Data Ingestion — Architectural Patterns Explored
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Data ingestion is a vital function that connects operational and analytical environments. Over two articles, I will delve into this essential process, which facilitates the movement of data from various sources within its operational context—often termed the 'operational plane'—to the analytical sphere, known as the 'analytical plane.' This transition is crucial for realizing the full potential of data-driven insights and analytics.
By enabling the generation of insights and the application of AI models, data ingestion is central to enhancing an organization’s analytical capabilities. The extent of analytical power is often tied to the variety of data sources that can be leveraged. Therefore, selecting effective data ingestion strategies is paramount. These strategies should be versatile enough to accommodate a broad spectrum of data, ranging from common operational tools like CRMs, ERPs, and financial systems to less conventional sources such as IoT devices, APIs, and various formats including documents, images, and videos.
When viewed from a wider perspective, data ingestion is a critical piece of the larger data platform framework within an organization. This platform often serves as a foundation for digital transformation efforts, assisting organizations in meeting their strategic goals. At its essence, a data platform comprises various architectural styles and a plethora of tools, all of which are integral to its operation and success.
In the first of two articles on data ingestion, this piece will investigate the architectural patterns that influence the choice of data ingestion technologies. I aim to clarify each pattern's core principles and their strategic implications for the data ingestion workflow. By outlining these patterns, I hope to identify and overcome the obstacles that can complicate what should be a straightforward yet vital task: integrating data into your analytical framework. Understanding the strategic importance of data ingestion is crucial for ensuring a seamless transition and effective utilization of data across an organization’s expansive data landscape.
Pattern 1: Unified Data Repository
The initial architectural model we will explore is the Unified Data Repository pattern, which utilizes a single storage system for both operational and analytical needs. This is often a Relational Database Management System (RDBMS), where the same database serves both daily operations and data analysis, thus eliminating the need for transferring data between separate storage systems.
Within this framework, two common sub-patterns arise:
- Virtualization — This involves creating virtual layers or views that provide an analytical perspective on operational tables in the database, allowing data to be accessed analytically without physical alterations.
- Duplication and Transformation — In this case, operational data is replicated in a format better suited for analysis, which can be achieved through stored procedures, materialized views, or directly within the operational application’s storage layer.
While this model simplifies data management and ensures access to raw data, it has notable limitations:
- Data Integration Challenges — The model struggles with integrating data from different physical databases due to its reliance on a single storage solution. Techniques like linked servers or cross-database queries can be employed, but they often introduce additional complexity and are generally less favored.
- Potential for System Interference — Concurrent operational and analytical processes on the same database can disrupt each other, increasing load and potentially degrading performance.
- Performance Trade-offs — The differing optimization needs of Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) systems mean that a system designed to serve both may not perform optimally for either.
- Tightly Coupled — This pattern fosters a strong interconnection between operational and analytical realms, reducing flexibility in either area.
Given these challenges, the Unified Data Repository is usually not advised for large datasets or multiple physical data sources. It may be adequate for smaller applications operating within a robust database where complexity is minimal.
Pattern 2: Data Virtualization
The next approach, Data Virtualization, employs specialized software to create a virtualized data layer over multiple data sources. This intermediary layer facilitates query execution, processing results from the original data sources to form a cohesive dataset for analysis.
Key advantages of this model include:
- Near-Real-Time Data Access — Data is queried directly from its source without physical relocation to an analytical database, offering rapid availability.
- Intelligent Caching — Data virtualization systems often include advanced caching features, reducing the load on source systems and enhancing performance.
However, there are concerns with this approach:
- Source System Limitations — If source databases are not optimized for specific query types, their performance issues can affect the virtual layer, especially if it relies on source responses.
- Network Overhead — A virtualization layer connecting to data sources across various network zones may encounter latency, hindering overall performance.
- Historical Data Tracking — Since the virtual layer doesn’t store data, it complicates historical analysis, often referred to as “time travel” across the data ingestion timeline.
It’s essential to recognize that particular data virtualization solutions might offer features to address these challenges. I recommend thoroughly testing any data virtualization solution within your specific infrastructure to understand its capabilities and limitations, allowing for proper scaling and adjustments to enhance integration and analysis.
Pattern 3: ETL
ETL, or Extract, Transform, Load, is a long-established approach to data processing. The process begins with data extraction from its source (Extract), followed by refinement on an ETL server (Transform), and culminates in loading the refined data into an analytics-oriented database (Load).
Numerous ETL tool providers have supported this methodology, offering various specialized transformation techniques and design styles. The common design involves a graphical interface that allows users to link Extract, Transform, and Load operations in a visual workflow. These processes can often be further customized through scripting or direct SQL queries.
The main benefits of ETL include:
- Centralized Logic — ETL processes allow for the consolidation of transformation logic in a single environment, facilitating not only data ingestion but also preparing data for analytical needs.
- User-Friendly Design — The visual aspect of ETL tools makes the data transformation process accessible, enabling users of varying skill levels to participate in creating data pipelines.
However, ETL also has its drawbacks, which have prompted the emergence of alternative models:
- Dependence on Specific Vendors — Relying on ETL tools can create vendor lock-in, making transitions to other platforms costly and complicated, especially if the tool’s pricing or features change.
- Performance Constraints — ETL transformations are performed by designated servers, which may not scale as effectively as the high-performance computing resources available in modern data warehouses, potentially leading to bottlenecks.
- Opaque Data Lineage — Simplifying into visual components can obscure the complexity of data transformations, making it difficult for those outside the ETL tool environment to understand and audit the data’s journey.
- Limited Scalability — While designed for broad use, ETL tools may lack robust capabilities for scaling and industrialization, which are crucial as data platforms expand.
- Rigidity — The inflexibility of ETL tools can result in workarounds that contribute to technical debt.
These prevalent limitations can often be mitigated by specific ETL vendors, particularly when integrated into comprehensive suites tailored for a particular cloud data warehouse. However, it is crucial to remain informed about the evolution of the ETL tool to ensure it aligns with changing data ingestion requirements, such as increasing data volumes or new data source types.
Pattern 4: ELT
ELT shares the foundational steps of ETL but alters the order and definition of these processes. In ELT:
- EL — The Extract and Load operations are performed first, transferring raw data directly to the data platform without immediate transformation.
- T — Transformation occurs afterward, converting raw data into actionable insights. Notably, transformation tasks can operate independently and on different schedules from extraction and loading.
This restructured methodology tackles several limitations of ETL:
- Enhanced Flexibility — Separating extraction/loading from transformation enhances adaptability, allowing for diverse tools to be selected for different data types and transformation standards.
- Aligned Performance — Transformation occurs within the data platform, utilizing its full computational power, making it particularly effective for managing large datasets with distributed computing engines.
- Improved Scalability — The inherent flexibility of ELT allows for the selection of transformation tools that excel in automation and scalability.
Despite these enhancements, the ELT model introduces new complexities:
- Governance of Multiple Tools — Employing various tools for extraction, loading, and transformation necessitates strict governance to manage licensing, pricing, update cycles, and support systems.
- Orchestration Challenges — A more diverse toolkit requires sophisticated orchestration based on Directed Acyclic Graphs (DAG) to ensure that transformations proceed only after successful data extraction and loading.
The ELT pattern is favored for its flexibility, but it requires a commitment to managing a multi-tool landscape and a complex orchestration strategy.
Emerging Patterns
Beyond the established patterns, new methodologies and frameworks continue to emerge. This section discusses two such trends: push and stream processing.
Push (vs Pull)
Traditional patterns are typically of the “Pull” variety, where the analytical plane actively retrieves data from the operational plane. In contrast, “Push” methodologies reverse this flow: the operational plane actively sends, or 'pushes', data to the analytical plane as soon as changes occur, such as Create, Read, Update, and Delete (CRUD) operations.
The push approach is often associated with streaming architectures but is not limited to them. It fundamentally involves the operational plane initiating data transfer to a designated endpoint in the analytical plane. This setup typically requires development teams to implement the push mechanism, either through separate components or enhancements to existing operational applications.
The primary advantage of this method is that it allows analytical teams to focus on transforming data into value without the distraction of creating ingestion pipelines, as operational systems handle data delivery. However, there are two significant drawbacks:
- Need for a Dedicated Application Development Team — This can be challenging with packaged software, SaaS solutions, or external hardware like IoT devices, where such a team may not be readily available. In such cases, establishing a specialized 'data integration team' may be necessary, but this can quickly become a bottleneck.
- Handling Push Failures — Pull-based architectures generally exhibit greater resilience to pipeline disruptions compared to push architectures. In the event of a pull failure, the analytical platform can restart the process. Conversely, if a push fails, the analytical platform may remain unaware of the missed push message. To mitigate this issue, push-based pipelines are often integrated into highly available streaming architectures designed for concurrent operation and robust availability.
The push pattern is most suitable for organizations with high software development maturity or those that can negotiate data-pushing capabilities when acquiring off-the-shelf solutions. In scenarios where this is not feasible, it may be wise to combine push with other data ingestion patterns to ensure seamless and efficient data integration.
Stream Processing
Stream processing, also referred to as event streaming, involves the continuous flow of data as it is generated, enabling real-time processing and analysis for immediate insights. These systems are essential for tasks requiring instant decision-making and support high-volume, low-latency processing for activities like financial trading, real-time analytics, and IoT monitoring.
When integrating stream processing with analytics, two prominent approaches emerge:
- Adapting ELT (or ETL) for Streaming — This involves extracting real-time events and loading them into data platforms, maintaining familiar workflows with novel data sources through specialized streaming consumers.
- Leveraging Streaming Caches — Centralized, durable streaming caches serve as high-performance repositories for event data. Some innovative patterns utilize these caches analytically, creating an efficient variant of shared data storage. A key consideration is the integration of streaming data with static data sources, which may not pass through the streaming cache.
The combination of streaming data and more static data is being outlined in architectural patterns like KAPPA and LAMBDA, which aim to unify both realms when necessary.
Conclusions
The strategic integration of data ingestion methods is essential in the evolving landscape of data analytics. This article has highlighted four core data ingestion patterns—Unified Data Repository, Data Virtualization, ETL, and ELT—each possessing unique benefits and limitations. As we examined these patterns, we noted the Unified Data Repository's simplicity but constrained scalability, the near-real-time capabilities of Data Virtualization accompanied by potential performance issues, the centralized control of ETL shadowed by possible bottlenecks and rigidity, and the flexibility and scalability of ELT offset by orchestration challenges.
Furthermore, emerging stream processing paradigms reflect the industry's shift toward real-time analytics. These methods, while still in their infancy, are paving the way for a more dynamic and immediate approach to data processing, accommodating the relentless pace of information generation.
In my next article, I will delve deeper into selecting the right data ingestion tool for your data platform, with a critical focus on the architectural pattern in which this tool will be integrated.