- What is a data pipeline?
- What is data integration?
- What are the issues with data integration and data pipelines?
- Palantir solution explained:
What is a data pipeline?
A data pipeline refers to the process of moving data from one system to another.
Modern data pipelines provide many benefits to the enterprise, including easier access to insights and information, speedier decision-making and the flexibility and agility to handle peak demand.
Data pipelines, by consolidating data from all your disparate sources into one common destination, enable quick data analysis for business insights. They also ensure consistent data quality, which is absolutely crucial for reliable business insights.
Broadly speaking, there are two types of data that pass through a data pipeline:
Structured Data: This type of data can be saved and retrieved in a fixed format. This includes device-specific statistics, email addresses, locations, phone numbers, banking info, and IP addresses.
Unstructured Data: This type of data is difficult to track in a fixed format. Email content, social media comments, mobile phone searches, images, and online reviews are some examples of unstructured data.
In order to extract business insights from data, you need dedicated infrastructure for data pipelines to migrate data efficiently.
What is data integration?
Data integration is the process of combining data from multiple source systems to create unified sets of information for both operational and analytical uses.
Integration is one of the core elements of the overall data management process; its primary objective is to produce consolidated data sets that are clean and consistent and meet the information needs of different end users in an organization.
What are the issues with data integration and data pipelines?
“Within the conventional approaches to data integration, there are assumed trade-offs, such as: Speed vs. Robustness, Democratization vs. Sophistication, & Efficiency vs. Flexibility.
- Speed vs. Robustness: It is assumed that in order to move quickly against complex data challenges, there must be a zero-sum exchange with robustness and stability. As the criticality of data outputs rises, it becomes more difficult to deliver timely pipelines — especially in settings where security and compliance are non-negotiable.
- Democratization vs. Sophistication: There is a growing desire to enable “citizen” data engineers and analysts, with applications and interfaces that are tuned for their skills and expertise. Yet lack of governance and simplicity means low/no-code solutions fail to meet the bar for production-grade data pipelining work.
- Efficiency vs. Flexibility: As data engineering work rises in complexity, there is a need to build declarative, streamlined experiences, which can provide small teams with technical leverage, as they build, tune, and scale the nuanced requirements of their domains. However, it is assumed that this streamlining comes at the expense of both optionality and the ability to switch among storage and compute paradigms.”
Over the past few years, in the midst of an increasingly volatile world and the necessity for agile responses, it was no longer feasible to entertain these (false) trade-offs.
Entire supply chains had to be stood up in days, such as those used to distribute every vaccine throughout the US and UK; simultaneous demand and supply shocks had to be assessed in near-real-time, tapping into data landscapes that were constantly in flux; sensor and IoT data had to be fused with a wide range of structured data, to support ever-fluid operations across infrastructure providers; among countless other examples, across the public and private sectors.
“These experiences served as the crucible for our engineering teams, requiring us to reimagine Palantir Foundry’s underlying data integration architecture from first principles. The result is what we’re excited to share a first glimpse of here: Foundry’s Next-Generation Pipeline Builder.”
Palantir solution explained:
Fundamentally, the Palantir Pipeline builder automates the process associated with creating a data pipeline.
“Writing data pipelines in Pipeline Builder is significantly faster than authoring them from scratch.”
Time-to-value: Pipeline Builder is designed to enable fast, flexible, and scalable delivery of data pipelines — enabling fluidity and time-to-value, while simultaneously enforcing robustness and security. Technical users can build and maintain pipelines more rapidly than ever before, focusing on declarative descriptions of their end-to-end pipelines and desired outputs.
Democratisation: Moreover, Pipeline Builder’s intelligent point-and-click, form-based interface enables citizen data engineers and less technical users to create pipelines without getting “stuck” in a simplified mode. Every pipeline, whether no-code or low-code, leverages the same git-style change management, data health checks, multi-modal security, and fine-grain auditing that permeates every other service across Foundry. Diverse teams can focus on describing business logic, rather than worrying about obtuse implementation details.
Rethink what better data pipelines can look like: Distilling our experiences across industries, we engineered a next-generation data transformation back-end, which acts as an an intermediary between logic creation and execution substrate. As users describe the pipeline(s) they wish to build, Pipeline Builder’s back-end works to write the transform code and automatically perform checks on pipeline integrity — proactively identifying and refactoring errors, and offering solutions to ensure healthy ongoing builds.
Writing data pipelines in Pipeline Builder is significantly faster than authoring them from scratch. Users can immediately begin applying transforms to data (structured, unstructured, IoT, etc.) without needing to instantiate environments and layer on boilerplate code.
