You need an ETL strategy to leverage the big data your organization collects.
ETL moves data from its source applications to a central repository for data warehousing. From there, you can analyze it and discover insights to help your business grow.
But without an overarching strategy, you'll struggle to make ETL work for you.
In this guide, we'll explore the core components of a successful ETL strategy, how to choose the best ETL tool and process, and actionable insights for successful implementation.
ETL stands for Extract, Transform, and Load. The first step to preparing an ETL strategy is understanding these three steps.
This process takes raw data and moves it into a central location where organizations can use it to make informed decisions and gain a competitive edge in their industry.
This step involves ingesting data from various sources such as databases, APIs, and spreadsheets.
Data ingestion is crucial to determine the data quality used for analysis and insights.
The extraction process can be complex as it involves using various tools and techniques to extract data for a more consistent workflow.
High-quality data can be used to create a reliable data pipeline.
Organizations can easily extract data from multiple sources if they have the right connectors, but not all ETL tools include these by default.
This step involves cleaning, enriching, and structuring the extracted data to make it suitable for analysis.
Transformation is essential as raw data sets are often messy, inconsistent, and unstructured. This reality makes it challenging to derive meaningful insights.
Transformation involves various techniques and tools, including data mapping, aggregation, filtering, and normalization. It may apply metadata and schema or convert between formats like JSON and XML.
Even though it can be complex, transformation is essential to turn raw data into meaningful insights.
Transformation can identify patterns, trends, and relationships that may not be apparent in the raw data.
This is where the transformed data is loaded into a target system, often a data warehouse or data lake like Snowflake, Google BigQuery, or Amazon Redshift.
The load process involves various techniques to ensure the data is loaded accurately and efficiently.
Proper loading enables organizations to gain insights and make data-driven decisions quickly.
Despite the "ETL" terminology, most of today's data pipelines use a cloud-based modern ELT process. Here's the difference between the two.
Traditional ETL involves extracting in a staging area and transforming data before loading it into a target system.
This process requires significant processing power, involving complex data transformations on local machines.
When dealing with large datasets, you'll need lots of computer resources to transform them into usable formats.
Best suited for smaller datasets where you need granular control over the data.
Enables complex transformation logic that may be difficult to implement in a modern ETL pipeline.
Slower processing compared to modern ETL processes.
Requires more complex and expensive infrastructure to handle unstructured data or data that requires significant transformations.
Maintaining and updating the ETL pipeline can be more difficult as data and business needs change.
Modern ELT (extract, load, transform) loads raw data into a cloud-based target data store and performs transformations after the data is loaded.
This approach takes advantage of cloud computing and is better suited for larger datasets.
It has faster processing and lower infrastructure costs.
Data engineers can extract, transform, and load data quickly and with near-infinite scalability.
Better suited for larger datasets where quick processing times are important.
Lower infrastructure costs compared to traditional ETL processes.
Better cloud compatibility since modern ETL processes are cloud-based.
Allows for more flexibility in terms of source data and targets.
Maintaining data over time can be easier since transformations are performed on the fly.
Less control over the data transformation process compared to traditional ETL processes.
It may not be suitable for highly complex transformation logic requiring granular data control.
It can be more difficult to troubleshoot and debug since transformations are performed after loading into the target destination.
Modern ELT may require additional data validation and testing to ensure the accuracy of the transformed data.
Choosing the right ETL process can be daunting, considering multiple factors. Here are the key factors you should consider when selecting an ETL process.
The ETL process must handle the size and diversity of data sources and connectors.
Traditional ETL may be more appropriate if you don't expect to process large amounts of data.
However, modern ETL is better for more volume or when using cloud-based data storage.
Real-time streaming is better suited for companies that require up-to-date data but costs more.
Batch processing, on the other hand, is more cost-effective for companies that can afford to wait for periodic updates.
Align your business requirements and goals with the capabilities of ETL tools.
For example, a modern ETL process that utilizes real-time streaming may be the best fit if your business requires real-time data processing for time-sensitive decision-making.
You must choose a compliant ETL tool and data repository if you have industry-specific regulatory or auditing requirements.
Businesses must evaluate the resources they have and the budget they have set in place.
Consider the total cost of ownership. Budgets should cover the hardware and software requirements needed to run the ETL process and the financial resources necessary to purchase or license the ETL tools.
You can choose either SaaS or open-source ETL tools. Remember that open-source ETL tools also require purchasing and managing the hardware.
Various industries use the ETL process to integrate, clean, and transform data for analysis, reporting, and visualization. Let's explore some common use cases for ETL.
Marketing analytics enables businesses to view their customer data from various sources in one place.
ETL can provide a unified dashboard view of marketing and sales interactions and behaviors by consolidating data from multiple data sources, such as sales, customer service, CRMs, and marketing campaigns.
These data analytics can help organizations gain insights into customer preferences, behavior patterns, and purchase history, allowing them to optimize marketing efforts and improve customer retention.
ETL can also be used for business intelligence by helping businesses identify new target markets, tailor their messaging to specific customer segments, and improve the overall customer experience.
Healthcare organizations often have to deal with patient data coming from different sources.
They can use ETL processes to consolidate this data into a single source of truth, making it easier to access and analyze.
This can help them gain a complete view of a patient's medical history, improve the accuracy of diagnoses, and ultimately improve patient care.
ETL processes can also help with compliance efforts by ensuring that all necessary data is available, up to date, and compliant with privacy regulations.
In finance, ETL can extract data from multiple sources, such as accounting systems, ERP platforms, and internal SQL or Postgres databases.
The goal is to create a single source of truth for financial data, ensuring its accuracy and timeliness.
Financial analysts and stakeholders can access the information they need to make informed decisions and comply with regulatory requirements.
ETL can also help automate financial reporting processes, saving time and reducing errors.
ETL plays a critical role in supply chain management by integrating data from multiple sources, such as vendors, distributors, and retailers, to provide a comprehensive view of the supply chain.
This allows for more informed decision-making, improved forecasting, and better data management.
Additionally, ETL can help improve customer satisfaction by providing real-time data on order status and shipment tracking.
ETL can be used in e-commerce analytics to transform data from various sources, such as website traffic, sales transactions, and customer behavior data.
You'll gain insights into customer behavior, sales trends, and product performance.
ETL can also help to integrate data from multiple e-commerce platforms, such as Amazon, Shopify, and eBay, to gain a holistic view of sales and customer data.
Business leaders can use these insights to make data-driven decisions and optimize e-commerce operations.
Social media analysis involves analyzing vast amounts of data from various social media platforms to extract valuable insights.
ETL can help consolidate data from different sources, such as Twitter, Facebook, and Instagram, in real time.
That semi-structured data can be used for performing sentiment, trend, and customer feedback analysis.
ETL can help brands better understand customer perceptions and opinions about their brand, products, and services.
Machine learning is also an excellent use case of ETL as it requires large amounts of data to be cleaned and imported in a single place.
ETL processes can convert data from various sources into a format usable for training machine learning algorithms.
For SaaS providers, you can embed ETL operations behind the scenes for data importing or data reporting functions.
One of the main reasons for building an ETL strategy is understanding the benefits ETL offers.
ETL can improve data quality and consistency by eliminating duplicate data and compiling data from multiple sources into a single comprehensive entry.
It can also standardize data formats and ensure data completeness.
ETL processes provide faster access to integrated data by automating data collection, transformation, and loading.
This eliminates the need for manual data integration processes, which can be time-consuming and error-prone.
ETL enables businesses to make better decisions by providing more accurate and comprehensive data.
It ensures that decision-makers can access consistent and reliable information by integrating data from multiple sources and transforming it into a standardized format.
ETL tools can automate the data integration process, thereby reducing the need for manual work.
This results in increased productivity and efficiency and reduced errors caused by manual processes.
Additionally, automated data integration can free up resources for other important tasks, such as data analysis and decision-making.
Effective ETL provides numerous benefits, as seen in the previous section. However, there are also challenges that organizations may face when implementing ETL processes.
The most common include:
Data quality issues are a common challenge in ETL processes due to the large volume and variety of data sources.
These issues can arise from inconsistencies, duplications, missing values, and errors in the data, which can affect the accuracy and completeness of the integrated data.
ETL developers must implement measures to identify and address these issues to ensure the quality and reliability of the integrated data.
Scalability and performance issues can arise when processing large volumes of data or when dealing with unpredictable data sources or large bursts.
ETL processes must be designed to handle these challenges and ensure that data is processed and integrated promptly and efficiently.
This can involve optimizing hardware and software resources, implementing parallel processing, and using distributed computing frameworks like Hadoop or Spark.
There can be challenges when integrating with legacy systems due to their older technology, especially on-premise architecture that's incompatible with modern ETL processes.
This can require additional effort and resources to ensure smooth integration.
Traditional ETL processes, while not ideal, are another option that can improve compatibility with legacy systems.
Organizations must ensure that sensitive data is protected throughout the ETL process and complies with regulations like HIPAA and GDPR.
Any breach in security or failure to comply with regulations can result in severe consequences, including fines, lawsuits, and reputation damage.
Additionally, data privacy concerns have become a growing issue, making it essential to protect sensitive data throughout the ETL process.
If you're developing an ETL strategy from scratch, follow these best practices to help streamline the data integration process.
Define clear goals and objectives for data integration. These will guide your strategy and the specific pipeline and tool you choose.
Use standardized data formats and structures to ensure consistency and compatibility throughout the ETL process.
Test and validate the data at each stage to identify errors or inconsistencies early on.
Monitor and maintain the ETL process regularly to ensure that it runs smoothly and efficiently and that any issues are identified and addressed promptly.
Choosing a tool is one of the most important decisions for your ETL strategy. The right integration tool can ensure everything runs smoothly, while the wrong tool can derail your plans and slow everything down.
Consider these factors:
Determine your company's specific needs and requirements before choosing an ETL tool.
Consider what you'll need for your data, including data types, volume, frequency, formats, transformation requirements, and more.
Look for an ETL solution that integrates well with your source systems, such as databases, cloud storage, or APIs.
If you already have a data storage solution like a data warehouse or data lake, choose a tool that integrates with it.
Portable integrates with long-tail data sources, and the team creates custom connectors on request.
A scalable solution will handle increasing amounts of data and data sources. A solution that becomes unwieldy or cost-prohibitive when scaling will hold you back.
Ease of use ensures that different team members can use the tool effectively.
Good vendor support is essential when issues arise or if you need assistance. Look for a tool with fast and friendly help on all plans.
A smart ETL strategy is a core capability of the modern data stack. It can help you extract, transform, and load mission-critical data into a target system. You can leverage the power of that data to make informed decisions, gain a competitive advantage, and drive growth and innovation in your organization.
Choosing an ETL tool that meets your needs is one of your most significant decisions. The right tool can make everything else much easier, but the wrong tool can hamstring your efforts with erroneous data.
If you can't find an ETL tool that integrates with the smaller but must-have platforms you use, consider Portable.
Portable is the best solution for long-tail ETL needs. We currently have 300+ plug-and-play connectors, with more on the way. And we create custom connectors with lightning-fast turnaround times. Try Portable for free today!