Data Engineering Best Practices For Scalable Systems

In a world driven by data, the difference between market leaders and followers often lies in how they manage their most valuable asset. Effective data engineering isn't just about moving data from A to B; it's the bedrock of reliable analytics, powerful machine learning models, and insightful business intelligence. But building robust, scalable, and secure data systems requires more than just tools, it demands a commitment to proven principles and a disciplined approach. Without this foundation, data platforms become brittle, expensive, and untrustworthy, undermining the very initiatives they were built to support.

This guide cuts through the noise to deliver a comprehensive roundup of essential data engineering best practices that address the entire data lifecycle. We move beyond theory to provide concrete, actionable strategies you can implement immediately. You will learn how to build resilient systems by applying principles across critical domains including:

• Automation and Orchestration: Designing and managing efficient data pipelines.
• Infrastructure as Code (IaC): Using tools like Terraform to create reproducible environments.
• Testing and Data Quality: Implementing checks to ensure data integrity and reliability.
• Security and Governance: Protecting data and managing access effectively.

From automating pipelines with Airflow to implementing IaC with Terraform and ensuring data quality with libraries like Great Expectations, we'll provide the practical knowledge needed to excel. These are not just recommendations; they are the core tenets that transform complex data challenges into strategic advantages. This article provides a clear roadmap for building and maintaining high-performance data infrastructure that delivers real, measurable impact.

1. Data Pipeline Automation and Orchestration

Manual data pipeline execution is a recipe for inconsistency, errors, and scalability bottlenecks. One of the most critical data engineering best practices is to implement robust automation and orchestration, which involves using specialized tools to schedule, monitor, and manage the dependencies of data jobs. This approach transforms a series of manual, fragile scripts into a resilient, self-managing system.

Orchestration frameworks like Apache Airflow, Prefect, or Dagster allow you to define your pipelines as code, creating Directed Acyclic Graphs (DAGs) that map out every task and its relationship to others. This code-based approach enables version control, collaboration, and repeatability. For instance, a common DAG might extract data from a PostgreSQL database, load it into a Snowflake staging area, run a transformation using dbt, and then notify a Slack channel upon completion or failure, all without human intervention.

Actionable Implementation Tips

To effectively implement orchestration, consider these strategies:

• Idempotent Operations: Design tasks to be idempotent, meaning they can be run multiple times with the same input and produce the same result. This is crucial for safely retrying failed tasks without causing data duplication or corruption.
• Comprehensive Monitoring: Implement detailed logging, metrics, and alerting from day one. Set up alerts for job failures, long-running tasks, and potential SLA violations to proactively manage pipeline health.
• Clear Dependency Management: Document all data sources, transformations, and dependencies. Tools like Airflow provide a UI to visualize these relationships, which helps in debugging and understanding data lineage. For those seeking agile and efficient data pipeline automation, exploring specialized platforms can also be highly beneficial; a deeper dive into CloverDX as an Agile ETL Tool shows how different solutions approach this challenge.

Automating your workflows reduces the operational burden on your team, improves data reliability, and ensures your data products are consistently up-to-date. If you want a more in-depth guide, you can learn more about building a robust data pipeline.

2. Data Quality and Validation

Untrustworthy data is worse than no data at all, as it leads to flawed analysis, poor business decisions, and a loss of confidence in data systems. A cornerstone of modern data engineering best practices is establishing systematic processes for data quality and validation. This involves implementing automated checks at every critical stage of the data pipeline to ensure data is accurate, complete, consistent, and timely, preventing bad data from ever reaching downstream consumers.

Frameworks like Great Expectations or Soda allow you to define data quality rules as code, which can be versioned and integrated directly into your CI/CD and orchestration workflows. For example, a pipeline moving financial transaction data could automatically validate that a transaction_amount column is never null, always positive, and falls within a statistically normal range. If a batch of data fails these checks, the pipeline can be configured to halt, quarantine the problematic data, and send an alert, ensuring data integrity is maintained proactively.

Actionable Implementation Tips

To build a robust data quality shield around your data assets, focus on these strategies:

• Layered Validation Checks: Implement checks at multiple stages: at ingestion to catch source system errors, post-transformation to verify business logic, and before loading into a data warehouse to protect analytical systems.
• Define Quality SLAs: Establish clear data quality Service Level Agreements (SLAs) with business stakeholders. These should define acceptable thresholds for metrics like completeness, freshness, and accuracy, and assign clear ownership for remediation.
• Automate Anomaly Detection: Use statistical baselines and machine learning to automatically detect anomalies. For instance, a sudden 50% drop in daily new user sign-ups could trigger an alert for investigation, a task that manual checks would likely miss.
• Centralize and Document Rules: Maintain a centralized, version-controlled repository for all data quality rules and their business rationale. Tools like Great Expectations' Data Docs can automatically generate documentation from your validation suites, making them accessible to the entire organization.

Implementing a proactive data quality strategy builds trust in your data platform and ensures that business intelligence and machine learning models are built on a foundation of reliable, accurate data. For those looking to delve deeper into this practice, Great Expectations offers a powerful open-source tool to get started.

3. Scalable Data Storage Architecture

A data storage architecture that cannot handle increasing data volumes and query complexity will inevitably become a performance bottleneck and a cost sink. Designing a scalable storage system is a fundamental data engineering best practice that involves selecting the right technologies and structuring data for long-term efficiency. This means choosing between data lakes for raw, unstructured data (like Amazon S3 or Google Cloud Storage), data warehouses for structured, query-optimized data (like Snowflake or BigQuery), and hybrid models like the data lakehouse.

This strategic approach allows organizations to manage petabyte-scale datasets effectively. For instance, Netflix leverages Amazon S3 as its central data lake, storing vast amounts of raw data in columnar formats like Parquet, which drastically improves analytical query performance for its recommendation engines. Similarly, platforms like Databricks are built on the concept of the data lakehouse, combining the low-cost, flexible storage of a data lake with the data management and transactional features of a data warehouse.

Actionable Implementation Tips

To build a truly scalable data storage architecture, focus on these key strategies:

• Partition and Cluster Your Data: Strategically partition your data based on frequently queried dimensions like date, region, or user_id. In warehouses like Snowflake or BigQuery, clustering on these keys physically co-locates related data, minimizing the amount of data scanned and accelerating query speeds.
• Embrace Columnar Formats: For analytical workloads, always use columnar storage formats like Apache Parquet or ORC. These formats compress data more efficiently and allow query engines to read only the specific columns needed, avoiding costly full-table scans.
• Implement Data Lifecycle Policies: Not all data needs to be instantly accessible. Use automated lifecycle policies in cloud storage (e.g., S3 Intelligent-Tiering) to move older, less frequently accessed data to cheaper storage tiers like Glacier, optimizing costs without manual intervention.

A well-designed storage layer is the bedrock of your entire data platform. It ensures that as your data grows, your systems remain performant, cost-effective, and capable of delivering insights. For a deeper look into modern storage paradigms, you can explore the architecture of the data lakehouse.

4. Data Governance and Metadata Management

Without a clear framework for managing data assets, even the most sophisticated pipelines can produce untrustworthy, inaccessible, or non-compliant data. Strong data governance and metadata management are essential data engineering best practices that establish the rules, processes, and controls for maintaining high-quality, secure, and discoverable data. This involves documenting data lineage, defining access controls, and ensuring data is handled responsibly throughout its lifecycle.

Platforms like Collibra, Alation, and open-source solutions like DataHub (originally from LinkedIn) provide centralized repositories to manage this information. For example, Airbnb's Dataportal system serves as a single source of truth for data discovery, lineage, and governance, empowering teams to find and trust the data they need. This proactive approach turns data from a chaotic liability into a well-managed, reliable corporate asset, ensuring that data products are built on a foundation of trust and compliance.

Actionable Implementation Tips

To build an effective governance strategy, focus on these practical steps:

• Centralize and Automate Metadata: Implement a centralized metadata repository using tools like Apache Atlas or DataHub. Automate metadata collection from sources like Snowflake, dbt, and orchestration tools to capture schema changes, ownership, and lineage with minimal manual effort.
• Establish Clear Ownership: Assign clear data owners and stewards to key datasets. These roles are responsible for defining data quality rules, managing access requests, and ensuring the data is well-documented and fit for purpose.
• Implement Data Classification: Create standards for classifying data based on sensitivity (e.g., PII, confidential, public). This classification drives automated access control policies and helps in managing compliance requirements like GDPR. Establishing clear guidelines for how long data should be kept is also crucial; for practical examples, explore guides on effective data retention policies.

By embedding governance into your data engineering workflows, you enhance data trust, accelerate data discovery, and reduce regulatory risk, making your entire data ecosystem more robust and scalable.

5. Infrastructure as Code (IaC) for Data Systems

Manually configuring data infrastructure is error-prone, difficult to scale, and nearly impossible to replicate consistently. A cornerstone of modern data engineering best practices is adopting Infrastructure as Code (IaC), a method for provisioning and managing infrastructure like databases, storage buckets, and compute clusters through version-controlled definition files. This practice treats your infrastructure configuration with the same rigor as your application code, ensuring predictability and auditability.

Frameworks like Terraform, AWS CloudFormation, or Pulumi allow you to declaratively define your entire data stack. Instead of clicking through a cloud console to create a Snowflake warehouse or a PostgreSQL instance, you write code that specifies the desired state. For example, Shopify uses Terraform to manage complex infrastructure across multiple regions, ensuring every environment is a perfect, automated replica. This IaC approach makes disaster recovery faster and less stressful, as rebuilding an entire environment becomes a matter of running a script.

Actionable Implementation Tips

To effectively implement IaC for your data systems, consider these strategies:

• Embrace Templating and Modules: Avoid duplicating code by using modules or templates for reusable components like a standard PostgreSQL setup or a Kubernetes cluster configuration. This keeps your codebase DRY (Don't Repeat Yourself) and easier to maintain.
• Version Control and Peer Review: Store all infrastructure code in a Git repository. Mandate pull requests and code reviews for any changes to enforce accountability, catch potential errors early, and share knowledge across the team.
• Isolate Environments: Use separate state files or workspaces for different environments (development, staging, production). This prevents accidental changes in production when testing modifications in a lower environment.
• Automate Security Scanning: Integrate tools like tfsec or Checkov into your CI/CD pipeline to automatically scan your IaC files for security misconfigurations before they are ever deployed.

Adopting IaC drastically reduces configuration drift, automates provisioning, and creates a transparent, versioned history of every change made to your infrastructure. If you're new to this concept, a good starting point is to learn more with a Terraform tutorial for beginners.

6. Modular and Reusable Data Components

Building data solutions from scratch for every new project leads to duplicated effort, inconsistent logic, and a maintenance nightmare. A core data engineering best practice is to adopt a modular design philosophy, creating composable, well-documented components that can be reused across different pipelines and projects. This approach treats data transformation logic, connection configurations, and processing tasks as building blocks, accelerating development and ensuring consistency.

Frameworks like dbt have popularized this concept with macros and packages, allowing teams to abstract complex SQL logic into reusable functions. For example, instead of every team writing their own sessionization logic, a central, version-controlled dbt macro can be created and shared. Similarly, companies like Spotify leverage a component-based architecture for their data processing, where standardized Spark jobs for tasks like data ingestion or feature engineering are templatized and reused, dramatically reducing boilerplate code.

Actionable Implementation Tips

To effectively build a library of reusable components, consider these strategies:

• Create a Central Repository: Establish a dedicated Git repository for shared components like dbt macros, Terraform modules, or Python libraries. This creates a single source of truth and simplifies discovery and consumption.
• Build with Parameterization: Design components to be configurable through parameters. For instance, an ingestion component should accept source and destination details as arguments rather than having them hardcoded, making it adaptable for various use cases.
• Version and Test Rigorously: Treat reusable components like software artifacts. Use semantic versioning to manage changes and dependencies, and ensure each component has comprehensive unit and integration tests to prevent downstream breakages.
• Establish Clear Governance: Define a process for contributing new components to the shared library. This should include requirements for documentation, testing, and code reviews to maintain high quality and prevent the repository from becoming a dumping ground for single-use code.

By embracing modularity, you empower your team to build more reliable and scalable data systems faster. You stop reinventing the wheel and start assembling proven, high-quality solutions.

7. Testing and Validation in Data Pipelines

Treating data pipelines as first-class software products means embracing rigorous testing, a practice that is non-negotiable for building reliable systems. Implementing a comprehensive testing strategy ensures that data transformations are correct, business logic is sound, and unexpected data changes are caught before they impact downstream consumers. This approach moves data engineering from a reactive, break-fix model to a proactive, quality-first discipline.

Frameworks like pytest for PySpark transformations and dbt's built-in testing capabilities have made data validation more accessible and powerful. For example, a common dbt test ensures a primary key column is both unique and not_null. More complex data quality checks can be written as custom tests, such as verifying that a discount percentage column never exceeds a certain value. This systematic testing is a cornerstone of modern data engineering best practices, preventing silent data corruption and building trust in the data products you deliver.

Actionable Implementation Tips

To effectively integrate testing into your data workflows, consider these strategies:

• Multi-Layered Testing: Implement tests at multiple levels. Use unit tests for individual transformation functions, integration tests to verify interactions between pipeline components, and end-to-end tests to validate the entire flow from source to destination.
• Realistic Test Data: Create and maintain a suite of test datasets that mirror the structure and variety of your production data. Crucially, include datasets that cover edge cases, null values, and malformed records to test your pipeline's resilience.
• Automate with CI/CD: Integrate your data tests directly into your CI/CD pipeline. Configure your system to automatically run all relevant tests on every code change (e.g., a pull request in GitHub), blocking merges if any tests fail. For a deeper look into the modern data stack and its components, you can read more about dbt Core.

By embedding testing throughout the development lifecycle, you significantly reduce the risk of data quality issues, speed up development by catching bugs early, and ensure your data pipelines are consistently accurate and reliable.

8. Monitoring, Logging, and Observability

A data pipeline that runs without any oversight is a black box waiting to fail. Implementing comprehensive monitoring, logging, and observability is one of the most essential data engineering best practices for maintaining data reliability and trust. This involves instrumenting every component of your data infrastructure to emit logs, metrics, and traces that provide real-time insight into pipeline health, performance, and data quality. It's the only way to proactively detect and resolve issues before they impact downstream analytics or business operations.

Platforms like Datadog, Prometheus, and the ELK Stack (Elasticsearch, Logstash, Kibana) are foundational to building a robust observability strategy. For example, a system might use Prometheus to scrape performance metrics from a Kubernetes cluster running Spark jobs, send structured logs from those jobs to Elasticsearch, and use Grafana to build dashboards that visualize everything from CPU usage to data freshness. This unified view allows teams at companies like Shopify and Netflix to quickly diagnose bottlenecks, debug failures, and ensure their complex data ecosystems are running smoothly.

Actionable Implementation Tips

To effectively implement monitoring, logging, and observability, consider these strategies:

• Implement Structured Logging: Log events in a machine-readable format like JSON. This makes logs much easier to parse, query, and analyze, especially at scale. Include contextual information like a correlation_id to trace a single data record's journey across multiple services.
• Establish Key Performance Indicators (KPIs): Go beyond simple pass/fail alerts. Track critical business and operational metrics such as data freshness (how up-to-date is the data?), data completeness (are there missing records?), and pipeline duration to monitor performance against established Service Level Agreements (SLAs).
• Create Role-Based Dashboards: Build different dashboards for different audiences. Engineers may need detailed infrastructure metrics and error logs, while business stakeholders might only need to see high-level data quality and freshness KPIs.

By building observability into your pipelines from the start, you move from a reactive "break-fix" model to a proactive one, ensuring your data platforms are resilient, reliable, and trustworthy. For a closer look at how modern tools facilitate this, you can explore the observability solutions offered by Datadog.

9. Data Security and Privacy by Design

Treating security as an afterthought is a direct path to data breaches, regulatory fines, and a loss of customer trust. The most effective data engineering best practices embed security and privacy directly into the architecture and development lifecycle from day one. This "security by design" approach means building systems where data protection measures like encryption, access control, and anonymization are fundamental components, not later additions.

This proactive stance ensures that as data moves through pipelines, from ingestion in a Kafka topic to storage in a Snowflake data warehouse, it is consistently protected. For example, financial institutions often implement tokenization to replace sensitive Personally Identifiable Information (PII) like credit card numbers with non-sensitive equivalents, a process that happens at the earliest point of data ingestion. This minimizes the "blast radius" of a potential breach, as the most sensitive data is never exposed in downstream systems like analytics sandboxes or development environments.

Actionable Implementation Tips

To effectively integrate security and privacy by design, consider these strategies:

• Encrypt Everything by Default: Implement end-to-end encryption for all data, both in transit (using TLS) and at rest (using AES-256). Cloud data warehouses like Snowflake and BigQuery offer this by default, but you must ensure it's enforced across all components.
• Implement Fine-Grained Access Controls: Use Role-Based Access Control (RBAC) to enforce the principle of least privilege. A data analyst should only have read access to the specific datasets they need, not write access to the entire production database.
• Leverage Data Masking and Anonymization: For non-production environments, use dynamic or static data masking to obfuscate sensitive data. This allows developers and QA teams to work with realistic data structures without exposing real customer information.
• Centralize Secret Management: Never hardcode credentials, API keys, or database passwords in your code or configuration files. Use a dedicated secrets management tool like HashiCorp Vault or AWS Secrets Manager to securely store and inject credentials at runtime.

Building security into your data platforms from the beginning is not just about compliance with regulations like GDPR or CCPA; it's about building resilient, trustworthy systems that protect your most valuable asset: your data.

10. Documentation and Knowledge Management

A data platform with poorly documented systems, pipelines, and schemas is a ticking time bomb. One of the most impactful data engineering best practices is to treat documentation not as an afterthought but as a core component of the development lifecycle. Comprehensive documentation captures critical institutional knowledge, accelerates onboarding for new engineers, and crucially, reduces the "bus factor" where essential information is siloed with a single individual. This practice transforms opaque, complex systems into understandable and maintainable assets.

Leading technology companies like Stripe and GitLab have set a high bar for documentation, integrating it directly into their engineering culture. The goal is to create a living, breathing knowledge base that evolves alongside the data platform. For instance, a well-documented data model in a tool like dbt would not only define columns and data types but also explain the business logic, ownership, and expected usage of the data. This level of detail empowers data consumers to self-serve confidently and helps engineers debug issues faster.

Actionable Implementation Tips

To effectively implement documentation and knowledge management, consider these strategies:

• Keep Documentation Close to Code: Embed documentation directly within your tools and codebases. Use README files in Git repositories, dbt docs for data models, and docstrings in Python scripts. This co-location ensures that documentation is updated as the code changes.
• Embrace "Docs as Code": Treat documentation files (e.g., Markdown files) like source code. Store them in version control, require peer reviews for changes, and integrate them into your CI/CD pipeline. This approach enforces quality and accountability.
• Document the "Why," Not Just the "How": Go beyond describing what a pipeline does and explain why it was designed a certain way. Documenting architectural decisions, trade-offs, and business context provides invaluable insight for future maintenance and iteration. For structured approaches, leveraging well-designed templates can ensure consistency and completeness; you can find useful technical documentation templates to get started.

By building a culture that values and rewards good documentation, you create a more resilient, transparent, and scalable data organization.

10-Point Comparison of Data Engineering Best Practices

Capability	Implementation complexity	Resource requirements	Expected outcomes	Ideal use cases	Key advantages
Data Pipeline Automation and Orchestration	High - workflow design and dependency handling	Orchestration platform, monitoring stack, skilled engineers	Reliable, repeatable job execution and scheduling	Complex ETL/ELT, dependent batch/stream jobs, large-scale pipelines	Reduced manual work, improved reliability, scalability
Data Quality and Validation	Medium - rule definition and integration	Validation tools, domain experts, monitoring	Higher data accuracy, fewer downstream errors	Reporting/BI, ML pipelines, regulatory data feeds	Prevents bad data, builds trust, early issue detection
Scalable Data Storage Architecture	High - requires careful design and tuning	Storage platforms (lake/warehouse), ops, cost management	Efficient storage, improved query performance, cost control	Petabyte-scale analytics, mixed data types, long-term retention	Handles growth, cost optimization, performance gains
Data Governance and Metadata Management	High - policy, ownership, tooling	Catalog tools, stewards, compliance processes	Discoverable, compliant, well-documented data assets	Regulated industries, large organizations, self-service analytics	Improves discoverability, compliance, lineage visibility
Infrastructure as Code (IaC) for Data Systems	Medium - coding and environment modeling	IaC tools, CI/CD, DevOps skills	Reproducible infrastructure, faster provisioning	Multi-environment deployments, disaster recovery, cloud infra	Consistency, repeatability, faster provisioning
Modular and Reusable Data Components	Medium - design and abstraction effort	Component libraries, governance, CI	Faster development, consistent transformations	Multi-team orgs, repeated ETL patterns, shared metrics	Reduces duplication, improves quality, speeds delivery
Testing and Validation in Data Pipelines	Medium - test strategy and data management	Testing frameworks, test data, CI pipelines	Fewer production bugs, safer refactors	Critical transformations, ML feature pipelines, release cycles	Catches bugs early, enables confident changes
Monitoring, Logging, and Observability	Medium - instrumentation and dashboards	Monitoring stack, storage, alerting, SRE time	Faster detection and resolution, performance insights	SLA-driven pipelines, high-availability systems	Rapid incident response, performance visibility
Data Security and Privacy by Design	High - security controls and compliance work	Encryption, IAM, audit tools, security expertise	Reduced risk of breaches, regulatory compliance	Sensitive data, payments, healthcare, finance	Protects PII, ensures compliance, builds trust
Documentation and Knowledge Management	Low to medium - process and discipline	Documentation tools, time from engineers, review process	Better onboarding, reduced tribal knowledge	Growing teams, complex systems, frequent handoffs	Speeds onboarding, reduces silos, preserves decisions

Building Your Future-Proof Data Strategy

The journey through the core tenets of modern data engineering reveals a clear, compelling narrative: success is not accidental. It is the result of deliberate design, disciplined execution, and a relentless commitment to quality. The ten practices we've explored, from pipeline automation to robust documentation, are not isolated recommendations but interconnected pillars supporting a resilient and high-performing data ecosystem. Implementing these data engineering best practices is the definitive step from merely managing data to strategically leveraging it as a core business asset.

We began by establishing the non-negotiable role of automation and orchestration, emphasizing how tools like Airflow or Dagster transform fragile, manual processes into reliable, self-healing workflows. This foundation allows engineers to focus on higher-value problems, a theme that echoes through every subsequent practice. Prioritizing data quality and validation isn't a downstream cleanup task; it's an upfront investment that prevents the costly propagation of errors, ensuring that every insight and every model is built on a foundation of trust.

From Individual Tactics to a Unified Philosophy

Adopting these principles requires a shift in mindset. Instead of viewing data infrastructure as a static set of servers and databases, we must embrace the dynamism of Infrastructure as Code (IaC) with tools like Terraform. This approach brings the same rigor, versioning, and reproducibility to our infrastructure that software engineers have long applied to application code. Similarly, building modular data components and implementing comprehensive testing are not just about improving efficiency; they are about fostering a culture of reusability and reliability that accelerates development and reduces long-term maintenance overhead.

The true value emerges when these practices converge:

• Scalability and Governance: A scalable storage architecture, whether in Snowflake or a data lakehouse, is only truly effective when paired with strong data governance and metadata management. Knowing what data you have, where it is, and who can access it is fundamental to unlocking its potential securely.
• Security and Observability: Embedding security by design is not a final checkpoint but an ongoing process. This proactive stance is powerfully augmented by a mature observability practice. Comprehensive monitoring, logging, and alerting don't just help you fix things when they break; they provide the deep system insights needed to anticipate failures and optimize performance proactively.

Your Actionable Path Forward

Mastering these concepts is a continuous journey, not a final destination. The landscape of data technology will inevitably evolve, but the principles of building robust, scalable, and secure systems will remain constant. By focusing on these foundational data engineering best practices, you are not just solving today's problems; you are building a future-proof data strategy that can adapt, scale, and deliver sustained value.

Your immediate next steps should be to audit your current environment against these principles. Identify the most significant gaps and prioritize them based on business impact. Perhaps it's introducing dbt for data quality testing, containerizing a legacy pipeline with Docker and Kubernetes, or implementing a formal documentation standard. Each incremental improvement builds momentum, reinforcing a culture of engineering excellence that will pay dividends for years to come. Ultimately, this disciplined approach transforms your data platforms from fragile liabilities into strategic enablers of innovation and growth.

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The journey to data maturity requires the right tools, processes, and, most importantly, deep expertise. If you're looking to accelerate this transformation and implement custom cloud and data solutions that deliver tangible results, Pratt Solutions offers the specialized knowledge in cloud infrastructure, automation, and data engineering to turn your strategy into a reality. Let's build something scalable and resilient together at Pratt Solutions.