Statistics show that about 44 zettabytes (i.e., 44 trillion gigabytes) of data were created globally by the beginning of 2020. We live in a digital age where data is an asset like gold, and businesses that fail to utilize their data effectively only find themselves at a competitive disadvantage.
Considering this, it is no surprise that data warehouses are an invaluable part of over 70% of businesses. With businesses now dealing with terabytes and petabytes of data, traditional reporting techniques with OLTP systems are no longer sufficient, and this is where the need for data warehouses arises.
If your organization is planning to build a data warehouse, you must understand what ETL (Extract, Transform, and Load) is and how the ETL layer in a data warehouse works. Continue reading to learn more about one of the building blocks of any ETL data warehouse.
Understanding ETL and Data Warehousing
The data warehouse architecture consists of multiple tiers, or layers, that the data passes through. Your data resides in various source systems, like web and cloud apps, databases, legacy systems, and more. From these source systems, you gather and transform your data in a staging database and move it into your data warehouse for reporting and analyses. This is the high-level view of how data flows within a data warehouse architecture.
There are various types of data warehouse architectures, but the most common is the three-tier architecture that consists of the following layers:
- Bottom Tier: This is where the database of the data warehouse exists. In this layer, the data is gathered, cleaned, transformed, and loaded from the source systems.
- Middle Tier: This is where the OLAP servers for the data warehouse exist that provide an abstracted view of the database. Here data is transformed into a structure that is suitable for analysis.
- Top Tier: This is the front-end layer accessible to the end-users and consists of the tools and APIs that can be used for reporting, visualization, or analysis of the data.
As such, it is important to know that the quality of a data warehouse depends on how seamlessly and reliably data flows among these tiers. This is where ETL plays a vital role. Each time you want to move data from one tier to another, you have to deploy an ETL pipeline. This is the reason an effective ETL development process is pivotal for the success of any data warehousing initiative.
What is ETL in Data Warehouse?
The section above discussed how the bottom tier of a data warehouse collects data from different sources. But how does all the data reach this layer? Through ETL.
ETL is the collective term for a set of processes used to build a unified data source for a business. To explain the ETL process in a data warehouse, further, here is a breakdown of how the ETL process works:
- Extract: The first step retrieves and combines data from all source systems for further processing.
- Transform: The second step cleans, formats, and structures the data so that standardized data is loaded into the data warehouse. Data transformation is essential because disparate source systems can have data in different formats and structures. For example, in one system, a customer’s full name may be stored together in the same column, whereas in another system, the customer’s name may be stored in separate columns for first, middle, and last In the transformation stage, data is converted into a consistent format for loading it into the data warehouse.
- Load: The final stage is where the data is moved from the source systems (or staging area) to the data warehouse.
How to Load Data in a Data Warehouse?
There are different techniques that you can use for loading, but the most widely used ones are:
- Full load: A full load, also known as a destructive load, involves removing or deleting all records from the data warehouse and then reloading everything from scratch. This approach enables simple and quick implementation and ensures data integrity in all cases (since data is replicated directly from source systems each time). However, there are quite a few drawbacks as well, the most obvious being the long loading times involved. Imagine having millions of rows of data and loading all of them each time whenever your data warehouse needs to be refreshed.
- Incremental load: In the incremental loading approach, you only write new data or data that has changed since the last load. This approach speeds up the loading process as compared to full load since the size of data to be written is much smaller. You can also use this approach to preserve the history of data. However, the disadvantage is that the incremental load requires setting up a complex mechanism to implement correctly.
What is the Difference Between ETL and Data Warehousing?
The difference between a data warehouse and ETL is basic. The process of ETL pushes the data into a data warehouse, while the data warehouse serves as a collection or storage place for all of the organization’s data.
The Role of ETL in Data Warehousing: Why is ETL Important in Data Warehouse?
Now that you know ETL is one of the core components of the data warehousing process, let’s shed light on its importance in building the DW architecture.
Ensures High-Quality Outputs
The first step of any data warehouse initiative is to figure out and plan how you will process the inputs (i.e., data from source systems) and generate the desired outputs (business intelligence and analytics). Businesses need a reliable and efficient ETL layer to extract and combine data from multiple data sources into their data warehouse. If the ETL process are not developed correctly, it can directly affect the quality of insights from your data warehouse.
Prepares Data for Multiple Levels of the Architecture
The role of ETL is not just limited to one part of your data warehouse architecture, but instead is a key component in all data transfers. For example, when you move data from source systems to a staging server, you create an ETL flow; when you move data from your staging server to the data warehouse, you create another ETL flow. Therefore, it is a crucial part of multiple stages in your data warehousing project.
Allows Job Scheduling and Process Automation
Coding and preparing dataflows and workflows can be a complex and time-consuming task since it involves building and piecing together various components of your data warehouse together. The process involves not only creating ETL scripts and SQL queries but also factoring in job scheduling (for automated data loading), data quality assurance, and monitoring into the process. This is why it is recommended to use an ETL tool to automate and simplify designing and implementing ETL processes for your data warehouse.
ETL Considerations for a Data Warehouse Initiative
Designing ETL processes for data warehousing presents various challenges to IT teams. It is important to understand and address the following when developing the ETL processes for your data warehouse initiative.
Understand the Data Sources
It is a good practice to design a data warehouse that is flexible to structural changes. This means that your data warehouse’s final design should be able to handle all kinds of incoming data, even when new sources and data streams are added to your business ecosystem. To achieve this, your ETL or data warehousing tool should have the ability to quickly make changes to the ETL processes after they have been deployed.
You might have data coming in from multiple database systems, such as MS SQL and Oracle SQL, different SaaS applications such as QuickBooks and HubSpot, and flat files such as CSVs and text files. You need to identify all these sources, profile them according to their type, understand how you will extract data from these sources, and list down the formats and structure of data used. You need to understand where your data resides and how it varies in order to perform the database ETL process accurately.
Determine How to Transform the Data
Once you know about your data sources and data types, decide how your data will be transformed and stored in your data warehouse. Continuing with the earlier example, at this stage, you specify whether you want to store the customer’s full name in a single column or as separate columns (for first, middle, and last names). Similarly, you need to determine what to do with missing and incorrect data values and how to fix inconsistencies and improve the overall quality of data retrieved from the data sources.
You will need to apply multiple transformations at different stages of the ETL pipeline. For this, ensure that your ETL tools have built-in transformations or supporting mechanisms to properly validate, clean, and convert the source data into the final form before loading it into your data warehouse.
Decide on How to Design & Develop the ETL Pipeline
After the design considerations, you should decide how you will develop your ETL pipelines. You can do it in two ways:
- Writing code for the ETL pipeline: This approach involves a dedicated team of data architects and engineers working on developing ETL pipelines. It involves writing long lines of code and scripts to retrieve, transform, clean, and load your data in the data warehouse. The benefit is that you have better control over how each element in the ETL process works. However, this approach entails long development times, erroneous implementations, high maintenance requirements, and performance issues.
- Using a third-party ETL tool: ETL tools are specialized solutions equipped with built-in features and functionalities to build your ETL pipelines in a code-free, intuitive development environment. With an ETL tool, you get a high-performance ETL engine, a range of ready-made features for transformations and process automation, native connectors to a wide range of sources, leading to quicker development times, higher flexibility, and cost savings.
Building an Integrated ETL Pipeline for Your Data Warehouse
The exact method for building an ETL pipeline for various stages of your data warehouse varies depending on your business requirements. However, here are some general steps and guidelines that you can follow for your initiative:
- Prepare a dataset for acceptable values: Analyze your data sources and prepare a list of permissible values for your data fields. For example, for marital status, the values can be single, married, separated, and so on.
- Extract data: Retrieve and combine data from all of your data sources into a single format for further processing. The data extraction process in ETL will vary from source to source. For instance, you might want to consume APIs for retrieving data from SaaS applications, whereas you can export records to CSV or directly query data from relational databases.
- Clean and deduplicate data: After extracting the data, deduplicate it to prevent writing the same data records multiple times and avoid redundancy. Next, use the dataset that you prepared in the first step above to validate and clean the data. For example, if you have a data field with the marital status ‘none’, you can set the flow to reject or discard such records to ensure data correctness.
- Apply transformations: Based on the design of your data warehouse, apply transformations to aggregate or otherwise alter your data values to match the required output. For example, to store the full name as a single column, ensure that the records are transformed so that the name fields are always concatenated.
- Hosting data in the staging area: While this is an optional step, it is generally recommended to load your transformed data into a staging area before moving it into the data warehouse. This layer makes it easy to diagnose any problems with the data and roll back the changes, if necessary.
- Load data into the data warehouse: Using the most suitable load technique (full load or incremental load), move the transformed data into the data warehouse. You can do this hourly, daily, weekly, or at whatever frequency necessary to meet your business requirements.
Improving the Performance of ETL Processes in Data Warehouse
Hiccups and issues anywhere in the ETL pipeline can be a cause of concern for businesses. Several things can go wrong in this area, but one of the most daunting is performance issues. Therefore, optimizing ETL processes in data warehouses is crucial.
What can you do when faced with performance issues in your ETL processes? Here are some effective optimization tips:
- Use incremental loads: If you are dealing with huge volumes of data, it is recommended to use incremental loads because loading gigabytes and terabytes of data each time with a full load can strain your systems and their overall performance. Even though increment loading requires more prep work for developing and automating the process, it can help you improve performance significantly.
- Remove irrelevant data points: You do not always need to store everything in your data warehouse. Identifying and removing extraneous data fields from your ETL pipeline can help address performance issues. For example, suppose you have files and images stored in transactional databases that have little to no analytical value. In that case, you can cut them out during the extraction phase of your pipeline.
- Identify and address bottlenecks: Monitor the logs for your ETL processes and observe the time taken by each stage and the number of records processed along with the hardware usage. You may find different kinds of bottlenecks in an ETL pipeline, and each needs to be addressed differently. Here are two examples:
- Your CPU might be at 100% capacity when running the ETL job while your memory and disk usage are minimal, which indicates that you can scale performance by upgrading or throttling your CPU.
- You might note that extracting or writing records from a specific table in the database always takes more time than others. This would indicate an issue with the table itself, such as the lack of indexes or partitions. You can improve performance by optimizing such tables in your source or target database.
Astera Centerprise: End-To-End ETL Tool for Data Warehouse
Designing and preparing the ETL pipelines for an enterprise data warehouse requires thorough planning and the right tools to ensure accurate data analytics.
Astera Centerprise is a top-rated data integration solution that offers robust features to help enterprises and SMEs build ETL pipelines for data warehouse projects. It features an enterprise-grade ETL engine that utilizes a cluster-based, parallel processing architecture to streamline complex processes and speed up your data to insights journey. It provides native connectivity to over 40 cloud and web apps, databases, and on-prem systems, ensuring seamless connectivity to transform your raw data into actionable insights.
To experience how Astera Centerprise’s superior functionality and drag-and-drop GUI can speed up your data warehouse initiatives, download a free trial here.