We’re told today’s world is data-driven. It’s hard to disagree. But, sometimes, it’s equally hard to comprehend how critical it is becoming for any given organization to ensure it can seamlessly integrate and manage high-quality data.
Fortunately, as the needs of modern organizations have evolved, so too have engineering best practices that encompass a wide array of processes and technologies—from ETL (extract, transform, load) data pipelines, data integration, and continuous integration to the management of big data, data validation, and real-time data processing.
Effective data engineering is now table-stakes—not only supporting the needs of data scientists, analysts, and end users but also ensuring that data sources, whether from data lakes or data warehouses, are efficiently transformed from raw data into high-quality datasets. This transformation is facilitated by rigorous data validation and data quality checks.
While these best practices need to be on point virtually all the time, it’s not always possible to keep up. So, increasingly, data contracts are demonstrating how they holistically make best practices even better.
Don’t worry—we’ll explain how. First, let’s agree on exactly which best practices we’re referring to.
It’s doubtful that any list of best practices—in any industry—is completely comprehensive. But it’s important to ensure said lists cover all common bases.
For our purposes here, we’ve referenced Joe Reis and Matt Housley’s excellent Fundamentals of Data Engineering to do exactly that. Here are eight data engineering best practices to keep in mind:
Invest in the long-term viability of data engineering in your organization by aligning all projects to business objectives. This ensures data teams can deliver measurable value and that leadership sees how projects contribute to the success of overall strategic goals.
Why wouldn’t a data engineer follow the data engineering lifecycle? Well, some sobering realities of working in enterprise organizations include resource constraints, time pressures, complexities in data environments, and even contradictory cultural norms.
Any number of these factors can make it tempting to skip steps or cut corners. However, decreasing data quality while increasing the chance of breaches and running afoul of compliance regulations can never be justified. It’s also why strict adherence to the entire data engineering lifecycle (e.g., data generation, storage, ingestion, transformation, serving, etc.) forms the cornerstone of any data environment.
Data engineering teams should build on this cornerstone—encouraging organizations to manage and cultivate data quality with the same care and strategic focus afforded tangible products sold to customers.
The ensuing shift in the collective perspective of treating data as a product enables data management to shift from a supporting role to that of a central, integral function.
Delivering on the promise of data as a product requires data architecture that can scale up and/or out to handle increased loads as needed. At the same time, performance optimization ensures this operational flexibility occurs efficiently (and smoothly), keeping data consumers and users happy.
By leveraging modular architecture, components can be independently developed, maintained, updated, or replaced. This simplifies maintenance and can mitigate risk, as failures are isolated to individual components.
Conversely, automation minimizes the risks of human-introduced errors and frees up valuable bandwidth among engineering team members. Overall, both increase agility and efficiency in data operations.
As waves of organizations implement digital transformation initiatives, data breaches are surging globally. With data becoming increasingly essential to modern business, data teams must enable and maintain aggressive safety postures to keep bad actors at bay while ensuring regulatory compliance.
Understanding the origin, purpose, and characteristics of data flowing throughout an organization is essential for maintaining data quality over time.
This understanding, in turn, requires effective documentation and metadata management, which additionally contributes to data governance and compliance efforts.
Finally, strong data governance practices should be essential, if not sacrosanct. As such, they should include clear and consistent definitions of roles, responsibilities, and data standards.
Effective data governance is crucial for supporting data quality, compliance, and operational efficiency. It ensures data use is ethical, legal, and aligned with regulatory requirements.
Data engineering teams must constantly fine-tune the specifics of these best practices to ensure they remain as practical as they are beneficial. This is especially true as data contracts are increasingly adopted and implemented by data-hungry enterprise organizations.
However, before understanding how data contracts impact data engineering best practices, it’s essential to know how they function.
Purpose and scope: At their most basic, data contracts define the purpose for which data is collected, processed, and shared. The established outline prevents data misuse by clearly establishing its scope usage limits.
Roles and responsibilities: Data contracts will also clearly delineate roles and responsibilities between data providers and data producers, users, and any third parties involved in data processes.
Data quality and standards: Drafted contracts will specify the quality and format of organizational data, ensuring it will be consistent and usable across all relevant systems.
Data governance: Contracts will address all relevant data governance mechanisms—typically including data ownership, retention, and deletion policies.
Compliance and auditing: In addition to collection, processing, and sharing, contracts will also outline how organizational data will remain compliant with relevant regulations—often including auditing rights to enforce compliance.
Security and privacy: Data contracts will contain provisions for protecting the confidentiality, integrity, and availability of data, complying with laws such as GDPR, HIPAA, or CCPA.
Breach and dispute resolution: Finally, data contracts increasingly outline provisions for handling breaches of contract, including penalties, dispute resolution mechanisms, and remedies.
Fluidity: Not all data professionals understand that data contracts are rarely static. Like best practices, they often need to be updated and fine-tuned to meet changing business needs, technologies, and regulatory requirements.
Global variations: Additionally, some data professionals underestimate the complexities introduced by international data transfers in increasingly global markets. By extension, these increase the complexity of data contract drafting and enforcement. But, ultimately, said contracts then better support organizations who navigate increasingly complex and nuanced data environments.
Technological implications: For some use cases, part of the data contract drafting process will outline specific technological requirements—potentially including encryption standards, interoperability protocols, and data formats that may impact existing or future IT systems and processes.
Cultural impacts: In some organizations, data contracts influence organizational culture by promoting data-centric approaches, such as treating data as a product. In these cases, data contracts may drive shifts in how teams and departments interface with each other.
Cost implications: On paper, complying with the standards and protocols data contracts outline can incur additional costs related to organizational data management. That said, these costs are often offset by enhanced data quality, risk reduction, operational efficiencies, and long-term savings.
Performance metrics: Data contracts can also incorporate performance metrics and service level agreements (SLAs). By doing so, contracts can help hold parties accountable for maintaining promised levels of service—impacting how data operations are managed and organized.
Ethical applications: Finally, contracts increasingly help govern the ethical use of data. For many organizations, this helps guide the governance of sensitive or personal information.
However, as AI, large language models, and machine learning evolve into a component of business-as-usual, this specific guidance that data contracts can provide will prove exceedingly valuable.
Based on even the most basic functionality of data contracts, it’s clear they can dramatically improve both data quality and organizational data management.
But as they do so, data engineers should be mindful of how contracts can impact the practices they adhere to daily.
Clarified expectations and stakeholder alignment are typical byproducts of the data contract drafting process. Structuring and documenting this alignment furthers the ability of data engineering teams to align efforts and projects with overall business goals.
Data contracts can also serve to bridge existing (or, perhaps, inevitable) communication gaps that grow between teams and stakeholders over time. As such, contracts not only promote valuable alignment with business goals but also help maintain them.
When a data contract defines quality standards, acceptance criteria, and data validation protocols, it also helps ensure that every phase of data generation adheres to standard protocols, reducing bottlenecks and ensuring efficient data handling.
This additional definitional fidelity can make them instrumental in helping enforce a holistic, dedicated approach to the entire data engineering lifecycle. A contract in place also serves as a bulwark against resource constraints and cultural pressures that may encourage team members to skip steps in certain situations.
Data contracts enable the “data as a product” concept, formalizing the need to treat data with strategic import and elevating the status of data through an organization.
In addition to enhancing the perceived value of data, contracts help ensure ongoing maintenance and improvement projects get the time, attention, and resources that product upgrades typically enjoy within organizations.
One critical aspect of data contracts involves defining all relevant SLAs. This ensures that designed architectures can meet organizational performance and scalability requirements, efficiently managing data volumes and dependencies.
Once in place, data contracts establish high standards and expectations for data handling and processing capabilities. As such, they can encourage data engineering terms to develop innovative new solutions for performance optimization techniques.
Data contracts that include specific security and privacy measures help ensure that all data handling conforms to the highest data protection standards. This includes using DevOps frameworks and continuous integration practices to maintain robust security postures.
In addition to ensuring organizational data meets both regulatory and internal security requirements, these measures also build trust with customers and partners—essential for organizations working to position themselves well in the face of ever-increasing scrutiny regarding data privacy.
Data contracts require detailed documentation, effective metadata management, and regular data quality checks to maintain a clear understanding of data flows and transformations, which are essential for data quality and governance. This practice ensures that all dependencies are tracked and managed effectively.
The comprehensive approach this supports also helps with compliance, auditability, and operational transparency, which, in turn, makes it far easier for data teams to trace data lineage and manage the data lifecycle.
Contracts solidify data governance by codifying roles, standards, and responsibilities. This strengthens data governance by making data use across the organization more consistent and ethical, ensuring consistent, ethical data usage.
Additionally, strong governance supported by contractual obligations can reduce legal risks while enhancing the organization’s reputation in handling critical and/or sensitive data.
Incorporating best practices in data engineering is essential for transforming raw data into high-quality datasets that drive informed decision-making. That’s what it takes for data to continue to function as the new oil.
But as big data grows into more and more of an understatement, data engineering best practices need to be as exceptional in practice as they are on paper. That will take a data contract at the tip of the spear, helping data engineering teams excel in providing a true end-to-end approach to data management that supports strategic goals and enhances overall performance, all in real time.
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