Organizations today use a ton of cloud services. Estimated averages range anywhere from a couple hundred per organization to almost 1300 per. Most of these come in the form of SaaS apps, all of which contain valuable data. On top of these apps, many organizations have legacy applications and various databases and pull data from outside sources like social media too.
ETL pipelines and data pipelines are instrumental in bringing data from all of these sources into a single place for meaningful data usage and analytics. Let’s look at both ETL pipelines and data pipelines, how they differ, and how to leverage both ETL data pipelines and data pipelines in your organization for more meaningful data use.
Before we get into ETL and data pipelines, let’s define a few terms.
What is ETL?
ETL – extract, transform, load – is a data integration process that makes data consumable for business intelligence and analytics. ETL lets you ingest (or extract) data from one data source – or many – transform that data, then load it into a receiving system like a data warehouse or data lake for processing. This process is typically handled by extracting raw data from a data source or sources, transforming the data on a separate data processing server, and then loading the transformed data into the data target.
What is a Data Pipeline?
A data pipeline is a set of steps that moves data from one system to another. Different types of data pipelines perform different operations through the data transit.
Standard data pipelines include:
- Batch data pipeline: A batch data pipeline periodically transfers bulk data from source to destination. It’s also a common choice for ELT or ETL processing.
- Streaming data pipeline: A streaming data pipeline continually flows data from source to destination while translating the data into a receivable format in real-time.
How ETL and Data Pipelines Relate
ETL refers to a set of processes extracting data from one system, transforming it, and loading it into a target system. A data pipeline is a more generic term; it refers to any set of processing that moves data from one system to another and may or may not transform it.
What is an ETL pipeline?
An ETL pipeline is simply a data pipeline that uses an ETL strategy to extract, transform, and load data. Here data is typically ingested from various data sources such as a SQL or NoSQL database, a CRM or CSV file, etc.
The data is then transformed in a staging area that’s sole purpose is to prepare the data into the format best suited for the data target (typically a data warehouse or database).
Let’s explore some common ETL data pipeline transformations to understand better how these types of data pipelines are used today.
ETL Data Pipeline Examples
- Join – Join allows related data from multiple different data sources to be merged into one data source for analytics purposes. For instance, an organization may want to join revenue data, or advertisement spending that spans offices, locations or affiliate companies.
- Aggregate – Data aggregation is a form of data summarization where relevant dimensions are grouped and relevant metrics are derived from that grouping. For instance, an aggregation procedure may sum up the total number of new accounts spanning multiple data sources. Here, similar columns (group metrics) in different data sources (dimensions) would be identified and summed.
- Split – Split is a process to transform unstructured or semi-structured data (such as strings, JSON, or XML files) into a more usable format for data analytics purposes. For example, given an address 5240 N Appletree Rd, San Diego California, 90210 maybe be split into appropriate columns:
Street: 5240 N AppleTree Rd
City: San Diego
State_Zip: California, 90210
ETL and the Shift to ELT
ELT stands for extract, load, transform – slightly different from ETL previously discussed. And although these seem quite similar their differences are actually quite profound. Rather than transforming the data on a processing server like ETL, ELT first loads the data into a data warehouse then transforms the data in the data warehouse.
Why is this important for today’s data pipelines? Efficiency.
Though ETL takes time to load data to a staging server for processing, it performs better once there. So, although ETL is the gold-standard approach for on-premises data centers, it is quickly being replaced by the more efficient data warehouse transformation. This edge in efficiency skyrocketed ELT’s popularity in IT environments that require highly efficient data processing.
At the end of the day, both ELT and ETL provide their fair share of pros and cons. So, what if you didn’t have to choose between ETL and ELT for your data pipeline?
No Need to Choose When you Choose StreamSets
Imagine orchestrating data pipelines in an intuitive, drag-and-drop abstracted interface that automates away a majority of the cumbersome data pipeline implementation process. With StreamSets, that’s a reality.
Rather than worrying about which ETL and ELT tool vendor will meet your organizational needs, with StreamSets, you no longer have to choose.
With StreamSets, you’ll be able to follow the simple processes outlined below to build effective, efficient, and robust data pipelines.
Through two engines, the SteamSets Data Collector Engine and the StreamSets Transformer Engine organizations can:
- Extract data from multiple sources
- Perform preprocessing transformations, i.e., to mask private data
- Load data into a repository
- Transform data as needed based on business requirements
StreamSets was built from the ground up with this dual paradigm at its core.
StreamSets touts dozens of pre-load transformation processors that you can drag and drop in a graphical user interface to greatly reduce the time to value and remove significant engineering overhead in bringing your data pipelines to market. To see these processors in action, here are seven examples of various data pipelines you can create in StreamSets.
