Before raw data can be used for analytics, it must first be converted into a form that can be easily queried and placed into a secure, centralized location. The ETL process is designed to accomplish these tasks. While the process used to be time-consuming and cumbersome, the modern ETL pipeline has made faster and easier data processing possible. Implementing a modern ETL process has significant benefits for efficiently building data applications and empowering data-driven decision-making.
What is the ETL Process?
ETL is an acronym that represents “extract, transform, load.” During this process, data is gathered from one or more databases or other sources. The data is also cleaned, removing or flagging invalid data, and then transformed into a format that’s conducive for analysis. The cleaned and transformed data is then often loaded into a cloud data warehouse or other target data store for analysis.
ELT: The Future of Data Pipelines
ETL pipelines first appeared in the 1970s, and a lot has changed since then. Today, organizations have access to more powerful ways to process and prepare data for use. Modern ELT (extract, load, transform) pipelines have significantly greater capabilities than their predecessors. With ELT, the raw data is extracted from its sources and loaded directly into the target data store. It’s then transformed as needed directly within the data store. Here are five benefits of using a modern ELT data pipeline.
Provide continuous data processing
Yesterday’s ETL pipelines worked well only with slow-moving, predictable data that fit into neat categories. Common data sources included CRMs, ERPs, and supply chain management (SCM) systems. Data gathered from these sources was typically loaded into an onsite data warehouse and stored in highly structured tables that made it easy to query using SQL and SQL-based tools. Data was typically processed in batches on a predefined schedule, resulting in data that was already hours or days old before it was ready for analysis.
Fast forward to today. Organizations collect massive amounts of data generated from many different sources including databases, messaging systems, consumer purchasing behavior, financial transactions, and IoT devices. This data can now be captured in near real time or real time with the modern ELT pipeline, since today’s technology is capable of loading, transforming, and analyzing data as it’s created.
Execute with elasticity and agility
Today’s ELT pipelines rely on the power of the cloud to rapidly scale computing and storage resources to meet current data processing and analytics demands. Modern cloud data platforms offer near-infinite data processing and storage capabilities. It’s unnecessary for organizations to plan in advance to accommodate anticipated surges in resources during periods of more intensive use.
Use isolated, independent processing resources
Legacy ETL pipeline configurations typically used the same computing resources to process multiple workloads. Running workloads in parallel on the same resource negatively impacts performance, resulting in longer wait times. In contrast, modern ELT pipelines separate compute resources into multiple, independent clusters with each workload receiving its own dedicated resources. This setup drastically increases the speed at which data can be processed, transformed, and analyzed. The size and number of clusters can rise and fall instantly to easily accommodate current resource demands.
Increase data access
Some data pipelines relied on highly skilled data engineers to build and maintain the complex constellation of external tools required to customize the ETL process to the unique needs of the organization. The resulting IT resource bottlenecks prevented timely access to relevant data, resulting in decisions based on stale data. However, modern and ideal ELT pipelines democratize data access by simplifying data processing, making the process of creating and managing data much less reliant on IT experts. This democratization of data allows business teams self-serve, accessing and analyzing relevant data independently. For example, Snowflake is known for its ease of use for SQL users, and other developers with different programming skills and preferences, including Java, Scala, and Python, can also build with Snowflake using Snowpark.
Are easy to set up and maintain
Legacy ETL pipelines relied on equipment and technologies that are costly to operate and maintain. To conserve computing resources, ETL projects were dependent on batch processing methods that were run during times when resource demands were low. This approach translated into data pipelines that were slow and complex. More importantly, this setup made it all but impossible for teams to use and analyze data much faster. Today’s modern ELT pipelines operate much differently. An architecture based on a cloud computing and storage solution such as Snowflake eliminates these constraints. Business teams can engage in data analysis at any time, accessing current data instantly to take advantage of time-sensitive insights.
Snowflake and Data Integration
Snowflake supports both ETL and ELT transformation processes, providing organizations with the flexibility to customize data pipelines to meet the needs of the business. And because Snowflake pairs seamlessly with data integration tools such as Informatica, Talend, and Matillion, organizations no longer need manual ETL coding and data cleansing. As a result, data engineers are freed to spend time developing advanced data strategies and pipeline optimization. Near-limitless computing and storage resources offer immediate access to validated and prepared data.
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