Do You Need a Data Pipeline?

Do you need a data pipeline? That depends on a few things. Does your organization see data as an input into its key decisions? Is data a product? Do you deal with large volumes of data or data from disparate sources? Depending on the answers to these and other questions, you may be looking at the need for a data pipeline. But what is a data pipeline and what are the considerations for implementing one, especially if your organization deals heavily with geospatial data? This post will examine those issues.

A data pipeline is a set of actions that extract, transform, and load data from one system to another. A data pipeline may be set up to run on a specific schedule (e.g., every night at midnight), or it might be event-driven, running in response to specific triggers or actions. Data pipelines are critical to data-driven organizations, as key information may need to be synthesized from various systems or sources. A data pipeline automates accepted processes, enabling data to be efficiently and reliably moved and transformed for analysis and decision-making.

A data pipeline can start small – maybe a set of shell or python scripts that run on a schedule – and it can be modified to grow along with your organization to the point where it may be driven my a full-fledged event-driven platform like AirFlow or FME (discussed later). It can be confusing, and there are a lot of commercial and open-source solutions available, so we’ll try to demystify data pipelines in this post.

Geospatial data presents unique challenges in data pipelines. Geospatial data are often large and complex, containing multiple dimensions of information (geometry, elevation, time, etc.). Processing and transforming this data can be computationally intensive and may require significant storage capacity. Managing this complexity efficiently is a major challenge. Data quality and accuracy is also a challenge. Geospatial data can come from a variety of sources (satellites, sensors, user inputs, etc.) and can be prone to errors, inconsistencies, or inaccuracies. Ensuring data quality – dealing with missing data, handling noise and outliers, verifying accuracy of coordinates – adds complexity to standard data management processes.

Standardization and interoperability challenges, while not unique to geospatial data, present additional challenges due to the nature of the data. There are many different formats, standards, and coordinate systems used in geospatial data (for example, reconciling coordinate systems between WGS84, Mercator, state plane, and various national grids). Transforming between these can be complex, due to issues such as datum transformation. Furthermore, metadata (data about the data) is crucial in geospatial datasets to understand the context, source, and reliability of the data, which adds another layer of complexity to the processing pipeline.

While these challenges make the design, implementation, and management of data pipelines for geospatial data a complex task, they can provide significant benefits to organizations that process large amounts of geospatial data:

  • Efficiency and automation: Data pipelines can automate the entire process of data extraction, transformation, and loading (ETL). Automation is particularly powerful in the transformation stage. “Transformation” is a deceptively simple term for a process that can contain many enrichment and standardization tasks. For example, as the coordinate system transformations described above are validated, they can be automated and included in the transformation stage to remove human error. Additionally, tools like Segment Anything can be called during this stage to turn imagery into actionable, analyst-ready information.
  • Data quality and consistency: The transformation phase includes steps to clean and validate data, helping to ensure data quality. This can include resolving inconsistencies, filling in missing values, normalizing data, and validating the format and accuracy of geospatial coordinates. By standardizing and automating these operations in a pipeline, an organization can ensure that the same operations are applied consistently to all data, improving overall data quality and reliability.
  • Data Integration: So far, we’ve talked a lot about the transformation phase, but the extract phase provides integration benefits. A data pipeline allows for the integration of diverse data sources, such as your CRM, ERP, or support ticketing system. It also enables extraction from a wide variety of formats (shapefile, GeoParquet, GeoJSON, GeoPackage, etc). This is crucial for organizations dealing with geospatial data, as it often comes from a variety of sources in different formats. Integration with data from business systems can provide insights into performance as relates to the use of geospatial data. 
  • Staging analyst-ready data: With good execution, a data pipeline produces clean, consistent, integrated data that enables people to conduct advanced analysis, such as predictive modeling, machine learning, or complex geospatial statistical analysis. This can provide valuable insights and support data-driven decision making.

A data pipeline is first and foremost about automating accepted data acquisition and management processes for your organization, but it is ultimately a technical architecture that will be added to your portfolio. The technology ecosystem for such tools is vast, but we will discuss a few with which we have experience.

  • Apache Airflow: Developed by Airbnb and later donated to the Apache Foundation, Airflow is a platform to programmatically author, schedule, and monitor workflows. It uses directed acyclic graphs (DAGs) to manage workflow orchestration. It supports a wide range of integrations and is highly customizable, making it a popular choice for complex data pipelines. AirFlow is capable of being your entire data pipeline.
  • GDAL/OGR: The Geospatial Data Abstraction Library (GDAL) is an open-source, translator library for raster and vector geospatial data formats. It provides a unified API for over 200 types of geospatial data formats, allowing developers to write applications that are format-agnostic. GDAL supports various operations like format conversion, data extraction, reprojection, and mosaicking. It is used in GIS software like QGIS, ArcGIS, and PostGIS. As a library it can also be used in large data processing tasks and in AirFlow workflows. Its flexibility makes it a powerful component of a data pipeline, especially where support for geospatial data is required.
  • FME: FME is a data integration platform developed by Safe Software. It allows users to connect and transform data between over 450 different formats, including geospatial, tabular, and more. With its visual interface, users can create complex data transformation workflows without coding. FME’s capabilities include data validation, transformation, integration, and distribution. FME in the geospatial information market and is the most geospatially literate commercial product in the data integration segment. In addition it supports a wide range of non-spatial sources, including proprietary platforms such as Salesforce. FME has a wide range of components, making it possible for it to scale up to support enterprise-scale data pipelines.

In addition to the tools listed above, there is a fairly crowded market segment for hosted solutions, known as “integration platform as a service” or IPaaS. These platforms all generally have ready-made connectors for various sources and destinations, but spatial awareness tends to be limited, as does customization options for adding spatial. A good data pipeline is tightly coupled to the data governance procedures of your organization, so you’ll see greater benefits from technologies that allow you customize to your needs.

Back to the original question: Do you need a data pipeline? If data-driven decisions are key to your organization, and consistent data governance is necessary to have confidence in your decisions, then you may need a data pipeline. At Cercana, we have experience implementing data pipelines and data governance procedures for organizations large and small. Contact us today to learn more about how we can help you.