Many organizations understand the important role of big data in business success and, thus, rely on it for generating business reports and making strategic business decisions. For their projects to be successful, they need to correctly gather, store, and generate insights from raw data. However, data collection with a view of generating useful insights is not an easy task. The challenge lies in the volume, variety, and velocity of big data. And the fact that most enterprises lack the computing power necessary to process huge amounts of data. Thankfully, the data engineering pipeline can, however, make things a lot easier. And that’s what we’re going to talk about today.
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If used in the right way, the data engineering pipeline can help you with creating clean data sources and generating useful insights. Read on to learn more about data engineering services and how data engineering pipeline can be used in your organization.
What is a data engineering pipeline?
A data engineering pipeline is the design and structure of algorithms and models that copy, cleanse, or modify data as needed. It also directly sources data to a destination like a data lake or data warehouse.
Simply put, a data pipeline streamlines and automates the flow of data from one point to another, and automates all the data-related activities in the pipeline. These include data extraction, data ingestion, data transformation, and data loading.
What happens inside a data pipeline?
Data processing is not a feature of every data pipeline. Typically, the primary purpose is to transfer raw data from database sources and SaaS platforms to data warehouses for use.
However, oftentimes, the role of the data engineering pipeline extends to carrying out some sort of transformation or processing of the data. This is because raw data loaded from a source might not be error-free or usable. And thus, it requires some alteration to be useful at its next node. The data engineering pipeline removes errors and resists bottlenecks or holdups, thus increasing end-to-end speed.
That said, cleaning data immediately after ingestion is crucial. This is because once erroneous or unfeasible data is ingested into databases for scrutiny or used to train machine learning algorithms, it may take plenty of time to reverse engineer the entire process.
Simultaneous processing of several streams of data also occurs inside data pipelines. The data is ingested in batches or via streaming. Hence, the data pipeline is compatible with any data source. And there’s no exact emphasis on the data destination. It doesn’t have to be data storage like a data lake or data warehouse.
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Designing a data pipeline
Each data engineering pipeline has different system layers. Each subsystem passes along data to the next until the data arrives at its destination.
Data sources refer to the lakes, wells, and streams where companies first collect data. They are the first subsystem in a data pipeline and are crucial to the overall design. Lack of quality data means there’s nothing to load and move across the pipeline.
Ingestion refers to the operations that read data gathered from data sources. In the plumbing industry, these are the equivalent of pumps and aqueducts.
Data is often profiled to assess its attributes and structure and how well it suits a business purpose. After data profiling, the data is loaded in batches or via streaming.
Batch processing is when data sources are extracted and worked on as a group in a chronological manner. The ingestion component reads, transforms, and passes along a set of records based on the pre-set criteria by developers and analysts.
Streaming is a type of data ingestion method where data sources output individual records or sets of data one by one. It is used by organizations that require real-time data for use with analytics or business intelligence tools that require the least possible latency.
After extraction from data sources, the structure or format of the extracted information may need to be modified. There are different types of data transformations. These include:
• Converting coded values into descriptive ones
Destinations are the equivalent of water storage tanks. Data that moved along the data pipeline ends up in a data warehouse as its main destination.
A data warehouse is a specialized database that contains all of a company’s error-free, mastered data in a centralized location. The data can be used in analytics, business intelligence, and reporting by data analysts and business executives.
Data pipelines are made up of software, hardware, and networking systems which adds to their complexity. Of course, all of these constituent components are susceptible to failure. And so, there’s a need to maintain smooth pipeline operations from data source to destination.
The quality of the data might be affected during the movement of data from one subsystem to another. Data can, for example, could become degraded or duplicated. These challenges grow in scale and impact as the complexity of the obligations grows, and data sources increase in number.
Data pipeline construction, monitoring, and maintenance are time-consuming and tedious. Therefore, developers should write the relevant codes to help data engineers perform performance appraisals and rectify any arising problems. Meanwhile, organizations should have dedicated employees to protect data flow across the data pipeline.
6 engineering strategies for creating resilient data pipelines
Conduct data audit
Perform a data audit before building a data pipeline. This means understanding the data models that came before yours—and learning the traits of the systems you’re exporting from and importing to, as well as the business users’ expectations.
Adopt a modular fashion that is adjustable when creating components or subsystems of your data pipeline. This is because you never know what you need until you make something that does not fit your purpose. The specifications aren’t straightforward until a business user requests a time series that they’ve just noticed they need but which isn’t supported by the system.
Constantly update your objectives
Your objectives will most likely continue to progress as you construct the data pipeline. That’s why you need to create a Google Document that you can always revisit and revise when needed. You should also request other stakeholders involved in the data pipeline to write down their objectives as well. You don’t want a scenario where someone assumes others share the same thinking as them.
Costs will likely exceed your budget. When creating a budget for a data pipeline, all these conventional personal fiscal rules apply:
• Quote expenses by an additional 20%
• Don’t spend money that is not yet in your possession
• Avoid recurring expenses
• Follow a budget plan
Create working groups
Have the data analyst, data engineer, data scientist, and business representatives work together as a unit in the data pipeline project. They should tackle problems as a group because it’s more effective than if they worked sequentially, forwarding requirements to one another. Functional groups create effective data pipelines at a lower cost.
Deploy observability tools
Observability tools allow you to peer inside your data pipeline. So when the pipeline is down, you can quickly diagnose the problem and fix it in no time. Observability includes:
• Monitoring: The dashboard offers an operational view of your system
• Tracking: Ability to deploy and track specific events
• Alerting: Notifications for anticipated and unforeseen events
• Analysis: Detection of anomalies and data quality issues
The benefits of implementing data pipelines
A data pipeline automates anomaly detection and rectification. And this opens up a plethora of promising opportunities for data practitioners, including:
Seamless data access
Data pipelines collect, aggregate, and store data generated by various devices in a centralized location. This entire process is automated and needs no human intervention. Thus, both internal and external data teams can access the centralized location’s data. This is as long as they have the right data access rights.
Save time and effort
Adopting a data pipeline sees the complete automation of data-connected activities. Hence, there’s less need for human intervention. When you include an inbuilt observability tool inside a data pipeline, anomalies are automatically detected, and alerts pop up. This minimizes the workload of data pipeline managers, who don’t have to waste time finding out the source of data errors.
Moreover, the complete automation of the data pipeline means requests by data teams are addressed promptly. For example, if the data analyst finds the data quality is not suitable, they can request and get replacement data within no time.
Better traceability of data flow
The flow of data across the pipeline involves a series of interrelated processes or activities that add to its complexity. Thus, it is hard to point out the source of the anomaly.
Picture a scenario where the end-user notices a chunk of data is missing. Of course, there are endless possibilities regarding data loss. It might have happened during data storage, data transmission, or because of a lack of a transitional process.
To recover the missing data, the end-user has to weigh up all the multiple scenarios and commence investigations. This is an uphill task. But with a data pipeline, fault detection is automated, hence increasing traceability.
Compatibility with assorted sources of data
Data pipelines are compatible with any data source. A process called data ingestion converts the data into a unified format, which reduces the workload of data teams. In this process, massive amounts of data from several sources are ingested into the pipeline, either in batches or in real-time. The data is then used to run analytics and meet business reporting needs.
Speed up the data life cycle process
The data life cycle involves the extraction of data from the source, where and how it is transferred, and ultimately the destination of the data. Operations within the pipeline are automated and implemented in a set order, thereby reducing human intervention. Because everything about the data movement occurs automatically, the data life cycle is accelerated inside the data pipeline.
High-quality training datasets
The success of machine learning models depends to a great extent on the input training dataset. Data pipelines clean and process raw data into useful data. Therefore, these high-quality training datasets can be used by artificial intelligence technologies and deep learning models.
Data sharing between data teams
Data teams enjoy seamless data access and sharing, thanks to data pipelines. The data gathered from the multiple devices in the field undergoes similar processing for varied applications. For example, data cleaning is a process performed by data engineers and data scientists before ingesting the data into machine learning or deep learning models. Hence, different teams within the same organization might be subjecting the same data to a series of similar steps. Storage of data also suffers from similar redundancy.
When data teams request data from a pipeline, they don’t have to repeat the data-related processes. This helps to save time.
When it comes to data pipelines, organizations have two options: either write their own code for a data pipeline or use a SaaS pipeline. Rather than spending time writing ETL code to create a data pipeline from scratch, enterprises can use SaaS data pipelines, which are quick to set up and manage.
Regardless of choice, the advantages a data pipeline brings to an enterprise are immense. The automation of data extraction, ingestion, and error detection reduces the workload of data managers and allows for seamless data access by teams. Other benefits include better analytics because of high-quality training datasets and faster detection and rectification of anomalies. See our data engineering services to find out more.
 Batini, C., Rula, A., Scannapieco, M., Viscusi, G.: From data quality to big data quality. In: Big Data: Concepts, Methodologies, Tools, and Applications. pp. 1934– 1956. IGI Global (2016), Accessed April 13, 2022
 Kaisler, S., Armour, F., Espinosa, J.A., Money, W.: Big data: Issues and challenges moving forward. In: 2013 46th Hawaii International Conference on System Sciences. pp. 995–1004. IEEE (2013), Accessed April 13, 2022
 Googleleadservices.com. Data Engineering Pipeline. URL: https://bit.ly/3k7fIss. Accessed April 13, 2022
 Jovanovic, P., Nadal, S., Romero, O., Abell´o, A., Bilalli, B.: Quarry: A usercentered big data integration platform. Information Systems Frontiers. pp. 1–25 (2020). Accessed April 13, 2022
 Appdirect.com. What is Saas Platform?. URL: https://bit.ly/3xLSRL5. Accessed April 13, 2022
 Marz, N., Warren, J.: Big Data: Principles and best practices of scalable real-time data systems. New York; Manning Publications Co. (2015), Accessed April 13, 2022
 Oracle.com. What is Data Warehouse. URL: https://bit.ly/3rJ5zXq. Accessed April 13,2022
 Striim.com. What is Data Ingestion, and Why This Process Matters. URL: https://www.striim.com/blog/what-is-data-ingestion-and-why-this-technology-matters/. Accessed April 13, 2022