A Guide to Data Pipeline Design: Low Latency or High-Throughput
By Dherik Barison
May 2025 | 15 min. read
Table of Contents
Why Does Data Pipeline Design Matter?
At Datastreamer, data pipeline design is the foundation of how data moves, from source to destination, while enabling transformation, enrichment, and delivery of actionable insights. Think of it as the blueprint that turns raw inputs into structured outputs, ready for analysis.
For example, a well-designed pipeline might pull social media data from TikTok, structure it, apply sentiment analysis, and store the results in S3. This is powered by Datastreamer’s modular, component-based architecture, which supports a wide range of real-time and batch use cases.
Each pipeline consists of four core components:
Ingress: Controls how data enters the system
Transformation: Structures the data (via tools like the Unify Transformer)
Operations: Filters, enriches, and routes the flow
Egress: Delivers the processed data to its destination
By combining these components, teams can customize their data pipeline design to meet specific performance goals, manage DVUs, and control costs. Some prioritize low latency and speed; others focus on deep processing and scale.
In this article, we’ll break down how Datastreamer supports both approaches—and how to choose the right pace for your pipeline strategy.
The Low Latency Pipeline: “The Sprinter”
A low-latency pipeline is designed for speed. The primary goal is to minimize delays and deliver filtered, enriched data in near real-time. These pipelines are ideal when you need to react quickly to fresh data and make fast decisions.
Typical characteristics of a low-latency pipeline include:
Rapid ingestion from live or frequently updated data sources
Minimal processing, focused on performance
Targeted enrichment that adds value without slowing down the flow
Fast delivery to downstream systems or applications
Let’s break down the component choices and strategies that support this setup.
Component Choices and Configuration Strategies for Speed
1. Ingress: Prioritize Fast, Continuous Inputs
For real-time performance, choose ingress components that offer speed and immediacy:
Live Feeds like Bluesky Live Feed, WebSightLine Augmented Instagram, and WebSightLine Threads Live Feed
Direct Push using Direct Data Upload for manual, on-demand data input
Event Streaming with Pubsub Ingress to react to data events in real time
Frequent Polling from providers like Vetric, Data365, or Brightdata using short polling intervals (when supported by the source API)
2. Transformation: Keep It Lightweight
Use the Unify Transformer to map various source formats into a single metadata schema with minimal overhead.
3. Operations: Focus on Fast, Value-Added Processing
To maintain low latency, apply only essential operations that contribute clear value:
Routing/Filtering with Lucene Document Filter and JSON Document Router to quickly sort or bypass data
Lightweight Enrichments such as Datastreamer Language Detection and basic field operations like Format, Map, or Concat
Optionally include the GenAI Sentiment Classifier if performance remains optimal
Custom Functions using Python scripts that are optimized for speed and simplicity.
4. Egress: Enable Instant Output
Choose egress components that support immediate data delivery:
Firehose Egress: For APIs or external applications that require live data
Webhook Egress: For integrations with external systems via API
PubSub Egress: For routing data into microservices or real-time processors
Inspector Egress: For monitoring and reviewing the pipeline’s behavior in real time.
A low-latency pipeline example:
Common Use Cases for This Pipeline Design
Real-time brand monitoring: Track and respond to mentions as they occur.
Live sentiment analysis: Monitor customer feedback for support teams.
Streaming fraud detection: Flag suspicious activity from transaction data.
Content moderation: Filter harmful or inappropriate content on the fly.
The High-Throughput Pipeline: “The Marathon Runner”
Unlike low-latency pipelines, a high-throughput pipeline is built for scale. Its primary focus is to efficiently process large volumes of data—often delivered in batches—making it ideal for deep analysis, archiving, or long-term storage.
These pipelines typically support comprehensive workflows where time sensitivity is less important than processing power and completeness.
Key characteristics of a high-throughput pipeline:
Bulk ingestion of large files or historical data from archives and data lakes
Complex transformations that involve multiple steps and advanced logic
Rich enrichment layers that extract deeper meaning from the data
Delivery to storage targets like data warehouses, searchable archives, or business intelligence platforms
This type of pipeline supports teams that need to analyze large datasets for research, reporting, or trend discovery—without the pressure of real-time responsiveness.
Component Choices and Configuration Strategies for Scale
When building a high-throughput pipeline, the goal is to efficiently handle large data volumes—often in batch mode—and extract deep analytical value. The component selection should reflect that need for scale, storage compatibility, and flexibility.
1. Ingress: Optimize for Batch and Bulk Data Intake
Choose components that are purpose-built for large files or historical data:
Cloud Storage: Leverage components like Amazon S3 Storage Ingress, Google Cloud Storage Ingress, and Azure Blob Storage Ingress for reliable, scalable intake.
Datastreamer Storage: Use Datastreamer File Storage Ingress or Datastreamer Searchable Storage Ingress—these are natively integrated and optimized for internal handling.
Historical or Bulk Sources: Connect to providers such as Bright Data (e.g. Bright Data Amazon Ingress) or Socialgist (e.g. Socialgist Reddit Ingress) for wide-scale data capture.
Component Choices and Configuration Strategies for Scale
When building a high-throughput pipeline, the goal is to efficiently handle large data volumes—often in batch mode—and extract deep analytical value. The component selection should reflect that need for scale, storage compatibility, and flexibility.
1. Ingress: Optimize for Batch and Bulk Data Intake
Choose components that are purpose-built for large files or historical data:
Cloud Storage: Leverage components like Amazon S3 Storage Ingress, Google Cloud Storage Ingress, and Azure Blob Storage Ingress for reliable, scalable intake.
Datastreamer Storage: Use Datastreamer File Storage Ingress or Datastreamer Searchable Storage Ingress—these are natively integrated and optimized for internal handling.
Historical or Bulk Sources: Connect to providers such as Bright Data (e.g. Bright Data Amazon Ingress) or Socialgist (e.g. Socialgist Reddit Ingress) for wide-scale data capture.
2. Transformation: Go Deep with Structured Data Conversion
Speed is less critical here, so you can employ more robust transformation layers:
Unify Transformer: Great for handling complex, multi-format data sources.
JSON Transformer: Ideal for mapping and restructuring nested or varied schemas.
3. Operations: Extract Meaning with Rich, Multi-Step Enrichments
This stage allows for in-depth processing, enriching your data before delivery:
Batch Processing: Use the Document Splitter to manage large payloads or parallelize components.
Advanced AI & NLP:
GenAI Classifiers: Apply tools like GenAI Category Classifier or ESG Classifier for detailed tagging.
Content Similarity Clustering: Group similar documents to aid categorization.
Product Sentiment Classifier: Assess sentiment tied to products or brands.
Custom Functions: Integrate NLTK or TextBlob logic for business-specific analysis.
File Processing: Extract insights from documents using PDF Files Text Extraction or LLM-based Text Parsing.
4. Egress: Store and Access Enriched Data at Scale
Depending on your use case, you can choose from a range of delivery targets:
Data Warehouses and Lakes: BigQuery JSON Writer, Snowflake Storage Egress, and Databricks Egress support enterprise-grade analysis.
Searchable Storage: The Datastreamer Searchable Storage Egress helps make large datasets easily navigable from within your platform.
Cloud Archive and Staging: Reuse the same storage solutions from Ingress to archive or stage output data.
ETL Platforms: With Fivetran Egress, integrate seamlessly into downstream ETL pipelines.
A high-latency pipeline example:
Common Use Cases for This Pipeline Design
Market trend analysis using historical social media data
Knowledge base creation from large collections of unstructured documents
ETL for dashboards that power business intelligence platforms
PII redaction at scale, such as with Private AI PII Redaction across document archives
Blending Approaches: Finding the Right Pipeline Pace
Not every pipeline fits neatly into a “sprinter” or “marathon” model. In many cases, combining low-latency and high-throughput strategies can help you meet both speed and scale demands. This is where a hybrid pipeline approach shines.
Using Router components like the JSON Document Router, you can split incoming data streams into multiple paths—one optimized for immediate, fast processing, and another designed for deeper enrichment or batch storage. This flexibility allows you to handle urgent and long-term use cases within the same pipeline architecture.
Key Questions to Identify Your Ideal Pipeline Design
To tailor your pipeline configuration, ask the following:
Data Source Nature: Are you working with real-time streams or periodic batch uploads?
Processing Depth: How much enrichment or transformation does your use case require?
Output Requirements: Who consumes the data, and what are their expectations for speed or detail?
Scalability: Can the pipeline support growth in volume, velocity, or both?
With Datastreamer’s modular architecture, you’re not limited to one path. You can build pipelines that adapt as your needs evolve, whether that means sprinting, running marathons, or doing both at once.
From Sprint to Marathon: Power Your Data Journey with Datastreamer
Datastreamer’s pipeline architecture is built to meet your unique data needs—whether you’re aiming for real-time insights with low latency or processing large volumes for batch analytics. Our platform equips you with the tools to build, tune, and scale your workflows at any pace.
To recap the two key pipeline models:
Low-latency pipelines are designed for speed, with minimal operations and rapid data delivery.
High-throughput pipelines support scalability, enrichment, and deep analysis of large datasets.
With a flexible, component-based architecture, Datastreamer helps you create the exact pipeline your use case demands. Whether you’re optimizing for milliseconds or managing millions of records, you’re in control.
Ready to build your pipeline strategy?
Explore key components to understand your options or talk to our team and get hands-on guidance. We’ll help you design a pipeline that fits your speed, scale, and analytics goals.
