Data Pipelines

What are Data Pipelines?

Data pipelines are used to move data from a source to a destination, such as a data lake or data warehouse. 

What are the components of a data pipeline?

A data pipeline is comprised of three steps: a data source, a data processing or data transformation step, and a data destination or data storage location. The data source is where the data comes from. Common data sources include databases, CRM systems, IoT sensors, and more. The data processing or data transformation step includes all of the operations that change data, including transportation, translation, sorting, consolidation, deduplication, validation, and analysis. The final step of a data pipeline, data storage, is where the transformed data is stored so that users are able to access it. Typical data storage locations include data warehouses, data lakes, and datamarts.

ETL pipelines are considered to be a subcategory of data pipelines. The main difference between an ETL pipeline and a data pipeline is that ETL pipelines can transform data in more ways than a data pipeline can. For example, an ETL pipeline can combine specific metric data to make it easier to analyze. ETL pipelines can also transfer data on a set schedule, such as when network traffic is slower instead of in real-time, allowing data to be transferred at regular intervals instead of continuously.

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What are the types of data pipelines?

Real-time pipelines

Real-time pipelines are often used in financial insights industries or enterprises that process data directly through streaming services, such as analytics and weather reporting. This system processes data instantly through an architecture with the capacity of processing millions of events at scale, providing incredibly reliable insights.

Open-source pipelines

Open-source pipelines are a budget-friendly system utilized by smaller businesses and the general public to move, process, and store data. Tools used to facilitate this type of pipeline are more affordable than those that facilitate real-time or cloud-based data pipeline systems. These systems are open to the public, requiring intentional customization in all use cases.

Cloud pipelines

Cloud pipelines are aptly named to utilize, transform, and analyze cloud-based data. By removing the need for on-site storage infrastructure, organizations can both collect and analyze data within a cloud-based structure. Cloud-native pipelines often include extensive security offerings due to the nature of the service.

Batch processing pipelines

Batch processing pipelines are one of the most popular choices for data pipeline storage systems. Often used to move and store massive amounts of data on a consistent basis, organizations utilize batch processing pipeline systems to translate and move their data to be stored and analyzed at a slower rate than real-time systems due to the sheer volume of data being moved.

Streaming pipelines

Streaming pipelines, along with batch processing pipelines, are the two most common forms of data pipeline. Streaming pipelines allow users to ingest both structured and unstructured data from a variety of different data sources.

What is data pipeline architecture?

Data pipeline architecture refers to the systems that connect the data sources, data processing systems, analytical tools, and applications.

Data pipeline architecture ensures all relevant data is collected, allowing data scientists to draw insights from data to target behaviors, promote efficiency in customer journeys, and amplify user experiences. Data pipelines take raw data, route it to an appropriate storage site, and transform it to actionable insights. The architecture is dynamically layered, beginning with intake and ending with continual oversight.

Foundationally, raw data involves an array of data points—far too many to gain insights from. The architecture of data pipelines involves the system created to capture, structure, and move data to draw insights from, and analyze for deeper understanding and utilization. This is often accomplished through automation, software, and data storage solutions.

Storage locations are determined depending on the format of data collected. Sending data to the correct storage location is a critical step in data pipeline architecture, with options for storing mastered data within a structured storage system such as a data warehouse or more loosely structured data within a data lake. Data analysts can gather data insights from loosely structured data within data lakes, or analyze mastered data within a central storage location. Without proper placement into a storage environment, there cannot be practical oversight within the architecture, further limiting future applications. 

HPE and data pipelines

HPE Ezmeral is a hybrid analytics and data science platform designed to drive data-first modernizations, enabling enterprises to unlock the value of their data wherever it lives. HPE Ezmeral powers HPE GreenLake analytics services to help customers unify, modernize, and analyze all their data across edge to cloud.

HPE Ezmeral helps unlock data’s value and innovate faster with choice, efficiency, and flexibility not available from niche and cloud-based solutions. It does this by:

Providing a unified software platform built on 100% open-source and designed for both cloud-native and non-cloud-native (legacy) applications running on any infrastructure on-premises or hybrid and multi-cloud environments.

Unifying data and modernizing apps with the industry’s first integrated data fabric optimized for high performance analytics. It accelerates time to insights by combining files, objects, event streams, and NoSQL databases into a single logical infrastructure and file system to provide global access to synchronized data.

Addressing the challenges of operationalizing ML models at enterprise scale with a solution that delivers DevOps-like speed and agility, combined with a cloud-like experience that accelerates your workloads.

Delivering a consistent experience across teams with a single platform that leverages a wide range of analytical and ML tools. Built-in automation and cloud-native experience simplifies connecting users and their tools to the right data, compute engines, and storage freeing teams to focus on unlocking data’s value.

Gaining freedom and flexibility with integrated open-source tools and frameworks into a unified hybrid data lakehouse. An integrated app store or HPE Ezmeral Marketplace enables the rapid creation of streamlined, customized engines and environments based on full-stack, validated solutions from trusted ISV partners.