Data Pipeline Best Practices for a Scalable Data Architecture

Why Are Data Pipelines Important?

Data pipelines find widespread usage in a range of business use cases. Before we understand why data pipelines are important, let's clear the air around data pipelines first.

Data Pipeline in a Nutshell

Explained simply, a data pipeline is the right combination of digital processing steps to move data from point A to point B. The data is moved from one point to another for storage and/or further data processing, like using it for analytics and combining it with other data.

In a data pipeline, the output of one step serves as the input for the next step. These steps transform and optimize data to a required state per the input requirements of the next destination.

Benefits of a Data Pipeline

A data pipeline serves multiple use cases for businesses and organizations, offering benefits like:

Data ingestion

Data ingestion is a process of pulling data from data sources and feeding it into a pipeline. This is achieved through API calls, webhooks, and replication engines. Data can be ingested into a data pipeline through two approaches:

• Batch ingestion: In batch processing, data sets are fed into a pipeline as a group according to a set schedule or a response to an external trigger

• Streaming ingestion: In stream processing, data is fed into a pipeline as and when it is generated in real-time

Data ingestion helps businesses collect, import, and process data for storage in a database or for further processing.

Data integrity

Since the entire data pipeline follows a pre-defined process, it ensures that all the collected data is processed and collected consistently. Thus, this ensures that all the information in reports is accurate and consistent.

Data quality

As data moves through the pipeline from one destination to another, it gets refined and cleaned, proving more meaningful to end users. You won't have to deal with inconsistent date formats, Excel formula errors, copy-and-paste discrepancies, and so on. This boosts your organization's productivity and performance.

Automation

A data pipeline automates the repetitive tasks of collecting, transforming, and loading data into different tools and apps. This frees up your time, allowing you to focus on other tasks. 

Visualization

In addition to processing the data, data pipelines also deliver key business insights. The processed data can be fed into the dashboard and reporting tools for effective visualization to make important business decisions.

Use Cases for Data Pipelining

Marketing data

A data pipeline can boost your marketing campaign by connecting all your data sources (IoT, social media, CRMs, and more) and creating powerful marketing reports. This further ensures that all the marketing data sets are fetched automatically through SaaS APIs for better analysis and educated decision-making.

E-commerce applications

A data pipeline can streamline e-commerce stores by integrating store data from point-of-sale systems and e-commerce platforms, thus enabling easy inventory management, personalized marketing, and customer segmentation.

Large data volumes

A data pipeline helps you make the most out of large volumes of data by converting raw data into high-quality information for business intelligence and analysis.

Risks of Not Having a Data Pipeline

Time-consuming data extraction

Not having a data pipeline means manually extracting all the data from different sources. This could work fine if you have to deal with one or two sources. But the whole process will consume a lot of time when you want to collect data from a number of source systems at different times and based on certain external conditions and triggers.

Raw data errors

A data pipeline processes raw data, ensuring it is fit for consumption at the next stage. Without a data pipeline, you can still have tons of data, but it would likely be raw, thus prone to errors and mistakes.

Data quality issues

There is always a probability of human errors, redundancy, the disparity in data formats, and missing data in the absence of a data pipeline.

Disparate data storage

Without a data pipeline, data is stored in multiple sources, making way for data silos, data redundancy, and disparity. You can also waste database storage space due to duplicative and redundant data sets.

Long-tail data hassles

Enterprises have an exponentially high number of data sources they extract data from. Managing a large number of data sources is tough, especially when you only have a handful number of professionals who do that. What happens if that person leaves? Managing all these sources and documenting the large-scale processes that satisfy different regulators and auditors is tedious and arduous.

Extra work for data engineers

Data engineers already have enough on their plates. Without a data pipeline, they will have to invest time in extracting and processing data, thus keeping their hands full.

That said, there are several helpful best practices to consider when establishing a reliable data pipeline.

Data Pipeline Architecture Best Practices

Establish a data catalog

A data pipeline makes it easier to extract data from multiple data sources. But there is always a lingering question about the whereabouts of data -- who owns them, how were they extracted, or which business process owns it.

While raw data is seldom useful, you might still want to look at it to ensure accuracy. If fed to a data pipeline, inaccurate data will only yield inaccurate results, a scenario you would never want to be in. This is why establishing a data catalog helps. Here is all you need to know about the data catalog.

Work in a cloud-based warehouse

A cloud-based pipeline enables the communication between different tools and platforms. This way, every entity involved in the process, whether data sources, data lakes, or data warehouses, can communicate among themselves. This will help you save time, effort, and money. Some popular cloud-based warehouses you can use for the process are SnowFlake, Amazon Redshift, and Google BigQuery.

Test regularly to achieve data quality

Testing a data processing process for quality ensures there are no bugs in the workflow. This verifies that the processed data is fit for end data consumers.

Set up alerts for real-time error detection

To ensure that the final data fits end users correctly, data scientists can set up alerts for real-time data errors like column changes, schema, null records, data SLAs, and more.

Use real-time streaming data

We know that data ingestion can be achieved through two approaches -- batch and real-time. As data is generated 24 hours daily, periodic or batch data ingestion can miss critical events. Stale data can prove useless and could even have catastrophic consequences, especially when the data is related to the security and safety of the organization.

Thus, using real-time streaming data is highly recommended to never miss any important detail. An even better approach is to have a data pipeline strategy that can handle batch and streaming data.

Design a maintainable data pipeline

An effective data integration strategy doesn't stop with a data pipeline. Rather, it starts from here. Once a data pipeline is created, modify and maintain it to ensure it aligns with your requirements.

Automate data transformation and data integration

You can incorporate DataOps or DevOps for data to enable continuous integration, development, and delivery into the pipeline using automation, machine learning, and AI. This will ensure your data is more available, reliable, and consistent.

8 Helpful Data Pipeline Tools

A data pipeline is important; these eight cloud-based and on-premises tools simplify the process. Let's have a look.

Data Pipeline Tool	Summary	Best For	Pricing
Portable	Portable is an ETL platform with 300+ connectors for easy deployment of data pipelines. The tool also provides new data connectors on request for no extra charge with free maintenance.	Portable is ideal for teams that, in addition to managing data pipelines, also want to extract insights from data.	Free plan with no limits on volume, connectors, or destinations for manual data processing. $200/mo for automatic data transfers.
Apache Airflow	Apache Airflow is an open-source data pipeline tool for creating, scheduling and monitoring data workflow. You can extract data from different sources, transform it, and load it to destinations.	Suitable for both startups and enterprises looking to scale up and customize their business processes	Free
Oracle Data Integrator	Oracle Data Integrator is a data integration platform that fulfills all data integration requirements – supports high volume, is compatible with batch loads, is event-driven, and more.	A right fit for organizations and businesses to support Big Data with the Oracle ecosystem	Oracle Data Integrator charges you on the basis of the computer instance. It costs $0.7742 OCPU (Oracle CPU) per hour.
AWS Glue	AWS Glue is a serverless data pipeline tool that helps you discover, prepare, move, and integrate data from multiple sources.	Best suited for applications primarily involving ETL and when you want to run jobs on a serverless Apache Spark-based platform	Hourly rate based on the number of data processing units (DPUs) required to run your job
Apache Kafka	Apache Kafka from LinkedIn is an open-source distributed publish-subscribe messaging event streaming platform capable of delivering data feed to data pipelines in real-time.	Capable of handling tons of data, Apache Kafka is the right fit for organizations looking to scale up.	Free
Kedro	Kedro is an open-source Python framework that allows you to create data pipelines. The tool helps you automate and reproduce data pipelines to facilitate the easy completion of regular tasks.	Best fit for projects meant to be built by large teams that need to be maintained over a long time.	Free
Joblib	Joblib facilitates lightweight pipelining in Python. It provides functions that help you to dump and load data easily.	Suitable for a large amount of data to save time and computational cost.	Free

Data pipeline vs. ETL: Key Functions and Differences

The terms data pipeline and ETL are often used interchangeably, but they have significant differences (and similarities).

ETL

Unlike ELT (Extract, Load, and Transform), ETL, meaning Extract, Transform, Load, is a type of data pipeline where data is extracted from a source, transformed or modified, before being loaded into a final data warehouse, cloud data lake, or other data repository.

Data Pipeline

Data pipeline is the process of moving data from one point to another. Data could be moved for various purposes -- easy storing, data processing, or combining with other data forms. Unlike ETL, a data pipeline may or may not include data transformation. A data pipeline performs various functions, like:

Differences and Similarities Between ETL and Data Pipeline

Differences

• ETL involves processes to extract data from a source, transform it, and load it into the destination. On the other hand, a data pipeline is a broader concept containing ETL as a subset. A data pipeline, similar to ETL, might involve data transformation and processing, but not always.

• ETL, typically runs in batches, meaning that a group or chunk of data is moved at a particular time. Let's say an ETL pipeline runs once every 24 hours. On the other hand, besides supporting batch processing, a data pipeline can also run on real-time data. You can handle incoming and ongoing data flow during data streaming.

• In ETL, data is typically loaded into a data warehouse or databank. However, in a data pipeline, the destination could be dictated by your requirements, allowing you to load data to any destination system, including a big data lake or Amazon Web Services bucket, or even trigger the next business processes by activating webhooks.

Similarities

Both the ETL and data pipelines are similar as these processes involve the movement of data. In a way, ETL is a subset of the data pipeline.

How Data Engineers Can Ship Faster

Data engineers and data scientists deal with a huge amount of data from disparate sources on a regular basis. Setting a data pipeline strategy to implement automation to process data saves time, boosts productivity, lets you derive critical insights, and assists you in decision-making.

Portable is a modern data management SaaS tool that empowers you by executing modern data pipelines with its 300 + data sources and connectors. Try it free today!

Next Up: Setting Up Data Integration

Thus, as evident, this entire process of ingesting, processing, preparing, transforming, and enriching data (whether structured, semi-structured, or unstructured) in a governed manner through a data pipeline leads to data integration.

Data integration, as the name indicates, integrates data with core business processes, breaking down information silos to derive key insights and analytics. So, how can businesses configure an efficient and effective data integration strategy? Find out in this detailed guide.