Data pipelines are the backbone of modern data management and analysis. Every second of every day we are creating tons of data, and we're also tracking all of them. The demand for data from end users grows every day, whether they are data analysts, data scientists, business analysts, or even reporters, writers, and C-suite executives. Instead of relying on manual data ingestion, processing, and loading, to move forward, you need to automate these processes. This is where a data pipeline comes in.
Data pipelines transport data from various sources, such as apps, sensors, social media, or CRMs like Salesforce and HubSpot, through a series of processing steps to a final destination, such as a data lake or data warehouse for further analysis, dashboarding, and reporting. The data pipeline process involves various stages and thus requires careful planning and management to ensure that data is delivered accurately and efficiently.
As your data gets bigger, and your pipeline becomes more complex, data pipeline management–the process of overseeing the entire lifecycle of a data pipeline, from design to deployment and maintenance–becomes essential. This involves defining the pipeline architecture, selecting the right tools and technologies, and establishing processes for monitoring and optimizing the pipeline's performance. In this article, we'll provide an in-depth look at data pipelines, data pipeline management, including best practices and common challenges, so you can leverage your data's full potential.
Data pipeline architecture
Software architecture is the heart of any automated process or infrastructural component. So is the case for data pipelines. Data pipelines are built to remove tedious, manual tasks, and reduce the risk of human error. To begin understanding data pipelines, we have to look at its core components.
The three core parts of a data pipeline are:
- Sources: where’s the data coming from?
- Transformation and mapping: how does the data need to be processed so it can be stored in its final destination?
- Destination: where will the data be stored?
Although separate, these three components interact with each other, and a robust data pipeline needs to account for how they work together in concert. For example, if your data will end up in a database vs. a data warehouse, or a data warehouse vs. a data lake, will determine if the data needs to be structured, and if there is a particular schema that data needs to follow.
Determining the transformation and processing steps needed for the data is another key aspect of data pipeline architecture. This includes identifying any data cleansing or enrichment steps that are needed, as well as any aggregations or summarizations that may be required. Will the data mainly be used for dashboarding, analytics, machine learning models, or reporting? Will any of the data be customer-facing? These questions can help guide whether and how the data is filtered, cleaned, and augmented.
Data pipeline design
Once you have a sense of your data pipeline blueprints, you can think about the design–how everything will be implemented. One key aspect of data pipeline architecture is scalability and reliability. A data pipeline should be able to handle an increasing volume of data without breaking down or becoming slow. This means designing the pipeline to handle parallel processing, load balancing, and other techniques to distribute workloads effectively.
There are different architectures that can be used for data pipelines, depending on the needs of the pipeline. Some common approaches include batch processing, streaming data pipelines, and hybrid architectures that combine batch and streaming. It's important to choose the right architecture for your specific data pipeline in order to ensure it is efficient and effective.
Deciding between batch processing and streaming data is an important consideration. Batch processing is suitable for large amounts of data that are not time-sensitive, while streaming data pipelines are better suited for real-time processing of smaller amounts of data. Ultimately, the right choice depends on the specific needs of your data pipeline and the goals you are trying to achieve.
In order to design scalable and reliable data pipelines, it is important to consider the following factors:
- Data volume: the amount of data that the data pipeline will process should be considered, as well as any potential future growth in data volume. This will help determine the resources and infrastructure to support the data pipeline.
- Data format and structure: the format and structure of the data can affect the design of the data pipeline. For example, structured data may be easier to process than unstructured data, and data in a flat file format may be easier to process than data in a nested format.
- Data processing requirements: consider the type of data processing required by the data pipeline when designing the architecture. This may include tasks such as filtering, aggregation, or data transformation.
- Data storage and retrieval: design the data pipeline with data storage and retrieval in mind. This may include considerations such as the type of data storage (e.g., relational database, NoSQL database) and the performance requirements for data retrieval (e.g., real-time data access versus batch data access).
When choosing the right architecture for a data pipeline, it is also important to consider the specific use case and requirements of the data pipeline. For example, batch data pipelines may be more suitable for regular data audits at a monthly cadence, while streaming data pipelines may be more suitable for updating product inventory, or other interactions with customers.
Data pipeline automation
One of the key benefits of data pipelines is the ability to automate bringing data from various sources into a centralized location. By automating data ingestion, processing, and loading, you can save time and ensure that data is being managed regularly and consistently, without as much expensive data scientist time. Here are a few ways to implement data pipeline automation:
- Scheduling data pipelines for regular execution: this helps ensure data is being regularly processed and updated, and can help prevent data from becoming stale. This can take the form of regularly ingesting and validating data, and updating dashboards.
- Using event-driven data pipelines: another option for automating data pipelines is to use event-driven data pipelines. Specific events, such as a new customer signing up or the completion of a specific task trigger these pipelines. This can be a useful approach for more complex data pipelines that require high flexibility and customization.
End-to-end data science in Einblick
Einblick is a powerful data science tool for your team’s end-to-end needs, with features such as
- Various data connectors so all your data is just a click away. You can also use our secrets menu to store any private credentials, such as API keys.
- Data profiler lets you assess data integrity and quality quickly.
- SQL and Python operators allow you to switch between languages for different parts of data processing.
- AutoML builds out machine learning pipelines using our progressive computation engine, and you can use the explainer and executor to understand key variables and apply the steps to new data.
- Schedules allow you to regularly and automatically execute common operations, update dashboards, refresh datasets, and more.
Check out this canvas a retailer built-out to use Einblick to predict customer churn. By scheduling and automating parts of the process, you can refresh these operators and the data as needed.
Once connected to your data sources, the canvas-based approach to data science makes it more intuitive to build and manage complex data systems, work with stakeholders, and prototype final use cases of your data. Whether you're just starting out with data pipelines or you're an experienced data engineer, Einblick can help you save you time and take your data management to the next level.
Data pipeline implementation
Integrating data pipelines with other systems and tools is a critical aspect of data management. This involves bringing data from various sources into a central location, such as a data lake or data warehouse, and making it available to downstream consumers, such as analysts and data scientists. You may have heard of ETL platforms. You can consider ETL (extract, transform, load) as a subset of the data pipeline. It's important to choose the right method for your specific data pipeline, considering factors such as the volume and complexity of the data, the desired level of automation, and the resources available for integration.
One of the key challenges in utilizing data pipelines, as with any other automation, is ensuring quality, integrity, and consistency. Having the right checks and balances in place will save you time, and give you peace of mind, so that your team can focus on the insights within all of the data.
Best practices for data pipeline management
Define clear goals and objectives:
What will we use the data pipeline for? Typical use cases include data analytics, machine learning, or reporting and dashboarding. Articulating the purpose of the data pipeline ensures the correct customization and implementation of the pipeline.
Identify the data sources and types of data:
To build an effective data pipeline, it's important to understand the various data sources that will feed into the pipeline, as well as the data that will be collected and processed. Your data pipeline needs to be built to handle a particular volume, variety, and velocity of data input. Building a data pipeline without a clear sense of this information would be like constructing a skyscraper without architectural blueprints.
Establish a clear chain of custody for the data:
To maintain data integrity and trustworthiness, it's critical to have a clear chain of custody for the data as it moves through the pipeline. This can involve tracking each stage of the data's journey, including ingestion, transformation, and delivery, to ensure that it is handled correctly and kept secure.
Implement robust security measures throughout:
Data pipelines often involve the movement of sensitive or confidential data, so it's essential to have security measures in place to protect the data from unauthorized access or tampering. This can include measures such as encryption, access controls, and firewalls.
Create a process for validating data integrity:
To ensure that the data being processed is of high quality and can be trusted, it's important to have a process for testing and validating data integrity. This can include measures such as validating input data against certain criteria, regular data backups, tracking changes in data, timely software patches, and training every team member about data management.
Challenges in data pipeline management
Poor data quality
When data is not properly cleansed, validated, or standardized before being integrated into the destination, inaccurate or incomplete data can affect downstream consumers.
Trouble with data integration
Data integration challenges can occur when different data sources have incompatible formats, schemas, or semantics. This can require significant effort to reconcile and harmonize data before the pipeline can process it.
When the pipeline is not designed or optimized to handle large volumes of data or high levels of concurrency, there can be delays in data availability and processing slowdowns, affecting all downstream activities, from analytics to machine learning models in production, dashboarding and reporting.
Security vulnerabilities can occur when security best practices are not followed. This can result in data breaches, unauthorized access, or tampering. The end result could be data loss, data exposure, or other risks that can affect the integrity and reliability of the pipeline.
Data pipeline performance
Optimizing data pipeline performance is essential for ensuring that data is available in a timely and reliable manner. There are several key considerations for improving data pipeline performance, including choosing the right architecture, selecting the tools and technologies, and tuning the data pipeline for different workloads.
Once you have a data pipeline built-out, it’s important to perform regular maintenance by tuning data pipelines for improved performance, and identifying and addressing bottlenecks in the data flow. This can involve optimizing data processing algorithms, improving data access patterns, and optimizing data storage layouts. To monitor data pipeline performance and identify bottlenecks, it's important to implement robust monitoring and optimization tools, such as New Relic and Datadog.
Data pipeline testing and monitoring
Testing data pipelines is essential for ensuring the quality and reliability of the data they produce. There are several types of testing that should be considered when building data pipelines:
- Testing data pipelines for correctness and completeness: this involves verifying that the data pipeline is producing the expected output and that all necessary data is being processed. They can achieve this through the use of unit tests, integration tests, and end-to-end tests.
- Testing data pipelines for performance and scalability: they should test data pipelines to ensure they can handle the expected volume of data and workloads. This includes testing the pipeline's ability to scale up and down as needed, as well as its ability to handle spikes in data volume.
- Testing data pipelines for security and compliance: it is important to ensure that data pipelines are secure and compliant with relevant industry and regulatory standards. This can involve testing the pipeline's ability to handle sensitive data, as well as its ability to meet requirements for data security and privacy.
Monitoring data pipelines is essential for ensuring their ongoing health and performance. Some key areas to monitor include:
- Monitoring data pipelines for errors and failures: data pipelines can encounter a range of issues, such as data quality problems, system failures, or security breaches. It is important to monitor data pipelines for these issues and to have a plan in place for addressing them when they occur.
- Detecting and addressing data pipeline performance issues: Data pipelines can experience performance bottlenecks or other issues that can impact the quality of the data they produce. It is important to monitor data pipelines for these issues and to take action to address them.
- Using data pipeline monitoring for continuous improvement: data pipeline monitoring can also identify opportunities for improvement, such as by identifying areas where the pipeline is underperforming or by identifying trends in data quality issues. By using data pipeline monitoring to drive continuous improvement, organizations can ensure that their data pipelines are always running at peak performance.
- The three main parts of a data pipeline are sourcing the data, processing the data, and storing the data in a final destination.
- Data pipelines help you automate various parts of the process saving you and your team time.
- Data pipeline architecture (high-level) and design (specifics) are critical for integrating data pipelines into your data ecosystem. These decisions should be based on knowledge and understanding of the sources, processing, and destination of the data, as well as any known end use cases of the data.
- Following best practices regarding data integrity and security are essential to maintain effective pipelines.
- Best practices include regular testing and monitoring of live processes.
Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.