Modern Modular Data Pipelines Example

-

 I would like to share with you my favourite example of the modern data pipeline.

It's amazing.

The first cool thing that we see is that this great pipeline is utilizing a full range of cloud services built for diverse use cases. Choosing the correct tool for each use case can be one of the key factors for the success of your idea, allowing you to get things running as fast as possible without reinventing the wheel.
Another cool thing is if we want to pull data from any non-trivial data source, like Twitter or Jira or GitHub, Azure Databricks is our first friend.
However, the most noticeable advantage of this pipeline, is that instead of having a monolithic data flow, this pipeline is actually multiple pipelines that are running in parallel. Short, simple, and independent pipelines. Multiple independent pipelines can work in parallel and on different frequencies. One pipeline failure would not impact others. This is an easy way to scale each pipeline separately to speed up only specific tasks and save money on keeping other operations on lower compute.

Comments

Post a Comment

Popular posts from this blog

SQL Awesomeness: Finding a QUALIFY query clause in the depths of the database ocean

-
Image
After so many years of writing SQL queries, I cannot believe I never came across a QUALIFY query clause. It feels like discovering a treasure in the depths of the database ocean. Even ChatGPT was not aware of it! QUALIFY query clause was invented by Teradata and is not a part of SQL Standart but apparently, multiple database vendors are supporting it, for instance, Snowflake and Databricks. QUALIFY clause solves the challenge when applying a window function as a query filter. SQL Standart and most database vendors will not allow to run of a window function as a part of a WHERE clause because window functions get evaluated after the HAVING clause. We typically create a CTE or subquery adding a window function to be able to filter on a it at a later query stage. Here is an example of how you can use QUALIFY to piece-of-cake filter on the window function.  I will use the Snowflake database, as an example. Let us consider that we have a list of queries and we need to find the last query ex
Read more

Look back and realize how far you came

-
Image
It appeared out of nowhere while I was having a strong argument with the ton of dust that had taken over my computer room. My former favourite book, "Advanced Transact-SQL for SQL server 2000" brought an upset look to my face, the one I usually get from our NetApp admin. He usually gives me such a look when I ask him for more disk space. At the time of SQL 2000 there were far less good SQL books available than today and amazing SQL programming tricks were living on Itzik’s book pages; and, similar to the Rubeus Hagrid’s “The Monster Book of Monsters” it attacked anyone who attempted to open it, unleashing wild and exciting brilliant ideas. Nowadays there are a lot of SQL books but not all of them are worth reading. Some of them should be strongly avoided and if you touch them you should immediately wash your hands and have a drink or three. A few month ago I was asked to technically review some new SQL Server book. Reading their missleading claims of how the optim
Read more

The Greatest Reasons to use or not to use a Centralized Data Access Architecture

-
Image
When developing a modern Data Platform Layer, one of the main decisions is whether to opt for centralized or decentralized data access architecture.  There is no “one-fits-all” solution, both have advantages and disadvantages. A Centralized Data Access Architecture would usually mean duplicating data from the operational layer into the analytical layer and applying various transformations to data to support and speed up data analytics. Operational online transaction processing Layer , where all microservices and their operational databases are located. Analytical Data Layer , where we would have data lakes that support data Scientists' work and a data warehouse, that supports Business Intelligence. Transformations, ETL or ELT data pipelines , which are moving data from the operational layer into the analytical layer. I f we opt for a Centralized Data Access architecture, what would be the benefits and the drawbacks? CONSISTENCY : consolidating data into a central location can
Read more