What does an ETL pipeline do?

It extracts data from disparate sources (either batch or stream processing), transforms it to clean and eliminate redundancies, and loads it into the destination database like ERP, CRM, data warehouse, etc.

How is SQL used in ETL?

SQL commands are used to extract data from source(s). SQL is instrumental in fetching data and creating different connections between tables. Whether the source is a data lake or a spreadsheet, SQL makes data extraction a seamless task for data engineers.

What is a simple example of a data pipeline?

ETL is used to extract, process, and push data. Consider an example of an eCommerce brand that uses different sales channels to market and sell its products. ETL can help eCommerce businesses integrate data into their CRM from disparate sources to fetch valuable patterns and insights into marketing campaigns, products, and customers.

What is the difference between ETL and ELT pipelines?

ETL pipeline transforms data at the middle tier before loading it into the destination database. Whereas ELT (Extract, Load, Transform) pipeline transform, process, and clean this data after loading it to the destination.

Is ETL a tool or process?

ETL is the set of processes implemented using different tools, languages, and methodologies.

What is the difference between a data pipeline and an ETL pipeline?

A data pipeline is an umbrella term for all data pipelines, including ETL pipelines. It doesn't necessarily transform data before loading it to the destination. ETL is the type of data pipeline that follows a rigid rule of processes comprising extracting, transforming, and loading the data into the destination database.

What is the difference between API and data pipeline?

Data Pipeline offers a single API to manipulate and fetch data from the source and load it into a target database or cloud (for instance, CRM, ERP, Google Cloud, etc.). Whereas APIs connect two data sources irrespective of source, destination, type, or structure.

Database/Cloud

What is an ETL Pipeline?

Q: What is an ETL Pipeline?

ETL pipeline is a set of processes, including Extract, Transform, and Load, to migrate data from data source(s) to a destination like a data warehouse.

In this helpful guide we'll discuss what an ETL pipeline is, why it's used, why ELT is a better option, and more.

Haziqa Sajid

May 31, 2023 • 8 min read

Overview

With businesses looking to get insights from their data and gain a competitive edge, the pressure is on data engineering teams to process and transform raw data into clean, usable, and reliable resources for downstream use in analytics, data science, and machine learning initiatives.

The ETL pipeline provides a data framework for collecting, cleaning, and analyzing data, making it easier for businesses to gain insights into their operations and make data-backed decisions.

Data pipelines are an important part of modern data architecture. But we should make it clear upfront that the ETL process has largely been replaced by the similar but more modern ELT process. You can read more about the differences between the two here. But much of what you will read in this article applies to both ETL and ELT.

What is ETL?

ETL, or Extract, Transform, Load, is a set of processes that involves extracting data from different sources, transforming it as a reliable resource, and loading it into destination systems.

Data Pipeline vs. ETL Pipeline... People often use the terms 'data pipeline' and 'ETL' interchangeably, but there is a distinction. A data pipeline is an umbrella term that covers all types of pipelines, including ETL, streaming ETL, and ELT.

Local or in-house ETL frameworks require in-house data engineers and network experts to keep the operations going. However, it is difficult for small and midsized businesses to bear the cost of managing in-house resources and ensuring security and compliance in local deployment.

Cloud ETL is based on distributed computing resources to ensure flexibility and efficiency. The most significant aspect of cloud ETL is it possesses no upfront costs, making it favorable for businesses of all sizes. Cloud ETL also provides enterprise-grade security to ensure regulatory compliance.

Highlights of ETL Pipeline
Hierarchy of the Processes	ETL pipelines are data pipelines used for extracting, transforming, and loading data from source systems into a target system or database.
Objective	The main objective of ETL pipelines is to move data from one system to another system while transforming it at the staging state.
Maintenance	They require significant maintenance because they are often built using custom code and scripts. Slight changes to the source or target database require updating the pipelines.
Data Latency	ETL pipelines involve latency as they typically process data in batches.
Flexibility	ETL pipelines are less flexible than other types of data pipelines because they are designed to work with specific source and destination databases.

ETL Pipeline Use Cases

If implemented efficiently, ETL data pipelines can help break down data silos and offer a single point of coordination for businesses to rely on. Below are critical ETL pipeline use cases that give you more control over your data and resources.

Cloud Migration and Cleaning – ETL pipelines enable data migration from legacy systems to the cloud with optimized utilization of resources. They are used to centralize relevant data sources to ensure a complete, normalized, and cleaned version of the updated records.
Integrating Data Into the Centralized Repository – ETL pipelines are used to fetch data from disparate sources to integrate data from different sources in data lakes or warehouses for data analytics and visualization.
Business Intelligence – ETL pipelines transform data in a structured format and prepare and clean it to perform analytics and derive valuable insights using different BI tools.
Enriching CRMs – ETL pipelines can fetch data from different customer-facing systems and converge it into a centralized repository to evaluate marketing KPIs concerning campaign results, customer behavior, engagements, ROI, etc.
Internet of Things – They also fetch real-time data from IoT devices through stream processing and push it to the target database for businesses to gather meaningful insights and make informed decisions.

Phases in the ETL Process

The ETL process consists of three main steps: Extract, Transform, and Load.

Extract

The first step involves exporting data to the staging area from various sources such as business systems, APIs, sensor data, CRMs, ERPs, transactional databases, etc. The data from the sources may be structured, semi-structured, or unstructured. Data connectors are often used to automate the data collection process.

There are two different ways to perform data extraction in the ETL process.

Partial Extraction – In partial extraction, only a subset of the data will be extracted to the target system, as opposed to the entire dataset. This can be done using a notification method where the source sends an update notification to the system when the data is being modified. Then, only the modified data is extracted. This method increases efficiency, reduces complexity, and ensures higher data quality.
Full Extraction – In some systems, the data structures and framework make it difficult to find updated records. In this case, you opt for full extraction. The extracted data needs data normalization to avoid duplication of records. This method can be much more time-consuming and resource-intensive than partial extraction.

Transform

In the staging area, data transformation involves converting and processing the extracted raw data into a format that different applications can use. The data is filtered, cleansed, and transformed into a supported format to prepare for further operations.

This step also ensures the data's quality, compliance, normalization, and integrity.

Learn more about data transformation:

Load

The loading step involves writing the transformed data from the staging area to a target database. There are two ways to load data into the target system.

Full Loading – This pushes all records from the staging area to the target database. By doing so, your data undergoes exponential growth, and eventually, it becomes challenging to manage the overwhelming volume of data.
Incremental Loading – This involves only loading additional or updated records to keep the data manageable. It is simpler and more manageable than full loading.

Differences between ETL pipeline vs. data pipeline

Benefits of ETL Processes

ETL processes involve different advantages that allow businesses to use, analyze, and manage data more efficiently and effectively. Some advantages include:

Resource Optimization – ETL pipelines eliminate manual intervention by streamlining data processing and improving data quality, leading to better resource utilization.
Improved Compliance – ETL pipeline allows businesses to ensure data privacy and protection compliances by following industry regulations and encryption protocols.
Simplified Migration to Cloud – ETL helps in migrating data to the cloud by seamlessly transforming and loading data into desired formats and locations.
Efficient Analysis – ETL solutions allow businesses to transform raw data into structured and organized datasets that are easier to analyze, leading to more efficient and accurate data analysis and business intelligence.
Clean and Reliable Data – ETL pipelines ensure that the final data loaded into the system remains clean from all redundancies, inconsistencies, and duplications so that businesses can access updated and reliable information to drive their business-critical operations.

Challenges with ETL

The ETL infrastructure poses some significant challenges and limitations. The primary reason behind these limitations is that the 'transform' process carried out during the data transition at a middle layer makes ETL design more complex. Some of these challenges include:

Building and maintaining reliable data pipelines is slow and difficult
Complex code and limited reusability of pipelines
Managing data quality in increasingly complex pipeline architectures is challenging
Insufficient data is often allowed to flow through a pipeline undetected
Extensive custom code is required to implement quality checks and validation at every step of the pipeline.
Increased operational load in managing large and complex pipelines
Pipeline failures are difficult to identify and solve due to a lack of visibility and tooling

Some of the downsides associated with ETL pipelines are solved by implementing an ELT process, which is considered more modern. Zuar's platform employs ELT for data collection. Learn the difference between ETL vs. ELT:

ETL Best Practices

When building an ETL data pipeline, it's important to follow best practices to ensure long-term success for your data pipeline. Listed below are five best practices for ETL pipelines.

Data Profiling – Prior to the extract stage of ETL, data profiling is recommended. This entails reviewing your source data to determine its quality, structure, and relationships with other data. This process helps to eliminate errors and ensures that your data is high-quality before being extracted.
Incremental Loading – As mentioned above, incremental loading is considered a best practice because it takes less time, is less resource-intensive, and can lead to fewer errors in your data.
Data Validation – This is the process of validating that the data has been loaded into your target system correctly. After completing the ETL process, it's important that all the data is accurate, consistent, and complete.
Error-Handling – There needs to be processes and mechanisms in place that detect and report errors that occur in the ETL pipeline. Errors in data pipelines can lead to disruptions in day-to-day operations, so it's important to be able to identify and fix them as quickly as possible.
Documentation – It's also important to document all aspects of the ETL pipeline. Where the data is coming from, how the data is being mapped, and how the data is being transformed are all examples of information that should be well-documented. This ensures that others in the organization can easily understand the data pipeline and that it can be maintained in the long term.

Automation & Optimization with Fully-Managed ELT

With the increasing complexity of applications and networks, businesses need easy-to-use, secure, and scalable cloud solutions. Fully-managed and streamlined ELT solutions are almost universally preferred over ETL solutions due to their better flexibility, scalability, and cost-effectiveness.

To embrace hassle-free and streamlined data architecture, opt for the fully-managed Zuar ELT platform, which automates end-to-end data workflows. By automating every stage of your data pipeline, you can focus on what matters; making data-driven decisions that drive your business forward.

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