Center for Data Pipeline Automation
Learn More

Challenges Data Pipeline Engineers Need To Overcome

Automation enabled by data engineers can help overcome common data pipeline challenges, which delivers benefits to all involved.

With the amount of data the average organization ingests on a daily basis increasing every year, the old ways of collecting, storing, and analyzing said data are not workable in a modern, real-time environment. 

There are plenty of methods available to automate and improve the capability and flexibility of data ingestion, one of those is the implementation of data pipelines which automate the movement of data between sources, applications, and devices. 

Data pipelines enable organizations to do more with data, by automating most of the processes involved in making data digestible for other applications. This includes aggregation, augmentation, enrichment, filtering, and grouping of data. 

Implementing data pipelines provides a whole host of benefits to an organization, but it is not a straightforward process. The topic of the challenges businesses need to overcome to get the most out of their data pipelines is not new. Here are some of the ones that are currently impacting businesses today. 

See also: What’s Changing Faster? Data Pipeline Tech or the Role of the Data Scientist?

1) Choosing the right hosting service

While some operations may be feasible on-premise, the industry in general is trending towards managed cloud databases, which have better integration with third-party analytics and pipeline tools alongside more scalability. 

2) Creating the pipelines

Knowing where data needs to go should be step one in developing a data pipeline. Organizations need to properly plan out what data to ingest, transform, and where the data journey should end. Ingesting too much data can create cost and storage issues, while not collecting enough can lead to inaccurate analytics and insights. 

3) Being flexible with schema

Due to data sources and events changing at a faster pace than previous generations of data analytics, organizations need to be flexible with data types and schema, to avoid defects in the extract, transform, load (ETL) process. 

4) Planning for scale

Organizations need to be more prepared to scale operations both up and down, rather than having one consistent amount of volume or time to batch import data. For organizations at the beginning of the journey, having a managed public cloud system reduces the chances of downtime. 

Benefits of addressing these data pipeline challenges

A variety of tools and skills are needed to address and overcome these data pipeline challenges. That is leading many organizations to explore ways to automate their data pipelines. With automation, the burden of creating the pipelines is removed from the data engineers. Plus, the data scientists and lines of business get instant access to the data they need to carry out their work.

Leave a Reply

Your email address will not be published. Required fields are marked *