Many businesses recognize the pivotal role that expansive data, or ‘big data’, plays in ensuring their success. As such, they often depend on it to formulate business reports and strategic plans. To achieve favorable outcomes from their ventures, these entities must competently collect, store, and extract insights from raw data. However, creating valuable insights from data is no simple feat, considering the substantial volume, variety, and speed of big data. Moreover, the lack of sufficient computing capabilities often impedes many enterprises from processing large amounts of data effectively. But there’s good news; the data engineering pipeline can significantly simplify these tasks, and that’s our discussion focus today.
When utilized appropriately, a data engineering pipeline can facilitate the creation of clean data sources and generation of valuable insights. Continue reading to broaden your understanding of data engineering services and how your organization can benefit from a data engineering pipeline.
Understanding the Concept of a Data Pipeline
A data pipeline encompasses a series of predefined operations that stimulate and transform data from diverse sources to a destination, enabling new value derivation. In its most basic representation, pipelines might solely extract data from a variety of sources, including REST APIs, databases, feeds, live streams, and more. The extracted data is then loaded to a destination like a SQL table housed in a data warehouse. Essentially, data pipelines serve as the bedrock of analytics, reporting, and machine learning capabilities.
The construction of data pipelines involves numerous steps, such as data extraction, preprocessing, validation, and storage, among others. These pipelines can be developed using a variety of programming languages and tools.
However, well-constructed data pipelines offer much more than just extracting data from sources and loading them into manageable database tables or flat files for analysts. They engage with raw data in numerous ways, including cleaning, structuring, normalizing, combining, aggregating, and more. Additionally, a data pipeline demands other activities like monitoring, maintenance, enhancement, and support of various infrastructures.
A data engineering pipeline refers to the design and structure of algorithms and models that replicate, cleanse, or alter data as needed. It also sources data directly to a destination, such as a data lake or a data warehouse.
In essence, a data pipeline simplifies and automates data flow from one point to another. It also automates all associated data activities, including data extraction, ingestion, transformation, and loading.
Exploring the Internal Mechanisms of a Data Pipeline
Data transformation isn’t inherently a component of all data pipelines. Their fundamental role usually involves transporting raw data from sources such as databases and SaaS platforms to data warehouses for utilization.
Nonetheless, in many instances, a data engineering pipeline’s responsibilities go beyond simply transferring data; they also entail executing transformations or processing of the data. The reason behind this is that raw data retrieved from a source might contain errors or might not be immediately usable. Consequently, it requires some modifications to become useful at its subsequent stage. The data engineering pipeline aids in eliminating errors and preventing bottlenecks or delays, thereby enhancing the overall speed of data movement. As such, purifying data immediately after it’s ingested is of utmost importance. This is due to the potential complications that can arise if erroneous or impractical data is assimilated into databases for analysis or utilized to train machine learning models, leading to a lengthy and complicated process to rectify the situation.
Data pipelines also facilitate the parallel processing of multiple data streams. The data is ingested either in batches or through streaming. As a result, a data pipeline can interact with any data source, with no specific focus on the data’s final destination. It’s worth noting that the end location doesn’t necessarily have to be a storage system like a data lake or warehouse.
Constructing a Data Pipeline
Each data engineering pipeline comprises several subsystem layers. The data travels from one subsystem to the next until it finally reaches its intended destination.
Data Sources Data sources symbolize the lakes, wells, and streams from where businesses initially gather their data. Being the first subsystem in a data pipeline, they are integral to the overall architecture. Without high-quality data, there would be nothing to load or transport along the pipeline.
Ingestion Ingestion involves the operations that read data obtained from the aforementioned data sources. This can be likened to pumps and aqueducts in the field of plumbing.
The gathered data is usually profiled to evaluate its attributes, structure, and its compatibility with a specific business objective. Post-profiling, the data is loaded either in batches or via streaming.
Batch processing occurs when data from sources are extracted and processed collectively in a sequential manner. The ingestion component reads, transforms, and passes on a set of records based on criteria predetermined by developers and analysts.
Streaming, on the other hand, is a data ingestion approach where data sources output individual records or data sets one after the other. It is commonly used by organizations requiring real-time data for analytics or business intelligence tools that demand minimal latency.
Transformation After data extraction, the structure or format of the gleaned information may necessitate modifications. There are various types of data transformations, including:
- Conversion of coded values into descriptive ones
- Filtering
- Aggregation
- Combination
Destinations Destinations represent the final storage tanks where the data ends up. A data warehouse typically serves as the primary destination for data moved along the pipeline.
A data warehouse is a specialized database that consolidates all of a company’s error-free, mastered data in a centralized location. This data can then be used for analytics, business intelligence, and reporting by data analysts and business executives.
Monitoring Data pipelines, comprising software, hardware, and networking systems, can be complex. Naturally, all these individual elements are prone to potential failure, necessitating the maintenance of smooth pipeline operations from source to destination. The quality of data might be compromised during its transit from one subsystem to another; it could become degraded or duplicated. Such issues magnify in scale and impact with the increase in complexity of tasks and the number of data sources.
Building, monitoring, and maintaining data pipelines can be laborious and time-consuming. Therefore, developers should formulate relevant codes to assist data engineers in conducting performance evaluations and troubleshooting any emerging issues. Simultaneously, organizations should allocate dedicated personnel to safeguard the data flow across the pipeline.
Six Engineering Approaches to Building Robust Data Pipelines
Perform Data Assessment Before embarking on the creation of a data pipeline, conduct a thorough data assessment. This involves comprehending the data models that preceded yours, acquainting yourself with the characteristics of the systems you’re exporting from and importing to, and understanding the expectations of the business users.
Adopt Incremental Development Embrace a flexible, modular approach when developing components or subsystems for your data pipeline. This is essential since you may not fully realize your needs until you’ve created something that does not adequately serve your purpose. Specifications often become apparent only when a business user identifies a new requirement that the current system doesn’t support.
Continually Revise Your Goals As you build the data pipeline, your goals will likely evolve. Therefore, it’s advisable to maintain a Google Document that you can revisit and revise as necessary. Encourage other stakeholders involved in the data pipeline to record their goals as well. This can help avoid misunderstandings or presumptions about shared objectives.
Ensure Cost-Effective Construction Costs can often exceed your initial budget. Therefore, when budgeting for a data pipeline, the following general financial guidelines should apply:
- Inflate cost estimates by an extra 20%
- Refrain from spending funds that aren’t yet available
- Minimize recurring expenses
- Stick to a budget plan
Establish Collaborative Teams Encourage collaboration among data analysts, data engineers, data scientists, and business representatives on the data pipeline project. Solving issues as a team tends to be more effective than sequential problem-solving where requirements are passed from one person to another. Such cooperative teams can produce efficient data pipelines at lower costs.
Utilize Observability Tools Observability tools grant insight into your data pipeline’s inner workings, enabling swift diagnosis and resolution of problems when the pipeline is down. Key aspects of observability include:
- Monitoring: A dashboard provides an operational overview of your system.
- Tracing: The capability to deploy and track specific events.
- Alerting: Notifications for expected and unexpected events.
- Analysis: Detection of anomalies and data quality concerns.
Advantages of Implementing Data Pipelines
The automation of anomaly detection and correction by a data pipeline ushers in numerous beneficial prospects for data practitioners, which include:
Unhindered Data Accessibility
Data pipelines facilitate automated collection, aggregation, and storage of data from diverse devices in a centralized repository, eliminating the need for human interference. Consequently, both internal and external data teams, provided they have the necessary data access rights, can easily access the data stored in this centralized location.
Conservation of Time and Effort
The integration of a data pipeline results in full automation of data-related tasks, reducing the need for human intervention. The inclusion of inbuilt observability tools within a data pipeline allows for automatic anomaly detection and alert generation, thereby reducing the tasks of data pipeline managers who no longer need to spend time identifying the source of data errors.
Additionally, the full automation of the data pipeline ensures that requests from data teams are promptly addressed. For instance, if a data analyst finds the data quality to be substandard, they can swiftly request and receive replacement data.
Enhanced Traceability of Data Flow
The complex nature of the data flow across the pipeline, involving a series of interconnected processes or activities, often makes it challenging to identify the origin of an anomaly. In a situation where the end-user observes missing data, the source of this loss could be anywhere from data storage to data transmission or even due to the absence of a transitional process. Determining the source of such an issue is a complex task. However, with a data pipeline, fault detection is automated, significantly improving traceability.
Compatibility with Diverse Data Sources
Data pipelines can seamlessly integrate with any data source. A process known as data ingestion standardizes the data into a unified format, lightening the load on data teams. This process allows for vast amounts of data from multiple sources to be ingested into the pipeline in batches or real-time, which can then be used to run analytics and fulfill business reporting needs.
Acceleration of Data Life Cycle
The data life cycle includes the extraction of data from the source, its transfer methods and routes, and ultimately, its final destination. Within the pipeline, operations are automated and executed in a predefined order, minimizing human intervention. This automation of data movement significantly speeds up the data life cycle within the data pipeline.
High-Quality Training Datasets
The efficacy of machine learning models relies heavily on the quality of input training datasets. Data pipelines transform and clean raw data into useful information, yielding high-quality training datasets, which can then be utilized by artificial intelligence technologies and deep learning models.
Data Sharing Among Data Teams
Data pipelines allow data teams to experience effortless data access and sharing. Data collected from various field devices undergoes similar processing for different applications. For instance, data cleaning is a step carried out by both data engineers and data scientists before feeding the data into machine learning or deep learning models. Consequently, different teams within the same organization might be processing the same data through similar steps. Similarly, data storage might also experience redundancy.
When data teams request data from a pipeline, they eliminate the need to repeat these data-related processes, saving valuable time.
Best Practices for Sustaining Data Pipelines
Maintaining a data pipeline comes with its share of challenges. One of the prevalent maintenance issues for data engineers is managing the reality that data sources are dynamic. Software developers constantly refine their software through feature additions, codebase refactoring, or bug fixes.
When such modifications alter the schema or interpretation of the data to be ingested, the pipeline risks experiencing failures or inaccuracies. Given the diverse array of data sources in modern data infrastructure, there’s no one-size-fits-all solution to accommodate schema and business logic changes in source systems. As such, adhering to best practices becomes essential for constructing a scalable data pipeline.
Here are some suggested best practices:
Introduce Abstraction
It’s beneficial to include a layer of abstraction between the source system and the ingestion process whenever possible. The source system owner should either maintain this abstraction or be aware of the abstraction method. For example, rather than ingesting data directly from a Postgres database, collaborate with the database owner to develop a REST API that extracts from the database and can be queried for data extraction.
Uphold Data Contract
If data ingestion happens directly from a source system’s database or via a method not explicitly designed for extraction, creating and maintaining a data contract offers a less technical solution for managing schema and logic changes. Essentially, a data contract is a written agreement between the owner of a source system and the team ingesting data from that system for use in a data pipeline. Ideally, a data contract should be written in a standardized configuration file, but a text document can also suffice.
Standardize Data Ingestion
The challenge in pipeline maintenance usually stems less from the number of systems from which we ingest, but rather from the lack of standardization among these systems. The issues are twofold: First, ingestion jobs must be tailored to handle a variety of source system types (such as Postgres, Kafka, etc.), resulting in a larger codebase and more maintenance. Second, ingestion jobs for the same type of source system can’t be easily standardized. For instance, even if we only ingest from REST APIs, the absence of standardized paging methods, incremental data access, and other features can result in the creation of unique ingestion jobs that don’t reuse code and can’t be centrally maintained. To tackle these issues, consider the following technical approaches:
- Endeavor to standardize and reuse whatever code is possible.
- Aim for configuration-driven data ingestion.
- Reflect on our abstractions.
Conclusion
In the realm of data pipelines, businesses are presented with two alternatives: coding their own data pipeline or employing a SaaS pipeline. Instead of investing time in writing ETL code to construct a data pipeline from the ground up, businesses can opt for SaaS data pipelines, which offer speed and ease in both setup and management.
Whichever option is selected, the benefits that a data pipeline provides to a business are considerable. Automated data extraction, ingestion, and error detection lighten the load for data managers and facilitate smooth data access for various teams. Additional advantages include enhanced analytics due to high-quality training datasets and swift identification and rectification of anomalies.