Everything You Need to Know About ETL (Extract, Transform & Load)

What Is ETL?

ETL stands for extract, transform, and load. It is a data integration process that extracts data from various data sources, transforms it into a single, consistent data store, and finally loads it into the data warehouse system. It provides the foundation for data analytics and machine learning in an organization. 

ETL allows businesses to integrate valuable data spread across multiple sources within the digital ecosystem and work with it. Proper ETL integration is an important aspect of organizational data strategy. Businesses can use ETL to extract data from legacy systems, cleanse and organize the data to improve data quality, and ensure data consistency so that specific business intelligence needs are addressed. The data is then loaded into a target database to create a consolidated view of enterprise data, which can lead to better business decisions. 

ETL vs ELT

ELT is another method of data integration, where instead of transforming the data before loading, the data is first copied to the target and then transformed. 

The key differences between ETL and ELT are:

• While ETL stands for Extract, Transform, and Load, ELT stands for Extract, Load, and Transformation.
• ETL loads data from the data source into the staging server and thereafter into the target system. But with ELT, data gets directly copied into the target system.  
• ETL cleanses sensitive and secure data before loading it into the data warehouse, thereby ensuring data privacy and data compliance. 
• ETL tool is usually used for data that is on-premises, relational, and structured, while ELT tool is used for scalable, cloud structured, as well as unstructured data. 
• It’s easy to implement ETL, whereas ELT requires expert skills for implementation and maintenance. 
• ETL can be more cost-effective compared to ELT

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Other Data Integration Methods

Besides ETL and ELT, some other data integration methods include:

• Change Data Capture (CDC) – captures changed source data only and moves that to the target system.
• Data Replication – replicates changes in data sources in real-time or batch by batch to a central repository.
• Data Virtualization – makes use of software abstraction layer to create an integrated view of data without actually loading or copying source data. 
• Stream Data Integration (SDI) – accepts data streams in real-time, transforms, and loads them onto the target system.  

How Does ETL Work?

The 3 steps of the ETL process ar— extract, transform and load. 

Step 1: Extract

The extraction process involves copying or exporting raw data from multiple locations called source locations and storing them in a staging location for further processing. 

Source locations can consist of any type of data, including SQL or NSQL servers, flat files, emails, logs, web pages, CRM, ERP systems, spreadsheets, logs, etc. 

Common data extraction methods are:

• Partial extraction with update notification
• Partial extraction without update notification
• Full extraction

Step 2: Transform

In the transformation stage of the ETL process, data in the staging area is transformed through the data processing phase to make it suitable for use for analytics. Raw data is converted to a consolidated, meaningful data set. 

Several tasks are performed on the data like:

• Cleaning and Standardization
• Verification and Validation
• Filtering and Sorting
• De-duplication
• Data audits
• Calculations, Translations 
• Formatting
• Data encryption, protection

Step 3: Load

In this final step of the ETL process, the transformed data is loaded onto its target destination, which can be a simple database or even a data warehouse. The size and complexity of data, along with the specific organizational needs, determine the nature of the destination. 

The load process can be:

Full loading – occurs only at the time of first data loading or for disaster recovery

Incremental loading – loading of updated data

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Benefits and Challenges of ETL (Extract, Transform, and Load)

ETL process improves data quality as data is cleansed before being loaded onto the final repository for further analytics. An automated data processing pipeline is provided to collect and format data without having to pass on data transformation tasks to other tools. 

However, ETL is a time-consuming batch operation, which is recommended for building smaller data repositories that do not need to be updated frequently. Other data integration tools like ELT, CDC, and data virtualization can be appropriately used to integrate larger volumes of data that require real-time updating.

ETL Supports Data Warehouses and Data Lakes

As organizations across industries need to store bulk data sets to support the increasing data needs, data warehousing has become common practice in recent times. Warehousing services like AWS Redshift and Google Big Query are offered by top cloud services. 

The rise in data complexity and the need to support numerous data sources have given rise to data lakes where users can store any type of structured and unstructured data at any scale. 

ETL supports both data warehouses and data lakes and provides an appropriate framework to extract data from any source, transform it and store it in the right storage service. Users can use an ETL pipeline with a data warehouse or data lake and create a centralized information base for every analytical need.  

ETL Tools

Some prominent ETL software tools are:

• Integrate.io
• Talend
• MarkLogic
• Oracle Data Integrator
• Amazon RedShift
• AWS Glue
• Matillion
• Azure Data Factory
• FlyData

Critical ETL Components

Some major ETL components to consider are:

• Managing Multiple Source Formats – to enable handling of various data formats
• Support for CDC (change data capture) – to allow incremental loading
• Auditing and logging – to ensure data can be audited post-loading and errors can be debugged
• Fault Recovery – to be able to recover gracefully in case of any problem in data movement
• Notification Support – built-in notifications to alert the user when data is not accurate
• Scalability – to be able to scale to support growing data volumes
• Accuracy – every data point must be capable of being checked at any stage 

Building Your ETL Strategy

ETL data integration can be achieved in one of two ways:

Building your own ETL – many organizations ask their developers to build their own ETL, which can be a time-consuming, expensive process prone to delays. 

Using ETL tools for data integration – most businesses today use ETL tools to incorporate their ETL strategy. Preferred for their speed, reliability, and cost-effectiveness, ETL tools are compatible with several data management strategies. These come with various data quality and data governance features. Choose the right ETL tool that connects all your data irrespective of source or destination. 

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Why Is ETL Important?

ETL helps ensure good data hygiene and improved data analytics. Plus, ETL tools perform many important business functions like:

• Reconciling different data formats to move data from legacy systems to modern technology
• Syncing external data from vendors, customers, suppliers
• Consolidating data from multiple overlapping systems
• Combining transactional data so that it can be understood by users

IoT Data Integration

The Internet of Things (IoT) is creating a connected global future for businesses through devices and systems that are part of the IoT. The IoT is producing huge volumes of data already, and the volume is predicted to grow at enormous rates in the future. ETL – particularly cloud-native ETL - will undoubtedly be essential to data integration and transformation from IoT sources. 

What is the Future of ETL?

In today’s ever-evolving data landscape, with the increasing importance of data lakes and cloud technologies, businesses need to be extra cautious so they can ensure that their ETL tools and strategies remain efficient, effective, and relevant even tomorrow. 

ETL Example

A typical ETL workflow example within an organization can include the following steps:

1. Connecting to a single or many data sources
2. Extracting batches of data into rows based on some criteria
3. Copying extracted data to the staging area
4. Transforming staged data 
5. Connecting to the target data warehouse and copying processed data for organized storage

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Conclusion

If you are considering working as a data warehouse professional or looking to advance your career in the field, gaining relevant skills will help you achieve your goals. Joining an online course offered by reputed e-learning leaders like Simplilearn could be your key to career success. Get started with our big data and analytics training today and get access to the best curriculum, training, career guidance, networking, certifications, and more.