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 that can be used to generate insights. As the data stack evolves, it is clear that we need to look at modern data pipeline 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 how modern data pipeline solutions can overcome it.
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:
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The increase in data volumes and the rapid rise of machine learning capabilities
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These heightened the demand for actionable data.
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The availability of cloud architectures
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This reduced the barrier to entry for organizations to adopt the technology.
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The introduction of Amazon Redshift in 2012
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This fundamentally changed the data warehousing and analytics landscape because elastic, scalable solutions working with big data became widely available.
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Anyone with a data set to analyze could put it to use.
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The emergence of microservices-based architectures
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This increased the need to move data between applications.
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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 data platform is built upon data pipelines. The purpose of a data pipeline is to transform raw data into actionable business insights. A pipeline automates:
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The process of ingesting data from a source
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Transforming data according to the requirements of the business
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Delivering the extracted data products to crucial stakeholders promptly
These three fundamental components make up a modern data 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. Modern data pipelines enable this by providing timely inputs to analytics processes.
To wrangle these complex topics, seven main features emerge and are required for modern data pipeline solutions to be fully functional.
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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.
Your modern 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, modern 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 modern 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.