Data Pipeline

What is a data pipeline?

It’s a data-driven world, but insights require information. Organizations typically house data in the cloud, on-premises, and on devices stored in data warehouses and data lakes, and sourced from applications, inputs, and other devices. Getting the right information in the right format to the right applications requires a path between locations and technologies.

Data pipelines ease data flow from source to destination while including necessary processing and transformation, making data easier to access and analyze.

Consider how water arrives at a drinking glass: it’s piped from a reservoir, processed through filters and sanitization, and, finally, flowed through pipes to a faucet. It’s a fitting analogy for how data travels, hence the term data pipeline.

What are the benefits of a data pipeline for a modern data strategy?

Data pipelines grab data from various sources and prepare it for reporting and analysis. For organizations with a modern data strategy, it’s a common approach to tearing down data silos and ensuring data-driven outcomes are fast, accurate, and trusted. Data pipelines are also typically automated for speed and reliability.

Key benefits of a data pipeline include:

• Enabling data-driven decision-making by providing structured data in appropriate formats for reliable business intelligence (BI) and analytics. 

• Improving business efficiency and scalability by automating data movement, processing, and transformation to reduce manual and provide insights regardless of data volumes.

• Ensuring data quality and consistency by incorporating data cleansing, validation, and standardization stages for adherence to good data practices and more trustworthy insights.

• Powering artificial intelligence (AI), machine learning (ML), and advanced analytics with the required data at appropriate volumes and with cleansing and processing needed for innovative AI models and advanced analytics initiatives.

• Increasing data processing and management efficiency by automating tasks to deliver business intelligence quickly.

Understanding the core components of a data pipeline

A typical data pipeline has different components, just like the analogous water pipeline, which might have tanks, pumps, and valves, and process water through filters or by adding chemicals like chlorine. 

The core components of a data pipeline are: 

1. A data source such as an application, device, or data warehouse provides the data.

2. Data extraction collects data from the sources using batch processing, API calls, and other techniques.

3. Data transformation processes data by sorting, filtering, cleaning, joining, validating, or reformatting to prepare data for the eventual report, analysis, or other output.

4. Data destinations store processed data such as analytics solutions, BI tools, or a data warehouse or data lake.

5. Data orchestration and monitoring optionally provide tools to manage workflows, monitor pipeline health, and alert managers to potential issues.

Types of data pipelines: ETL, ELT, and more

Data delivery and processing have traditionally taken one of two confusingly similar forms:

• Extract, transform, and load (ETL) techniques extract data from a source, transform it on a separate processing server, and then load it to a destination like a data warehouse.

• Extract, load, and transform (ELT) processes extract data, load it into the destination, and perform transformations directly within the destination.

 Both ELT and ETL are considered data pipelines. However, other data pipeline types include:

• Streaming data pipelines, also known as real-time data pipelines, process data quickly and continuously as it arrives at its destination. Use cases like credit card fraud detection and dynamic pricing use streaming data pipelines.

• Batch processing data pipelines process data at scheduled intervals for analytics and outputs that are less time-sensitive, such as periodic reporting.

Data pipeline best practices

Preparation can ease the process of defining, building, and managing data pipelines. Here are several best practices and helpful tools to ensure any data pipeline flows well:

• Use a data catalog to discover, categorize, and govern data while providing accurate data source and destination information for effective data pipeline strategies.

• Discover data sources to be included in data pipeline efforts and, if multiple sources should be connected, incorporate appropriate data transformations.

• Evaluate requested outputs and analyses and collaborate with eventual data consumers to understand their goals, which will inform how data is transformed and the type and frequency of data pipeline best suited to reaching those goals.

• Ensure robust data governance by creating a single source of truth for data sources and governance policies, tracking and monitoring data lineage, and streamlining data governance compliance.

• Take advantage of AI innovations built for data initiatives that accelerate data discovery, streamline data documentation, and improve data quality at scale.

Alation boosts data pipeline value

Data pipelines automate data collection, processing, and delivery to increase the accuracy and speed of data-driven insights and improve AI outcomes. However, they need access to trusted data. Data catalogs are a crucial first step in ensuring data filling the pipelines is organized, accessible, and trusted.

Alation helps data-driven organizations source trusted information for data pipelines, AI assets, or any objective. Teams and technologies get the information they need to learn, decide, and act confidently.

Alation also delivers:

• Universal search across decentralized databases, lakehouses, file systems, and other locations.

• Trusted AI initiatives built on a foundation of governed, accurate data.

• Increased compliance and data privacy by balancing visibility with accountability.

Discover how Alation improves data pipelines by scheduling a product demonstration.

Next steps: Learn more about data pipelines

There’s always more to learn about data pipelines. Continue exploring with the following resources