What Is Data Pipeline Architecture?
Everyday business operations produce astonishing amounts of data that can yield valuable insights into the company's health and success.
Data pipelines are complex transport mechanisms driven off a combination of software, hardware and networking components. Having well-designed data pipelines is key to achieving faster data insights thru data driven and consumption driven analytics. First, however, it's important to understand the role that data pipeline architecture plays.
Data Pipeline Architecture
Modern data pipeline architecture refers to a combination of code and pre-configured tasks (for merging, forking and transforming) data from its source into data lakehouse.
Key considerations to bear in mind as you device an efficient pipeline architecture:
- Throughput: Throughput is the amount of data a pipeline can process in a set period of time.
- Reliability: Systems within the pipeline are fault-tolerant, meaning they can continue to operate without interruption even when one or more components fail.
- Audit Traceability: Built-in mechanisms for auditing, logging and validation help to ensure your data is in good condition when it reaches its destination.
- Latency: Latency is the amount of time it takes data to travel through the pipeline from its source to its destination. This could be streaming (near-real times) or batching (including micro-batching) each requiring special considerations
What Is Data Pipeline Architecture Used For?
High volumes of data flow into businesses every day. A well-built data pipeline will make that data accessible when you need it, boosting your organization's analytics and reporting capabilities.
Here are some of the key benefits of good data architecture:
- Data consolidation: Pipelines extract data from multiple sources, condensing it into one package to provide a comprehensive analysis. A well-structured pipeline only delivers necessary information to the user — any extraneous data stays in place.
- Administrative control: A secure data pipeline allows system administrators to limit dataset access to specific users or teams.
- Improved vulnerabilities: Moving data involves multiple transfers between storage locations. Those transfers may require appropriate formatting and integrating to make data fit for purpose and use. Having a well-built data pipeline ensures repeatability and code reusability while also helping incorporate statistical checks and balances delivering trusted data for consumption.
Designing Data Pipelines
Data pipeline architecture consists of many layers that overlap until the data reaches its final destination. Each layer is essential to effectively formatting and delivering data where it's needed the most. Pipeline essentials:
- Data sources: These are typically consumer and business facing transactional systems (like reservation, POS, banking systems etc) that are necessary for a deeper understanding. A modern data engineering SaaS vendor platform supports many source system connections thru simple plug-in interfaces (ODBC, API, native).
- Ingestion method: Ingestion refers to the processes that pulls data from their sources. There are two main processing methods: batch-based and streaming. Each has its pros and cons, and the type you use will depend on your organization's data needs.
- Transformations: Most applications require raw data to undergo specific formatting or structural modifications, mostly business driven. These transformations ensure the data is eventually fit for purpose and use. Possible changes include filtering, aggregation, combination and mapping coded values to more descriptive ones. Aggregation is a significant transformation because it includes database joins (and unions), in which related datasets combine to form comprehensive packages of information.
- Destinations: Your data's destination is the centralized location at the end of the pipeline. Structured data will go into data warehouses for analytical use, while less structured (raw) data will be left in the untransformed raw-layer where data scientists and analysts can easily access it for niche needs.
- Monitoring: The monitoring layer is what keeps your pipeline functioning properly. The pipelines should inherently support and incorporate monitoring, logging and alerting to allow data engineers to proactively identify and fix problems that arise.
Common Types of Data Pipeline Architecture
The two main types of data pipeline architecture are batch-based and streaming pipelines. You should choose your type based on your intended application, as their characteristics make them best suited for specific use cases.
Batch-based pipeline architecture formats and transports preexisting chunks of data on either a scheduled or manual basis. It extracts all the data from the source and applies business logic thru appropriate transformations before sending the data to its destination.
Typical use cases for batch-based data pipelines include payroll processing, billing operations or generating low-frequency business reports. Because these processes tend to take long periods of time, they usually run during times of low user activity to avoid affecting other workloads.
Streaming data pipelines process changed data in or near real-time. All other data remains untouched, reducing the necessary computing resources.
Streaming pipelines are most effective for time-sensitive applications, such as gaining insights into the most recent changes in a dataset. Common examples include cybersecurity applications, customer behavior insights, fraud detection and critical reports for operational decisions.
Most organizations will benefit from combining both types of pipeline architecture. Having both gives data experts the flexibility to adjust their approach depending on the use case and lets you keep up with the increasing global data production rates.
Build Your Data Pipeline With Dextrus Today
Businesses have the option to either use a SaaS pipeline or develop their own. While an in-house approach might seem like a practical choice, building your own pipeline can take time and resources away from other essential tasks, ultimately affecting your business intelligence strategy. Most organizations will find that using a SaaS channel is more practical and cost-effective than creating one from scratch.
Dextrus is a high-performance end-to-end cloud-based data platform that lets you quickly transform raw data into valuable insights. With Dextrus, you can build both batch and streaming data pipelines in minutes. You can model and maintain easily accessible cloud-based data lakes and gain insights into your data with Dextrus' accurate visualizations and informative dashboards.
Dextrus is an excellent choice for your organization, offering:
- A low learning curve with no complicated code.
- Effortless data preparation.
- Query-based and log-based CDC features.
- Quick insight on your datasets.
- High throughput.
- Very low data latency.
- Self-service ingestion.
- Data-driven pipeline configuration.
- Cloud-based platform configuration.
Request a free demo today to see how Dextrus can complete your enterprise's data pipeline.