PRACTICAL CHALLENGES OF IMPLEMENTING A DATA PIPELINE

A data pipeline includes a series of steps that are executed sequentially on each dataset in order to generate a final output. The entire process usually involves complex stages of extraction, processing, storage, and analysis. As a result, each stage as well as the entire framework requires diligent management and adoption of best practices. Some common challenges while implementing a data pipeline include:

SLOWER PIPELINES DUE TO MULTIPLE JOINS AND STAR SCHEMA

Joins allow data teams to combine data from two separate tables and extract insights. Given the number of sources, modern data pipelines use multiple joins for end-to-end orchestration. These joins consume computing resources, thereby slowing down data operations. Besides this, large data warehouses rely on star schemas to join DIMENSION tables to FACT tables. On account of its highly denormalised state, star schemas are considered less flexible to enforce the data integrity of dynamic data models.

SLOW DEVELOPMENT OF RUNNABLE DATA TRANSFORMATIONS

With modern data pipelines, organizations are able to build functional data models based on the recorded data definitions. However, developing functional transformations from these models comes with its own challenges as the process is expensive, slow, and error-prone. Developers are often required to manually create executable codes and runtimes for data models, thereby resulting in ad-hoc, unstable transformations.?

NUMEROUS SOURCES AND ORIGINS

The dynamic nature of data-driven applications requires constant evolution and are often ingesting data from a growing number of sources. Managing these sources and the processes they run is often challenging as these expose data with different formats. A large number of sources also makes it difficult to document the data pipeline’s configuration details, which hampers cross-domain collaboration in software teams.?

COMPLEXITY IN SECURING SENSITIVE DATA

Organizations host petabytes of data for multiple users with different data requirements. Each of these users has different access permissions for different services, requiring restrictions on how data can be accessed, shared, or modified. Assigning access rights to every individual manually is often a herculean task, which if not done right, may lead to the access of sensitive information to malicious individuals.

GROWING TALENT GAP

With the growth of emerging disciplines such as data science and deep learning, companies require more personnel resources and expertise than job markets can offer. Combined with this is the fact that a typical data pipeline implementation requires a huge learning curve, thereby requiring organizations to dedicate resources to either upskill existing staff or hire skilled experts.

Shortly, I will come up with some of best practices to implement & mitigate the challenges data pipeline, Thanks.