Data Pipelines 101: Architecture and Implementation

Data pipelines are the backbone of modern data operations. They help teams collect, move, and transform data at scale—so you can go from raw information to real insights, fast.

In this guide, we’ll break down the key concepts behind data pipelines, explore common use cases, and share best practices for designing and managing them effectively.

Whether you’re starting from scratch or looking to fine-tune an existing setup, this guide will give you a solid foundation to build smarter, faster, and more scalable data workflows.

What is a data pipeline?

A data pipeline is a series of automated processes that enable the movement, transformation, and storage of data from one or more source systems to a destination system. These pipelines are crucial in the modern data ecosystem, where vast volumes of data need to be processed quickly and accurately to provide actionable insights.

Data pipelines are designed to streamline the flow of data between systems. They automate the steps that would otherwise be manual, reducing the risk of human error and inefficiencies. 

The goal of a data pipeline is to ensure that raw data is transformed into a usable format and delivered to a destination where it can be analyzed, visualized, or used by applications.

The core function of a traditional data pipeline can be broken down into several stages:

Data collection: Data is collected from various sources before it is ingested into the system. This includes both structured and unstructured data formats. This step sets the foundation for data ingestion and ensures that the correct, relevant data is captured for the downstream processes.

• Databases: Structured data pulled from relational databases like SQL, NoSQL, and cloud-native data stores.
• APIs: Data collected via API calls from third-party services or internal platforms.
• IoT devices: Sensor data generated by IoT devices, such as manufacturing sensors, environmental monitoring systems, or wearable devices.
• User inputs: Data generated through user interactions, such as form submissions, clicks, or purchases on websites or apps.
• Files: Data that comes in the form of logs, CSVs, XML, or JSON files, often stored on cloud storage or servers.

Data ingestion: The process of moving data from the collection phase into the data pipeline, where it can be prepared for processing. This involves bringing the data from various collection points (databases, APIs, IoT devices, etc.) into the system for further handling.

• Batch ingestion: For large data sets that don’t require immediate processing, batch ingestion is used. This method collects data in bulk at scheduled intervals (e.g., nightly uploads).
• Stream ingestion: For real-time applications, stream ingestion allows data to be continuously ingested as it is generated. This is commonly used in scenarios like live analytics, fraud detection, or sensor data collection.
• Data formats: Data can be ingested in various formats such as CSV, JSON, XML, Parquet, or Avro, depending on the source system and the storage capabilities of the data warehouse or lake.

Data transformation: After ingestion, data transformation is the process of converting raw data into a more useful format for analysis. This may involve:

• Cleaning: Removing or correcting invalid, incomplete, or duplicate data.
• Formatting: Standardizing data formats, such as date and time representations or categorical labels.
• Enrichment: Adding context or metadata to data, like geolocation information, user profiles, or other external data sources.
• Aggregation: Combining data from multiple sources to create a unified view of the data, such as merging customer purchase data with inventory data.

Data storage: Once the data is transformed, it needs to be stored in a destination system. Data storage is the phase where the data is saved to a permanent system that allows for efficient retrieval and analysis. Common destinations include:

• Data Warehouses: Structured repositories designed for fast query performance and reporting, such as Snowflake, Amazon Redshift, or Google BigQuery.
• Data Lakes: Large-scale storage systems that can store structured, semi-structured, and unstructured data, such as Amazon S3, Hadoop, Databricks, or Azure Data Lake.
• Cloud Storage: Scalable, managed storage systems that offer flexible data management and retrieval, often used for backups or less structured data.

Data delivery: The final stage of the pipeline is data delivery, where the transformed and stored data is made available to the end users, systems, or applications. Depending on the use case, data delivery can take many forms:

• Business intelligence (BI) dashboards: Data is sent to BI tools like Tableau, Power BI, or Looker to provide visualizations and insights to business users.
• Machine learning (ML) models: Processed data is fed into machine learning models for predictions, classification, or anomaly detection.
• APIs and reports: Data is made available through APIs to other systems or delivered as reports for operational purposes.

Why are data pipelines important?

Data pipelines serve as the foundation for modern data architectures. They are essential for transforming raw data into actionable business insights, powering a variety of use cases, including:

• Business intelligence: Data pipelines enable organizations to generate real-time or near real-time reports and dashboards, allowing decision-makers to access timely and accurate insights.
• Machine learning (ML): Automated pipelines help in the preparation of data for machine learning models, ensuring that models are trained with high-quality, up-to-date data.
• Data integration: Pipelines enable organizations to integrate data from disparate sources, creating a unified view of business operations. This is crucial for ensuring consistency across systems.
• Real-time analytics: With real-time data pipelines, businesses can gain instant insights into operations, customer behavior, and more. This capability is especially valuable in fast-paced industries like finance, ecommerce, and telecommunications.

In short, data pipelines are critical because they automate and streamline the entire data processing workflow, ensuring that organizations can efficiently collect, process, and analyze data at scale.

Key benefits of data pipelines

• Automation and efficiency: By automating the flow of data between systems, data pipelines reduce the need for manual intervention, thus improving operational efficiency and freeing up resources for other tasks.
• Data quality and consistency: Pipelines ensure that data is cleaned, transformed, and validated before it reaches its destination, improving the quality and consistency of the data.
• Scalability: Modern data pipelines are built to scale with the growing volume of data. Whether an organization is processing gigabytes or petabytes of data, pipelines can be designed to handle increased data load without sacrificing performance.
• Faster time to insights: Data pipelines enable businesses to gain insights faster by ensuring that data is processed quickly and delivered to the right systems on time.
• Flexibility: With the right pipeline architecture, organizations can adapt to changing business needs. They can easily integrate new data sources, adopt new technologies, or scale the pipeline to meet increased demands.

Data pipeline fundamentals and core architectural components

A data pipeline consists of several key components:

• Data sources: The origin points for raw data, including databases, APIs, files, and IoT devices.
• Connectors: Interfaces that automate data flows and pass data between sources and destinations using solutions like Fivetran. 
• Transformation engines & platforms: Software solutions and tools that clean, enrich, model, and format data—such as Coalesce.
• Orchestration tools: Platforms that manage the sequence, scheduling, and execution of pipeline streams and tasks.
• Destinations: Data warehouses, lakes, or AI & analytics platforms where transformed data is stored, including Snowflake, Databricks, and Microsoft Fabric.

Common data pipeline architecture patterns

There are several types of data pipeline architectures, each designed to meet different business needs:

• Batch pipelines: These pipelines process data in large, scheduled batches. They’re commonly used when real-time data isn’t required, and the goal is to handle large volumes of data at once. For example, an ecommerce company might use a batch pipeline to analyze daily sales data at the end of each business day.
• Real-time pipelines: Real-time data pipelines continuously process data as it is generated or ingested. These pipelines are ideal for applications that require up-to-the-minute insights, such as fraud detection, inventory tracking, or recommendation engines. For instance, a financial services firm may use a real-time pipeline to analyze transactions and detect fraudulent activities instantly.
• ETL (extract, transform, load): The traditional ETL pipeline extracts data from source systems, transforms it into the desired format, and then loads it into a destination system. While this approach is effective for batch processing, it can be slower when handling large volumes of real-time data.
• ELT (extract, load, transform): In contrast to ETL, ELT pipelines load raw data into the destination first, then perform the transformation within the destination system (such as a cloud data warehouse). This model is more suitable for cloud-native architectures, offering flexibility and speed, especially when working with large data sets.
• Hybrid pipelines: Hybrid pipelines combine batch and streaming processes to accommodate diverse data requirements. These pipelines allow businesses to process both historical data (batch) and real-time data (streaming) within the same framework. This approach is often seen in large organizations with complex data needs.

Data pipeline orchestration concepts

In the world of data engineering, data pipeline orchestration refers to the management of data processing tasks—such as data extraction, transformation, and loading (ETL/ELT)—in an automated and structured manner. Orchestration tools ensure that tasks are executed in the correct order, dependencies are respected, and the pipeline runs on schedule without requiring manual intervention.

Data pipeline orchestration tools also handle critical functions such as error handling, retries, and logging, which are crucial for maintaining the health and reliability of pipelines in production environments.

Key Capabilities of Orchestration Tools

Effective orchestration tools enable the following capabilities:

1. Task scheduling and execution: Orchestration tools allow for the scheduling of tasks at specific intervals (e.g., hourly, daily, or in response to events) to ensure that the pipeline runs at the desired frequency. Scheduling also helps optimize resource utilization by running jobs during off-peak hours or when resources are available.
2. Task dependencies: Data pipelines often involve multiple interconnected tasks, where the output of one task serves as input for the next. Orchestration tools manage these dependencies to ensure that tasks are executed in the correct order. For instance, a transformation task may only be triggered after data has been successfully extracted from the source system.
3. Error handling and retry logic: Orchestration tools incorporate error handling to ensure that pipeline failures are detected early and addressed promptly. If a task fails, orchestration tools typically trigger retry mechanisms or alerts, ensuring that failures are resolved without manual intervention. This is especially important in data-driven applications where uptime and consistency are critical.
4. Parallel execution: To improve the performance of data pipelines, orchestration tools can execute independent tasks in parallel. This can significantly speed up data processing, especially in pipelines that handle large data sets or require complex transformations.
5. Monitoring and logging: Orchestration tools provide robust monitoring and logging capabilities, allowing teams to track the status and performance of each task within the pipeline. These logs are valuable for troubleshooting, optimizing pipeline performance, and ensuring transparency across teams.
6. Resource management: Effective orchestration optimizes the use of compute resources (e.g., cloud storage or processing power), ensuring that pipelines run efficiently without overburdening system resources. This is especially important for cloud-native platforms like Snowflake and Databricks, where resource allocation can directly affect costs.

Common data pipeline use cases

• Business intelligence pipelines: BI pipelines automate the extraction, transformation, and loading of data into reporting systems, enabling businesses to generate actionable insights and make informed decisions quickly.
• Machine learning data pipelines: These pipelines support ML workflows by automating data preprocessing, feature engineering, and model training, ensuring models are trained on high-quality, up-to-date data.
• Operational data pipelines: Operational pipelines automate the movement of data across business systems, enabling real-time operational decision-making and improving efficiency in day-to-day processes.
• Real-time analytics pipelines: Real-time analytics pipelines process streaming data and deliver insights instantly. These pipelines are used in environments where timely data is critical, such as fraud detection, predictive analytics, and live reporting.
• Data product pipelines: Data product pipelines provide the infrastructure for delivering data-driven products and services, enabling businesses to monetize their data or offer personalized customer experiences.

Designing data pipelines

Designing a robust data pipeline requires thoughtful planning and a comprehensive understanding of the business’s data requirements. The process involves several stages that help define the objectives, choose the right technologies, and ensure that the pipeline is both efficient and scalable. Below, we delve deeper into the essential stages of data pipeline design:

Requirements gathering

The first step in designing a data pipeline is to gather the business and technical requirements that the pipeline needs to fulfill. This process helps align the pipeline with the specific objectives of the organization, ensuring that it meets both immediate and future needs. 

Key considerations during this phase include:

• Business goals and objectives: Clearly define the business outcomes expected from the pipeline. For instance, are the primary goals to provide real-time insights, perform batch data analysis, or support machine learning models?
• Data sources and outputs: Identify the data sources (e.g., databases, APIs, cloud storage, IoT devices) and the desired outputs (e.g., reports, dashboards, ML models). Understanding these will guide decisions about what data needs to be ingested, processed, and how it should be structured for consumption.
• Data volume: Estimate the amount of data that the pipeline will handle daily, weekly, or monthly. This helps determine the scale of the system and informs decisions about storage and processing capabilities.
• Latency and throughput requirements: Determine the acceptable latency (time delay) for data processing and the required throughput (data volume handled per unit of time). This is crucial for selecting between batch or real-time processing.
• Compliance and security: Define any compliance requirements (such as GDPR, HIPAA, etc.) and security measures (e.g., encryption, data access controls) that the pipeline must meet.
  

By thoroughly gathering these requirements, you ensure that the design phase focuses on the right goals and priorities, avoiding misalignment between pipeline capabilities and business expectations.

Source system analysis

Once the business requirements are defined, the next step is to analyze the data sources that will feed into the pipeline. Source systems often vary in terms of their quality, accessibility, format, and volume, and evaluating these factors is critical to ensure smooth integration into the pipeline. 

Key areas to consider during source system analysis include:

• Data quality: Assess the quality of data from each source. Is the data clean, complete, and accurate, or will it require significant preprocessing to meet quality standards? Inconsistent data from source systems can lead to errors and inefficiencies downstream.
• Data format: Understand the format of data being ingested (e.g., JSON, CSV, Parquet, XML). Some sources may require additional transformation steps to convert the data into a consistent format for processing.
• Volume and velocity: Analyze how much data is generated and how frequently it is updated. Are you dealing with large, batch-based data sets, or real-time, continuously streaming data? This impacts your choice of pipeline design and infrastructure.
• Data access and integration: Evaluate how easily you can access the data and integrate it into the pipeline. Is the data stored in on-premise systems, or is it cloud-based? What APIs or connectors are available for extracting the data? Are there any access restrictions or limitations?
• Data freshness and reliability: For time-sensitive data, assess the freshness requirements—how quickly should data be ingested, processed, and delivered? Additionally, ensure that the source systems are reliable and resilient to minimize downtime or data loss.
  

By analyzing the source systems, you can identify any challenges that may arise during the data pipeline design and determine the necessary strategies to mitigate them.

Pipeline pattern selection

Selecting the right data pipeline pattern is a crucial decision in the design process. The pattern will determine how data flows through the pipeline, how transformations are applied, and how data is processed and delivered. Different business needs require different pipeline patterns. 

Here’s a breakdown of common patterns:

• Batch processing: Best suited for applications where real-time data is not required, and data can be processed in scheduled intervals. For example, generating weekly reports or aggregating sales data over a month. Batch processing is efficient for handling large volumes of data that do not require instant insights.
• Real-time processing (Streaming): For use cases where immediate insights are required, such as fraud detection, personalized recommendations, or IoT monitoring. Streaming pipelines process data as it arrives, enabling near-instant analysis and actions.
• ETL vs. ELT: Depending on your data processing needs, you may choose between ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) patterns. ETL is preferred when you need to transform the data before loading it into a storage system. ELT, on the other hand, loads the data into the destination first and then performs transformations using the compute power of the destination system, typically in cloud-native environments.
• Lambda and Kappa architectures: Lambda architecture combines both batch and real-time processing layers, whereas Kappa simplifies this by focusing entirely on real-time processing. The choice depends on whether your system needs to handle both historical data and real-time data or just real-time data.
  

Choosing the right pipeline pattern ensures that the pipeline aligns with business needs in terms of data speed, complexity, and processing volume.

Technology evaluation

Once the pipeline pattern is selected, it’s important to evaluate the technology stack that will power the pipeline. The right set of tools and platforms will help you scale the pipeline, meet performance requirements, and ensure smooth integration with other systems. 

Key evaluation criteria include:

• Scalability: Consider whether the tools can handle future growth in data volume, velocity, and variety. Cloud-based platforms like Snowflake, Databricks, or Coalesce provide scalability and elasticity to accommodate growing data sets.
• Ease of integration: Choose technologies that integrate well with your existing infrastructure and data sources. Tools should support seamless data ingestion, transformation, and storage without requiring extensive custom coding.
• Cloud compatibility: Ensure that the technology stack is cloud-native or cloud-compatible, as cloud platforms (AWS, Azure, Google Cloud) offer cost-effective, scalable solutions for managing large-scale data pipelines.
• Performance: Evaluate the processing power and efficiency of the tools. For example, distributed computing platforms like Apache Spark or Databricks offer high-performance processing capabilities, ideal for large data sets and complex transformations.
• Cost and licensing: Factor in the costs associated with each tool or platform, including storage costs, compute resources, and licensing fees. Open source solutions like Apache Kafka and Apache Airflow may be cost effective for certain use cases, while commercial platforms may offer more integrated, user-friendly features.
• Support and documentation: Consider the level of support available from the tool vendors and the quality of their documentation. Tools with active communities and extensive resources can help accelerate development and troubleshooting.

By thoroughly evaluating the technology stack, you ensure that the pipeline is not only effective but also optimized for long-term success. To learn more about data transformation tools and how to evaluate them for your business needs, read our comprehensive buyer’s guide. 

Architecture design principles

Designing the architecture of the data pipeline involves creating a framework that ensures scalability, fault tolerance, and efficiency. Consider these key design principles:

• Scalability: The pipeline should be able to handle increasing data volumes over time. This requires choosing technologies that scale horizontally (i.e., by adding more resources) to accommodate growing data sets.
• Fault tolerance: The pipeline should be designed to recover from failures. This includes implementing features like automatic retries, error handling, and data checkpoints. Redundant components can help ensure that a failure in one part of the pipeline doesn’t disrupt the entire system.
• Efficiency: Optimize the pipeline for performance, considering factors like data partitioning, caching, and parallel processing to minimize latency and maximize throughput.
• Modularity: Design the pipeline to be modular and reusable. Break the pipeline into smaller, manageable tasks (e.g., ingestion, transformation, storage) that can be independently maintained or updated.
• Data governance: Ensure that the architecture supports data governance practices, such as data lineage tracking, access controls, and compliance with regulations like GDPR and HIPAA.

Scalability considerations

A pipeline must be designed with future scalability in mind, as data volumes and business needs are likely to grow over time. Key considerations for scalability include:

• Horizontal scaling: Ensure that the pipeline can scale by adding more compute or storage resources as needed. Cloud data platforms like Snowflake and Databricks offer dynamic scaling capabilities that automatically adjust resources based on workload demands.
• Load balancing: Implement load balancing mechanisms to distribute tasks evenly across multiple processing units, preventing bottlenecks and improving throughput.
• Elasticity: Leverage cloud-native services that can automatically scale up or down based on data processing requirements. This reduces infrastructure costs and ensures optimal resource utilization.

Performance requirements

Optimizing the pipeline for performance involves selecting efficient processing methods and technologies that can meet throughput and latency requirements. Key optimization techniques include:

• Parallel processing: Use distributed processing frameworks like Apache Spark or Databricks, which can split tasks across multiple nodes for faster processing.
• In-memory computing: Leverage in-memory processing techniques, where data is loaded into memory for faster access and manipulation, reducing reliance on slower disk-based operations.
• Data partitioning: Partition large data sets into smaller, manageable chunks to speed up processing and improve query performance. This is particularly useful for batch processing and data storage in cloud data lakes or warehouses.
• Query optimization: Ensure that transformation queries are optimized for performance (and your data platform) by using indexing, caching, and query execution strategies that minimize computational costs. 
  • Learn how you can optimize Snowflake data transformation with Coalesce to reduce compute costs.

By carefully considering these factors, you ensure that your data pipeline is not only efficient and scalable but also capable of meeting both current and future business requirements. 

Each stage of pipeline design—from requirements gathering to technology selection and performance optimization—plays a critical role in building a system that can support the growing demands of modern data workflows.

Data pipeline implementation approaches

When designing data pipelines, organizations have several implementation strategies to choose from. The choice of approach depends on factors such as the nature of the data, processing requirements, and scalability goals. Below, we explore the various pipeline implementation strategies—each with its pros and cons.

Traditional ETL-based Pipelines

ETL (Extract, Transform, Load) pipelines are the traditional model for processing data. In this approach, data is extracted from various sources, transformed into the desired format, and then loaded into a destination system, such as a data warehouse. ETL is typically used for batch processing scenarios where data is processed in predefined intervals, such as daily or weekly.

Pros:

• Data quality assurance: Transformations are applied before loading the data into the destination, ensuring that only clean, formatted, and standardized data is ingested. This reduces the likelihood of issues downstream.
• Simplified data modeling: Since transformation occurs before loading, the destination system doesn’t need to perform complex transformations, making data modeling and querying easier and more efficient.
• Ideal for batch processing: ETL is an excellent choice when large volumes of data need to be processed in batches (e.g., nightly reports or analytics).

Cons:

• Limited scalability for real-time data: Traditional ETL is not suitable for real-time data processing, which is increasingly required for many modern business use cases (e.g., fraud detection or personalized marketing).
• Performance overhead: Transformations can be resource-intensive and time-consuming, especially when dealing with large data sets, leading to delays in data availability.
• Complexity in handling incremental data: Handling incremental data and real-time updates requires complex workarounds, which can lead to higher operational complexity.

Modern ELT pipelines

ELT (Extract, Load, Transform) pipelines have become more popular with the rise of cloud-based data warehouses, such as Snowflake, Databricks, and Google BigQuery.

 In ELT, data is extracted from the source and loaded directly into the destination system before transforming it within the data warehouse. This approach leverages the processing power of modern cloud data warehouses for transformation tasks.

Pros:

• Cloud-native scalability: ELT takes full advantage of the scalability of cloud data platforms. Since transformations happen after loading, you can scale compute resources dynamically to handle larger data sets.
• Faster data availability: Data is made available in the destination quickly after extraction, allowing users to start querying raw data immediately. Transformations can be done as needed, enabling quicker insights.
• Flexibility: ELT allows for more flexibility in data transformation, as transformations can be modified easily in the destination without requiring changes to the extraction process. It also supports a wide variety of transformation techniques, including machine learning models.

Cons:

• Requires robust data governance: Since data is loaded in its raw form, ensuring data quality and consistency can be challenging. Without proper data governance, the destination system may end up with a large amount of unclean or incomplete data.
• Increased load on the destination: Cloud data warehouses perform the transformations after data is loaded, which can put significant computational load on the destination system. If not managed properly, this can lead to high costs and performance bottlenecks.
• Dependency on cloud compute resources: The effectiveness of ELT depends on the capabilities of the cloud compute environment, which may lead to cost unpredictability and potential performance issues with very large data volumes.

Real-time streaming pipelines

Real-time streaming pipelines are designed to handle continuous data streams. These pipelines process data as it is generated, enabling immediate insights or actions. 

Stream processing systems like Apache Kafka, AWS Kinesis, or Apache Flink are typically used in this setup. Real-time pipelines are suitable for use cases like monitoring, fraud detection, or personalized recommendations.

Pros:

• Immediate insights: Real-time pipelines process data instantly, allowing businesses to act on the most up-to-date information. This is critical for applications that require low-latency, such as fraud detection in financial transactions or real-time recommendations in ecommerce.
• Continuous data flows: Enables continuous data ingestion and processing, which is ideal for applications involving IoT, customer behavior analytics, and live tracking.
• Scalability: Stream processing systems are designed for horizontal scalability, allowing the pipeline to scale effortlessly to handle growing volumes of data over time.

Cons:

• Complexity in design and maintenance: Real-time streaming pipelines are complex to build and maintain. They require specialized tools and frameworks to handle data synchronization, event processing, and fault tolerance.
• Higher infrastructure cost: Processing real-time data continuously can be resource-intensive, and maintaining the necessary infrastructure for streaming (e.g., Kafka clusters, processing frameworks) can be costly.
• Data consistency challenges: Ensuring data consistency in real-time streaming can be difficult, especially when handling large volumes of event-driven data. Issues like out-of-order events or message loss require careful management.

Hybrid implementation strategies

Hybrid pipelines combine batch and streaming processing to handle both large historical data sets and real-time data simultaneously. 

Hybrid pipelines allow organizations to leverage the advantages of both approaches, providing the flexibility to work with diverse data sources and processing requirements.

Pros:

• Comprehensive coverage: Hybrid pipelines provide the ability to process both real-time and historical data, making them suitable for a wide range of applications that require both batch reporting and real-time analytics.
• Flexibility: Organizations can choose the best processing model for each data type. For example, real-time data might be streamed for immediate processing, while historical data can be handled in batches for deep analytics.
• Efficient resource utilization: Hybrid systems allow you to balance the load between batch and streaming systems, optimizing the use of resources based on the data flow needs.

Cons:

• Increased complexity: Implementing and managing hybrid pipelines can be more complex than using a single approach. It requires coordination between batch and real-time systems, which can lead to higher maintenance overhead.
• Data duplication risks: Ensuring consistency between batch and streaming systems can result in potential data duplication or synchronization issues, requiring sophisticated management of data pipelines.
• Resource overhead: Hybrid pipelines may require multiple infrastructure components (e.g., batch processing systems, streaming services), increasing resource overhead and potential cost.

Build vs. buy considerations

When it comes to building a data pipeline, organizations must decide whether to build a custom solution or use an out-of-the-box product like Coalesce. Both options have their advantages and challenges.

In the data world, it’s not always just about cost or control; it’s about efficiency, risk, and the tradeoffs between technical debt and technical advantage. When it comes to data transformation—which sits at the intersection of engineering scalability, analytics maturity, and business agility—this decision gets even more nuanced. Which is why we’ve written a complete breakdown on the topic of building vs. buying a data transformation tool here.

Build (in-house development):

Pros:

• Bespoke: Custom-built pipelines can be designed to meet the specific requirements of the organization, ensuring full flexibility and control over how data is handled.
• Full ownership: Organizations have complete control over the architecture, allowing for optimization based on unique use cases and specific performance criteria.

Cons:

• High development costs: Building a custom pipeline requires significant resources and expertise. Development time, ongoing maintenance, and troubleshooting costs can be substantial.
• Complexity: The complexity of building and scaling a custom pipeline increases with the volume and variety of data, requiring specialized knowledge in system integration, data engineering, and cloud infrastructure.
• Deployment time: A custom-built pipeline can take months or even years to develop and deploy, especially when scaling for large volumes of data.

Buy (vendor solutions):

Pros:

• Quick deployment: Out-of-the-box solutions like Coalesce offer rapid deployment, enabling organizations to get started immediately without the need for custom development.
• Built-in best practices: Prebuilt solutions come with optimized, industry-standard practices and built-in functionality for monitoring, error handling, and governance.
• Lower maintenance overhead: With a managed solution, much of the infrastructure management, scalability, and updates are handled by the vendor, reducing the internal operational burden.

Cons:

• Less customization: While prebuilt solutions offer flexibility, they may not be fully customizable to meet every specific need of an organization.
• Vendor dependency: Relying on a third-party vendor means that any issues, feature updates, or changes in business needs require collaboration with the vendor.
• Ongoing subscription costs: Subscription fees for managed solutions can add up over time, especially if usage or user seats scales significantly.

Choosing the right data pipeline strategy: Balancing customization, scalability, and time to value

Choosing the right implementation approach for your data pipeline depends on your organization’s specific needs, the complexity of the data you’re processing, and your long-term scalability goals. 

While traditional ETL is well suited for batch processing, ELT and real-time streaming offer flexibility and scalability for modern, cloud-native environments. Hybrid strategies combine the best of both worlds, while custom-built pipelines provide total control at the expense of development resources and time.

In contrast, buying a vendor solution like Coalesce offers ease of use, rapid deployment, and built-in best practices—ideal for organizations looking to minimize time-to-value and reduce operational complexity.

By weighing the pros and cons of each approach, you can design and implement the data pipeline architecture that best aligns with your business objectives.

Build data pipelines faster and smarter with Coalesce

Design, transform, and automate your data pipelines—all in one place.

Coalesce brings speed, flexibility, and governance to data pipelines, with a visual-first platform built for today’s cloud data warehouses.

• Get a Demo >
• Take a Virtual Tour >