Data Pipeline Architecture: A Guide For Business Users

Scraping Robot
July 26, 2023

Many businesses use data scraping processes to collect and organize information from websites. From there, they can set up data sets to analyze for business purposes. You may want to track a competitor’s website or keep up with consumer sentiment to understand current market trends.

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After collecting the information, you’ll need data pipeline architecture to facilitate information processing. Here, we’ll break down the purpose of a data pipeline and how they ultimately help business users.

What Is Big Data Pipeline Architecture?

What Is Big Data Pipeline Architecture?

Data pipeline architecture is the structures and processes used to clean, copy, manipulate, and transform data. It’s the starting point for data engineers looking to extract insights from the information pulled in by your data collection processes, including web scraping tools.

Organizations use these pipelines to move information from information starting points, including:

  • Spreadsheets
  • SaaS platforms
  • Databases
  • Customer relationship management (CRM) platforms
  • Web analytics tools
  • Social media

Once the data gets into the pipeline, businesses can move it to target destinations like data lakes and warehouses. Companies can have developers create data pipeline architecture with code and backend data processes. Some companies prefer using Software-as-a-Service (SaaS) tools designed for this purpose. Either way, a lot of work goes into assembling a viable and robust data pipeline.

Why Do You Need Data Pipeline Architectures?

Why Do You Need Data Pipeline Architectures?

The need for information is only growing. Data has become a valuable asset, and businesses are doing everything they can to gain as much value from it as possible. That’s only feasible if you have a reliable and stable framework for analysis operations.

The need for complex analytics isn’t limited to large enterprises. A small business may also have a large and involved set of analytics requirements to support its data needs. From there, you have business users with unique information requests.

For example, you can have a marketing team that wants information on the channels customers like to use when engaging with the brand. Developers might want information on the performance of the company website. You also have business executives who want details on the most successful initiatives for bringing in revenue.

You can accommodate these requests if you have an optimal data pipeline architecture to support data scraping and other information collection processes. The data infrastructure can ensure that your company pulls in all relevant information, stores it, and makes it available in the necessary format.

What Are the Components of a Data Pipeline?

What Are the Components of a Data Pipeline?

Big data pipelines manage data collection, processing, and implementation. The goal is to ensure that the more information you pull in, the lower the margin of error when using the data for business purposes.

The complete cycle of a modern data pipeline starts at the source information and ends where you deposit the data. From there, team members access the information to serve their decision-making processes. Artificial intelligence (AI) algorithms may also use the data to break down business information.

A data pipeline flow usually consists of the following.

1. Collection

Also called the ingestion layer, this process brings information into the data pipeline. Here, analysts apply tools that connect to data sources using various protocols or application programming interfaces (APIs).

For example, a streaming data pipeline architecture would work with data from a constantly moving source and deliver it to an information storage target. You see this type of architecture used in real-time applications.

2. Ingestion

The collected information moves to a storage layer for additional review. The storage layer can be a relational database like SQL Server or an unstructured storage object like Hadoop or Amazon S3. Some companies catalog and profile the information to make it easier to analyze.

Before you can work with data collection, you must figure out which information to extract by executing data profiling. That involves reviewing the characteristics and structure of information to determine how well it fits your purposes.

Below are two common types of ingestion:

  • Batch ingestion: Batch processing involves extracting record sets and performing the same functions collectively. The ingestion mechanism runs sequentially to read, process, and output record groups based on the criteria outlined by analysts and developers. These jobs run on a scheduled basis, so no new information enters the data pipeline architecture until completion.
  • Streaming ingestion: Unlike batch processing, data automatically passes into a streaming pipeline architecture as single records or information units. Streaming ingestion is typically used when there’s a need for a real-time data pipeline architecture with minimal latency.

3. Preparation

This phase, also called the transformation, involves molding data into a state ready for modeling, analysis, or use in other downstream processes. You start by cleaning the data and eliminating any inconsistencies, errors, and outliers that would affect its integrity. In addition, you would look for and remove duplicates, handle missing or incomplete values, and correct formatting issues.

After cleaning, information from different sources is pulled into a comprehensive view. Here, you may integrate other data sets based on common attributes. You may also need to deal with conflicts and handle any discrepancies in the formatting and structure of the data. From there, you transform the data to align with the needs of downstream processes.

4. Consumption

Once the data is ready, businesses can apply different mechanisms to make the information available to business users. They can use query tools and APIs or gain direct access to the data store. Analysts can apply statistical analysis or machine learning techniques to locate patterns within data sets.

From there, they can capture the insights and pass them to business leaders to help with decision-making. Other users can tap into the data and format it in a visually pleasing manner: dashboards, reports, charts, graphs, or interactive visualizations.

What Are Some Common Pipelines Used in Modern Data Pipeline Architecture?

What Are Some Common Pipelines Used in Modern Data Pipeline Architecture?

Below is an overview of patterns often used to construct data pipeline architecture.

  • Batch processing: Information gets processed in one large batch at regular intervals.
  • Real-time streaming: Information gets processed as it moves through the pipeline in real time.
  • Lambda: Lambda architecture combines the benefits of batch and real-time processing. It moves information through the pipeline in parallel. Businesses can output information from both layers to create a unified data layer.
  • Extract, transform, and load (ETL): The ETL pattern extracts information from data sources, updates it to the required format, then loads it to a target data repository. Businesses often use ETL processes for data warehousing, migration, and integration.
  • Event-driven architecture: This pattern focuses on capturing and processing system events. That allows data to be processed and passed through the data pipeline architecture depending on the action or workflow triggered by an event.
  • Data replication and synchronization: Data is replicated or synchronized across various databases and systems. Organizations typically use data replication and synchronization for data backup, disaster recovery, and maintaining data consistency across different data repositories.

What Are Some Data Pipeline Architecture Best Practices?

What Are Some Data Pipeline Architecture Best Practices?

Regardless of the data pipeline architecture used, organizations should track what’s happening within all system components. Failures can occur at any point, so you need to establish monitoring, logging, and alerts to ensure data engineers can step in and resolve issues that might crop up. Below are other best practices for setting up and working with data pipeline architecture.

  • Map and learn dependencies: Use an automated solution to track and document the dependencies within your data pipeline. It’s hard for humans to keep up with a complex pipeline and the associated documentation, so this reduces the chance of introducing changes that could harm its overall operations.
  • Set up service level agreements (SLAs): Match each data pipeline architecture to a specific use case. Capture details, like when consumers expect refreshed data, hourly, daily, or as it happens. That will determine how you ingest information.
  • Consider the data: Account for the type of data you will be working with when setting up a pipeline. For example, your choice will likely differ when dealing with structured versus unstructured data. In some cases, developers may need to build a custom solution.
  • Automate your pipeline: With so many available information sources, including data scraping robots powered by web proxies, it makes sense to automate your data pipelines. Having them in a modular, automated, and flexible format makes introducing changes to adapt to new business requirements easier.

Data pipeline information should be available only to those with a business need. Organizations should have an access management solution to control who can obtain protected or sensitive information.

Setting up a data catalog for data governance and compliance is also a good idea. Data catalogs provide descriptive metadata information on data assets like tables and metrics. Businesses should consider automating updates to data catalogs to ensure nothing is missed.

Don’t get bogged down in finding the perfect data pipeline architecture. Focus on what works for your organization and the needs of its various users. That helps you avoid building something that doesn’t work for your data environment.

Find Solutions That Fit Your Data Needs

Find Solutions That Fit Your Data Needs

Data pipeline architecture makes it easier for organizations to move data collected from different resources to various source systems. Once you gather information, the pipeline cleans, formats, and prepares the data for further downstream consumption. Every area in your company then has access to reliable information for different purposes, including data analysis and marketing.

Scraping Robot prides itself on the quality of its data scraping tools. We provide everything businesses need to establish hassle-free information collection processes to support their data pipeline architecture. Discover the benefits for yourself by signing up for a free trial of our product.

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