What is a data pipeline?
Monitoring data pipelines end-to-end is essential for data engineering teams looking to develop a deeper understanding of their data metrics and how to improve compute performance. Improving your data pipeline architecture and reliability is essential to keeping track of your data metrics. Using data pipeline observability tools are valuable assets to have as a data team member. These tools can track the entire data journey while increasing the efficiency and reliability of your data pipeline. The best features for managing your data pipeline are often found in automated data pipelines software that monitors a company’s data performance throughout the entire pipeline.
While you are aware that the data pipeline is crucial to understanding your organization’s data metrics, you might not have considered automating data pipelines or using comprehensive data pipeline architecture examples to base your data pipeline framework and data quality framework. If you have continuously operated with manual data metrics, then the idea of switching to a platform to automate your data pipelines may seem daunting. Successfully automating data pipelines is possible if data teams choose platforms suited to the team’s needs. Customizable platforms are essential to navigating data pipelines without wasting too much time and energy on collecting data metrics.
Furthermore, it would be helpful for you to consider reviewing a data pipeline architecture diagram to learn more about the various components and layers that make up data management. A data pipeline architecture diagram, such as the one offered by Acceldata, is crucial to fully understand the different layers of data management, including the data ingestion layer, the processing layer, etc. With Acceldata’s expert data pipelines software, data teams can confidently collect essential data metrics and create a robust data pipeline architecture. Acceldata is vital for data teams that need to increase pipeline efficiency and reliability while also tracking a data journey from origin to consumption.
Types of Data Pipelines
As you navigate the landscape of data pipelines and tools to manage pipelines, you will want to seek data pipeline examples that are important to consider. This is especially true for anyone seeking software specifically catered to data pipeline for beginners. Before successfully implementing data pipeline tools like Acceldata, you must know essential terminology commonly heard when discussing a data pipeline.
To begin, you should make yourself aware of the different types of data pipelines. Data pipelines are commonly divided into two sections: batch processing and streaming. Additionally, data pipeline tools are best implemented when you understand the data pipeline stages. The data pipeline process can be split into three main stages: sources, processing, and destination. The sources stage involves the initial process of capturing data that teams can then analyze. Following this, the processing stage occurs when the data is manipulated based on your company's requirements. Finally, the data pipeline's destination stage involves sending data to its destination, typically a data lake or data warehouse, for detailed analysis.
Knowing key data pipeline terminology, while not required when using high-quality software like Acceldata, is a valuable asset to encourage intelligent discussions about your team’s data pipeline. The best data pipeline framework prioritizes predictable and scalable data pipelines that make measuring and analyzing your key data metrics easier.
Data Pipelines vs. ETL
When discussing data pipeline tools for your organization, you may commonly encounter the term ETL. Hearing data pipeline compared to ETL can make identifying the distinction between the two challenging. ETL is short for “extraction, transformation, and loading” and is a term to describe subprocesses that may occur within a data pipeline. Therefore, the core distinction between data pipeline vs. ETL is that ETL is a part of the data pipeline process that occurs depending on your company’s needs and desires.
It is helpful for data teams to look at an ETL data pipeline example to better understand ETL and its role in the data pipeline. Additionally, you should learn about any unfamiliar terminology related to data pipelines. "Data pipeline" is a more generic term than ETL, which focuses on specific data pipeline stages. Additionally, you should seek more information on comparing a data pipeline vs. data warehouse. A data warehouse is the final destination in the final stage of the data pipeline. Because Acceldata’s software covers all parts of the data pipeline, data teams using Acceldata can:
- Improve data visibility
- Allow access to data from multiple different sources and hybrid data lakes and warehouses
- Align data with business outcomes
- Integrate with your other data systems
Data Pipeline Tools
Data teams can use data pipelines tools aimed at helping teams analyze data metrics found in the data pipeline. While there are many different data pipeline tools accessible to data teams, you must seek only the best data pipeline tools and platforms. By choosing high-quality software like Acceldata, your data team can create a comprehensive data pipeline framework that guides you through the end-to-end pipeline process.
As you seek out data pipeline platforms with unique features and tools, you should ensure that you find software suited to your team’s needs. Not all data pipeline orchestration tools are created the same, and some tools may be more suited to your individual needs than they would be for a different team. Ideally, your data team will implement platforms with data pipeline tools, open-source access, and comprehensive data to help understand the pipeline. Additionally, you will want to find software like Acceldata that offers ETL data pipeline tools. These tools are essential to ensuring that you do not miss any step of the data pipeline and collect crucial, accurate data at every stage of the journey. With Acceldata’s numerous data pipeline tools, your data team can gain valuable insights into how your data performance compares to competitors and what changes can be made in the data pipeline to optimize performance.