Data drives businesses. From customer information to reports from different teams, data is the cornerstone of daily operations for most organizations.
However, all this data comes from varying internal and external sources. So how do companies collect, organize and use this information? They need a way to aggregate data, store it and transform it so that analysts can understand it.
This is done through a data pipeline.
Businesses can set up custom pipelines to truly harness the power of data, especially when they use automated tools.
In this article, we’ll explain why businesses need reliable data pipelines, dive into the key components for building a data pipeline and explore the main types of data pipeline architectures.
Why you need a data pipeline
Data pipelines help with four key aspects of effective data management:
Centralized data collection and storage
A data pipeline improves data management by consolidating and storing data from different sources into one platform or destination.
Improve reporting and analytics
A pipeline can speed up data processing and transformations, leading to better reporting and analytics. An effective pipeline can help garner significant insights that foster innovation and growth.
Reduce load on backend systems
A pipeline moves data to an on-premise or cloud storage system rather than burdening backend systems and operational databases.
Ensure data quality and compliance
A standardized data pipeline with dynamic security systems can maintain data integrity, prevent errors and ensure compliance.
How to build a data pipeline
There are six critical elements involved in building data pipelines.
Let’s take a look.
1. Data sources
First, you need to organize your data sources. Any system or software that generates data for your business is a data source. Data sources include internal platforms used for work and communications, as well as customer-facing software. Examples of data sources include application APIs, the cloud, relational databases and NoSQL.
Data sources can generally be divided into:
- Analytics data about user behavior that shows how your customers interact with your software
- Data collected from third-party sources that are useful to your company
- Transactional data related to sales and products
Most SaaS vendors host hundreds or even thousands of data sources, while businesses also host multiple sources on their own systems. Your pipeline relies on quality data collection, so the sources are the first component to consider.
Collection, also known as ingestion, is the next step. Here, data from multiple sources is integrated into the pipeline using tools like Fivetran.
The ingestion process also includes protocols for how the data from various sources will be combined and incorporated into your pipeline. There are specific criteria for how the data must be collated. Your ingestion protocols ensure that these requirements are met.
Depending on your platform, you can build pipelines that support both batch and streaming pipelines.
In batch ingestion, data is periodically collected and stored based on predetermined schedules by developers and analysts. They can also set criteria for ingestion and external triggers.
Most companies use batch processing to analyze large amounts of historical data to gain insights from activities that happened in the past.
An alternate framework is stream processing, where the pipeline collects, transforms and loads data in real-time. This method is ideal for quick data processing and is commonly used by enterprises that rely on low latency for their applications and analysis.
Many organizations use both batch and streaming pipelines to serve various analytical and business intelligence needs.
Once the data is ingested, it needs to be processed or transformed into consumable information that analysts can use. This layer of the pipeline — a sequence of actions called data integration — includes validation, clean-up, normalization and transformation.
There are two standard data integration methods: Extract, Transform, Load (ETL) and ELT Extract, Load, Transform (ELT). The integration method you choose will determine your data pipeline architecture. We’ll discuss this in more detail in the next section.
In the processing stage, developers and managers work together to determine how the data will be transformed, such as enriching specific attributes and deciding whether all the collected data is useful or only parts of it.
Processing also involves standardization, where all the data you’ve collected is presented in a uniform format and follows the same measurement units, fonts, colors, etc. Developers also build mechanisms to clean up data by removing errors and redundant data.
After the data is processed, organizations have to choose a destination. A destination is a centralized location that houses all the modified and cleaned data. From here, analysts and data scientists can use the data for reporting, business intelligence and more.
The destination can be a data warehouse, data lake or data lakehouse. Data can be stored in on-premise systems or in the cloud.
A data warehouse is typically used for structured data, while a data lake is used for unstructured data. A data lakehouse is a hybrid of the other two and enables both structured and unstructured data storage and management.
The destination can also be an API endpoint, analytic systems or business intelligence tools.
Your workflow decides the structure of tasks and processes within the data pipeline, including their dependencies. It is a series of steps that dictate which jobs are performed when and how, considering the interlinked relationships between them.
Here’s an example of an ELT workflow:
A data pipeline’s workflow can have both technical and business-oriented dependencies. For example, data that needs to be held before further validations is a technical dependency. Cross-checking data from different sources to ensure its accuracy is a business dependency.
Developers can alter the workflow to match project and business objectives. An effective workflow makes monitoring and management easier.
Since data pipelines are complex systems with multiple components, they must be monitored to ensure smooth performance and quick error correction.
Developers must build monitoring, logging and alerting mechanisms that also consider how to maintain data integrity.
When a source is offline or the network is congested, data engineers must be able to rely on monitoring and alert mechanisms so that they can act on it instantly.
Consistent monitoring ensures error-free data integration and facilitates advanced analysis.
Data pipeline architecture
Data pipeline architecture outlines how all the elements listed above work together. One key factor that determines your architecture is whether you choose the ETL or ELT framework for data integration.
In the ETL process, data is extracted and transformed before it’s loaded into storage. Here’s an illustration:
Organizations can rely on ETL for:
- Faster analysis: Since the data is transformed and structured before being loaded, the data queries are handled more efficiently. This allows quicker analysis.
- Better compliance: Organizations can comply with privacy regulations by masking and encrypting data before it’s loaded into storage.
- Cloud and on-premise environments: ETL can be implemented in data pipelines that rely on cloud-based and on-premise systems.
Despite its merits, companies are moving away from ETL because of the slow loading speeds and the need for constant modifications to accommodate schema changes, new queries and analyses.
ETL is also not suited for large volumes of data and scaling becomes difficult when the number of data sources increases. It is best used for smaller data sets that require complex transformations or in-depth analysis.
In the ELT process, the data is extracted and loaded into data lakes before it’s transformed. Here’s an example:
ELT is ideal for:
- Automation: ELT allows teams to standardize data models, which promotes automation and outsourcing.
- Faster loading: The ELT framework loads data before the transformation, providing immediate access to the information.
- Flexible data format: ELT supports both structured and unstructured data, so it can ingest data in any format.
- High data availability: If you’re using business intelligence platforms or analytic tools that don’t require structured data, they can instantly collect and act on data from the data lake.
- Easy implementation: ELT can work with existing cloud services or warehouse resources, easing implementation roadblocks and saving money.
- Scalability: Since most ELT pipelines are cloud-based, companies can easily scale their data management systems using software solutions. Modifying pipelines on cloud providers is faster, cheaper and less labor-intensive than the physical on-premise changes required to scale an ETL pipeline.
The only downside of using ELT is that analysis might be slower when handling large volumes of data since the transformations are applied after all the data is loaded. But this is mitigated on feature-rich, fully managed ELT solutions like Fivetran.
Which framework should you use?
Choosing between ETL and ELT changes your entire data pipeline architecture since two major processes — loading and transformation — are interchanged.
The method you choose depends on your business use cases, but most modern companies use cloud-based, automated ELT pipelines due to their speed, flexibility and scalability.
Technical considerations for data pipeline architecture
There are five vital factors that affect your data pipeline.
A data pipeline’s primary purpose is seamlessly moving data from the source(s) to the destination. This prevents manual file movement, along with the time constraints and errors that come with it.
Automation is vital for seamless data movement from source to storage. Your data engineers must build a system that extracts, loads and transforms data on a predetermined schedule without relying on manual triggers or configurations.
Automations should also apply to data transformations and monitoring. Your developers and engineers should create a pipeline that efficiently provides valuable data for analysts to work with and develop reports, dashboards and insights on.
Monitoring mechanisms must automatically and instantly send errors when issues arise.
A data pipeline fails its purpose if it interferes with core business processes when running or if the data it presents is too stale to be useful.
Organizations often use change data capture (CDC) to ensure that relevant data is delivered on time without disrupting business operations. There are multiple CDC approaches that capture updates based on triggers, time stamps or logs.
Another key performance-related technical consideration is the parallelization and distribution of the data pipeline architecture so that the pipeline can handle increased loads or demands.
Developers and engineers must also compartmentalize and buffer sensitive operations to mitigate bottlenecks.
An unreliable data pipeline hinders analysis. Several factors can affect the reliability of a data pipeline, including synchronization failures, schema changes, bugs and hardware failures. For example, an ETL pipeline can be derailed by deleted columns and tables.
Common issues like network outages, failed queries and memory leaks can also hinder operations. That is why data engineers should construct an idempotent data pipeline, where primary keys are used to identify unique records and prevent duplicate or erroneous record entries.
Scalable data pipelines are vital for business growth. As your company develops, you want pipelines that can handle more data sources, higher data volumes and complex performance requirements.
Expecting engineers to constantly build and maintain connectors for each new data source is time-consuming and frustrating. It also leads to slower insights. For growing businesses, it’s best to design a system that programmatically controls your data pipeline.
Security and compliance are key to storing sensitive customer, client and business data. Organizations must comply with regulatory standards to ensure that no personal information is stored or exposed within their data pipelines.
Many organizations use ETL for this, as it allows them to encrypt data before its storage. However, an ELT pipeline with process isolation and robust security features, such as data encryption in transit and rest and the blocking or hashing of sensitive data before storage, can ensure compliance while providing superior performance.
Designing, building and implementing a data pipeline is a complex, labor-intensive and expensive process. Engineers must build the source code for every component and then design relationships between them without any errors. Moreover, a single change can necessitate the entire pipeline being rebuilt.
That is why most organizations choose to buy instead of build.
A fully managed data pipeline solution can help centralize all your data and deliver insights faster while being affordable. Try out Fivetran today to see how easy it is to manage your data on our platform.