A Sneak Peek Into the Modern Data Structure

In any company, the primary function of IT is to streamline the different processes and make the same more efficient. That has been and always will be a focal point since the dawn of software/applications. The dynamic of how to serve is what really changes (and who is serving, and what is served, etc..). Knowing what is being developed, what is being used, who is developing, and who is consuming makes tracking the lineage of any technology much easier. The magic is found in the spaces in between

Every aspect of IT is experiencing rapid growth

The technology environment is changing everywhere, depending on your point of view. In the last ten years, we saw a wide range of technologies, such as TIBCO, Dell Boomi, Integration, Warehousing, Cloud, Analytics, Data Science, and now Data. We have observed that there is a fractal growth of intriguing ideas within it, regardless of where you are. Modern Data Stack is an example of fractal expansion in the data stack!

About Modern Data Stack

Although Snowflake’s roots are in data warehousing, and it is definitely where the company’s strength lies, the company is already expanding horizontally to cover the complete end-to-end data flow. Snowflake is no longer a data warehouse; it is instead a Data Cloud. Snowflake is, unsurprisingly, synonymous with a data warehouse; we’ll have to wait and watch if the new branding spreads among practitioners and users.

Stage 1: It started with ETL. Until recently, this was the de facto standard (pre-data lake and Cloud data warehouse era). The crucial thing to remember is that the transformation happens before the data is loaded. It used to be quite smooth, but it is no longer the first choice for any data pipeline requirement.

Stage 02: With the advent of low-cost cloud storage (AWS S3, Azure Blob Storage, Redshift, Google Bigquery, and so on), it’s now more cost-effective to extract everything, load it into the data warehouse, and then perform the transformation.

Stage 03: Now that the T is no longer connected to the EL, one of the primary issues is ensuring that everything runs in the correct order; in other words, how do we ensure good synchronization? Assume you’re loading data from ten different sources and want to produce an aggregated table once all of the data has been loaded. When and how do you plan on undergoing a transformation? You’ll have incomplete data if you start too soon, and you’ll lose time if you start too late. This is where the orchestration layer, the next component of the Modern Data Stack, comes in.

Stage 04: Since we now have many tools performing distinct jobs (ingestion, transformation, and orchestration), a data quality check must be performed. In the past, products like Informatica, Mulesoft, and Dell Boomi did everything for you. Within the tool, you could write assertions, unit test cases, test packages, and so on.

If you look closely, you’ll see a pattern: the Modern Data Stack divides the massive ETL tool capability into individual tools. This not only allows consumers to choose one tool for ingestion and another for transformation, but it also avoids vendor lock-in. If you’re looking to leverage the power of Snowflake, Flye has got all the expertise you need!