Data Integration is the process of combining data from multiple sources into a unified view for downstream applications or analytics. It allows data teams to centralize data for other uses to maximize query performance (from the consolidated data store) and minimizing the impact on back-end data sources.
Traditionally, data integration has involved three key processing components: extracting data from the sources, transforming into the unified view, and loading the unified data into the destination. In some cases, all three operations are contained in the same physical process, while in others, the steps are separated and run individually. And the actions can come in different orders, with the most common ones ETL and ELT.
Data integration is often used for analytics purposes, sending the resulting data to analytical databases, data warehouses, or data marts, and organizing the data in specific analytic formats (star-schemas, multi-dimensional, etc.). But new purposes for data integration include enriching data within an application such as CRM and bringing together all the data need for a new application such as customer or supply chain 360.
ETL stands for extract, transform, and load , one type of process used for data integration. Each of the three steps is distinct and can be complicated. But they are interrelated and come together into a complete process to deliver a unified set of data.
Here is how the steps work:
ETL processes can be pieced together in ETL tools, hand-coded, or a combination thereof. An ETL process could be a single synchronous process, or steps can be separated and run individually. In the latter, there will often be an intermediate data store used to manage the in-flight data.
ETL processes can also range from simple – extract from a single source and transform it into an analytical form – to complex – bring together many sources to transform and feed into one or more sophisticated analytical schemas (e.g., star schema). Organizations may have ETL processes that feed each other. A complex process may feed a wide range of data into a data warehouse, and then a simpler process may move data from a data warehouse to a departmental OLAP cube.
ELT, or extract, load, and transform , is a new data integration model that has come about with data lakes and cloud analytics. Data is extracted from the source and loaded into a destination still in its original or raw form. The raw data is transformed within the destination to a second form that is then ready for analytics.
In practice, we see two primary use cases:
ELT processes on data lakes can be sophisticated as they are often dealing with complex data formats, multiple data sources, and a great deal of preparation. Cloud analytics ELT from SaaS applications tend to be simpler, taking data from 1 or 2 SaaS sources, loading it, then transforming it into a denormalized form.
ETL is the more traditional method of doing data integration, has well-known best practices and approaches, and skills are easy to find. There are a good number of tools on the market that support traditional ETL approaches.
However, developing ETL processes can be slow, cumbersome, and costly. Since it requires specialized tools and skills, dedicated teams are needed. These teams will typically have project backlogs, forcing analytics teams to wait. Some of the legacy ETL products also carry a very high price tag.
ELT is more modern and embraces agile methodologies and cloud technologies. As such:
Hence, ELT’s more modern approach can be far more efficient and effective.
At the heart of your modern data stack would be your analytics engine. This would be a cloud data warehouse – Snowflake, Amazon Redshift, Google Big Query, or Azure Synapse – or a data lake engine such as Databricks Delta Lake. The other tools in your data stack would integrate with your analytics engine, using it for compute and storage. These tools would be:
Datameer is a powerful SaaS data transformation platform that runs in Snowflake – your modern, scalable cloud data warehouse – that combines to provide a highly scalable and flexible environment to transform your data into meaningful analytics. With Datameer, you can:
Datameer provides a number of key benefits for your modern data stack and cloud analytics, including:
All of these benefits will create highly efficient and effective analytics engineering processes and expand the use of data in the business.