In any business today, countless data sources generate data, some of it valuable. This data might go on to be used for business intelligence and many other use cases. But you cannot use that data as it’s gathered, primarily due to data inconsistency and varying quality.
The challenge here is twofold: connecting these inconsistent data sets in multiple formats and leveraging the appropriate technology to derive valuable insights. ETL serves as the foundation to overcome this challenge. This article digs deeper into:
- ETL processes, benefits and challenges
- Common ETL tools
- Differences between the ELT processes
What is ETL?
ETL refers to the three processes of extracting, transforming and loading data collected from multiple sources into a unified and consistent database. Typically, this single data source is a data warehouse with formatted data suitable for processing to gain analytics insights. ETL is a foundational data management practice.
In its early days, ETL was used primarily for computation and data analysis. Many organizations now use ETL for various machine learning and big data analytics processes to facilitate business intelligence.
Beyond simplifying data access for analysis and additional processing, ETL ensures data consistency and cleanliness across organizations. Organizations also use ETL to:
- Improve data quality
- Store legacy data
- Get a consolidated view of all the data throughout the business
Today, ETL is being used in all industries, including healthcare, manufacturing and finance, to make better-informed decisions and provide a better service to the end users.
Processes of ETL
ETL comprises three steps: Extract, Transform, and Load, and we’ll go into each one.
Step 1. Extract
In this phase, raw data is extracted from multiple sources and stored in a single repository. The raw data sources include:
- Customer relationship management (CRM) systems
- Machine data and Internet of Things (IoT) sensors
- Email repositories
- Web pages
- Enterprise Resource Planning (ERP) systems
- SQL or NoSQL servers
- Data from APIs
- And more
The source data can be structured, unstructured or semi-structured and in various formats, such as tables, JSON and XML. The Extract step includes validating the data and removing or flagging the invalid data. Data can be extracted in a few ways:
- Online extraction. Connect directly with the data source and extract the data.
- Offline extraction. Instead of extracting directly from the data source, the data is extracted to a separate staging area.
- Incremental extraction. Here, you extract only the data that has changed.
- Full extraction. In this, you extract all the data.
Step 2. Transform
The Transform phase involves converting data into a format that allows it to be loaded into consolidated data sources. During this phase, raw data gets processed in a staging area. The processing can include the following tasks:
- Removing duplicates
- Validating and cleaning data to avoid errors
- Mapping data to the required format
- Revising data formats to ensure consistency
- Filtering to remove unnecessary data
Additionally, you can apply advanced data transformation steps depending on the requirements. For example:
- Data summarization to reduce the size of the data set
- Splitting data into multiple columns
- Data joining
- Data derivation and creating new values
- Data encryption to comply with data privacy laws
(See how data normalization relates to the Transform step.)
Step 3. Load
Once the data extraction and processing steps are complete, the final process is loading the transformed data into the data warehouse from the staging area. This process is well-defined, continuous and automated, and usually the loading happens batch-wise. The following are methods for data loading:
- Complete data loading. This loading typically happens during the initial load. It involves extracting and transforming the data set from the data source to the data warehouse.
- Incremental data loading. Regularly loading only the updated data between the source and target systems. The ETL system should store the date and time the data was last extracted.
- Batch loading. Loading the data set batch-wise and periodically if it is too large.
- Streaming incremental load. This streams data continuously, with a major caveat: this loading type is suitable for smaller data sets.
Use cases for ETL
The use of ETL tools has rapidly grown in the past few years for common use cases like:
Data warehousing for Business Intelligence (BI)
Collect, analyze and organize data for business intelligence tasks like OLAP (online analytical processing), business reporting and creating dashboards from the data.
ETL processes are used to extract, transform and load data to cloud storage and databases when businesses migrate their systems and data to/from on-premises data centers to cloud environments.
Machine learning and artificial intelligence
With large data sets, you can create accurate machine learning models and perform classification and clustering tasks. The high-quality data in data warehouses can significantly accelerate the performance of ML models.
IoT data integration
For example, a lot of data can be generated on a manufacturing floor from multiple sensors. ETL allows for the automatic consolidation of this streaming data into a single location for further analysis. This applies most commonly to supplies, like in industrial, retail or manufacturing settings.
Market data integration
Marketing data can exist in different sources, like social networks, third-party websites, and web analytics sites. ETL allows for the collection of this data in order to analyze and visualize the impact of their marketing strategies.
ETL tools and their benefits
ETL tools allow automation of the tasks involved in these three processes when creating ETL pipelines. The major companies that provide ETL products and services include AWS, Google, IBM, Oracle and Microsoft. A few examples of ETL software tools include:
Here are the benefits you gain by integrating ETL tools into your business.
Improve data quality
Original data is frequently inconsistent, with missing values, errors and duplicates that prevent true business insights. ETL tools provide automated data cleaning steps like removing duplicates, replacing missing values, and removing errors, which help organizations improve data quality. It allows them to always maintain a standard data set readily available for various organizational tasks.
The transformed data in a data warehouse facilitates easy and fast access to the data. ETL tools can also include more performance-enhancing functionalities such as parallel processing and symmetric multi-processing. It allows business intelligence (BI) tools to directly query the data warehouse without processing the data. Thus, the tools can deliver reports and other results faster.
Reduce unnecessary costs
Maintaining data from multiple sources involves a lot of effort and resources. It will also create unnecessary maintenance costs for organizations. Raw data can be discarded or archived in another central repository when it is in consolidated data sources. Therefore, you’re both reducing costs and making it easier to store data in central data repository.
Unlike manual ETL processes, ETL tools automate every step, from data extraction to generating business value. This enables organizations to focus on adding value to the organization rather than performing mundane, time-consuming tasks.
Ensures data security
Encryption, data backup, recovery methods and data access controls integrated into ETL tools offer the necessary data security and privacy. This feature reduces the risks of unauthorized access and cyber threats to the organization.
(Understand vulnerabilities, threats and risk.)
Provide data visualization
ETL tools provide an easy-to-use user interface to visualize and access data. It allows even people with minimal programming experience to perform required tasks. Some tools support drag-and-drop functionality.
Challenges for ETL processes
Though a standard process in any high-volume data environment, ETL is not without its own challenges.
High data complexity and volume
Data with complex aggregations and large volumes may exist. This can slow the ETL extraction and transformation, or you may need to combine methods.
High network latency
The network latency can be a bottleneck for the efficiency of the ETL process when a large volume of data needs to be extracted and loaded.
Lack of enough computing resources
Memory, storage space and processes can slow down if the ETL system doesn’t have enough CPU. However, cloud-based ETL tools can often scale resources up and down based on the resource requirements.
Data from raw data sources is often inconsistent. Plus, data formats can vary over time. There can be requests for new features, such as adding more data columns. All of this requires ETL workflows to change frequently to support these needs — which can be an additional cost and overhead for the company.
Lack of understanding of data transformation requirements
Transformation is the most critical step in ETL. It can be a time- and resource-intensive task based on the required transformation steps. If you underestimate the transformation requirements, the transformed data will be inaccurate and incomplete, making it impossible to load it into the data warehouse.
This is where many ETL processes fail. Crucially, you’ll need to clarify the exact requirements for data transformation.
Lack of training and awareness
Employees in companies may need to be trained well enough to handle ETL data pipelines. Additionally, they should be trained to handle the data carefully with well-established protocols to ensure data safety.
ETL is the process of integrating data from multiple data sources into a single source. It involves three processes: extracting, transforming and loading data. In the current competitive business environment, ETL plays a central role in most use cases like data warehousing, building ML and AI applications, market data integration, and cloud migration.
Today, a wide variety of ETL tools on the market can automate these three processes. ETL tools provide many benefits, including improving data quality, the performance of the ETL processes, productivity and data security. However, there are several challenges for ETL, which you need to be aware of and take necessary steps to mitigate.
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