A data pipeline includes a set of processes and tools to combine, automate, compute, and transform data from a source to a destination.
What is a Data Pipeline?
A data pipeline is a set of processes and tools that combine, automate, and transform data from a source system to a destination. An end-to-end data pipeline includes several tasks such as collecting raw source data from heterogeneous sources, integrating data, joining data with other sources, storing data, adding derived columns, data analysis, and delivering insights.
A typical data pipeline consists of a workflow that defines the sequencing of jobs and maps their dependencies. The other elements that constitute the data pipeline include:
- Data source: a place where a data pipeline extracts data. Data sources include CRMs, RDBMS, ERPs, and IoT device sensors.
- Data ingestion: it is the process of combining data from multiple sources into a unified view. Integration steps include ingestion, data cleansing, and ETL.
- Computation: involves data analytics and computation to derive new insights. Data pipelines use batch processing and stream processing as their data extraction methods.
- Presentation: involves sharing insights using emails, SMS, push notifications, dashboards, and microservices.
Why is a Data Pipeline Important?
Well-managed data pipelines offer businesses access to well-structured and consistent datasets. Data constitutes the primary building block of any AI model and hence building systematic data pipelines matters for AI success.
In a machine learning context, data pipelines help eliminate error-prone, time-consuming, and manual workflows involved in shifting data between various ML stages and thus help avoid data bottlenecks.
Data pipelines provide the following benefits to businesses:
- Actionable insights by quickly integrating, analyzing, and modeling raw data
- Confident and faster decision making as data is processed in real-time reflecting up-to-date information
- Enhanced business agility backed by modern cloud-based data pipelines
- Supports performant ML applications
Importance of Monitoring Data Pipelines
Modern ML applications demand building reliable, cost-effective, and fast data pipelines managed by data pipeline tools. Before you build your data pipelines for ML projects, you must be aware of the data sources, destinations, business objectives, and the applicability of different data engineering tools.
Data engineering frameworks such as Apache Hadoop/Hive, Airflow, Presto, Apache Spark help build and orchestrate reliable data pipelines to serve your ML project needs.
Later, you can set up appropriate data pipeline monitoring practices using Censius AI Observability platform to ensure
- Optimal resource utilization
- Scaled productivity
- Better auditing and accountability
Censius AI Observability Platform maintains the sanity of your production models by governing the model input pipelines for drift so you don’t have to constantly monitor your incoming data. It keeps a constant watch on your data pipelines so that you can focus on building more intelligence.