24 February 2023 | Noor Khan

Founded in 2002, our client has been around for over two decades and is an internationally known company dealing with broadcasting data for commercial use. With a mission of making high-quality technology and content affordable for everyone, they have established themselves as a market leader.
The client deal with real-time broadcasting and commercial data, and the data needs to be available in near real-time. Their existing ETL infrastructure built on AWS technologies such as Redshift was not able to meet their expectations and requirements. Our highly experienced engineers presented the solution of leveraging Databricks for the whole ETL process for the many benefits it offers including parallel processing with multiple clusters.
The client was dealing with considerably large datasets including the likes of 215 million records of commercial data being processed on an hourly basis, and 9 million records of content data being processed hourly. This data created eighty reports which need to be produced quickly and efficiently.

To significantly improve the speed of data availability and reporting, the client required a solution which would enable the parallel processing of multiple data clusters. The ETL pipeline infrastructure was built in line with customer requirements and the data flowed through multiple stages:

Each stage carried out the processing tasks such as de-duplication, validation and cleansing to ensure the data is loaded to the destination was clean, without gaps and delays.
Databricks is a brilliant technology used for ETL processes with user-friendly dashboards to track the entire process and spot any errors. Our highly skilled data engineering team are proficient in Databricks and has utilised it for many client projects, therefore they were able to make the recommendations to help the client overcome their challenges.
The solution was built with automated error resolution in place which is offered by Databricks. Our engineers were able to set a number of tries that the system would make before the error was reported for manual intervention. This helps the data engineering team ensure that there are no data drops and delays and errors can be resolved quickly and efficiently. There are two main errors that there is a potential for occurring and these include:

Our client are thrilled with the high performance of the new and improved ETL infrastructure built on Databricks which is driving a new speed and efficiency for their reporting. Our expert data engineering continues to provide an ongoing operational monitoring and support service to the client to ensure data availability and accessibility at all times.
Ardents' team of highly skilled data engineers are proficient in world-leading data technologies and can make recommendations based on your unique needs and requirements. Whether you have a preferred tech stack or want expert guidance on the technologies right for your data and business, we can help. Are you facing any of these challenges:
If you are, get in touch today to find out how we can help you unlock the potential of your data.
Accelerating market research by automating data collection with OCR technology. [...]
Leader logistics software provider Our client is a leading logistics software provider in the UK. With over 3 decades of experience in the industry, they continuously look to innovate with technology. Their range of software products includes a warehouse management system and removal management software. They aim to remove the complexity of software and bring [...]
Well-established logistics software provider Our client is a software products company providing software to the logistics industry and their main product was administration solution software for removal companies. With almost three decades of experience, our clients are leaders in the removals sector. Since the start, they have gone from strength to strength in becoming a [...]
At Ardent, we have spent years helping organisations design, modernise and operate the data foundations behind critical reporting, analytics and decision-making. That experience gives us a clear view of what now separates AI-ready businesses from those still struggling to get value from their data. It is not the amount of data they hold, or even [...]
From Stable Infrastructure to Adaptive Intelligence Most organisations do not need more data. They need their existing data to work better. At Ardent, we spend a significant amount of time inside large-scale client data platforms that are already mature, operational, and delivering value. These are not greenfield environments. They are complex ecosystems built over years, [...]
When the Warehouse Starts Doing the Work In our previous piece, we explored how ETL (Extract, Transform, and Load) is evolving into adaptive, intelligent systems. In Redshift environments, we are now seeing what that shift looks like in practice. For most of its life, Amazon Redshift has been treated as the final step in the [...]