ETL: Connecting All Your Data

Businesses are collecting vast amounts of data from various sources on a regular basis. But the challenge lies in making sense of this ever-growing complexity and volume of data and using it to drive advantageous decisions. This is where ETL comes in.

ETL is a crucial aspect of data management that provides accurate, timely, and relevant outputs, allowing businesses to consolidate data from diverse sources, identify trends, and make informed decisions quickly.

This blog post will provide an in-depth overview of ETL, its components, utility in various fields, challenges, and solutions.

Highlights:

• ETL is a data integration process used to acquire, remodel, and redirect data from multiple sources into a new centralized location, such as a data warehouse or data lake.
• Cloud computing is a system that provides the infrastructure for data processing and storage. However, ETL is necessary for managing and preparing data for analysis in this cloud environment.
• ETL pipelines must integrate and synchronize data from diverse sources, which can be complicated by system compatibility and scalability limitations. Not recognizing the warning signs of these issues can result in data quality and consistency problems.

What is ETL?

ETL is a data integration process used to acquire, remodel, and redirect data from multiple sources into a new centralized location, such as a data warehouse or data lake.

This process has passively been in use since the 1970s, when the need for centralized data repositories first arose. However, it wasn’t until the 1980s-90s transition period, with the advent of data warehouses, that purpose-built ETL tools came into existence. These tools helped extract data from siloed systems, transform it into a suitable format, and load it into data warehouses.

Data engineering makes up the heart of ETL as the discipline comprises automating ingestion, transformation, delivery, and sharing of data for analysis via repeatable data pipelines. A data pipeline consists of data-processing elements that help move data from source to destination and often from raw to analytics-ready format.

These tools enable comprehensive data integration while allowing different types of data to work together. They make it possible to migrate data between various sources, destinations, and analysis tools. The ETL process has become an integral part of data management strategies and is crucial in generating valuable insights through the production of accurate and relevant business intelligence.

Today, reverse ETL is also becoming common, where cleaned and transformed data is sent back into the business application for processing.

ETL Process

The ETL process is a vital component of any data integration strategy, and it involves three core stages: Extract, Transform, and Load.

ETL Extract

In this phase, data sources are identified, and specific rows, columns, and fields are extracted from these sources, including transactional databases, hosted business applications, and other data storage platforms. It’s essential to estimate data volumes from each data source to create suitable plans for every ETL stage. Extracting data without negatively affecting source systems or response times is critical.

To execute complex strategies, data must travel freely between systems and apps. In this first ETL step, structured and unstructured data is imported and consolidated into a single repository from a wide range of sources, including existing databases, legacy systems, and cloud environments.

There exist three common methods for data extraction:

Update Notification
One of the simplest ways to extract data is through update notifications. This involves having the source system notify when a record has been changed. Many databases provide this mechanism, and SaaS applications offer similar functionality through webhooks.

Incremental Extraction
Some systems can identify modified records and provide an extract of those records. In the steps following extraction, the system needs to identify these changes and propagate them down. However, deleted records may not be detected using this method.

Full Extraction
For some systems, reloading all data is the only way to extract it as they cannot identify which data has been changed. This method requires retaining a copy of the last extract to check which records are new. However, it involves high data transfer volumes and is recommended only for small tables as a last resort.

ETL Transform

Transformation is a critical phase of the ETL process that involves cleaning and aggregating data to prepare it for analysis. There are two ways to approach this step: multistage data transformation and in-warehouse data transformation. The latter has become the default approach due to the increased performance and scalability of modern analytics databases and the ability to write in-database transformations in SQL.

The process of data transformation involves several sub-processes, including cleansing, standardization, deduplication, verification, sorting, and other optional tasks. Transformation improves data integrity, removes duplicates, and ensures that raw data is compatible and ready to use. Rules and regulations can be applied during this phase to ensure data quality and accessibility and to meet reporting requirements.

Some of the most common types of transformation include:

Basic transformations

• Cleaning: Resolving inconsistencies and missing values in the data.
• Deduplication: Identifying and removing duplicate records.
• Format revision: Converting character sets, units of measurement, and date/time format.
• Key restructuring: Establishing key relationships across tables.

Advanced transformations

• Derivation: Applying business rules to data to derive new calculated values from existing data.
• Filtering: Selecting specific rows and/or columns.
• Joining: Linking data from multiple sources.
• Splitting: Separating a single column into multiple columns.
• Data validation: Validating data against predetermined rules.
• Summarization: Calculating total figures and storing them at different levels as business metrics.
• Aggregation: Combining data elements from multiple sources and databases.
• Integration: Standardizing data names and values for the same data element across different sources.

ETL Load

The final stage of the ETL process is storing the transformed data in a target system, such as a database, data warehouse, or analytics platform. To ensure that the target environment isn’t negatively impacted, it’s important to consider the intended use of the data during the load stage. Some common uses of loaded data include machine learning-based fraud detection, business intelligence and analytics, real-time alerting systems, and site search tools. For instance, when loading data into Amazon Redshift, it’s recommended to use infrequent large batches instead of small frequent ones to avoid overwhelming the system and causing performance issues.

Loading data into a warehouse can be done through two methods:

Full Load

• All data from the source is loaded into the warehouse in a single dump.
• This is the best method to use for initial data loading.
• It is straightforward and requires less effort.
• It takes more time to complete than incremental loading.

Incremental Load

• Only new and updated records are loaded at regular intervals.
• Done in two ways: streaming and batch.
• Streaming incremental loading is better for small data volumes, while batch incremental loading is more efficient for large data volumes.
• Requires more effort as the ETL must be checked for new/updated rows.
• Recovery from issues can also be harder.

When deciding which method to use, it’s important to consider the size and complexity of the transformed data and the frequency of updates to the source data.

Critical Components of ETL

When choosing an ETL process, it’s important to consider the following critical components:

1. Incremental loading: Support for change data capture allows for incremental loading, which updates the analytics warehouse with new data without a full reload of the entire dataset.
2. Auditing and logging: Detailed logging within the ETL pipeline is necessary for auditing data after loading and debugging errors.
3. Handling multiple data formats: The process should be able to handle various data formats from diverse sources, such as APIs and databases.
4. Fault tolerance: The ETL system must recover gracefully, ensuring data can move through the pipeline even when encountering problems.
5. Notification support: Notification systems are necessary to alert users when data is inaccurate. This includes proactive notification to end users when API credentials expire, passing errors to developers to debug, and utilizing systems-level monitoring for networking and database errors.
6. Low latency: Decisions made in real-time require fresh data, so minimizing latency is critical.
7. Scalability: All components of the ETL process must scale to support arbitrarily large data volumes as companies grow.
8. Accuracy: Data must not be dropped or changed in a way that corrupts its meaning, and every data point should be auditable at every stage in the process.

What Are Connectors in ETL?

Connectors refer to the software components or modules that enable ETL tools to connect to various data sources and targets. They allow the tools to seamlessly perform data operations by providing a standardized interface. Most of these tools provide a library of pre-built connectors for popular data sources, such as SQL Server, Oracle, and MySQL, as well as generic connectors for other data sources.

Some common types of connectors include:

• Database Connectors: Allow ETL tools to connect to relational databases, such as MySQL, Oracle, and SQL Server.
• File Connectors: Allow connections to various file systems, such as CSV, JSON, and XML.
• Cloud Connectors: Connect to various cloud-based data sources.
• Big Data Connectors: Help in connecting to various big data platforms.
• Web Service Connectors: Facilitate connections to various web services, such as SOAP and REST.

ETL vs ELT

ETL (extract, transform, load) and ELT (extract, load, transform) are both methods to move data from various sources to a common data store. The primary difference between these methods is the order of operations.

 

Is ETL Needed for Businesses in the Cloud Era?

As more and more businesses shift their operations to the cloud, many people wonder if ETL is still relevant. The answer is a resounding “yes.” In fact, it has become particularly important in the cloud era, where enterprises are faced with more complexity, a faster pace, and larger data volumes.

Cloud computing provides the infrastructure for data processing and storage. However, ETL is necessary for managing and preparing data for analysis in this cloud environment. Its role in the cloud arena is the same as in a traditional data warehouse – to collect and transform data from multiple sources and prepare it for analysis in business intelligence tools. The rise of cloud computing has made it possible to perform extraction, transformation, and loading activities in a cloud environment, where computing resources are delivered over the internet.

The cloud environment provides flexible and scalable computing resources that can be accessed on demand, which is ideal for managing large amounts of data. The data may come from a variety of sources, including on-prem databases, cloud-based applications, or third-party data sources. ETL enables the collection and preparation of this data for use in business intelligence tools, such as data visualization software necessary for gaining insight into business performance and identifying areas for improvement.

Traditional vs Cloud-native ETL Tools

Traditional ETL tools are on-premise and require a dedicated IT team to set up and maintain. They typically operate on a batch-oriented processing model and may have limitations in scalability, performance, and flexibility.

On the other hand, cloud-native ETL tools are designed to run natively in the cloud, leveraging the scalability and reliability of cloud infrastructure. They offer a pay-as-you-go model and are typically easier to use, with drag-and-drop interfaces and pre-built connectors. Cloud-native ETL tools also can handle both batch and real-time processing, making them more versatile for modern data integration needs.

Benefits of ETL in Data Management

ETL enables organizations to extract data from multiple sources efficiently, transform it to meet their specific needs and load it into a target system. Some of the key benefits of using ETL in data management include:

Improved data quality and consistency

Standardizing data across different sources helps ensure data consistency and accuracy by reducing ineffectuality and errors. Data is cleaned and validated during the transformation process, which further improves its overall quality and consistency.

Reduced data errors and duplication

Data is thoroughly validated and checked for accuracy before it is loaded into the target system. Elimination of duplicate records leads to a reduction in storage space and a more streamlined database.

Increased efficiency and productivity

ETL processes automate the extraction, transformation, and loading of data, reducing the time and effort required to perform these tasks manually. They can be scheduled to run at specific times, freeing up staff to focus on other tasks. This enables organizations to process large volumes of data quickly and efficiently, improving productivity and reducing the risk of errors.

Data validation and cleansing

Data is checked for completeness, accuracy, and consistency during the transformation process. The cleansing techniques remove or correct erroneous data, leading to improved data quality and increased trust in the data.

Data integration and synchronization

Organizations can integrate data from multiple sources and synchronize it with their target database or data warehouse. This integration leads to improved data analytics, as data is combined from multiple sources to provide a more comprehensive view of the business. Data synchronization ensures that the data in the target database or data warehouse is up-to-date and accurate.

ETL Architectural Process: Best Practices

ETL architecture needs an architecture-first mindset, focusing on the organization’s mission, vision, goals, objectives, requirements, and capabilities. By taking the time to understand the organization’s needs and requirements, the architecture can be designed to positively impact the business, resulting in improved data quality, consistency, and increased productivity.

The process of designing the architecture consisted of 5 elaborate stages:

I. Understanding Organizational Requirements

To determine whether to build or buy an ETL solution, understand the organization’s data sources, usage, and latency requirements.

Identifying Data Sources: Organizations should prioritize the most critical data sources and decide how much time they are willing to invest in building new integrations. Startups and growing businesses often extract data from standardized sources like MongoDB, MySQL, Salesforce, and Google Analytics.

Determining Usage: Data usage, including who will use it, how they will use it, and where it will be loaded, is essential. The data’s purpose will affect how it is processed and delivered, such as provisioning data for analytics, which differs from using data to power products like Uber or Airbnb.

Considering Latency: The frequency of data updates depends on the nature and intended use. For systems alerting, it would need to be updated every second, whereas for financial reporting, once a week may suffice.

Build or Buy Decision: Organizations can decide whether to build or buy an ETL solution based on their requirements and data sources. A pre-existing solution is suitable for businesses that extract data from standard sources and use it primarily for analytics. Custom solutions may be necessary for companies that require real-time data analysis, such as Uber.

II. Auditing Data Sources

Building a profile of the data sources used by a business is essential in the architecture process. This includes considering production databases, as well as data sets in SaaS tools used for sales and marketing, customer support, and operations.

Common Data Sources: Production databases such as MongoDB, MySQL, PostgreSQL, MariaDB, Microsoft SQL Server, and Oracle are often utilized. Sales and marketing sources include web analytics, marketing automation tools, email service providers, and ad data sources. Customer support sources include CRM, help desks, and customer chat tools. Operational sources like payments, accounting, and shopping cart platforms are also critical.

Identifying Top Priorities: Identify top priorities, such as consistent financial data, to make tradeoffs between data completeness and speed of project completion.

III. Determining Data Extraction Approach

Once the data sources have been audited, determine the approach to extract data. Whether using a pre-built connector or custom code, the approach must align with the organization’s requirements.

Two questions guide the approach to data extraction:

1. What sources are being used?
2. What methods of data extraction will be used?

The answers to these questions will help decide the most efficient method of data extraction for the business.

IV. Building Cleansing Machinery

Presently, ETL systems focus on loading minimally processed data into the warehouse and leave most of the transformations for the analytics stage. However, some basic data cleansing is still required during the ETL process.

Restructuring data: The structure of data in the source data store can be different from the structure defined in the destination data store. Thus, data may need to be restructured before loading into the warehouse.

Data typing: Data must be typed when entering the system. This type is to be maintained throughout the ETL process to ensure consistency.

Data schema evolution: This consideration applies not only to internal systems but also to external data sources like SaaS APIs. Systems should be prepared to accommodate changes in data schema without human intervention as products and APIs evolve over time.

V. Managing the ETL Process

The final step in designing the architecture involves defining the necessary functionality to manage the ETL process, guaranteeing reliable and accurate delivery of data to the organization while building trust in its accuracy.

Job scheduling: The process should always run automatically or be triggered as required. The schedule for operation should be based on the needs of each data source.

Monitoring: If any system failures or data inaccuracies are detected during testing, one will want to know about it. Businesses should integrate their monitoring system with other alerting systems used in their technology infrastructure.

Recovery: Businesses need to prepare for any probable failure of their ETL process. The architecture must plan for failures and have mechanisms in place to recover from them.

ETL testing: Every step must have checks and balances in place to test data accuracy. Common data inaccuracies and their testing rules should be taken into consideration.

Security: Security measures must ensure that only necessary data is transferred securely. Storing sensitive customer information in analytics warehouses is not recommended unless absolutely necessary.

Key Challenges in ETL

When selecting an ETL product, keep in mind that the process can be complex and comes with inherent challenges. It is crucial to consider how these challenges may affect business and data usage.

Scaling

Building a scalable ETL pipeline is crucial, but it’s easy to overlook long-term maintenance requirements. Shortcuts taken during development may result in a pipeline that’s not easily expandable. Therefore, keep scalability in mind throughout the design process. Additionally, customer development should be considered as part of scaling to ensure that the pipeline meets the needs of the end users.

Data Transformation

Data transformation is a critical stage in the ETL process, and inaccuracies can have far-reaching consequences. One common mistake is tightly coupling different elements of the pipeline, which can make it difficult to modify or scale the pipeline later. Building a pipeline based only on the current data scale may also be problematic. It’s crucial to consider future data growth and design one’s pipeline to handle growth effectively.

Diverse Data Sources

ETL pipelines must integrate and synchronize data from diverse sources, which can be complicated due to system compatibility and scalability limitations. Not recognizing the warning signs of these issues can result in data quality and consistency problems. Focus on fundamental best practices rather than relying on tools and technologies to ensure that a business pipeline can handle diverse data sources effectively.

Handling Data Quality and Consistency

Data quality and consistency problems can arise from a range of issues, such as incorrect data mapping or errors in the ETL process. Implementing appropriate data quality checks and testing procedures is essential in maintaining data accuracy. Additionally, consider security measures, such as securely transferring data and limiting access to sensitive information to protect against data breaches.

ETL Tools

ETL tools used to derive commercial solutions can be broadly categorized into three types:

Commercial Tools

Commercial ETL tools are software products purchased or licensed from a vendor and may be deployed on-premise or on the cloud. These tools are designed to streamline the process by providing pre-built connectors, a drag-and-drop interface, and pre-configured transformations. Some popular commercial tools include:

Intellicus: A commercial ETL tool that offers comprehensive data integration capabilities. It is highly customizable and scalable, with robust features for data profiling, quality management, and governance. The platform is designed to help businesses easily extract, transform, and load data from multiple sources for effective analysis and decision-making.

Informatica PowerCenter: As a widely used ETL tool for large enterprises, it has a drag-and-drop interface and offers a range of advanced features for data integration, including data quality and profiling, data masking, and data governance.

Microsoft SQL Server Integration Services (SSIS): This tool is part of the Microsoft SQL Server suite of tools and is commonly used by organizations that already use Microsoft products. It has a graphical interface for building ETL workflows and supports a variety of data sources.

IBM InfoSphere DataStage: This is designed for large-scale data integration projects. It offers a range of advanced features, including parallel processing, job scheduling, and real-time data integration.

Open-Source Tools

Open-source ETL tools are free software products that can be downloaded and used by anyone. These tools are often cloud-based and offer a lower-cost alternative to commercial tools. Open-source tools are highly customizable and provide a wide range of connectors and transformations. Some of the popular open-source ETL tools are:

Apache NiFi: This is a powerful, web-based tool that allows one to create data flows by dragging and dropping processors onto a canvas. It supports a wide range of data sources and provides a real-time view of data as it flows through the system.

Talend Open Studio: This is a popular open-source tool that offers a wide range of connectors for different data sources. It has a drag-and-drop interface for building ETL workflows and includes features for data quality and governance.

Pentaho Data Integration: A part of the Pentaho Suite of tools, it has a graphical interface for building ETL workflows and supports a variety of data sources. It also includes advanced features such as data profiling, cleansing, and masking.

DIY Scripts

DIY ETL scripts are hand-coded solutions built in-house by an organization’s IT department. They offer the most flexibility but also require the most technical expertise. DIY scripts can be designed to meet specific business requirements and can be customized as necessary. However, the development and maintenance of a DIY ETL solution can be time-consuming and expensive. Some of the best examples of these solutions include:

Python: Python is a popular programming language for building DIY scripts. It has a large ecosystem of libraries and tools for working with data and provides a lot of flexibility for building custom ETL workflows.

Unix shell scripts: Unix shell scripts can be used to build simple extraction, transformation, and loading workflows that run on a schedule. They are lightweight and can be easily customized to meet specific requirements.

Microsoft PowerShell: PowerShell is a scripting language used widely in the Microsoft ecosystem. It can be used to build ETL workflows that integrate with other Microsoft tools and services.

ETL Use Cases

ETL utility is varied and widespread across different industries. They include:

Healthcare data integration

Healthcare organizations need to manage an enormous amount of patient data. ETL tools can help consolidate this data, improve accuracy, and make analysis easier. These tools can integrate data from various sources, including electronic health records, billing systems, and third-party data sources. They can also assist with HIPAA compliance by automating the process of de-identifying data and helping protect patient privacy. With ETL tools, healthcare providers can ensure they have access to accurate data for patient care, research, and operations.

Financial services data warehousing

Financial services organizations, including banks and insurance companies, deal with a vast amount of financial data. They need to manage this data in a secure, accurate, and timely manner. ETL solutions can help financial institutions consolidate and integrate data from different sources, including transaction data, market data, and customer data. Using these tools, organizations can create a centralized data warehouse that enables them to make informed decisions, detect fraud, and manage risk.

Business intelligence

ETL tools play a critical role in business intelligence, which involves analyzing data to gain insights into business performance. These solutions can extract data from various sources, transform it into a consistent format, and load it into a data warehouse or lake. This provides a centralized source of data for analysis, reporting, and visualization. Business intelligence teams can use ETL tools to create complex data pipelines that handle large volumes of data and automate data integration tasks.

Retail data analytics

Retailers can use ETL tools to integrate and analyze data from various sources, including point-of-sale systems, e-commerce platforms, social media, and customer loyalty programs. By consolidating this data into a centralized data warehouse or lake, retailers can gain insights into customer behavior, inventory management, and marketing effectiveness. ETL tools can also help retailers detect fraud, manage supply chains, and improve operational efficiency. Retailers can use these tools to automate the data integration process, which helps them analyze data faster and make more informed decisions.

Emerging Trends in ETL Technology

The technology landscape for ETL is constantly evolving, and there are a few trends that are emerging as key drivers of innovation.

Cloud-based ETL Solutions

The move towards cloud-based ETL solutions is rapidly gaining traction. Cloud-based extraction, transformation, and loading tools offer advantages over traditional on-premise tools, such as greater scalability, lower upfront costs, and faster deployment times.

Big Data Integration and Processing

As data generation continues to grow at an unprecedented rate, big data integration and processing have become a key priority for many organizations. ETL tools that can handle large volumes of data and support real-time processing are increasingly in demand.

Self-service ETL Tools

Another trend gaining popularity is the use of self-service ETL tools. These tools enable business users to perform data integration tasks without relying on IT teams, reducing the time and resources required to complete projects.

Predictions for the Future of ETL

While it is difficult to predict exactly how the ETL landscape will evolve, here’s what industry experts forecast:

Increased Automation and Machine Learning Capabilities

ETL tools will continue to become more automated and incorporate machine-learning capabilities to help identify patterns and anomalies in data. This will enable organizations to identify insights and opportunities from their data more easily and quickly.

More Advanced Data Profiling and Mapping Features

The tools will become more sophisticated in their ability to profile and map data, enabling organizations to manage their data integration projects more accurately and efficiently.

Greater Focus on Data Governance and Security

Finally, as organizations become more aware of the importance of protecting sensitive data and ensuring compliance with regulations, it is expected that there will be a greater emphasis on data governance and security in ETL tools. This will drive the development of more robust security features and compliance capabilities within the tools’ design.

Conclusion

ETL plays a vital role in ensuring data integration, accuracy, and consistency for businesses of all sizes. With the right tools and processes, organizations can efficiently extract data from various sources, transform it into a usable format, and load it into a target system. As technology continues to evolve, ETL processes are also expected to become more streamlined, automated, and cloud-based, with an increased focus on data governance and security. It’s essential for businesses to choose the right solution that meets their unique needs and supports their growth and data management goals.

About Intellicus

Intellicus is a business intelligence and analytics platform that offers a comprehensive suite of ETL features. The platform supports cloud and on-premise deployment and offers intuitive drag-and-drop interfaces for easy data transformation. Intellicus also provides data profiling, validation, and cleansing capabilities to ensure data accuracy. The advanced ETL features include real-time data synchronization, schema mapping, and support for a variety of data sources, including big data platforms.

Click here to learn more.