The biggest difference between a data pipeline and ETL is that ETL is a type of data pipeline. Therefore, while every ETL workflow is a data pipeline, not every data pipeline is an ETL process.
Both approaches offer a seamless data integration solution. Let's quickly summarize the differences:
|Consideration||Data Pipeline||ETL Pipeline|
|Use Cases||Data analytics, process automation, product development||Primarily data analytics|
|Latency||Real-time or batch||Primarily batch|
|Deployment||On-premises or cloud-hosted||On-premises or cloud-hosted|
|Sources||SaaS applications (CRM systems, ERP platforms, etc.), relational databases, files, webhooks||SaaS applications (CRM systems, ERP platforms, etc.), relational databases, files, webhooks|
|Destinations||Data warehouses, transactional databases, data lakes, SaaS applications||Data warehouses, transactional databases, data lakes|
|Best Suited For||Data analysts, data engineers, analytics engineers, business intelligence teams, software engineers||Data analysts, data engineers, analytics engineers, business intelligence teams, software engineers|
A data pipeline is a set of processes that move data from one place to another. Data pipelines are commonly used in data management and can support a variety of use cases, including real-time data streaming, batch processing, and data migration.
In general, a data pipeline involves extracting raw data from various sources, which may include structured data (e.g. databases) and unstructured data (e.g. logs and documents).
The extracted data is then transformed, which may involve cleaning, filtering, and organizing the data to make it more usable. This process may involve using tools such as SQL to manipulate the data. The transformed data is then loaded into a target data store, which could be a database, a data warehouse, or a data lake (e.g. Amazon Web Services (AWS) S3 buckets).
By using a data pipeline, organizations can more effectively manage and utilize their data to support various business processes and drive better outcomes.
To load data into a data warehouse using an ETL pipeline (extract, transform, and load), the first step is to extract the data from the source system. This could involve APIs or other interfaces to access data from applications or databases. The extracted data can be in various formats, such as CSV or JSON, and may include both structured and unstructured data.
Next, the data is transformed to clean, filter, and organize it in preparation for ingestion into the data warehouse (Google BigQuery, Snowflake, Amazon Redshift, etc.). The transformed data is then loaded into the data warehouse - defining a schema to ensure that the data is structured in a way that is consistent with the data warehouse's requirements.
Overall, ETL pipelines are an important tool for loading data into a data warehouse, allowing organizations to more effectively manage and utilize their data to support various business processes and drive better outcomes.
Whether you want to power predictive analytics, machine learning models, or simply move social media data into your warehouse for data analysis, you need a simple solution.
Wouldn't it be great if you didn't have to build your own data processing pipeline? If things just scaled to with the big data sets that exist across your company?
You wouldn't have to learn an open-source framework, write Python code, or worry about the validation of large volumes of data.
Without these bottlenecks, you can spend your time on data analysis, writing SQL, and managing data transformations to turn raw data into business insights.
Lucky for you, no code ETL tools can streamline these workflows and make it simple to sync datasets into your analytics environment.
Here's how you get started with using Portable for ETL / ELT data pipelines.
Create your account (no credit card necessary)
Connect a data source
Authenticate your data source
Select a destination and configure your credentials
Connect your source to your data warehousing environment
Run your flow to start replicating data from your source to your destination
Use the dropdown menu to set your data flow to run on a cadence
Ready to get started? Try Portable today!