and activation platforms are tools that can be used as part of an integration strategy when companies have centralized their data processing around a data warehousing solution or analytical data store such as Snowflake, Google BigQuery, Amazon Redshift, AWS S3, Azure Synapse, or Databricks. These platforms allow companies to extract data from their data warehousing solution or analytical data store and load it back into source systems or other databases. This can be useful when data transformation has taken place in the warehouse and the transformed data needs to be synced back into source systems or other databases.

Reverse ETL and activation platforms typically include features such as validation to ensure that the data being loaded is accurate and consistent. They also include no-code connectors that can easily configure the reverse ETL process without the need for specialized technical expertise. By using reverse ETL and activation platforms, companies can improve the flow of data between their data warehousing solution or analytical data store and other systems, helping to support various business processes and drive better outcomes.

4. Data collection and creation tools

Data collection and creation tools are typically used upstream of the data integration process and are used by data engineers to collect raw data and create the data needed to power downstream business decisions. These tools can help to ensure data quality by providing features such as rigorous schema definitions and anomaly detection.

Data collection and creation tools can be used to create data from various sources, including websites, mobile apps, IoT devices, and other first-party systems. The collected data is typically loaded into a staging area (as flat files or event streams) where it can be prepared for downstream data analysis. This may involve handling issues such as duplicates and ensuring that the data is in a usable format.

Overall, data collection and creation tools are an important part of the data integration process, allowing organizations to effectively manage, utilize, and process data to support various business processes and drive better outcomes.

How do you get started with ETL data integration?

It's easy to get started with an ETL solution.

1. Sign up for free (no credit card required)

2. Connect your data source

3. Authenticate with your data management environment (Snowflake, BigQuery, Redshift, etc.)

4. Move data

Why wait? Get started today.