According to a McKinsey report, ”the best analytics are worth nothing with bad data”. We as data engineers and developers know this simply as “garbage in, garbage out”. Today, with the success of the cloud, data sources are many and varied. Data pipelines help us to consolidate data from these different sources and work on it. However, we must ensure that the data used is of good quality. As data engineers, we mold data into the right shape, size, and type with high attention to detail.
Fortunately, we have tools such as Apache NiFi, which allow us to design and manage our data pipelines, reducing the amount of custom programming and increasing overall efficiency. Yet, when it comes to creating them, a key and often neglected aspect is minimizing potential errors.