What is a Data Pipeline? Types, Benefits, Use Cases

Data pipelines are automated processes that move data and allow you to reliably deliver data to multiple destinations for real time analysis or do batch processing. Data pipelines also make it easier to collect, process and deliver accurate data at scale across industries.

Every day, organizations are creating large amounts of data across all their applications, databases and other systems. This is part of an increasing global trend with the projected creation of 463 exabytes of data per day by 2025. As such manual processing is no longer viable.

Automated data pipelines provide reliable frameworks for extracting, processing and delivering data from many different sources to a variety of analytical destinations. By providing repeatable work flows these systems ensure consistent delivery while decreasing the need for manual intervention.

Robust pipeline architecture provides rapid analytics, higher quality analytics and scalable operations for business intelligence and machine learning applications.

What is a data pipeline in modern data systems

Automated systems using workflow orchestration to move data between different locations, apply transformation to the data and deliver it to be used by analysts for decision-making and for operational use. These types of systems are called data pipelines.

The primary characteristics distinguishing a data pipeline from a simple transfer include automation, repeatability, execution of a predefined workflow based upon a schedule or trigger, application of standard rules and systematic error management.

In terms of reliability the stakes are high, as indicated by Gartner research, which states that poor data quality results in an average loss of $12.9 million per year for each organization.

Production data pipelines must provide failure, interruption and quality error resolution without data loss. Modern data pipelines are designed with continuous monitoring capabilities that detect failures and automatically recover from them.

Data pipelines act as a backbone to connect operational systems to analytical environments and enable the consolidation of distributed information.

Core components of a data pipeline architecture

The complete architecture of a data pipeline is comprised of five connected layers that manage the lifecycle of data from its generation or creation all the way to its use by consumers of data.

Below are the primary functions of the five core components of the data pipeline:

How data pipelines process and move data

Sequential processes are used in a data pipeline to direct data from creation to usage with transformation and quality control.

Step 1. Data creation: Source systems generate data as a result of transactions or events. Change capture techniques track which records have changed to be captured.

Step 2. Data ingestion: Pull based methods include querying source systems at periodic intervals, push based methods involve receiving streams of published changes to your system. Batch processing performs large scale operations on bulk data and real-time data ingestion performs data collection as it happens.

Step 3. Validation: Validating schema determines that the data conforms to the defined structure. Complete validation identifies missing fields. Duplicate validation eliminates duplicate entries in order to ensure data accuracy.

Step 4. Transformation: As part of the ETL process data is extracted from its original format, transformed into another format and loaded into a data warehouse or data lake. Calculations and enrichment are applied based upon business rules.

Step 5. Loading: Data is written to data warehouses and data lakes by transforming operations. Data is stored most efficiently using partitioning and compression.

Step 6. Consumption: The final step in a data pipeline is when datasets can be consumed for analytics, reporting and machine learning via business intelligence applications and APIs.

Understanding different types of data pipelines

Each organization chooses a type of data pipeline architecture based upon their velocity needs and how much time they are willing to spend waiting for data.

A batch pipeline runs on a schedule, processes large amounts of data collected together and executed as part of a single event. It makes sense when an application can tolerate some delay in receiving its data. It also allows for maximum use of resources.

Streaming pipelines processes data continuously as each new event occurs and provides nearly immediate availability. It is best suited for high velocity sources such as IoT devices and financial transactions where minimum latency is required.

An ETL process pipeline extracts data, performs transformations and loads the result. The three tasks occur in separate environments allowing for the creation of very complex logic.

An ELT process pipeline first loads raw data into a target and then executes any necessary transformations via the processing capability of the target. Since over 60% of all corporate data is now stored in the cloud, ELT uses cloud warehouse processing capabilities.

Hybrid architectures include both batch and stream components, which allow organizations to strike a balance between low latency and high efficiency. Organization may choose to stream data to support mission critical real time applications and use batches to analyze historical data.

Key benefits of using data pipelines

Implementation of a data pipeline benefits an organization in both operational and analytical areas affecting business performance.

• Data accuracy improvement: Elimination of manual entry errors such as data format discrepancies and missing updates through systematic validation and standardized transformation improves the reliability of data.
• Automation of workflows: Automation of repetitive tasks through scheduled execution reduces resource utilization for higher level analytical work while reducing the time required for insight development for all stakeholders.
• Speed of analytics: Quick delivery of reliable and timely data ensures that stakeholders have access to the most current data when they need it. This also ensures that the dashboards remain current through the predictability of updates.
• Ability to scale: The ability of pipelines to process large amounts of data as well as multiple additional data sources with no increase in proportionate effort through the use of distributed processing enables organizations to scale their operations.
• Reducing risk: The implementation of standard processes, comprehensive monitoring capabilities and automation of error handling processes provides organizations with the protection against data loss enabling business continuity.

Common data pipeline use cases across industries

The data pipeline supports a multitude of analytical and operational use cases across many industry sectors including finance, healthcare, retail, manufacturing and technology.

Real-Time Lead Scoring Model for a USA-Based Tech Company

A U.S.based integrated technology services firm was experiencing unstructured lead flows into their organization through a variety of channels making it difficult for them to evaluate or prioritize these leads. The organization wasted time qualifying many low value leads.

Hitech Analytics created an algorithmic lead scoring model using keywords to standardize and clean data as well as assess other important attributes such as location and company size. The model then scored each lead based on its priority level and directed the highest priority leads to sales team members.

The end results were:

• 50% increase in sales rep productivity
• 200% improvement in MQL to SQL conversion
• Faster closings and strengthening revenue pipeline

Challenges in managing data pipelines at scale and their solutions

Companies with large scale production data pipeline infrastructure experience a number of issues that can be solved by implementing methodical processes to maintain both the dependability and performance of their infrastructure.

Schema drift and poor quality data

• Challenge: Poorly formatted and changing schema cause problems during integration.
• Solution: Validation rules help to ensure all data meets standards for the application. Schema enforcement tools can identify when structural or logical changes have been made to the schema.

Pipeline failures, component dependencies

• Challenge: A failure upstream in the pipeline causes a failure in the downstream systems due to the cascading nature of the pipeline.
• Solution: Component decoupling will isolate them from each other so if one fails it will not affect the others. Retries may also be used to automate recovery of failed components.

Gaps in monitoring and observability

• Challenge:Lack of visibility into the operations of the pipeline will cause delay in identifying the problem and resolving it.
• Solution:There is centralized monitoring that collects and reports on the health of the pipeline. Alerts will notify the team as soon as there is an issue.

Constraints related to scalability and performance

• Challenge: As the volume of data increases the infrastructure becomes overwhelmed which results in delays.
• Solution: Data will be processed in a distributed manner (horizontal scaling) and data will be partitioned and processed in parallel.

Increasing cost, inefficient operation

• Challenge: The increased cost of the infrastructure puts pressure on budgets.
• Solution: Data will be processed in a way that is optimized to reduce computational overhead, and data will be stored based on its life cycle. This means that less frequently accessed data will be archived.

Conclusion

Modern businesses depend on data pipelines to build a strong foundation of data driven decision making and reliable analytics. Choosing appropriate data pipeline types based on latency requirements and transformation complexity will help ensure that your systems are both useful and sustainable.

By developing a strong architecture, organizations can use their data pipelines as a competitive advantage by providing faster insights, better quality data and scalable operations to support today’s business needs and tomorrow’s initiatives in machine learning and real time optimization.

HitechAnalytics

At Hitech Analytics, we understand that each company has different needs, business goals and technology environments. With advanced analytics, you can make right decisions, prepare for the future and leverage intelligence from huge data volumes. We embed analytical intelligence into your everyday data and turn it into actionable insights.