HomeArchitectureHow to build robust data pipelines for data science projects

How to build robust data pipelines for data science projects

While businesses have large volumes of data, they do not draw valuable insights from it. One significant data challenge that every organization faces is data silos. Only through data pipelines can you consolidate data from multiple disparate sources for your data scientists to fetch business insights easily. In addition to bringing your data onto a single storage unit, data pipelines ensure data consistency and avoid redundancy. 

Some organizations follow manual processes to collate their data into Excel sheets. However, this leads to many errors and throws your team down a rabbit hole. It becomes an impossible task once you bring in real-time data sources.

With data pipelines, you can begin with a simple flow and then scale it to accommodate enormous data streams. Building robust data pipelines is a no-brainer if you want to extend your data science projects to their full potential.

What is a data pipeline?

A data pipeline is the set of processes you employ to collect raw data from its source to where you want to store it. For example, you collect user data from social media, CRM, and transactional databases. This information is like a goldmine for your organization. 

But data scientists can’t work their magic to produce consumer insights on fragmented data. Using data pipelines, you can assemble data from all these sources to a single location like a data lake or warehouse. It forms an essential part of data analytics. 

Some benefits of a data pipeline include:

  • Provides flexibility to scale data analysis needs smoothly.
  • Ensures your data is consistent, reliable, and accurate.
  • Allows standardization of raw data into a structured and uniform format
  • Facilitates easy integration of new data sources.
  • Helps extract business insights faster.

Building a robust data pipeline

Organizations today need data-driven guidance to make effective and profitable decisions. They must build a systematic flow to harvest voluminous data from sources through processing and monitoring mechanisms and to the destination. 

Here are the key components of a data pipeline.

Data sources

Sources are where your data pipeline will begin, as they are the origination point of data. Any platform or system that generates the data for your business can be a data source. They could be Relational Database Management Systems (RDBMS), CRMs, ERPs, and social media integrations. Some organizations may even have data captured by IoT devices. Any source that generates information about user behavior and interactions, commercial transactions, and any other third-party information can be added to your pipeline.

Data ingestion

Ingestion is collecting data from multiple sources and moving it through the pipeline. Organizations have a range of options for data ingestion, including a replication engine, a webhook, and API calls. You can follow one of the two methodologies to collect data.

  1. Batch processing is where you collect data at regular intervals in batches by scheduling data extraction to avoid taxing your overall system.
  2. Streaming is when you want real-time data to be loaded into your analytical model, you can use this model to keep the data flowing continuously.

Data transformation

In this stage, you process the data collected into a format your data scientists can use. To transform the data, run a set of tasks to produce an outfit in a form that can be stored in the destination repository. These tasks include data classification, normalization, validation, and redundancy elimination. They are usually done through Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) models. You can implement an automation strategy by defining robust governance policies to enable streamlined workflow for repetitive tasks like reporting.

Data storage

Following the transformation, the structured and uniform data can now be stored in a repository. The destination could be a data lake or data warehouse. Some organizations load this data directly onto their visualization platforms to generate analytical reports instantly. 

Data Governance

Once your data is sorted and stored in a repository, it needs to determine rules and access policies. The governance policies ensure that the data is used properly for business growth and is not exploited. You can achieve this through multiple layers of security, including encryption, monitoring, and access control.

Step-by-step process to building a data pipeline

Step 1: Decide the use case for your data pipeline

Your approach to building a data pipeline will depend on your objective. It can be used to 

  • Analyze data sets and investigate hypotheses or identify anomalies
  • Generate infographics, charts, or other visualizations
  • Power your machine learning algorithms and generate predictions 

Step 2: Identify your data sources

Get a detailed understanding of all the platforms, tools, or systems that possess the information you can use to churn business insights. After you understand your potential data sources, consider the data format (JSON, XML, TXT, etc) you will be dealing with.

Step 3: Determine your ingestion process

In this phase, pick one or more communication layers, like HTTP and gRPC, to collect the data from the sources. Also, choose one of the two ingestion processes we discussed earlier – batch processing and streaming. You can explore third-party tools to ingest data. 

Step 4: Outline your processing strategy

Your transformation strategy will define how you get the processed data. Depending on your goal and incoming data format, the number of steps in the processing plan may vary. You can select one of the following processing approaches:

  • ETL: In this processing architecture, you begin by extracting data, followed by its transformation, after which the structured data is loaded into the repositories. This style is followed when storing the data in a data warehouse.
  • ELT: In ELT-based architectures, your first step is to extract and load the data into the destination, i.e., data lake. Here, you execute the transformation process. This method is proper when using the exact data for various business requirements and across departments.

Step 5: Set up data storage

The data storage plan depends on the transformation strategy and your business goal for the data. You can have a data warehouse to store the processed data, a data lake, or visualization tools to use the same information for multiple insights.

Step 6: Define data monitoring and governance principles

Once you have all the other pieces, jot down the policies to streamline the data workflow. Decide how minutely you want to observe the entire process and other access controls. It is important to set strict policies since the data output can transform your business approach.

Data pipelines for a foundation for your data science applications

When organizations talk about data analytics and business insights, they limit their conversation to data science, visualization, and analysis. However, emphasizing on creating an effective data pipeline will help you make smarter decisions faster by putting the work into the ever-growing data. We have outlined the components of building a future-proof pipeline and a step-by-step guide. Given its essential role, ensure you put in considerable time and resources when building a robust data pipeline.


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