MetaRouter’s Perspective on Data Warehouse Loading: A Shifted Left Approach

By Greg Brunk, Head of Product at MetaRouter

Where we are at now.

In the realm of data integration and warehouse loading, MetaRouter adopts a distinctive approach that challenges the conventional data processing models employed by enterprise ELT toolings such as Fivetran. Our philosophy revolves around what we call the "shifted left" narrative, disrupting the traditional data loops and offering a more streamlined and efficient solution. When we say “shifted left,” we mean “at the point of collection.”

Typical data warehouse loading models involve multiple revision cycles to mold raw data into a usable structure. These cycles include loading raw data, performing cleanup to eliminate non-compliant data, normalizing everything into valid schemas and finally, enriching and restructuring for business use. This process, known as data loops, demands considerable time and effort, often delaying the point at which data becomes actionable.

What sets MetaRouter apart is our commitment to shifting identity enrichment, compliance cleanup, normalization, filtering and structuring to the left, meaning at the point of collection—in real-time, in transit. 

Where we're going.

And as a result, companies save significant time (and headache) by having MetaRouter do this heavy lifting upfront –we eliminate the need for time-consuming cleanup cycles, allowing data science and engineering teams to spend less time on data quality and governance and more time implementing business-critical activities. This also drives better ROI because data is already actionable when initially loaded into the warehouse. In other words, our strategic approach allows users to spend more time leveraging data for insights rather than managing its complexities.

This shifted left methodology is not confined to a specific use case; it applies universally across various warehouse applications, including adtech reverse-ETL, analytics, Customer Data Platforms (CDP) and clean rooms.

Once the data reaches the storage and is poised for loading into a Data Warehouse, it's already positioned close to its required state. Unlike traditional models that rely on complex Extract, Transform, Load (ETL) operations to manage schemas and structures at this stage, MetaRouter's advanced warehouse loading capabilities minimize the need for intricate post-load operations.

We are also a truly multi-cloud solution from a data delivery standpoint, working with Data Warehouses like AWS, Azure, Google Cloud Platform, Snowflake and Databricks.

Essentially, MetaRouter's entire product is designed as an advanced warehouse loader. Rather than burdening the integration process with a myriad of one-off technologies, we perform the intricate work upstream in our cluster. This design choice empowers integrations to be smaller and simpler. MetaRouter efficiently handles the heavy lifting, ensuring the data is primed and ready for immediate action upon arrival in the Data Warehouse.

MetaRouter's approach transforms data loading from a step in the process to a strategic advantage that maximizes the time spent deriving insights from data rather than managing its intricacies.

‍