The success of any data analytics work depends on the ETL process. Without a solid extraction, load and transformation plan, no data can be manipulated to derive actionable insights or to build ML models. And with the rise of streaming data, ETL has started playing a more important role in collecting and analysing data on a real-time basis. It’s time to improve traditional data pipelines so that they are aligned with modern data analytics requirements. Contact Aceso Analytics to optimise your ETL pipeline and accommodate the rising demand for streamlined data analysis.
At Aceso Analytics, we employ modern data pipeline best practices enabling you to build an ETL pipeline that ‘just works!’
Documentation is the soul of building a good data pipeline. A well-documented data pipeline makes sure that everybody knows how the steps are related to each other. When you work with a large amount of data, chances are your data pipeline might introduce one or two bugs. If you have a well-documented data pipeline, troubleshooting becomes easier in such cases.
Architecting an ETL process is not generic in nature. Every Data Scientist worth his salt knows that the ETL process can differ significantly depending on how the data is going to be used and why the data is being collected (in other words, what challenges the ETL process is going to address). Optimising and improving the data pipeline requires an awareness of the purpose of data collection and the way the data is going to be used. At Aceso Analytics, conducting this requirement analysis comes first, always.
Visualising the ETL process helps the organisation to understand how the data gets manipulated as it passes through the pipeline. This visualisation of the data pipeline is extremely important – it enables us to tweak the pipeline as and when the data or the requirement changes. Every data pipeline has to evolve over time, and this is only possible if we have a data lineage in place.
This is the age of everything-as-a-code, and data analytics is no different. However, if you are abstracting away your data pipeline by converting it into a giant, inseparable code, then it will become too hard for you to pinpoint the bug if any failure takes place. Therefore, at Aceso Analytics, we break the ETL scripts into workflows, thereby bringing the concept of microservices into the data pipeline.
As part of ETL best practices, most companies try to reduce data platforms and use a primary platform where all of their data gets dumped. While this gives centralised control over data, unrestrained data dumps can turn your data storage platform into a data swamp. As part of a streamlined ETL strategy, we store enterprise data in an organised way leveraging metadata and partitioning on top of a clear roadmap.
Forget everything. Forget tools; forget technology. The principal strategy that we employ to improve data pipelines is assessing the role of data in your organisation. Once you have the clearest idea about how your enterprise data fits into the success of your company, everything else will gradually fall into place.
At Aceso Analytics, our data pipeline optimisation starts with understanding what data is important to your business, why it is important, and how exactly it will solve the problems that you are facing. Our philosophy – Don’t shape your data pipeline around the tools and technology. Shape it around your requirement and then use the tools needed to meet that requirement.
Learn more about how we can help you and have your questions cleared.