ETL and data engineering are critical processes that let you extract, transform and load data from multiple sources.
Combined, they help your organization store, access, and use its data in an efficient and organized manner and make data-driven business decisions that positively impact your growth.
In this article, we'll guide you through everything you need about ETL and the solutions data engineering teams can implement to ensure an efficient ETL pipeline.
The ETL process involves three main steps:
This phase entails retrieving data from multiple sources, including APIs, files, and databases.
There are various data extraction techniques used depending on the data source, including:
The data transformation step involves modifying the extracted data into a unified, usable format that can be easily loaded into the target data warehouse.
Specifically, it entails cleaning up unnecessary information from the data, performing aggregations, handling validation, and finally standardizing the data into a consistent format.
This is the final stage in the ETL process. It involves taking transformed data and loading it into the target database for analysis, visualization, machine learning, or other uses.
Loading can involve different strategies based on the target data's storage system.
Loading data into a data lake involves less structured data that can be transformed later.
Loading is the final step of the ETL process and determines how data is stored, its usability in data visualization and downstream applications, and how users access it.
Data engineering is essential in the data analytics (predictive, prescriptive, descriptive, diagnostic) and management sector.
ETL and ELT are the most common data engineering approaches used to integrate data from multiple sources and prepare it for analysis.
While these terms are often used interchangeably, there's a significant difference between the two.
ETL involves extracting data from multiple sources, transforming it using specific schema, and loading it into a data warehouse.
ELT means Extract, Load, Transform. It entails retrieving data from multiple sources and loading it into a data lake before transforming it.
The primary difference between ETL and ELT is the data transformation timing. In ETL, data is transformed before it's loaded, whereas, in ELT, data is loaded before it's transformed.
ELT is more flexible and cost-effective for cloud-based sources. But ETL can be useful for specific data regulations or privacy requirements.
Numerous organizations across different industries use ETL for the following tasks:
Data warehousing involves collecting, integrating, and storing data from multiple sources in a data repository like a data warehouse.
The data is then analyzed for business intelligence using tools like Power BI.
You'll gain insights into business operations, make data-driven decisions, and improve company performance.
ETL is a powerful approach for migrating data into the cloud.
It allows for efficient and accurate data transfer while ensuring that the data is transformed and loaded in a way compatible with the cloud environment.
This assists companies in making the most out of cloud computing's scalability, flexibility, and cost-effectiveness while preserving their existing data assets.
ETL transforms data from multiple sources into a common structure for easy analysis.
With ETL, firms can combine data from disparate sources without the need for intricate manual processes.
This lets you get insights into your operations, make informed decisions, and enhance performance.
Real-time data processing entails processing data as it streams in.
This allows your firm to quickly detect and respond to anomalies and also identify opportunities, and improve overall business performance.
In ETL for real-time data processing, companies use tools known as stream processing frameworks, including Apache Kafka, Apache Flink, and Apache Spark Streaming.
These tools can handle high-volume data streams and provide features like windowing, state management, and fault tolerance.
These features ensure that data is processed accurately and efficiently in real-time.
Following data management best practices ensures that the data is of high quality and ready for accurate analysis and decision-making.
ETL is used to standardize formats and clean data to make sure the data is accurate, complete, and consistent across different systems.
ETL tools can automate the data cleaning and standardizing process, making managing large data volumes easier and more efficient.
Even though data engineering and ETL are closely related fields in data management, they aren't the same.
ETL is a specific process within data engineering. It involves retrieving data from different sources, changing it into a form that can be easily analyzed, and then loading it into a target system.
In contrast, data engineering entails a much broader range of activities related to data systems. These activities include development, management, and optimization.
Data engineering involves using tools to build and manage the infrastructure required for data storage, analysis, and processing.
Data engineers create systems that turn raw data into useful information. This information can help organizations make decisions.
They work with data scientists and architects to ensure data is stored correctly, collected when needed, and assessed best.
Data engineering is about managing data from beginning to end. It helps make data analysis more accessible by creating valuable systems and processes.
Data engineering requires various data science skills. This may include knowledge of software development and programming languages, querying languages like SQL, data modeling, database design, distributed systems, cloud computing, and machine learning.
ETL, on the other hand, is a type of data engineering. ETL is focused on data movement and transformation rather than the broader data management context.
ETL developers are responsible for maintaining ETL pipelines and ensuring data is correct using specific tools and technology.
ETL is an essential data engineering component. It enables data to be moved, transformed, and loaded from source to target systems.
ETL pipelines help companies organize their data. It puts all the data from different places into a centralized repository.
These pipelines help ensure data is clean, processed, and stored correctly for the data engineering team as a whole.
ETL developers and data engineers have some similar tasks, but there are also differences between them.
Companies hire ETL developers and data engineers who work alongside data analysts. The job of the analytics team as a whole is to analyze and manage the company's data.
ETL developers design, develop, and maintain ETL processes (when structured and unstructured data is imported into a repository).
ETL developers use ETL tools to move data. They set up pipelines to transfer data from one system to a central repository like a data warehouse.
In contrast, data engineers design, build and maintain the systems required for data processing, storage, and analysis.
They work with data architects, scientists, and other stakeholders to build pipelines and algorithms to effectively use data.
An ETL developer can become a data engineer with additional training and skills in programming languages, distributed systems, and cloud computing.
They may also learn to use tools and technologies commonly used in data engineering, like Python, SQL, Airflow, Tableau, Hadoop, and more.
The path to becoming a data engineer will vary depending on personal goals, experience, and opportunities.
|Key Differences||ETL Developers||Data Engineers|
|Focus||Designing, developing, and maintaining ETL processes||Designing, building, and maintaining data infrastructure and architecture|
|Skills needed||ETL tools, SQL, data modeling||Programming languages, distributed systems, cloud computing, machine learning, database design, data modeling|
|Responsibilities||Design and develop ETL pipelines||Design and build data pipelines for data processing, storage, and analysis|
|Average Salary||$90,000 - $130,000||$100,000 - $150,000|
ETL tools are essential to modern data engineering workflows. They help companies take data from different sources and turn it into the same format.
With the many cloud ETL tools available in the market, choosing one that fits your company's needs can be challenging.
These are some of the top ETL tools for data engineering:
Portable is the best data integration tool for teams with long-tail data sources.
The platform offers long-tail connectors for over 300 unknown data sources, thus giving users various options to meet their data integration requirements.
Portable stands out because it has long-tail connectors unavailable on most other platforms.
Fivetran is a cloud-based solution that supports integration with Snowflake, Azure, BigQuery, and Redshift data warehouses.
One of the major limitations of Fivetran is its lack of long-tail and custom integrations.
It supports more than 150 connectors, and it uses automated schema detection and mapping to simplify the ETL process while offering personalization options for complex data requirements.
Airbyte is a cloud-based open-source data integration site that provides a reliable and scalable way to transfer data from different sources to a data lake or warehouse.
Since it's open source, users can customize and extend the platform to meet their data integration needs.
Airbyte simplifies data integration by providing a user-friendly web-based interface to allow end users to set up and manage data pipelines without having any coding skills.
The site also supports various connectors, including popular ones like file storage systems.
Integrate is a data pipelining tool that can simplify the ETL process.
It has a simple and intuitive interface for creating data pipelines between various destinations and sources, eliminating data integration pain points.
Integrate.io is a one-stop shop for all data integration requirements, including observability and reverse ETL.
The site supports over 100 popular SaaS applications and data stores like Microsoft Azure SQL and Slack.
This data integration site offers various ETL and ELT capabilities. Users can integrate and connect data from different sources and load it to a target destination.
Talend's user-friendly drag-and-drop interface lets users manage and create a complex data integration workflow without coding skills.
The site also has various pre-built templates and connectors, making it simple to connect to common data sources.
It supports cloud-based and on-premises deployments for cloud platforms like Google Cloud.
AWS Glue is a fully managed ETL service from Amazon intended for analytic workloads and big data.
Since it's fully managed and end-to-end, it's designed to simplify moving data between data processing and storage services.
The data platform also automates data discovery, cataloging, and transformation. This makes data integration with AWS easier.
Since AWS Glue uses serverless architecture, it can automatically scale down or up to meet the workload's needs without needing users to manage any infrastructure.
Top data engineering teams typically use a mix of free ETL tools and commercially supported ETL solutions to streamline and automate their data operations.
ETL is a necessary process that is critical to any data engineering framework.
Portable is an ETL tool that helps you integrate long-tail sources into your data workflow. We have 350+ ETL connectors ready out of the box and develop custom integrations in as little as a few hours.
If your data engineering team struggles to find connectors for long-tail data sources, Portable is your answer. Try Portable free today!