The Power of Data Pipelines: Better Together with your data for Enhanced Data Management 

In today’s digital-first world, data is not just a byproduct of business operations; it is a foundational asset driving decision-making, innovation, and growth. With the explosion of data across various systems; however managing, processing, and utilising it effectively has become a monumental challenge. Enter data pipelining, a transformative solution that brings the “better together” ethos into data management. By decoupling data acquisition from downstream processing, organisations can unlock unprecedented flexibility, scalability, and cost-efficiency. 

This blog explores how data pipelines enable organisations to work smarter, not harder, in managing their growing data needs. The explosion in popularity of such tooling is the high cost of traditional Security Information Event Management (SIEM) ingestion licencing, given the explosion in data size.  It also has downstream data retention cost issues, as opposed to the cheap cloud storage we have become used to. This has led many organisations to seek tools such as data pipelines.  We’ll reference SIEM throughout to use as an example, however all data can be applied to data pipelining. 

The Data Challenge: Triple Vs in Focus 

Historically, managing data was more straightforward. Smaller data sets, limited sources, and vendor-provided connectors made it feasible to funnel all data into one destination; often a central repository like a database or security tool (e.g. a SIEM). However, today’s data landscape is defined by: 

• Variety: An expanding array of structured, semi-structured, and unstructured data sources. 

• Volume: Explosive growth in the amount of data generated by systems, users, and devices. 

• Veracity: The need to process data with varying levels of quality and trustworthiness. 

This trifecta creates significant challenges for organisations that rely on outdated or rigid approaches to data management. Overwhelming volumes of data lead to noise, alert fatigue, and ballooning ingestion and storage costs. Additionally, vendor lock-in can stifle innovation and flexibility, preventing organisations from fully leveraging their data assets. 

Decoupling as a Data Management Game-Changer 

The key to addressing these challenges lies in decoupling data acquisition from downstream processing. Instead of a monolithic approach where all data is sent to a single platform or vendor solution (as in the case of most SIEM’s) a data pipeline serves as an intermediary. It provides control over what data is sent where and in what format. 

For instance: 

• Critical, real-time security alerts might still be directed to a SIEM. 

• Logs and telemetry data that don’t require immediate action could be stored in cost-effective alternatives, such as data lakes or object storage systems. 

This decoupling approach introduces unparalleled flexibility and control into the data management process. Organisations can optimise costs, improve operational efficiency, and make better use of their data without being constrained by vendor-specific limitations.  Data can be used more widely across an organisation, as well as applied data methods such as ML, to better understand the data for example for predictive analytics, among many other applications.  

Core Capabilities of a Data Pipeline Tool 

A modern data pipeline offers a suite of capabilities that empower organisations to take full control of their data lifecycle. Here are some of the most impactful features: 

1. Data Collection with Replay Options

Data pipelines enable seamless integration with multiple sources, whether the data is pushed or pulled. Some advanced tools also allow for data replay, enabling organisations to retrieve historical data from low-cost storage and feed it back into their systems for analysis or troubleshooting.  This can also be utilised for e2e testing. 

2. Data Shaping and Enrichment

With pipelines, data isn’t just collected—it’s transformed. Through enrichment and shaping, organisations can add context to raw data, making it more meaningful and actionable. For example, data can be enriched with additional metadata, normalised, or mapped to a standard information model to streamline its use across various tools and systems. 

3. Data Reduction

Not all data is equally valuable. Pipelines help organisations reduce noise by filtering out irrelevant or redundant information, retaining only the data that provides meaningful insights. At the same time, full-fidelity data can be archived in low-cost storage for later retrieval if needed.  Very popular and ROI justified for heavy SIEM users. 

4. Data Routing

Gone are the days of rigid one-to-one relationships between data collectors and destinations. Pipelines enable multi-source, multi-destination routing, ensuring data is delivered where it’s needed most—whether that’s a SIEM, data warehouse, or analytics tool.  As mentioned this opens up audiences for the data, as well as data engineering and science applications. 

5. Open Storage and Engineering

Data pipelines allow organisations to break free from proprietary storage formats, storing data in open and cost-effective solutions. This enables a more dynamic data strategy where data can be processed and analysed using the best tools for the job, not just those tied to a specific vendor. 

6. Scalability and Resilience

Modern pipeline tools are built for scale, offering robust cloud-native architectures that support high availability, cost-efficient data egress, and hybrid cloud/on-prem configurations. 

Benefits of Data Pipelining: Why “Better Together” Matters 

By embracing a data pipeline strategy, organisations can achieve significant benefits that address the core challenges of modern data management: 

1. Cost Optimisation

With pipelines, organisations can store data in the most cost-effective way possible, reducing the expensive ingestion and storage costs associated with proprietary platforms. By trimming unnecessary data before it reaches costly systems, organisations often see a reduction in data volumes by 20–30%.  This creates instant reduced ingestion costs and ROI. 

2. Flexibility Across Disciplines

Data pipelines decouple data from specific applications, enabling cross-functional teams—including security, data science, and business intelligence—to access, use, and transform data without constraints. Masking, shaping, and enrichment functions ensure data is tailored to each team’s needs. 

3. Enhanced Insights and Knowledge

Decoupling pipelines give organisations a deeper understanding of their data flows, enabling them to discover and act on insights that might otherwise be hidden in a single system. Open data models ensure transparency and accessibility across platforms. 

4. Faster Time-to-Value

Prebuilt integrations, intuitive interfaces, and community-driven resources make modern data pipeline tools easy to adopt. Teams can quickly onboard, configure, and start realising value from their pipelines, whether it’s improving security postures or streamlining analytics workflows. 

5. Vendor Independence

By storing data in open formats and routing it to multiple destinations, organisations avoid vendor lock-in and maintain the freedom to adapt their tech stack as needs evolve. 

6. Improved Security and Compliance

Pipelines can be configured to anonymise or mask sensitive data before it reaches external systems, ensuring compliance with regulations like GDPR or HIPAA while still enabling robust analysis and reporting. 

Example  – How Cribl Specifically Addresses These Themes 

Cribl, a leader in the data pipeline space, provides a platform designed to meet the diverse needs of modern organisations. Its flagship products—Cribl Stream and Cribl Edge—offer functionality perfectly aligned with the benefits outlined above.  

Here’s how Cribl delivers on the promise of “better together” data management: 

1. Comprehensive Data Collection

Cribl Stream collects data from virtually any source, including cloud platforms, logs, metrics, and third-party tools. Its universal collectors ensure compatibility across disparate systems, and its replay functionality allows organisations to retrieve historical data for future analysis without disrupting operations. 

2. Powerful Data Shaping

Cribl simplifies data enrichment and transformation with prebuilt packs that map data to standard information models, such as Splunk CIM or Microsoft Sentinel’s ASIM. This out-of-the-box functionality eliminates the heavy lifting typically required for data normalisation, accelerating time-to-value. 

3. Efficient Data Reduction

By filtering out low-value data at the pipeline level, Cribl helps organisations significantly reduce the volume of data sent to high-cost platforms. For example, security logs can be trimmed to retain only critical information while still archiving full-fidelity data in a cost-effective storage solution. 

4. Flexible Data Routing

Cribl excels in multi-source, multi-destination routing. Organisations can send enriched data to their SIEM, raw data to a data lake, and anonymised data to a business intelligence platform—all from a single pipeline. 

5. Open Data Storage

Cribl’s architecture supports open storage formats, enabling organisations to avoid the pitfalls of vendor lock-in. By storing data in formats like S3 or Parquet, organisations retain full ownership and control of their data, unlocking new opportunities for advanced analytics and machine learning. 

6. Ease of Use and Scalability

Cribl’s intuitive interface and prebuilt packs make it easy for teams to adopt and use. Its cloud-native design ensures scalability, enabling organisations to handle growing data volumes without compromising performance or incurring excessive costs. 

7. Testing and Validation

Cribl’s replay and simulation features allow organisations to test new pipelines, validate data transformations, and fine-tune configurations in a controlled environment. This minimises risk and ensures that deployments meet organisational requirements before going live. 

8. Community and Ecosystem

Cribl’s strong user community and ecosystem of partners make it easier for organisations to find support, share best practices, and access additional resources. This collaborative approach accelerates adoption and drives continuous improvement. 

Conclusion: Unleashing the Potential of Data Pipelines with Cribl 

Data pipelining is a transformative approach to modern data management, and Cribl stands out as a leader in this space. By addressing the challenges of variety, volume, and veracity, Cribl empowers organisations to achieve flexibility, cost savings, and enhanced insights. Whether you’re decoupling data acquisition or optimising multi-destination routing, Cribl ensures that your data strategy is as agile and effective as possible. 

With Cribl, the “better together” mindset isn’t just a slogan—it’s a reality. By aligning teams, systems, and strategies around a unified data pipeline, organisations can unlock the full potential of their data and drive meaningful results across the business. Now is the time to explore how Cribl can transform your data strategy and help you stay ahead in a competitive, data-driven world.