Identifying Rampant Data Pipeline Challenges
A data pipeline is a series of digital processes that collect, change, and deliver data from beginning to end. Data pipelines are used by businesses to replicate or move data from one source to another so that it can be stored, analyzed, or merged with other data. Individuals managing data pipelines commonly experience challenges and we want you to be acquainted with a few of them.
Variety of Data Sources
Organizations rely on a broad web of data sources and ever-changing applications. Consider all the data integrations and paths you're involved with. Who oversees everything? Managing all the sources and the large-scale procedures that come with them is complex and documenting all pointers in a way that pleases auditors or regulators is much more complex.
Protecting Confidential Information Causes Issues
You may not want everyone to have access to sensitive or confidential data and thereby may impose restrictions on how the data can be worked on or shared via a big data pipeline. You can anonymize all of your datasets, but that has its own set of drawbacks, including the possibility of missing information that is crucial for analysis or testing.
Using Data to Scale
Analysts must be able to automatically scale their data storage. They may be obtaining data for your application, enterprise, or infrastructure analytics from a single device, system, or group of sensors today, but there may be an over-abundance tomorrow. How can you keep up with the increasing amount and velocity of data? Your on-premises hardware and data store will limit you, necessitating sharing and replication. When they go wrong, though, they need hours of troubleshooting and rework. A controlled system that automatically scales as your data grows is the ideal solution.
Experimenting with Data
Building Data Pipelines on extract, transform, and load (ETL) procedures present distinct challenges. A flaw in one phase of an ETL process can result in hours of intervention, compromising data quality, losing consumer trust, and making maintenance difficult. For transactional data with a static schema, ACID (atomic, consistent, isolated, and durable) databases are reliable.
Companies who want to gain insights from their data lack the staff resources and experience needed, thus this is an area where they should concentrate their efforts. In 2020, there was a scarcity of 250,000 data scientists. Data applications can be difficult to administer and troubleshoot manually by an amateur. Specialists with PhD experience are preferred.
Ameex can help your business in overcoming Data Pipeline challenges in a seamless way. Connect with us right away!