In today's data-driven world, businesses are constantly looking for ways to extract insights from their vast amounts of data. Usually, this involves implementing new technologies and techniques to deal with the escalating volumes of data. One technology that has been gaining popularity in recent years is the ELT (Extract, Load, Transform) pipeline. As with any data pipeline, an ELT pipeline follows the basic principles of getting data from A-to-B but follows different conventions than your typical ETL pipeline. As companies experiment with ELT pipelines, many of them see a decent performance boost over their existing technologies and approaches.
In this blog post, we will cover some of the most frequently asked questions about ELT pipelines. Some of the topics covered will include the key benefits of ELT pipelines, how they work, and some common use cases. Most importantly, we will also cover the key differences between ELT and ETL (Extract, Transform, Load) pipelines and explore the limitations of ELT pipelines. Lastly, we will take a look at one possible ELT solution, Arcion. We will discuss how Arcion provides comprehensive end-to-end data integration and processing capabilities for ELT. If you want to learn more about ELT pipelines and how they can help your business level up its data game, read on!
What is a Data Pipeline?
Most people in technology are familiar with what a data pipeline is, or at least the concept of it. A data pipeline, in the most simple definition, is a sequence of processes or technologies that move data from a source to a destination. The data moved through these pipelines is typically used for processing or analysis. The pipelines and the processes encapsulated inside them ensure data is properly formatted, cleansed, and transformed by the time it is put to use. Each data pipeline will have various stages that data will move through. In general, data pipelines can usually be divided into data ingestion, storage, processing, analysis, and visualization. Let’s take a look at each step in a bit more detail, including the tools which could be used for each step.
The data ingestion stage is the start of a data pipeline. This stage involves extracting data from various sources such as databases, APIs, logs, files, or any other source that contains data needed for downstream processes. Some common platforms used for data ingestion include Apache Kafka, Apache Flume, and Amazon Kinesis.
Once data is ingested, it needs to be stored in a central repository. Data storage options have grown over the years but common solutions include data warehouses and data lakes. Popular technologies used for data storage are Hadoop, Amazon S3, and Google BigQuery.
After the data has been moved into storage, the process of cleaning, transforming, and enriching data takes place in the data processing stage. The processes executed here help to make the incoming data usable for analysis. Common tools used for data processing include platforms such as Apache Spark, Apache Beam, or Apache Flink.
After the data has been processed, insights can start to be harvested. In the data analysis stage of the pipeline, data is analyzed to uncover insights and trends. This is the stage where the business value starts to be delivered. Many different tools exist to assist with data analysis, many requiring a background in programming languages and data science. The most popular tools used for data analysis are languages like SQL, Python, or R.
Optionally, analyzed data may be displayed visually. Although optional, most businesses do have some sort of visualization tool configured to allow users to easily see insights and trends. This makes the results of the pipeline more accessible. The data is commonly presented in graphs, charts, or dashboards. Popular data visualization tools include Tableau, Power BI, and QlikView.
What is an ELT Pipeline?
Data pipelines come in quite a few different flavors. As we have seen above, generic data pipelines involve a sequence of processes that move data from its source to its destination with the destination receiving data in an already prepared or transformed state. An ELT pipeline (Extract, Load, Transform) takes a slightly different approach by doing some of the processes in a different order and using different technologies. Below we will explore what an ELT pipeline is, its key benefits, how it works, and some common use cases.
Key Benefits of ELT Pipeline
Like any modern data pipeline, the primary benefit of using an ELT pipeline is the ability to handle large volumes of data quickly and efficiently. Unlike more generic pipeline approaches, Instead of transforming the data as it is being ingested, an ELT pipeline extracts the data and loads it into a target data store first. The benefit of this approach is that it allows for faster data ingestion while reducing the load on the source systems. Once the data moves from source to target in its raw state, the transformation step processes can then be performed with the target data store. Since the target data store is often a data warehouse or data lake, it can easily transform the data using SQL, Python, or R, depending on what the platform supports. Another advantage to using an ELT approach is that it enables more flexible data modeling and accommodates a wider variety of data types and structures.
How an ELT Pipeline Works
Now knowing the benefits of an ELT pipeline, it makes sense to dive a bit deeper into how an ELT pipeline works. The ELT pipeline process can be broken down into three steps, as denoted by its acronym: Extract, Load, and Transform. The first step in the ELT process is to extract data from its source. Most data is extracted from sources such as a database, an API, or a file. Once the data is extracted from the source systems, it is loaded into a target system, such as a data warehouse or data lake. There are quite a few platforms where data can be loaded but some of the most popular include Amazon Redshift, Google BigQuery, or Snowflake. These platforms can handle massive volumes of data and have a huge amount of computing power available at their disposal, which becomes important in the next step. The final step in the ELT pipeline process is to transform the data that has been loaded into the target platform. Leveraging the compute resources of the target platform, data is cleaned, transformed, and enriched using languages such as SQL, Python, or R. By the end of the pipeline processes, the data is prepared and ready for analysis.
Common Use Cases for ELT Pipelines
ELT pipelines are commonly used in scenarios where there is a need to process large volumes of data quickly and efficiently. In almost every industry that deals with data, data pipelines will likely be used pretty heavily. Let’s look at a few examples of how ELT pipelines are used across various industries.
E-commerce companies use ELT pipelines to collect customer data, process it, and generate insights on customer behavior, purchase patterns, and product recommendations.
Financial institutions use ELT pipelines to collect financial data, analyze it, and create customized reports for their clients.
Healthcare organizations use ELT pipelines to collect patient data, analyze it, and create personalized treatment plans based on patient histories.
Social media companies use ELT pipelines to collect user data, analyze it, and provide personalized recommendations to their users.
Internet of Things (IoT)
IoT companies use data pipelines to collect sensor data, process it, and generate insights on device performance, predictive maintenance, and asset management.
An ELT pipeline is an efficient and effective way of processing large volumes of data. By extracting data and loading it into a target data store first, an ELT pipeline reduces the load on source systems and enables more flexible data modeling.
The Importance of ELT Pipelines
The rapid growth in the volume and variety of data that companies collect and the process has led to the emergence of advanced data processing technologies. One such technology is the ability to run ELT pipelines at scale. ELT pipelines have become an essential component of modern data stacks and are a major driver in helping businesses extract the maximum value from their data.
Most modern ELT pipeline technologies can auto-scale and take on increasingly heavy loads of data and analysis. Since the transformation is done after the data has been loaded, ELT pipelines use the massive compute power in modern data storage solutions to transform data more quickly and efficiently. This is important since having data ready for analysis as quickly as possible can bring the most value from said data.
When it comes to the importance of ELT pipelines in modern data processing systems, their impact cannot be overstated. ELT pipelines offer some massive benefits to modern organizations that are trying to harness their data. Let’s take a look at some advantages of using ELT pipelines in your data stack.
One of the first benefits most users see is an increase in pipeline efficiency. ELT pipelines are designed to handle large volumes of data, making them ideal for businesses that need to process massive amounts of data quickly. The fact that ELT pipelines transform the data after it is loaded into the target platform reduces the load on source systems and pipeline infrastructure. This approach also enables organizations to experience faster data ingestion and reduced risk when it comes to data loss or corruption.
ELT pipelines are more flexible than traditional ETL pipelines because they allow for more extensive data modeling. Data can be transformed and cleaned on the target platform using tools like SQL, Python, or R which offer lots of advantages in terms of speed and flexibility. By using the target platforms’ infrastructure and compute power, ELT pipelines allow for more complex data structures and a wider variety of data types.
Somewhat in sync with the point above on increased efficiency, ELT pipelines are more cost-effective when compared with standard ETL pipelines. By reducing the load on source systems, ELT pipelines can save businesses money on hardware and infrastructure costs since the transformation is now happening on the target. Additionally, the ability to use cloud-based data stores and processing tools means that businesses can avoid costly on-premise infrastructure with the benefit of easy scalability from the cloud resources.
ELT pipelines have helped shape modern data stacks by enabling businesses to take advantage of cloud computing and big data platforms to do the heavy lifting. Modern data stacks use a vast combination of technologies and tools, ELT pipelines play a critical role in these stacks. ELT pipelines enable businesses to process data more effectively, making it more accessible and usable for analysis. This, in turn, enables businesses to make data-driven decisions that can help drive growth and improve efficiency.
ELT vs ETL: Key Differences
In the world of data integration, there are two commonly used approaches: ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). Both ETL and ELT are data integration methodologies that support the functions, obviously in different orders, of collecting data from various sources, cleaning it, transforming it, and loading it into a data store for analysis. However, there are some key differences between ETL and ELT, and understanding these differences is essential for choosing the right data integration approach for your project.
ETL, as the name suggests, involves three primary steps: Extract, Transform, and Load. In an ETL pipeline, data is first extracted from various sources, transformed using a variety of data processing tools, and then loaded into a data warehouse or data store, where it can be used for analysis. ELT, on the other hand, reverses the order of the transformation and loading steps. In an ELT pipeline, data is first extracted from various sources and loaded into the target data store, and once the data is loaded, it is transformed on the target platform.
One of the key differences between ETL and ELT is the order of the transformation and loading steps. In ETL, where data is transformed before it is loaded into the data store, the benefit comes when dealing with complex data structures and a wide variety of data types. By transforming the data before loading it into the data store, businesses can ensure that the data is clean and accurate before it gets loaded into the target. In some cases, this reduces the risk of errors and inconsistencies within the data. In ELT, where data is loaded into the data store before it is transformed, the benefit comes when dealing with large volumes of data, as it can reduce the load on the source systems. By loading the data into the target data store first, businesses can also take advantage of the processing power and scalability of modern cloud data stores.
Another key difference between ETL and ELT is the level of complexity involved in each approach. ETL pipelines typically involve more complex data modeling and transformation steps, which can require specialized skills and expertise. ELT pipelines, on the other hand, are generally more straightforward, as the transformation and cleaning steps are performed using tools that most data professionals are already familiar with. This can also help with the cost-efficiency and timeline of implementing an ELT solution.
Now that we’ve spoken about the differences, your next question will likely be: “So, which approach is better for your business?”. The answer to this question depends on several factors, including the size and complexity of your data, the processing power and scalability of your data store, and the skills and expertise of your data team. For businesses that deal with complex data structures and a wide variety of data types, ETL may be the better approach. ETL pipelines can be designed to handle these complexities and ensure that the data is transformed and cleaned accurately before it is loaded into the data store. For businesses that deal with large volumes of data and need to process it quickly, ELT may be the better approach. ELT pipelines can take advantage of the processing power and scalability of modern data stores, allowing businesses to process large volumes of data quickly and efficiently.
It may make sense to break this down a bit further to give you some examples of where each approach makes sense or doesn’t. Let’s take a quick dive into where each approach may be better than the other by looking at some common use cases for data pipelines.
In data warehousing use cases, using ETL may be a better approach. Since ETL pipelines can handle complex data modeling and transformation steps while still staying performant, it makes more sense to use them here. ETL pipelines can ensure that the data is clean and accurate before it is loaded into the data warehouse which can help reduce the risk of errors and inconsistencies in the data.
In real-time analytics use cases, ELT may be a better approach, as it can handle large volumes of data quickly and efficiently. ELT pipelines can load data into the data store in real-time, allowing businesses to analyze and respond to changes in the data in real time. When time is of the essence, ELT makes more sense and this is especially true at scale.
In data migration use cases, ETL may be a better approach, as it can handle complex data mapping and transformation steps that are sometimes required when migrating from one platform to another. ETL pipelines can ensure that data is transformed and migrated accurately from one system to another. This brings a decreased risk of errors and inconsistencies compared to using an ELT pipeline for the same use case.
In machine learning use cases, ELT may be a better approach, as it can handle large volumes of data quickly and efficiently. Machine learning use cases tend to thrive as data scales and data ingestion and analysis can be handled in real-time. ELT pipelines can load data into a target platform to be processed and analyzed using machine learning algorithms to train and deploy machine learning models.
All-in-all, the choice between using an ETL or ELT pipeline depends on several factors, including the size and complexity of your data, the processing power and scalability of your data store, and the skills and expertise of your data team. ETL is a better approach for complex data modeling and transformation, while ELT is better for large volumes of data and real-time analytics. Understanding the differences between the two can help businesses choose the right approach for their specific needs.
Best ELT Pipeline Tool - Arcion
When it comes to creating ELT data pipelines easily, the right too can make all the difference. Of all the tools available, we built Arcion so that it is much simpler to implement and maintain than other tools and approaches.
Arcion is a go-to solution for many enterprises who are looking to select a data pipeline tool that is scalable, reliable, and extremely easy to configure and use. It provides robust data pipelines that offer high availability, streaming capabilities through log-based CDC, and auto-scalable features. Available with multiple deployment options, Arcion can migrate data to and from on-prem data sources, cloud-based data sources or a mix of both. Arcion’s partnership with Snowflake and Databricks has led it to become a preferred tool when creating real-time ELT pipelines.
The zero-code approach to configuring Arcion allows users to easily get Arcion up and running and build their data pipelines without writing a single line of code. Arcion can be set up and configured strictly through configuration files or by using Arcion’s intuitive and easy-to-use UI to set up pipelines in a matter of minutes. Compared to homegrown solutions or ones that mix-and-match a bunch of different technologies, Arcion makes implementation smooth by providing 24/7 support through extensive documentation, tutorials, blogs, and customer support.
Let’s take a look at some specific features that will benefit you while building ELT data pipelines with Arcion.
Many other existing data pipeline solutions don’t scale for high-volume, high-velocity data. This results in slow pipelines and slow delivery to the target systems. Arcion is the only distributed, end-to-end multi-threaded CDC solution that auto-scales vertically & horizontally. Any process that runs on Source & Target is parallelized using patent-pending techniques to achieve maximum throughput. There isn’t a single step within the pipeline that is single-threaded. It means Arcion users get ultra-low latency CDC replication and can always keep up with the forever-increasing data volume on Source.
100% Agentless Change Data Capture
Arcion is the only CDC vendor in the market that offers 100% agentless CDC to all its supported 20+ connectors. Arcion reads directly from database logs, never reading from the database itself. Previously, data teams faced administrative nightmares and security risks associated with running agent-based software in production environments. You can now replicate data in real-time, at scale, with guaranteed delivery - but without the inherent performance issues or the security concerns of having to install an agent to extract data from your pipeline sources.
Data Consistency Guaranteed
Data consistency is a crucial piece in implementing robust ELT pipelines. Arcion provides transactional integrity and data consistency through its CDC technology. To further this effort, Arcion also has built-in data validation support that works automatically and efficiently to ensure data integrity is always maintained. It offers a solution for both scalable data migration and replication while making sure that zero data loss has occurred.
Automatic Schema Conversion & Schema Evolution Support
Arcion handles schema changes out of the box requiring no user intervention. This helps mitigate data loss and eliminate downtime caused by pipeline-breaking schema changes. This is possible by intercepting changes in the source database and propagating them while ensuring compatibility with the target's schema evolution. Other solutions will reload the data or re-do the snapshot when there is a schema change in the source databases. This causes pipeline downtime and requires a lot of computing resources which can quickly become expensive! Arcion does not require this to be done, making it more efficient and cost-effective.
Pre-Built Enterprise Data Connectors
Arcion has a robust library of pre-built data connectors to allow for easy integration with your favorite databases and data sources, including Snowflake. Unlike other ELT tools, Arcion provides full control over data while still maintaining a high degree of automation. Data can be moved from one source to multiple targets or multiple sources to a single target depending on your use case. This means that if you branch out into other technologies, you’ll already have the capability within Arcion to handle your new sources and targets without the need for another pipeline technology.
Intuitive UI To Setup Pipelines In Minutes
As with a zero-code platform, you can set up and configure Arcion CDC using its intuitive UI in minutes - with no custom code required.
Limitations of ELT Pipelines
Every technology, regardless of how great it is, will always have some limitations or challenges to overcome. This is certainly applicable to ELT pipelines, and while ELT pipelines offer several advantages, they also have some limitations that businesses need to consider before implementing them. Some of the key limitations of ELT pipelines include factors around data quality, data security, technical expertise, cost, and lack of control in the output of transformations in the data. Let’s take a look at each of these aspects in more detail.
ELT pipelines assume that the data being loaded into the target data store is already in a usable format. However, this is not always the case, and if the data quality is poor, it can cause issues downstream in the pipeline. The data does not need to be perfect, but sources should ensure that there is a fair amount of data quality since it will be landing directly into the target platform. This means that businesses need to ensure that their data is clean and consistent before loading it into the target data store, which may or may not always be possible.
With ELT, the data is loaded into the target data store before being transformed, which can increase the risk of data breaches. This means that businesses need to ensure that their target data store is secure and that the right access controls are in place to prevent unauthorized access. It’s also important that the data coming into the platform, along with being audited for quality, should be audited for any data that is not necessary and could be seen as a security risk. This type of data includes things such as credit card numbers or social insurance numbers, that may not be necessary to store.
While ELT pipelines are generally easier to set up and maintain than ETL pipelines, they still require some technical expertise to set up and configure. This means that businesses may need to invest in training or hiring staff with the necessary skills and expertise. Having staff that has previous experience with ELT pipelines and the tools behind the scenes can be necessary to have a successful project or at least one that gets implemented in time without major difficulty.
ELT pipelines can be more expensive than ETL pipelines, as they require more processing power and storage to handle the large volumes of data being loaded into the target data store. This means that businesses need to consider the cost implications of implementing an ELT pipeline and ensure that the benefits outweigh the costs. The number of transformations required and the complexity can quickly skyrocket the cost of running an ELT pipeline at scale.
Lack of Control
With ELT, the transformation is done within the target data store, which can limit the amount of control businesses have over the transformation process. Some ETL tools have more friendly interfaces that can make configuring and tweaking transformations easier than changing SQL, Python, or R code used within ELT pipelines. This means that businesses may not be able to implement custom transformations or ensure that the data is transformed in a way that meets their specific business needs.
In summary, while ELT pipelines offer several benefits, they also have some limitations that businesses need to consider before implementing them. These include data quality issues, data security concerns, the need for technical expertise, potential cost implications, and a lack of control over the transformation process. Only by weighing these limitations and challenges against the benefits of ELT pipelines can businesses make an informed decision about whether ELT is the right approach for them.
In conclusion, ELT pipelines are becoming increasingly popular among businesses as they offer several benefits over traditional ETL pipelines. With real-time analytics, simplified data integration workflows, and improved scalability, ELT pipelines can help businesses gain insights into their data quickly and efficiently. ELT pipelines offer another tool in the data pipeline toolkit which can be a major advantage to certain use cases over more traditional pipeline approaches.
However, businesses need to carefully consider the limitations of ELT pipelines that were mentioned in this blog as they can have a major impact on the data landscape of an organization. It's also important to select an ELT pipeline tool that provides comprehensive end-to-end data integration and processing capabilities, a powerful transformation engine, flexible and scalable architecture, and robust security features.
As discussed above, one such ELT pipeline tool that checks all of the boxes of ease, security, and scalability is Arcion. Arcion is an excellent choice for businesses looking to streamline their data workflows and drive data-driven insights through ELT pipelines. To get started with Arcion, chat with one of our experts today to unlock the power of cloud-native ELT pipelines.