In a practical world, data is always associated with one or more action(s). For example, even a single comment on a content post, triggers social media metrics, search engine metrics, etc.
For every single one of these processes, the data has a source and a destination, which is, a data pipeline.
Modern businesses require scalable mediums that allow them to process and transition data between server and client systems, to and from storage, online applications, and on-premise/off-premise data sources. The more sophisticated the medium, the better control you have over your data storage, traffic, and of course, pathways.
What is a data pipeline?
A Data Pipeline is a pathway through which information moves from your data sources to your applications and client systems. The pipelines facilitate accessibility among your storage systems, Cloud, databases, and Big Data framework.
Such a pipeline essentially consists of three segments:
- The source
- The process flow(s)
- The destination
Once a data request is made, if it doesn’t need to be processed, it is ingested in the pipeline. If there are linked processes, they continue to run concurrently, till the desired result is obtained and delivered to the destination.
The destination in this case can be anything; destinations can range from being a storage system, to a database, to a view, and data lakes. The in-between processes can include data augmentation, enrichment, transformation, and filtering.
The ideal pipeline, e.g. the AWS data pipeline model, must always be sanguine about your data storage; this, in turn, helps with a speedy and efficient working of your web processes.
Resource availability, task dependencies, process continuity, and notification systems continue to function based on how you orchestrate your pipeline. Speaking realistically, as a business and its products scale up to support more users, the pipeline’s complexities also need to scale up.
This is due to the following reasons:
- Your business scales up to incorporate various, heterogeneous data sources and silos
- Your business processes rely on external data sources to a great extent
- You have to spend more time merging your tool chain to aggregate your data
- You need to put in a lot of manual intervention required in orchestration, intervention, and monitoring your data stack
The better the orchestration of the data pipeline, the more resilient it is against disruptions or scalability challenges. The better the pipeline functionalities, the better the health and efficiency of your data stack.
This leads us towards gleaning what a data stack is.
Data Pipeline vs. Data Stack
What does your data pipeline do?
What is a data pipeline’s purpose? What is the purpose of its components? To fine-tune and optimize your data analytics, you must understand the role a pipeline plays in your day-to-day data operations.
Usually, raw data is ingested from multiple data sources (pipeline origin). The data source can be anything, ranging from an API, a webhook, or even a push mechanism.
A data pipeline gets your data from point A to point B, where the journey between source A and destination B may comprise multiple processes. Some data repositories include famous databases like RedShift, AWS, Snowflake, and much more.
Your data can be headed to any kind of storage repository. Alternatively, the destination can also be another application waiting for its input data. A common destination is Google Sheets, which can be connected to any data repositories for best results.
The changes in your data, from one state to another, can be achieved through sorting, validation, verification, and standardization. This process is known as transformation. During the transformation process, your data undergoes a change from its raw form to a more legible form, wherein it can be analyzed and transformed further.
If the data is not in a raw state when it is presented at the source, it can be immediately ingested. Otherwise, it undergoes processes known as Batch Processing or Stream Processing.
Batch processing is where the data is collected periodically at the source and then sent off to the destination. Stream processing manipulates the data and gets loaded immediately after creation.
The data pipeline workflow typically comprises sequencing technical or business-oriented dependencies. Technical dependencies are responsible for the collation of data from their respective sources, so they can be queued, validated, and delivered to their destination efficiently.
Business dependencies, as a process, necessarily cross-verifies data from one source to another with an intent to assess the accuracy of the data, before it is consolidated.
Businesses have to continuously monitor their data pipelines to make sure that the pipeline is integrating with other pipelines, so as to maintain the overall integrity of the data stack.
Monitoring routines must have provisions to notify administrators of disruptions in the network, at the source, or in the destination.
What does your data stack do?
For broad stroke purposes, think of your data pipeline as the veins and arteries of your data stack. Therefore, in layman’s terms, data stacks compile, organize and make your data more intelligible.
Any point of origin where the data is loaded for processing and transferring to a new point is called a source. This could be another application such as CRM, ERP systems, SaaS systems, and many more. The data could also be headed for storage from the source.
The data pipeline is responsible for the actual transference of data and processing. Now, this can include structured, unstructured, or semi-structured data. It could be configured to perform its task at specific intervals, or in real-time. The pipelines allow you to access your data as required, which makes the storage systems as their source.
ETL or Extract-Transform-and-Load solutions are processes that are responsible for the extrapolation of raw data and its conversion to a readable form for actions.
By making your raw data more legible, you can derive meaningful insights from subsequent analytics. It also helps businesses design better machine learning and automation models.
The destination database is a repository for your data, which is transferred via the pipeline. Ideally, the pipeline streamlines your destinations so that the data stack performs at an optimal level. The destination can be a warehouse, a data lake, or a relational database.
Business Intelligence tools
Your analytical processes and intelligence tools rely on pre-conditioned models to derive inferences from your data. The more reliable the data pipeline, the more performant the data stack is. In other words, these tools make your reporting and visualizations more accurate and trustworthy.
How to Build a Data Pipeline
A data pipeline is considered viable and functional only when it is able to handle variable workloads. A single data source point can be associated with multiple pipelines.
Hence, emphasis must be laid on the architecture of the pipeline, to employ the best available architecture. The idea is to replicate the architecture across every pipeline in the data stack.
The best-case scenario for an organization is to implement GDPR, HIPAA, and CCPA compliant pipelines for your business data. As far as the pipeline architecture is concerned, you can either employ Batch or Stream Processing oriented architecture for best results.
Alternatively, you can employ Lambda Architecture, which combines the best of both processing types. Lambda architectures are one of the best solutions in this modern, Big Data driven use cases.
Warehousing helps you maintain a structured repository of your data, so that business processing tools can gain intelligent insights easily. Modern businesses rely on serverless, multi-cloud data warehouses that support their Agile CI/CD.
Such data warehouses are also immensely scalable, which helps businesses incorporate emerging tech such as AI-automation, ML, etc. for their analytics.
Transforming is merely the conversion of unrefined data into readable data for analytical purposes. Unstructured data is converted into structured data, and semi-structured data is further refined for easily applicable analytics.
Analytics ready data is operationalized using ML and AI-based processes. The integration of such technologies enables complex and large analytics operations.
How does the AWS data ipeline model succeed?
- The AWS pipeline model is primed for data mobility and transformation with automated and intelligent error handling, scheduling, and dependency management
- Your pipeline is more resilient in an ideal model. Automation ensures failed processes are retried automatically, and your pipeline moderators are notified of the disruptions
- Pre-conditions and custom pipeline logic are easier to integrate. The AWS Data Pipeline model uses a number of pipeline templates to boot. This helps you recreate the ideal pipeline schema based on your bespoke use case.
- Scalability and cost become the least of your worries with an integrated AWS Cloud toolchain
A data pipeline is an essential part of every organization’s analytical journey, as it helps get the data to the right place. Not only does it connect the data sources, but also delivers data to the right destinations.
If you want more information on how you can tweak your data pipeline to reflect the right data operations, then check out this guide to the modern data stack below.