What is ETL, and how does it compare to modern, streaming data integration tools? As real-time data pipelines become a necessary standard, we’ll cover how ETL, ELT, and real-time streaming ETL work, major differences, and which to choose based on your data architecture and needs.
ETL stands for Extract, Transform and Load, and is a three-step process used to consolidate data from multiple sources. At its core, ETL is a standard process where data is collected from various sources (extracted), converted into a desired format (transformed), then stored into its new destination (loaded).
ETL is not new. In fact, it’s evolved quite a bit from the 1970s and 1980s, where the process was sequential, data was more static, systems were monolithic, and reporting was needed on a weekly or monthly basis.
In this step, the focus is first to understand what form and what format the data is in and the systems that generate the data. Then decisions need to be made to figure out how and how often to connect to access each data source. It could either through a recurring nightly batch processes, triggered on occurrence of specific events or actions or in real-time.
Challenges extracting data:
In this second step, raw data is cleaned, formats are changed, and data is aggregated so it's in the proper form to be stored into a data warehouse or other sources, so it can be used by reporting tools or other parts of the business.
Data transformation activities:
Challenges transforming data:
Challenges in this step are directly tied to computing power and resources available. The more data that needs to be transformed, the more computationally and storage intensive it can become.
In this step, the transformed data is stored in a place that applications and reporting tools can access. This could be as simple as an unstructured text file to more complex data warehouses. The process varies widely depending on the nature of the business requirements and the applications and users the data serves.
Things to consider after loading data:
ETL tools are used to help make it easier to connect to multiple data sources, transform the raw data and import it into databases with as little customization and coding as possible. They typically have GUIs that help users specify what they want to happen with the data and parse the overwhelming volume of data.
Over time, ETL tools have become ESB (Enterprise Service Bus) systems or Enterprise Application Integration as it gathers distributed data across applications, platforms and departments in a business. In a world where data is generated by all layers of the stack, from mobile devices to servers to applications to users, ETL tools are a crucial part of business operations.
ETL was created during a period of monolithic architectures, data warehouses and relational databases. Batch processing was enough to satisfy data management requirements.
Modern data sources and modern data formats are more ephemeral in nature, unstructured, and in larger volumes. The exponentially large volumes of data breaks ETL pipelines at the seams. The more time and resources it takes to transform that data, the more the source data queues back up and the process quickly breaks down.
Modern ETL pipelines will stress test all three phases of data extraction, transformation and load.
All the requirements of the transformation phase of ETL like data cleansing, enrichment and processing need to be done more frequently as the number of data sources and volume skyrocket.
There is also opportunity to handle important data that could generate better business insights that can be fed into machine learning and AI algorithms is made possible with the conversion of batch-processed ETL to streaming STL.
With the rise towards cloud-native applications, Kubernetes, and microservices, the industry is shifting towards streaming ETL with real-time stream processing using Kafka. Learn more about the how ETL is evolving.
An alternate process called ELT (Extract, Load, Transform) such that the source data is directly loaded into a database and then workers will transform the data when it can.
This became popular because of cloud infrastructure and the rise of cloud data warehouses where the cloud’s processing power and scale could be used to transform the data.
Modern data management continues to be challenging with the increasing volume and variety of data, the complexity of the data pipeline and the emergence of data streams and event streams.
ETL has evolved in many ways, where Extract, Transform and Load are concurrent processes operating on real time data pipeline.
What if data could be automatically extracted, transformed, and loaded as continuous, real-time streams?
Not only would it enhance operational efforts and reduce work, it’s the only way to deliver your data slices off of always up-to-date sources, whether they’re coming from hundreds or billions of daily events from different devices, locations, or even cloud computing, or bare metal servers as a single source of truth.
Out of ETL, ELT, and real-time data streaming, streaming technology has become the most widely adopted for many reasons. Real-time streams are achieved by using a stream processing framework like Apache Kafka.
Instead of a linear, batch ETL process, the focus is to direct the stream of data from various sources into reliable queues where workers automatically transform the data, store the data, analyze the data, and report on the data concurrently.
If you have primarily legacy infrastructure and a monolithic setup and batch processing is adequate for your business needs, keep it simple and stick with your ETL set up.
If you find that your transformation process can’t keep up with all the source data coming in, consider using ELT.
If you’re dealing with a massive amount of real-time data streams, you should start evaluating how to adopt a real-time data pipeline that will work for your business requirements.
Whether you’re looking to integrate data, build a real-time data pipeline, or modernize legacy data architectures, Confluent provides seamless data integration across unlimited sources, any infrastructure, at any volume in real-time with 24/7 support.
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