What is ETL, and how does it work?

ETL, which stands for extract, transform, and load, is a process of combining data from multiple sources so it can be cleaned and organized in a single system, typically a data lake or data warehouse, for storage, analytics, and reporting.

How does ETL work?

Extract, transform, and load (ETL) is a three-step computing process. It is designed to enforce specific data types and data validity standards onto data pulled from multiple sources with the end goal of loading it into a single, central repository.

Let’s look at these three phases more closely. 

Extract

First, data is extracted from the source systems. These sources may include databases, apps, CSV and XML files, SQL servers, web pages, email, APIs, IoT, and social media. It’s common for data from these sources to have different forms of organization: structured, unstructured, semi-structured, and distinct formats. 

When extracting data, raw data is sent from these disparate sources to a single staging area. Basic validation checks are run to detect missing and corrupt files, but the data typically arrives unaltered in preparation for the second phase.

Transform

Now, it’s time for the raw data to be processed. During this phase, data in the staging area is validated, cleansed, and converted into a format or structure that matches the target system. This involves new rules and functions being applied to the data.

Transformation processes include:

• Data cleansing: Using interpolation to identify and fill in missing data values and null values
• Data validation: Ensuring data follows a consistent format and is within a valid range 
• Data aggregation: Conducting calculations to create totals, averages, and counts for vital metrics such as revenue, purchase value, profit margin, and product rating
• Data mapping: Defining relationships between similar elements for different data types. For example, mapping “customer_name” from the source system to ‘full_name” in the target system
• Data enrichment: Creating new columns and data from existing data to make it more relevant and valuable

Load

Data is loaded into the target system during the final phase. The transform and load ETL processes move data from the staging area to its final destination, typically a data warehouse. Data is loaded in full, not in stages, for the first data migration. Then, incremental data changes are usually made during refreshes to save time and resources thereafter.

ETL vs ELT

ELT (extract, load, and transform) is a similar data transformation process using the same concepts but in a different order. ELT also exports data from various sources during the first step. However, rather than loading this data to a staging area ready for transformation, it is added directly to a target database. Only after uploading is the data then converted to the required format.

ETL is generally preferable for stricter forms of data processing. This method cleanses and transforms data before it’s uploaded to a new database, which is preferable for data governance, compliance, and data security. ETL allows the redaction of sensitive data prior to uploading, for example, which offers improved privacy outcomes. In contrast, ELT involves ingesting masses of raw, unfiltered data.

Let’s quickly look at when you might prefer ELT to ELT and vice versa.

• Advantages of ETL: Data is cleansed, transformed, and of the highest quality before loading. ETL is best for complex transformations and compliance-driven workflows involving smaller sets of structured data.

• Advantages of ELT: In a word: Speed. High volumes of data can be loaded quickly. ELT is best for unstructured data in big data environments and for tasks such as machine learning and AI models where raw data is required.

What are the benefits of ETL? 

The extract, load, and transform process brings structure and organization to raw data. Large sets of unstructured data are unwieldy and difficult to analyze, making it challenging to extract insights and value. 

Having a centralized repository – a data catalog with clean, reliable, and actionable data – can be transformative for tasks including reporting and analytics, market analysis, performance monitoring, and more.

1. Data consolidation

ETL consolidates data into a single source of truth (SSOT). When you have one reference point for your data, everyone can work with accurate and reliable information, which improves the quality of core processes and decision-making. Everyone is essentially singing from the same hymn sheet. There is no confusion or inefficiency, which can drag on productivity and continuity. 

2. Data transformation

Data is not only consolidated but transformed with errors, duplicates, and inconsistencies removed, leaving you with a standardized and high-quality data set. The extraction and transformation methods involve exhaustive checks and validations. These rules are set up before execution during the design and development phase, giving you full control over the quality of your data.

3. Data integrity 

ETL underscores compliance and governance efforts by maintaining the accuracy, consistency, and integrity of data, which are required to meet regulatory requirements for GLBA, GDPR, APA, and other forms of legislation. ETL tools create audit trails, set data retention policies, ensure data encryption, and automate reporting. All of these are critical for compliance.

4. Data accessibility

Bringing together data from multiple sources into a single data lake or data warehouse improves accessibility and efficiency. ETL transforms data into a structured format that’s easier for non-technical users to understand. This allows different departments with varying expertise to interact with data. High-quality data is at people’s fingertips and not locked away in raw, unintelligible formats.

ETL use cases and case study 

ETL is widely used across industries and can be applied to improve data quality for various scenarios. Common uses include business intelligence (BI), data warehousing, cloud migration, predictive maintenance, and compliance and governance.

Let’s look at a specific case study in more detail.

Real-time analytics for business intelligence

British automotive services company Cox Automotive adopted a modern ETL solution after growing frustrated with “data drift” changes in the characteristics of input data, which decreases the accuracy of a data model over time.

Rather than analyzing data, analysts were spending 80% of their time “wrangling it into shape” and trying to get it prepped and ready for the actual analysis platform.

ETL offered a solution by supporting the creation of a central repository where data assets from every business unit would reside. The data lake was able to handle data environments dynamically, so pipelines remained relevant and accurate, even when sources and structures changed. 

This was transformative for BI: analysts could finally concentrate on data analysis and visualization.

Other use cases include:

• Data migration: ETL can facilitate and streamline data migration when upgrading legacy systems, merging enterprise data systems after an acquisition, or moving to cloud storage platforms
• Compliance: ETL can consolidate, cleanse and standardize data for compliance with regulations. ETL tools combine data from transactional databases, marketing tools, and support logs and then transform and load it into a centralized repository complete with audit trails and reports.

How to choose the right ETL tool? 

Selecting the right ETL tool for data capture and integration will make the process easier. ETL tools are software. They extract, transform, and load data into your target system. A good tool integrates all these processes while balancing factors, including ease of use, scalability, and cost.

When selecting a tool for data engineers, follow these steps.

Step 1. Assess your needs

Start by creating a strategy for data integration. Determine the amount of data and types you want to consolidate and define the business initiatives that will be supported. You should have a clear understanding of what data needs to be moved, where it will end up (target system), and how it will be used.

Step 2. Evaluate transformation capabilities

Next, you need to match your objectives with transformation elements. How does your data need to be cleansed and collated for end users to extract insights easily? You should also consider whether you need real-time processing or machine learning functionality. 

Common transformation processes include deduplication, derivation, aggregating, joining, splitting, cleaning, mapping, and filtering.

Step 3. Create a shortlist

Now that you know what you need to achieve, start shortlisting viable tools. You want these tools with strong transformation features that match your requirements and criteria. Popular tools include AWS Glue, Google Cloud Data Flow, Microsoft SQL Service Integration Services (SSIS), and Azure Data Factory.

Step 4. Conduct research

Research the tools on your shortlist to find the perfect match. Ideally, tools should be scalable and flexible and integrate with existing databases and cloud platforms if you aren’t planning a wholesale migration. 

Consider the costs, too: evaluate upfront fees and long-term operational costs, including storage systems, computing resources, and maintenance.

Step 5. Perform proof-of-concepts (and live demos)

Industry solutions need to be tested. Invite vendors to demonstrate their capabilities and perform a proof-of-concept (PoC) to put the ETL tool through its paces in a live environment. This will allow you to test scalability, error handling, and speed before finalizing a deal.

Future trends in ETL 

ETL will need to evolve to cater to growing demands in frequency, scale, and speed from business users who are prioritizing data integration for a host of business activities. It will also be key to unlocking the full potential of the cloud, generative AI, and real-time processing. 

Several key trends that will shape the future of ETL include:

• Cloud-native ETL: Challenges with the amount of data loaded will be addressed by cloud-native ETL solutions capable of transforming large batches by scaling infrastructure and deploying parallel processing. Expect more businesses to migrate to cloud data warehouses and leverage native tools.
• Real-time data pipelines: Analysts don’t want to wait days to leverage new data. Real-time data pipelines with stream processing will deliver continuous data integration so they can extract insights instantly and make smarter, faster decisions, enhancing business agility.
• AI/ML-powered ETL: The power of AI will bring about a step-change in ETL tooling and capabilities. Engineers will be able to automate data discovery, checks, and transformations. 
• Serverless ETL: Businesses will reduce overheads and streamline maintenance with serverless ETL solutions. Running data pipelines without having to manage infrastructure will free time and resources.
• Data lakes and data mesh: The lines between different tools and architecture will be blurred as ETL tooling more closely integrates with data lakes and data mesh architectures. This will change data capture, empowering businesses to easily manage and analyze big sets of diverse data.

Summing up 

Extraction, transformation, and load processing (ETL) bring data out of the darkness and into the light. By combining data and storing it in a single location, data is no longer a burden, a source of frustration holding teams back. It transforms into a complete, flexible, and scalable asset you can derive value from it every day.

With cloud-based ETL data tooling now available, there has never been a better time to consign legacy systems to the past and embrace a new age of data management.

Recordpoint can perform a data migration using the ETL methodology. We can help you extract all of your data from the content source and then cleanse, build, test, and migrate it to our cloud-based platform. 

Do you want to manage your data in one place and reap the benefits of automatic record ingestion, indexing, and classification? Contact us today to start your journey to a brighter data future.

FAQs

What are the differences between ETL and ELT?

Both ETL and ELT use the same processes but in a different order. ETL (Extract, Transform, Load) involves cleansing and applying transformations before data is loaded into a database. In contrast, ELT (Extract, Load, Transform) loads the data first and then applies transformations afterward.

How often should ETL processes be run?

ETL processes can be automated to run regularly or triggered by input events to keep data accurate and relevant. The frequency depends on business needs and data type. ETL can run continuously for real-time data analysis. However, smaller data repositories may only need refreshing weekly.

What is an example of ETL?

Consider a retail company gathering data from multiple stores and sources — online orders, point-of-sale (POS) systems, and inventory databases, to create a single data warehouse. The data is extracted, cleaned, and converted or aggregated before being loaded onto a data platform.