If your company uses raw data, properly managing its flow from the source to the destination is essential. Otherwise, the transfer process may not be successful, resulting in errors, duplicates or data damages. On top of that, the amount of online data and its sources is constantly growing, further complicating its extraction.
The solution is building a data pipeline architecture – it helps to ensure the information is consistent and reliable while eliminating manual work of data extraction. In today’s article, we’ll dive deeper into what data pipeline architecture actually is and how you build a solid one for your team.
To understand its architecture, let’s first need to look at the data pipeline as a single unit. Simply put, a data pipeline is a system where data is transferred from the source to the target system. However, an ever-growing number of disparate data sources requires something more sophisticated, and here’s where the data pipeline architecture enters the scene.
That said, data pipeline architecture is a system that collects, organizes, and delivers the online data. This system consists of data sources, processing systems, analytic tools, and storage units, all connected together. Since raw data may contain irrelevant material, it may be difficult to use it for business analytics and intelligence. A data pipeline architecture arranges such data so it’s easy to analyze, store, and gain insights from it.
As it was mentioned in the beginning, the volumes of online data are growing daily, requiring large data pipelines to handle it. But what are the exact reasons behind the system's importance?
Ready-to-use and available for different teams. First of all, with data pipeline architecture allowing businesses to handle data in real-time, they can analyze it, build reports, and gain insights. A sophisticated infrastructure can deliver the right data, in the right format, to the right person.
Data from multiple sources in one place. A data pipeline architecture combines information from multiple sources, filters and delivers only the required data. This way, you don’t have to take additional steps acquiring the data separately or get flooded with unnecessary information.
Convenient transferring process. In addition, a robust data pipeline architecture allows companies to easily move data from one system to another. Typically, when moving data between systems, you have to transfer it from one data warehouse to another, change formats or integrate the data with other sources. With a data pipeline, you can unify data components and build a conveniently-working system.
Enhanced security. Finally, a data pipeline architecture helps companies restrict access to sensitive information. For example, they can modify the settings so that only certain teams are able to see certain data.
A data pipeline delivers information from the origin to a data warehouse; also, it can organize and transform data along the way. Let’s take a look at each architectural element and what it’s for.
Origin, which, in other words, is the entry point for all data sources in the architecture. The most common types of origins are application APIs, processing applications, or a storage system like a data warehouse.
Dataflow is the process of data being transferred from the starting point to the final destination (more on that later). One of the most common approaches towards dataflow is called an ETL pipeline, short for Extract, Transform, Load.
Extract refers to the process of acquiring data from the source. The source can be anything from a SQL or NoSQL database, an XML file or a cloud platform that holds data for marketing tools, CRM, or transactional systems.
Transform is all about converting the data format so it’s appropriate for the target system.
Load is the part where data is placed into the target system, like a database or data warehouse. The target system can also be an application or a cloud data warehouse such as Google BigQuery, Snowflake, or Amazon RedShift.
Destination, as the name suggests, is the final point the data is moved to. Typically, the destination is a data warehouse or a data analysis/business intelligence tool, depending on what you’ll be using the data for.
Monitoring is the routine of tracking whether the pipeline is working correctly and performs all the required tasks.
There are two approaches your business can take towards a data pipeline: you can use a third-party SaaS (software as a service) or build your own. If you go with the latter, you’ll need a team of developers who’ll write, test, and maintain the code for the data pipeline.
Of course, they’ll require various tools and technologies for it – let’s take a look at the most common ones used for building a data pipeline:
Amazon Web Services (AWS) – cloud computing platforms and APIs provider. AWS is relatively easy to use, especially compared to its competition. It offers multiple storage options, including the Simple Storage Service (S3) or Elastic Block Store (EBS), usually used for storing large amounts of data. Also Amazon Relational Database Service provides performance and optimization for transactional workloads.
Oxylabs Scraper APIs – public data acquisition solutions. Next on the list, there are Scraper APIs – SERP Scraper API, E-Commerce Scraper API, and Web Scraper API. These three tools are designed to scrape public data from any website, search engine or e-commerce marketplace. The tools deliver real-time data in a structured JSON and CSV format, making it convenient for future use.
Kafka – distributed event store and stream-processing platform.
With the help of Kafka Connect and Kafka Streams components, this tool is designed for building robust data pipelines, integration, mission-critical, and streaming analytics applications.
You can use Kafka to combine messages, data, and storage – the components of these units (i.e., Confluent Schema Registry) so you can build a proper message structure. Meanwhile, SQL commands allow filtering, transforming, and aggregating data streams for continuous stream processing with ksqlDB.
Hadoop – open source framework for storing and processing large datasets. Hadoop is ideal for processing large, already-distributed datasets via multiple servers and machines simultaneously. To process the data, Hadoop utilizes the MapReduce framework and Yarn technology: this way, the tool breaks down the tasks and quickly responds to queries.
Striim – data integration and intelligence platform. Striim is an intuitive, easy-to-implement platform for streaming analytics and data transformations. The tool features an alert system, data migration protection, agent-based approach, and the possibility to recover data in case any issues occur.
Spark – open-source unified analytics engine for large-scale data processing. Spark allows you to merge historical and streaming data; it supports Java, Python, and Scala programming languages. The tool also gives access to multiple Apache Spark components.
To really grasp how a data pipeline architecture works, let’s look at some examples. There are three common types of data pipeline architecture: Batch-based, Streaming, and Lambda. The main difference between these examples is the way the data is being processed.
In the Batch-based Architecture, the data is processed in bundles periodically. Say you’ve got a customer service platform that contains large amounts of customer data that needs to be pushed to an analytics tool. In this scenario, the large amounts of data entries would be split into separate bundles and sent to the analytics tool bundle-by-bundle.
Here’s a visual representation of the Batch-based architecture:
In the Streaming Architecture, data is being processed one-by-one in whole units. In this scenario, the data is dealt with as soon as it’s received from the origin, contrary to the Batch-based architecture, where it’s done periodically.
Here’s a visual representation of the Streaming Architecture:
Finally, the Lambda Architecture is a mixture of both Batch-based and Streaming approaches. It’s a rather sophisticated system, where data is both processed in certain periods of time by batches and by whole units. The Lambda Architecture allows both historical and real-time data analysis.
Here’s how the Lambda Architecture would look like:
By moving, transforming and storing data sets, pipelines enable businesses to gain crucial insights. Yet, with the ever-growing amounts of online data, data pipelines must be robust and sophisticated enough to ensure all the operations go smoothly.
What is a data pipeline?
A data pipeline is a system where publicly available online data is moved from the source to the database. The system includes all the elements and procedures of a data movement from the beginning to the end, including the origin the data is scraped from, the ETL dataflow, and the destination data travels to.
What is the difference between data pipeline and ETL?
A data pipeline is a system where data is transferred from the source to the target. Meanwhile, ETL – short for Extract, Transform, Load – is a part of a data pipeline.
ETL is the process of transferring data from a source (i.e., a website), to a destination, typically a data warehouse. Extract refers to acquiring data from the source; transform refers to modifying the data for loading it into the destination, while load is the process of inserting the data into the storage unit.
What are some data pipeline examples?
Batch-based, Streaming, and Lambda are the most common examples of a data pipeline architecture. The main difference between them is the way the data is being processed.
In a Batch Architecture, data is being processed in bundles periodically; in a Streaming Architecture, data units are being processed one-by-one as soon as they are received from the origin. Finally, the Lambda Architecture is a mixture of both approaches.
About the author
Senior Content Manager
Roberta Aukstikalnyte is a Senior Content Manager at Oxylabs. Having worked various jobs in the tech industry, she especially enjoys finding ways to express complex ideas in simple ways through content. In her free time, Roberta unwinds by reading Ottessa Moshfegh's novels, going to boxing classes, and playing around with makeup.
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