Organizations that prioritize data enjoy a significant advantage over their less data-centric counterparts, including achieving a 4% lead in productivity and 6% higher profits. However, harnessing your vast volumes of data efficiently can be a challenge. Without a robust solution, your data landscape can quickly become a chaotic mess.
The answer is to leverage data pipelines to develop a single source of truth for all your business data by integrating information from software like CRMs, marketing platforms, financial systems, and more.
Read on to learn more about what a data pipeline is and how it could transform your approach to data management.
What Is a Data Pipeline?
A data pipeline is an automated system that ingests raw data from various sources within your business, such as customer relationship management (CRM) systems, marketing platforms, financial records, and more. It then processes and transforms this information according to predefined rules and business logic set by you and your team. The processed data is then loaded into a centralized data warehouse or repository for further use.
Why Your Business Should Use Data Pipelines
The power of modern data pipelines lies in their ability to comprehensively integrate information from any source. This allows you to access valuable insights within your data to inform important decisions. Let’s look at the main reasons for implementing a data pipeline:
- Simplifies Data Integration: A data pipeline reduces the complexity of integrating data from systems such as databases, applications, and web services used in your business and aggregates it in a central location.
- Transforms and Cleanses Data: Standardization and validation rules clean your information and ensure high quality. Issues, such as missing entries or duplicate records, are addressed, and you get access to precise, trustworthy data.
- Processes Data in Real Time: With continuous intake, the pipeline keeps your analysis updated as new inputs arrive. This enables your business to react rapidly to changing market conditions.
- Enhances Scalability and Flexibility: As needs evolve, a pipeline solution can expand its capabilities. Additional sources or analytics tools can be incorporated without disrupting your teams’ operations.
- Automates Processes: Rule-driven workflows automate previously manual processes like data preparation. This saves time and reduces the risk of human errors.
Data Pipeline vs. ETL Pipeline
While similar, data and ETL pipelines take different approaches to data management. Let’s take a look at their notable distinctions:
Data Pipeline
A data pipeline handles various sources like databases, APIs, files, and streams. It supports complex transformations and enrichment to process data in real-time or near-real-time. It offers the following features:
- Integrates data from multiple sources
- Applies transformations and enriches data
- Processes data in real-time or near-real-time
- Built to scale with your business needs
- Manages data quality with validation
- Provides visibility with governance and lineage
- Loads data into various destinations
ETL Pipeline
An ETL pipeline follows a more rigid, batch-oriented process that focuses on extracting data from sources, transforming it, and loading it into a target warehouse or database. Primarily for structured data, the approach places less emphasis on real-time processing or streaming data. It is distinguished by these key attributes:
- Extracts from sources and loads to a destination
- Operates in batches on a schedule
- Designed for traditional databases
- Less flexibility compared to data pipelines
It’s important to consider these differences if you want a solution optimized for your unique needs. An ETL pipeline focuses on delivering batch loads into data warehouses. However, as your needs change over time with additional data sources and new requirements, a data pipeline allows more flexibility.
How Does a Data Pipeline Work?
A data pipeline works by orchestrating a series of steps to collect, process, and deliver data from various sources to its intended destination. Here’s an overview of the key components and processes involved in a typical data pipeline:
- Data Ingestion: This step involves collecting data from all your important sources, such as databases, APIs, files, streaming information, and your devices.
- Data Transformation: Your business’s raw data often requires cleaning, filtering, grouping, and normalizing to fit your goals and needs.
- Data Enrichment: Additional helpful sources can be linked to enhance the data you already have, providing more context and insights for your team.
- Data Validation: Rules and checks are applied to ensure data quality and completeness meet your predefined standards.
- Data Integration: The transformed and enriched data is combined in a unified, consistent format from various places. This can help eliminate data silos that form across the many tools your employees use.
- Data Loading: The processed data is loaded where it’s needed most. These include data warehouses, data lakes, or other analytics platforms your business relies on.
- Metadata Management: Detailed metadata, including data lineage, is captured and maintained throughout the pipeline. This enables governance, auditing, and traceability of your information.
- Monitoring and Orchestration: The pipeline is continuously under observation, and errors or failures are handled automatically. This ensures a reliable and uninterrupted flow of the data your business relies on.
6 Types of Data Pipelines
There are different varieties of data pipelines. Each type offers distinct advantages and is suited to different use cases, from traditional batch processing to real-time analytics and cloud-native environments. Let’s have a detailed look at each type:
1. Batch Processing Pipelines
With batch pipelines, data can be gathered in large volumes and processed in chunks at scheduled intervals, such as hourly or daily. This works well if insights from large amounts of information aren’t needed immediately. Batch pipelines allow efficient handling of data that doesn’t constantly change.
2. Real-Time/Streaming Pipelines
Real-time pipelines continuously ingest and process information for time-sensitive or dynamic data as it arrives. This provides immediate visibility, so your business can react quickly to new trends. It’s perfect when up-to-the-minute analytics are important.
3. Hybrid Pipelines
A hybrid pipeline leverages the strengths of both batch and real-time methods. Batch processing can handle historical data, while real-time processing manages current data streams. This flexibility means you get historical context with the latest updates.
4. Cloud-Native Pipelines
Designed specifically for the cloud, cloud-native pipelines help scale easily without infrastructure limits. This approach offers elasticity and optimized costs thanks to cloud services.
5. Data Lake Pipelines
A data lake pipeline makes exploring different types of unstructured information easy when handling large volumes of diverse raw data. By discovering trends across all your business data, you gain new perspectives.
6. Containerized Pipelines
Using containerization brings consistency and portability. The same pipelines can deploy anywhere without worrying about different environments, simplifying maintenance and updates for your business.
Challenges in Building and Managing Data Pipelines
Implementing and managing effective data pipelines presents several challenges that must be worked through to ensure smooth operation and optimal performance. Here are some difficulties you may encounter:
- Data Variety and Volume: Businesses deal with structured, unstructured, and semi-structured data from many sources that must flow through the pipeline. However, a modern data integration platform can support various data types and provide advanced transformation and validation capabilities.
- Data Quality: Data from different systems may not always match up or might often contain errors. This can negatively impact how your employees make decisions based on the resultant insights. The solution is an automated pipeline that ensures high-quality data regardless of where it originates.
- Real-Time Processing: Your business needs to access fresh, up-to-date insights. However, quickly processing large volumes of data while maintaining performance can test even the best systems. Streaming analytics and event-driven architectures supported by scalable infrastructure can handle growing data volumes and increasing velocities.
- Integration: Connecting your pipeline to your business’s existing tools, platforms, and sources isn’t always straightforward. Proprietary formats and legacy software constraints can prevent smooth integration. Your solution must be compatible with your various systems and applications, enabling seamless data flow and interoperability.
Why Boomi is the Best Option for Building Your Data Pipelines
While data pipelines promise a host of benefits for your organization, building and managing them effectively can present significant challenges. Dealing with difficulties such as disparate data sources, maintaining data accuracy and consistency across systems, and processing information promptly, can hurt efficiency and decision-making.
However, the right solution can help you overcome these obstacles. This is where the Boomi Integration Platform comes in. Among its many advantages, the Boomi Integration Platform offers:
- Automated workflows: Move data seamlessly between any source or target, saving your team valuable time on manual processes.
- Governed data flows: Ensure high-quality, consistent information across all systems your employees use.
- Real-time data integration: Your business will have access to the freshest insights to drive smart decisions.
- Scalability: Boomi’s integration platform can support growing volumes of data as your business expands.
Learn more about modern concepts of managing data pipelines, data fabrics, and endpoints by downloading this free ebook.