The top 7 features of modern data pipelines

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As the amount of data generated by industries increases daily, the importance of reevaluating the data stack has never been greater. Studies predict that by 2025, the world will create approximately 463 exabytes of data each day. Given this vast amount of data and its value, a modern data infrastructure is needed to store, transform, and extract meaningful insights. To start the process, data scientists should democratize their data and make it available to all appropriate organizational stakeholders.

What does the modern data stack look like? While the definition is still evolving, there are several viewpoints. The literature varies on what characteristics truly comprise a modern data stack.

Modern data pipelines play a crucial role in providing data used to generate insights. As the data stack evolves, it is clear that we need to look at data pipelines solutions through a new lens. Its features will undergo enhancements to meet the needs of today’s data scientists and analysts. Information technology leaders are still trying to understand how this data generation paradigm best fits into their organization. This blog explores this problem and the solutions available.

The shift in the data platform market

The technology for modern data stacks represents recent changes in the industry. Several trends coalesced into expressing the need for new and improved solutions. These include but are not limited to:

  • The increase in data volumes and the rapid rise of machine learning capabilities

    • These heightened the demand for actionable data.

  • The availability of cloud architectures

    • This reduced the barrier to entry for organizations to adopt the technology.

  • The introduction of Amazon Redshift in 2012

    • This fundamentally changed the data warehousing and analytics landscape because elastic, scalable solutions working with big data became widely available.

    • Anyone with a data set to analyze could put it to use.

  • The emergence of microservices-based architectures

    • This increased the need to move data between applications.

With these burgeoning trends, efficient data pipelines have become an essential part of the IT infrastructure. Although these technological developments are all relatively recent, they are proving an unprecedented demand in this industry. At this point, stakeholders need a robust, flexible solution to manage and monetize an organization’s data, one of its most valuable assets.

This data management solution starts with data pipelines.

What is a data pipeline?

The foundation of the modern platform is built upon data pipelines. The purpose of a data pipeline is to transform raw data into actionable business insights. A pipeline automates:

  • The process of ingesting data from a source

  • Transforming data according to the requirements of the business

  • Delivering the extracted data products to crucial stakeholders promptly

These three fundamental components make up a pipeline. As the name implies, they need to be highly scalable, given the volumes of data. They must also be timely so analysts can utilize these up-to-the-minute data products.

Strategic and tactical decision-making based on data is no longer a quarterly, scheduled function of the business. Instead, it is a continual process. Data pipelines enable this by providing timely inputs to analytics processes.

To wrangle these complex topics, seven main features emerge and are required for the modern data pipeline solutions.

  1. 1. Use of Cloud Data Warehouses

Modern data stacks should use cloud data warehouses as a foundational technology. This data management solution enables them to reap the benefits of performance and scalability that would otherwise be cost-prohibitive. Elastic workloads provide the flexibility to scale up when needed and scale down when not in use. Many companies run periodic workflows that peak at extremely high volumes but then scale down to almost zero in between runs.

Managed services provide tremendous benefits to organizations where data accelerates the business. Unless your company’s core value proposition is data, as with DropBox, it is not worth investing in your data platform’s on-premise hardware or software. The better choice to use cloud data warehouses allows users to focus on their core business functions rather than data infrastructure.

  1. 2. Multi-cloud and multi-engine support

Your data pipelines should be able to run on multiple cloud environments as well as numerous data analytics engines. This is important for two reasons.

The first is that cloud providers are continuously making upgrades to their capabilities. It is a highly competitive market with fast iterations. You don’t want to be locked into one cloud provider because moving workloads over to another cloud vendor is challenging. Ideally, data pipelines should be robust enough to run on the three major cloud providers: Azure, AWS, and GCP.

The second reason involves efficient and cost-effective data stacks processing for your business. To ensure this, your data pipeline creation platform should support execution on different processing engines. For example, conventional data stores such as relational databases or file systems often work better using Spark for applying transformations. However, if your data is located in a modern store such as Snowflake, it is more efficient to use Snowflake for transformations.

  1. 3. Robust pipeline orchestration capabilities

Pipeline orchestration capabilities are used to manage the entire pipeline lifecycle. This begins with the creation of pipelines and then triggering the execution of workflows. You may need:

  • Pipelines to run on demand or on a scheduled basis (hourly, daily, weekly, etc.)

  • The platform to monitor execution and generate alerts in the case of failures

  • Logs to be accessible

  • The platform to make it easy to iterate quickly once corrections are made

  • The monitoring to include visibility of infrastructure resource utilization

  • Orchestration capabilities to include the ability to connect related pipelines and data sets and migrate pipelines to different environments if needed

In summary, your pipeline tool should be designed to scale up to match your increasing demands and down when fewer pipelines need to be executed. Data processing should be separated from data storage so it can be efficiently scaled and costs can be managed.

  1. 4. Ability to unit test your pipelines

As data volume increases daily, pipelines consume more resources that take longer to complete. Thus, it becomes essential to have regular checks as the pipeline progresses through transformations. If a pipeline fails certain validity checks, you should be able to terminate the pipeline instead of waiting for it to complete. This also helps avoid pushing inaccurate data into destination tables.

However, before consuming significant amounts of resources, the ability to effectively unit test is critical. That shortens the development cycle by quickly identifying and fixing any errors before the workflows run through entire data sets.

  1. 5. Data Versioning

Platforms should enable the versioning of data sets to make it easier to roll back in production if necessary. In case of accidental deletions, having backups and previous versions available for recovery is vital to maintaining pipeline operations. There are several ways to maintain data versions, so be sure to find the right data management solution that meets your needs.

  1. 6. Data Lineage

Data lineage helps you track your data’s transformation from source to destination.

Data lineage serves to answer three primary questions:

  • Which pipelines are using a particular dataset as a source?

  • What transformations are applied to source datasets before writing into a destination?

  • Which pipelines are contributing to the generation of a destination dataset?

It can also tell you how a particular field was derived and how it is being utilized in different pipelines. This is a tremendous help when estimating the impact of altering existing pipelines, especially those with many dependent consumers already well tested.

7. Pre and post-workflow hooks

You may need to perform certain actions in many scenarios after the transformed data is made available. Additionally, there may be situations when you need to run custom code before the actual transformation pipeline begins. In these cases, your data pipeline platform should have hooks to plug-in custom logic before or after the primary workflows are run.

Snowflake + Spectra: The data pipeline solution

Thankfully, Snowflake is a leader in all these aspects. The Spark engine has made considerable efforts to optimize the use of processing resources. Recently, they introduced dynamic resource allocation, which helps release unutilized data resources more effectively.

The Spectra product has all of these features already built-in and ready to help your organization monetize its data. Spectra can help you build out your modern data platform so that your business can focus on data insights without spending enormous time maintaining infrastructure. Want to learn more about Fosfor? Contact us to schedule a demo.


Mahesh Jadhav

Technical architect for Spectra

Mahesh Jadhav is the technical architect for Spectra. He is an Oracle-certified Java professional with 10+ years of experience and a hands-on expert in Apache Spark, Kubernetes, Big data, Spring boot, and application development. Mahesh is actively involved in technical design of the platform as well as in putting strategies around performance, security and packaging aspects of the product. Tunning Apache Spark jobs is something he loves and has contributed immensely in this area. Building a data platform is something that challenges him.

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