ETL processing has been around since the 1970s but 40 years later many organizations continue to suffer from the constraints of traditional batch processing, where large data volumes are moved in pre-ordained time windows, usually to take advantage of low network congestion. Traditional tools work quite well with relational databases, but can run into issues when dealing with semi- or unstructured data.
Definition
ETL (Extract, Transform, Load) is the act of moving data from one location to another. In the extract phase, hetero- or homogeneous data is extracted. In the transform phase, it is transformed into the proper data format(s). In the final data load phase, it is loaded into a database, data warehouse, or data mart.
The Problems with Traditional ETL
With an older ETL tool, organizations extract and ingest data in prescheduled batches, typically once every hour or every night. But these batch-oriented operations result in data that is hours or days old, which substantially reduces the value of data analytics and results in missed opportunities. Marketing campaigns that rely on even day-old data could reduce their effectiveness.
Many traditional processes need a dedicated service window. However, they often conflict with existing service windows. In addition, for global organizations active 24 hours a day, a nightly window required for batch data processing is no longer realistic.
ETL Processing and the Modern Data Pipeline
With the recent data explosion, traditional extract, transform, load has become a liability for data-driven organizations that require continuous data integration for near real-time business insights. This is why many enterprise businesses are augmenting or even replacing traditional extract, transform, load processes with a cloud-based modern data pipeline.
Modern data pipelines offer the instant elasticity of the cloud and a significantly lower cost structure by automatically scaling back compute resources as needed. They can provide immediate and agile provisioning when data volume and workloads grow..These pipelines can also simplify access to common shared data, and they enable businesses to rapidly deploy their entire pipelines without hardware limitations. The ability to dedicate independent compute resources to workloads enables them to handle complex transformations without impacting the performance of other workloads.
With traditional solutions, the only way for multiple business applications to pull from centralized data is.to invest in tools that extract data from data marts, transform it into the proper querying format, and then load it into individual databases. ETL processing typically require a large set of external tools for extraction and ingestion. It often takes months for a team of experienced data engineers to set up such a process and integrate the tools, which creates bottlenecks from day one. In addition, it requires even more time to set up the process required for ongoing maintenance.
The massive enterprise shift to cloud-built software services combined with ETL and data pipelines offers the potential for organizations to greatly improve and simplify their data processing. Companies that currently rely on batch processing can begin implementing new continuous processing methodologies without interrupting current systems. Instead of costly rip-and-replace, the implementation can be incremental, starting with certain types of data or areas of the business.
Snowflake and ETL
Snowflake's platform provides the elasticity and flexibility needed to move from traditional ETL to a modern data pipeline. Snowpark is a developer framework for Snowflake that allows data engineers, data scientists, and data developers to execute pipelines feeding ML models and applications faster and more securely in a single platform using SQL, Python, Java, and Scala. Using Snowpark, data teams can effortlessly transform raw data into modeled formats regardless of the type, including JSON, Parquet, and XML. Seamlessly handling both structured and semi-structured data, Snowflake can help organizations process incoming data streams without batch scheduling and scale up, down, or out to meet rapidly shifting data requirements. In addition, the Cloud Data Platform offers the Data Sharing, allowing businesses to easily share and access data across the org, different business units, disparate geographies, or partner networks.