7 critical factors to consider when building your data pipeline

Vikas Agarwal

Big Data, Data Analytics, Data and Insights

Data Pipelines are created with processes to ingest, transform and serve data. Along with this, data pipelines are expected to support the process of data observability and job orchestration impacting the length of the pipeline. The complexity is immense once we start drilling down. Hence is significant to keep certain principles in mind while developing your data pipeline.

Data pipelines let businesses derive value from their raw data. There are a number of ways in which enterprises can organize their data pipeline. But we shouldn’t make the mistake of generalizing the functions of the data pipeline. Ideally, we understand data pipelines to be frameworks that enable enterprises to move raw data from Point A, the source to Point B, the destinations while applying some transformation in between. Just like the physical pipelines that’ll need connectors, flow controls, interoperability stands, etc. similarly data pipelines consist of complex components that together work to take the raw data from Point A to serve the analytical ready information at Point B.

Before we begin a discussion on the critical 7 components, let us first throw light on some basics

Defining Data Pipeline

A data pipeline is an aggregate of data processing activities, methodically put together. Data is ingested in the beginning of the data pipeline, facilitating a series of cross-dependent functionalities to generate data that is ready to be analysed. Data pipeline is used to create business solutions by filtering out the unnecessary part and improving the relevant data’s functionality.

The significance of Data Pipeline

Data is the foundation of a successful business and data centric decision making is critical as never before. We are using data driven analysis for business problem solving. However, gathering the relevant data and generating insightful information out of it is not as simple as it seems. Data ingestion, consolidation and analysis take both time and efforts. They are prone of vulnerability and shifts. There is also a certain cost associated with data architecture. Niche data handling and potency of human labour are also factors that could pose challenges in efficient data pipeline.

Data pipeline are the constructs that make it easier for data to be combined from significant sources into a singular location. This data is then employed with series of functionalities to come up with meaningful data sets. Data pipeline is capable of simplifying the data processing operations. It is time and cost efficient and eliminates human effort.

Data Pipeline Process & Architecture

The design of systems that are able to clean, replicate and modify the sourced data along with transmitting the analysed data to the destination such as data lakes and warehouses are known are data pipeline process or architecture.

Let us look at the step wise architecture of data pipeline, serving end to end enterprise solutions. Broadly we can look at the below mentioned five processes within the data pipeline architecture

Data Collection
Collecting data from a number of sources including web and mobile applications is required to initiate the process within data pipeline
Data Ingestion
Once we are done with the retrieval and collection of data it will be required to be ingested into the data pipeline to trigger a series of functions. The aggregated data forms the data lake.
Data Preparation
The ingested data needs to be extracted, transformed and loaded to make it relevant for analysis that can generate relevant insights. In this stage the data is stored in the data warehouses where it goes through multiple processes.
Data Computation
Analysing, processing the data to generate valuable insights from the data preparation stage is the essence of data computation. Both historical paradigms and real time paradigms to data processing are applied to come up with data insights and performance.
Data Presentation
After the data has been processed looking at the inferences and insights presented by the data on an external interface is vital for an overall understanding of the valuable insights. The data needs to be visually understood at this stage. The exhaustive data needs to be presented in simple format upon dashboards to be understood by technical and non-technical members of the enterprise.
As we see the data pipeline architecture encompasses the entire process right from collection to presentation, making business decisions scientific and relevant.

7 key factors to consider when building the efficient data pipeline

There could be multiple factors that one can overlook while getting your pipeline up and running. These could be because of timeline pressure or mishandling of data by the personnel. With a data pipeline, the data analysts and scientists in your organization will have the necessary support to apply critical thinking and creativity to handle data more efficiently.

But if you ignore any of these below-mentioned factors, there could be serious business consequences. Ensure you pay attention to the following while when you go about selecting tools and technologies for creating your data pipeline

Gain understanding of the engine in use
It is quite straightforward to write the code however the manner in which the code gets executed is largely dependent on the engine that is in use. If you are unaware of how exactly is the engine running, it may become difficult to optimize your code to perform better and handle failures and challenges.
Also, a lot of tools are based upon Apache Spark. With these tools, the possibilities of automation is quite limited. Spark can handle multiple task; shuffling data between nodes, garbage collection and the likes. However, when the amount of data that needs to be processed begins to increase, these functions may start to degrade. At this point, the expertise of data managers come into picture. They’ll need to configure the tools to be capable of handling the load and making code as productive as possible.
Hence always understand your engine to be able to scale significantly; be it Google, EMR or Databricks
Writing efficient output for destination system
A lot of the tools focus mostly on the data processing aspect of the data pipeline completely ignoring the preparation of output for the destination framework. There could be indication of data’s readiness to query however practically it may not so happen. At the end you want a ‘query able’ data that is quick and optimized. There could be challenges like the data needing separate update upon metadata catalogue or the data is not structured or partitioned efficiently. To manage these challenges there could be requirement of more code however it may generate some extra friction in the development and later on during maintenance.
Consistently managing scheduling and orchestration
Pipelines are able to do multiple things. They are created of multiple tasks. It is essential to orchestrate them time to time and consistently. For example, whenever there is a new data, it is essential to ingest and run the pipeline. It will not work if you’ll have to manually ingest, upload, re-order or reschedule the data each time. There are also tools that support this function within data pipeline’s setup functions. Apache Airflow is one of the examples. This also implies that the data engineers need to learn, understand and scale up more systems and tools. Rather their time must have been spent on creative analysis and logical interpretation of the data on top of the pipeline to give it meaningful format for business to understand.
Scaling & performance to go hand in han
Most of the data pipeline tools provide good user experience. When you run your data through them, they seem to fit fine in the data pipeline. However, if you consider scaling up, you may end up frustrated. However, it is easy to conflate the auto scaling feature with an improvement in data ingestion and processing thereof all through out. A host of tools are able to auto scale without any intervention. There could however remain other challenges also. Like the data while processing may not get evenly distributed throughout the nodes that have been added while scaling. You’d need to keep a check on all of these bottlenecks and onboard mechanism to resolve the issues.
Remaining flexible towards change
There may be at times the need to constantly ingest, manage, update & troubleshoot the data pipeline. This may drive data engineers crazy! The data plumbing phenomena is real. There is constant chasing of fixing pipeline crash, resolving bugs, addressing the irrelevant data for its relevancy and the likes. This can lead to added layers of complexity especially while working with the data lakes, real -time data streams or managing schema evolution. It is simple to write code and move the data batch from an app to database, but what when data engineers would want to add additional fields or reduce the refresh time from days to hours or even add a new data source? There may be chances that you’ll need to rerun your pipeline when the application logic evolves or there are some edits in the business rules. How do we manage these situations? You should not need to manually rebuild, rearchitect or scale your system to adjust the necessary changes.
Understanding your target users
Now a days enterprises are not in favour of making huge investments in proprietary legacy systems that are expensive and demand more time and efforts. Most of the progressive organizations have moved toward open core architecture. Open source is a great revolution which allows you to scale and perform better with the community support. However open source is not as simple. You’d need developers and engineers who’d know how to work with this tech revolution, otherwise enterprise is bound to suffer. Also where would be the developers working is also significant. Will they be working at your location or at the vendors? Will they be executing the code or would be writing and debugging the code? The skills the users want to acquire in-house will be deciding factors in understanding the tools that you’ll want to use.
The choice of language
It is also important to take a look at the choice of language that enterprises may make to develop their data pipeline. SQL is a familiar language for both data engineers and data consumers. It is quite portable and very accessible. It has very low barriers to entry and almost anyone can learn it. It is also popular. On the other hand, tools of GUI are difficult to test, automate and even port. Programming languages like Python, Java are known to have long learning curves. Hence everyone in your organization should not be expected to learn them if they’d want to understand data.

Ending Note:

Enterprises are getting aware of the data pipeline and are demanding a robust architecture. However, it is best to keep the process as simple as possible. We are transitioning towards a self-service model in the web world. WordPress and Wix are more popular examples. Just as it is possible to build websites or online stores without the knowledge of HTML or Java, similarly it is possible to create enterprises critical data sets without the skills of Python or Scala. Also, we see an amalgamation of AI tools within the data pipeline architecture. This will let enterprises come up with new products almost immediately!

The future of data pipeline is exciting however the key is in keeping the system as simplified as possible.

Vikas Agarwal

Vikas Agarwal is the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.

7 critical factors to consider when building your data pipeline

Big Data, Data Analytics, Data and Insights

Defining Data Pipeline

The significance of Data Pipeline

Data Pipeline Process & Architecture

7 key factors to consider when building the efficient data pipeline

Ending Note:

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