{"id":8983,"date":"2021-06-16T13:56:34","date_gmt":"2021-06-16T12:56:34","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=8983"},"modified":"2024-03-28T13:48:29","modified_gmt":"2024-03-28T13:48:29","slug":"five-more-practices-that-will-ensure-a-successful-data-pipeline-project","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/our-thinking\/five-more-practices-that-will-ensure-a-successful-data-pipeline-project\/","title":{"rendered":"Five more practices that will ensure a successful data pipeline project"},"content":{"rendered":"

Managing the flow of information from a source to the destination system forms an integral part of every enterprise looking to generate value from their data. <\/span><\/p>\n

Data and analytics are critical to business operations, so it\u2019s important to engineer and deploy strong and maintainable data pipelines by following some essential practices.<\/span><\/p>\n

This means there\u2019s never been a better time to be a data engineer. According to <\/span>DICE’s 2020 Tech Job Report<\/span><\/a>, Data Engineer is the fastest-growing job in 2019, growing by 50% YoY. Data Scientist is also up there on the list, growing by 32% YoY.<\/span><\/p>\n

But the parameters of the job are changing. Engineers now provide guidance on data strategy and pipeline optimisation and, as the sources and types of data become more complicated, engineers must know the latest practices to ensure increased profitability and growth.\u00a0<\/span><\/p>\n

In our <\/span>data pipeline playbook <\/span><\/a>we have identified eleven practices to follow when creating a data pipeline. We touched on six of these practices in our <\/span>last blog post<\/span><\/a>. Now we talk about the other five, including iteratively creating your data models as well as observing the pipeline.\u00a0 Applying these practices will allow you to integrate new data sources faster at a higher quality.<\/span><\/p>\n

About this series<\/b><\/p>\n

This is part five in our six part series on the data pipeline, taken from our<\/span> latest playbook<\/span><\/a>. First we looked at the basics, in <\/span>What is a data pipeline<\/span><\/a>. Next we looked at the <\/span>six main benefits of an effective data pipeline<\/span><\/a>. Next we considered the <\/span>\u201cmust have\u201d key principles of data pipeline projects <\/span><\/a>\u00a0and in part four, we looked at the <\/span>six key practices needed for a data pipeline<\/span><\/a>. Now we go into details of more of those practices, before finishing off our series in part six with a look at the many pitfalls you can encounter in a data pipeline project.\u00a0<\/span><\/p>\n

Practice Seven: Observe the pipeline<\/b><\/h2>\n

Data sources can suddenly stop functioning for many reasons – unexpected changes to the format of the input data, an unanticipated rotation of secrets or change to access rights, or something happens in the middle of the pipeline that drops the data. This should be expected and means of observing the health of data flows should be implemented. Monitoring the data flows through the pipelines will help detect when failures have occurred and prevent adverse impacts. Useful tactics to apply include:<\/span><\/p>\n