Today, diverse data plays an essential role in creating effective business strategies for growth. However, collecting data from different sources requires building different types of data pipelines, regardless of the sector or industry in which your organization operates. Although there is no shortage of data in the digital world, streamlining the data collection process becomes a significant challenge for companies. This is primarily because of the nature of raw data. Since the raw data comes in different types and sizes, companies often need to build complex data pipelines.
But which type of data pipeline is suitable for your business? Are there any benefits of implementing a data pipeline in your technology stack? Well, look no further! We have got a complete guide on various types of data pipelines to answer all your queries.
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What is a Data Pipeline?
A data pipeline is a series of aggregated data-processing activities that transfer data from one system to another. Generally, a data pipeline is divided into two major components: a source from which data is collected and a destination or sink where it is stored. However, the use of data pipelines often goes beyond just moving data. Based on the business requirements, data is transformed before storing it in the destinations like a data warehouse or a data lake. In the case of transformations, they may have processing steps like validation, cleaning, and more before storing the data.
A data pipeline starts with data extraction and continues through various stages, where each stage produces an output that serves as the input for the following stages. The process continues till the pipeline is complete. For example, data might flow from a business application to a data warehouse, from a data lake to another database, or even from a source to business intelligence applications.
Given how data pipelines work, you might confuse it for an ETL (extract, transform, and load) process. However, note that the ETL process is a subset of a data pipeline. A data pipeline might not include the transformation phase that an ETL pipeline usually includes before storing the data in the destination.
Types of Data Pipelines
Now that you know what a data pipeline is, let’s move forward to different types of data pipelines depending on the kind and flow of data:
- Batch Processing Pipelines: These types of data pipelines are used to transfer a large amount of data in batches, recurringly or in regular periods. Organizations use batch-processing channels to extract data from the source, apply operations, and transfer to the sink later. The entire process is not done in real-time, varying execution times from minutes to a few hours. Many organizations also utilize batch processing data pipelines to extract and ingest it into a more extensive system like data lakes for later processing and analysis.
These types of pipelines are generally used for traditional analytics—using gathered data (historical data) for decision-making.
- Real-Time/Streaming Data Pipelines: These types of data pipelines are used to draw insights from constantly flowing data in near real-time (within seconds or even milliseconds). Real-time analytics enables businesses to receive up-to-date operational information, respond swiftly, and devise solutions for intelligent performance monitoring. Contrary to batch processing, a streaming pipeline continuously ingests changing data, and updates metrics, reports, and summary statistics in response to each available event. Such types of data pipelines are generally used to deal with live-streaming or fluctuating data, like in financial stock markets.
- Cloud Native Data Pipelines: These data pipelines utilize cloud technologies to transfer and process the ingested data. These types of data pipelines offer a robust infrastructure, higher scalability, and better cost-efficiency compared to on-premises data pipelines. Many reliable cloud-native pipeline services are provided by industry leaders like AWS DMS, Hevo Data, and Equalum.
- Open-Source Pipelines: Open-source data pipelines are cheaper than commercially available data pipelines, as it is available publicly for people to download and utilize based on their requirements. The no-cost factor and ease of access also enable you to edit the source code, making them flexible to your requirements. Many open-source tools, like Apache Kafka, offer free pipeline development services. However, you need to have some experience and expertise to explore these types of data pipelines.
- On-Premises Data Pipelines: These types of data pipelines are a counter-example of cloud-native pipelines. Generally, there is a belief that cloud-native pipelines lack data security as they are hosted on cloud service provider’s servers. Therefore, many businesses, especially highly-regulated organizations, opt for on-premise data pipelines to aim for better security and control.
Benefits of a Data Pipeline
Are you still wondering why your data needs to be consolidated using pipelines? The simple answer is the proliferation of cloud-based services. Most companies use a suite of applications for different purposes: marketing, sales, logistics, etc. This segregates company data into multiple data silos.
Having separate data silos makes it challenging to fetch segments of relevant data. This is where data pipelines come in handy. They ensure that your data flows precisely where you want while maintaining security.
Here is a list of some more benefits of data pipelines:
- Allows data centralization: Data centralization enables you to work cross-functionally and ensure data transparency across your organization. Data pipelines enable centralization by consolidating it from multiple sources and transforming it to store it at the desired location.
- Ensures data security: While building different types of data pipelines, you often set up security guidelines that can protect your data during transit. These guidelines become easier to replicate once you build a data pipeline that abides by the necessary security principle. This ensures that security practices are readily included when you develop subsequent pipelines and new data flows.
- Monitoring and governance: As organizations grow, their requirements to manage data with pipelines increase, and its governance becomes more crucial. With tools like Apache Airflow, you can conveniently monitor numerous data pipelines simultaneously and can scale data operations efficiently.
- Adds flexibility and agility: Pipelines provide a framework for observing and responding to your data flexibly. You can do so in real time with streaming data pipelines and periodically with batch data pipelines. Both leverage extensible, modular, and reusable pipeline structures improving your company’s data engineering capabilities.
- Helps in data standardization: The act of transforming raw data into a standard and uniform format so that analysts can evaluate and draw conclusions from quality data is known as data standardization. Data pipelines facilitate easier standardization of your data by determining the source, functions, transformations, and destination of data.
A data pipeline is a series of defined actions that facilitate data flow from a source to a sink/destination. This dataflow can also incorporate data transformation to convert your raw data into a required format suitable for storing and analysis. Depending on your requirement and the kind of data, there are different types of data pipelines, including batch processing, streaming, open source, cloud-native, and on-premises data pipelines. All data pipeline types ensure flexibility and agility in your data flow and maintain data integrity and security.
However, without some fundamental technical expertise and experience in programming, it becomes difficult to harness the above-mentioned benefits of a data pipeline. To simplify the implementation of data pipelines, you can opt for cloud-based automated ETL tools like Hevo Data which offers 150+ plug-and-play integrations.
Visit our Website to Explore Hevo
Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
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