Transform Your Business with Expert Cloud Data Architecture Consulting Services

Yes, we help you select the best cloud data platforms and technologies tailored to your specific needs.

With the breadth of infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS) and software-as-a-service (SaaS) offerings available across AWS, Google Cloud and Microsoft Azure, we guide you to the right mix of services, depending on your preference for control versus leveraging fully-managed services.

We advise you on the best cloud data services for your workloads - whether you are managing structured, semi-structured or unstructured data - and your required processing latency, from batch to near real-time to real-time. 

For your data analytics and business intelligence use cases, we assess your requirements and provide recommendations on the best cloud data warehouse solution for your specific needs: Snowflake, BigQuery or Redshift.

For building a cloud data lake, we help you select a cloud storage service, like S3, Google Cloud Storage or Azure Data Lake Storage; as well as the processing engine to use, such as EMR, Glue, Dataproc or HDInsight.

Where a cloud data lakehouse is the optimal choice for your needs, we help you get set up on Databricks and enable you to start unlocking business value from your data quickly.

If your needs require a hybrid data architecture, we help you leverage solutions like Dremio or Presto, to enable you to perform unified analytics across your cloud and on-premises data.

With our deep hands-on experience across multiple cloud providers and cloud data services, our experts provide you with objective advice to enable you adopt the best cloud data platforms and technologies to address your specific needs.

Yes, we provide full-lifecycle cloud data engineering services to execute on the strategy outlined and architecture designed by our expert cloud data architects.

Our team includes some of the best cloud data engineers, data scientists and AI/ML engineers in the industry, who complement the deep expertise of our cloud data architects.

We have hands-on, real-world experience building advanced cloud data platforms and products across industries, equipping us to handle your most complex use cases.

Whether your architecture calls for a cloud data warehouse on Snowflake, BigQuery or Redshift; a cloud data lakehouse on Databricks or a cloud data lake on Amazon S3 or Google Cloud Storage; our cloud data engineers have the cutting-edge, cloud-certified skills and experience to build the best cloud data solution for your exact needs.

We seamlessly scale from prototyping your tailored cloud data architecture to a full production deployment of your custom cloud data solution.

We accelerate advanced analytics and actionable insights in the cloud by solving key challenges for you:

• Break down data silos: Perform analytics on and gain insights from your data across your cloud data lake, cloud data warehouse and cloud data lakehouse, as well as your relational and NoSQL databases; all without having to move or copy your data.
• Rapidly gain data-driven insights: Go from data to insights quickly with seamless access to interactive SQL, real-time streaming analytics and ML-enhanced dashboards.
• Transform unstructured data: Leverage AI to unlock value from your documents, images, audio, video and more.
• Experiment freely: Quickly test your hypotheses and iterate without long-term commitments.
• Adapt on-demand: Elastically scale your data analytics resources to meet your changing needs.
• Pay-as-you-go: Pay only for the resources you use with no upfront costs.

Partner with us to transform your siloed data into strategic assets, rapidly gain actionable insights from your data and deliver business value through experimentation and iteration.

Here are some key ways WSaaS can help you successfully leverage generative AI and large language models (LLMs) to streamline your workflows and boost your productivity:

• We start by thoroughly understanding your specific business goals, use cases and data landscape. Based on your needs, we advise you on the optimal generative AI strategy, balancing performance, cost, time-to-value and risk management.
• We conduct a rigorous evaluation and benchmarking of leading generative AI vendor solutions and open-source models to best address your specific requirements.
• We define an end-to-end generative AI delivery roadmap, spanning model architecture, hyperparameter tuning, training methodology, data curation, data labeling and cloud infrastructure.
• Working closely with your teams, we architect a secure, scalable cloud AI infrastructure on AWS, Google Cloud, Azure, Snowflake or Databricks, depending on your needs.
• We build the cloud AI infrastructure you need to cost-efficiently scale your compute capacity with GPUs/TPUs for high-performance model training and inference.
• We help you select then fine-tune foundation models like Llama, GPT and Falcon on your proprietary datasets to achieve state-of-the-art performance and accuracy for your target applications.
• We stand up an AI/ML platform for you, like Amazon SageMaker, Google Cloud Vertex AI or Azure Machine Learning, and we implement industrial-grade AIOps/MLOps to enable you take your models from experimentation to production-ready deployment with monitoring, observability and stability.
• We advise you on strategies to continuously refine and enhance your model's capabilities through several techniques, such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF); as well as through vector database-powered retrieval augmented generation (RAG).

Our proven cloud data architecture methodology is based on years of real-world experience designing and building advanced cloud data solutions across industries.

Our methodology consists of three key phases:

1. Discovery: We start by deeply understanding your business context, use cases, workflows, capabilities and entities. The Discovery phase is key to designing an architecture that is tailored to your exact needs. Our cloud data architecture consultants leverage their extensive industry experience to comprehensively and accurately identify and capture your specific requirements.
2. Assessment: Next, we perform a detailed assessment of your current data landscape and data infrastructure. By analyzing your current data flows, pain points, challenges and constraints, as well as your technology environment, we can design the optimal data architecture to solve your specific challenges.
3. Delivery: Finally, we partner with you to deliver the cloud data solution. We provide a clear roadmap, MVP scoping, agile plans and cost analysis, as well as your return on investment (ROI). Our proven cloud data engineering operating model enables us to execute with speed, while delivering the highest quality cloud data solutions for your needs.

Our cloud data architecture operating model enables us to design sophisticated cloud data solutions aligned to your strategic objectives, while our hands-on cloud data engineering expertise equips us to deliver high-quality results for you quickly.

We combine strategic advisory with practical execution to ensure your success.

We align your cloud data strategy tightly to your digital transformation objectives by architecting agile and flexible systems tailored to empower your business with speed and adaptability in the face of constant change.

Our cloud data strategy is capability-first and optimized for the speed, agility and flexibility you require to achieve your digital transformation objectives.

We equip you with digital transformation accelerators - proven, reusable capabilities like cloud-native APIs; containerized or serverless microservices; and infrastructure-as-code (IaC) templates - tailored to your specific architecture.

Our accelerators empower your teams to rapidly build, enhance and scale your products, leveraging best practices and cutting-edge capabilities that we have refined from extensive and deep real-world experience.

We partner with you to infuse your digital transformation strategy with your cloud data strategy roadmap.

We then work with you every step of the way to deliver tangible outcomes on your cloud and digital transformation goals.

Digital transformation is a journey not a destination.

So we enable you with the future-proof cloud data strategy and execution capabilities you need to embrace constant change and innovation as a first-class objective.

We tailor our approach to modernize your legacy systems and migrate them to the cloud based on your specific needs, timeline and budget.

We take a pragmatic modernization approach ranging from:

• Lift-and-Shift Replatforming: We get your systems to the cloud quickly, while starting to leverage the benefits of the cloud, such as scalability and availability. We replatform your legacy systems to infrastructure-as-a-service (IaaS) or platform-as-a-service (PaaS) offerings, prioritizing speed and simplicity for faster time-to-value.
• Re-architecting and Refactoring: We redesign your applications to take full advantage of cloud capabilities by leveraging cloud-native patterns, such as decoupling (separation of storage from compute; loosely-coupled microservices), serverless, containerization, automation (DevOps/GitOps and CI/CD) and right-sizing/pay-per-use cost models.

We can architect your application to take advantage of best-of-breed cloud-native offerings; such as Snowflake, Databricks, Amazon Redshift and Google BigQuery; and modern languages, libraries and frameworks, such as Python, Apache Spark, Apache Airflow, Apache Kafka, Delta Lake, Kubernetes, TensorFlow and PyTorch.

Our approach to modernizing your legacy systems and migrating them to the cloud balances pragmatism, risk mitigation, cost-effiency and agility goals to determine the optimal migration and refactoring strategy for your specific needs.

Our bespoke, consultant-led approach equips us to handle your most complex legacy modernization requirements.

We ensure smooth integration between your legacy systems and cloud architecture through a pragmatic approach tailored to your specific environment and needs. 

For migrating data and analytics workloads, we leverage cloud services like AWS Database Migration Service, Google Database Migration Service and Azure Database Migration Service.

Such database migration services enable one-time or continuous data replication from your on-premises databases to the cloud in a serverless, pay-per-use model that is fast and easy to set up.

Where needed, we convert your legacy schemas and refactor code to align with cloud-native best practices.

Our consultants advise you on the optimal migration and modernization strategy based on your timeline and budget.

As part of your cloud migration, we can maintain a hybrid architecture for you as long as you so choose, by enabling seamless federated data processing and analytics on your on-prem and cloud data.

We leverage solutions like Dremio and Presto to provide a unified interface for you to query data across your legacy on-premises systems and your cloud environment.

Our tailored approach to integrate your legacy systems with your cloud architecture balances your business continuity with rapid time-to-value.

Implementing a multi-cloud or hybrid data architecture does introduce complexity.

Therefore, we simplify such complexity for you by designing and building a streamlined, robust, secure and cost-efficient multi-cloud or hybrid data solution tailored to your specific needs.

We take an unbiased approach to architecting multi-cloud solutions, selecting the optimal combination of cloud providers and services for each of your workloads.

For example, we can leverage Databricks or Snowflake to perform your big data processing and analytics, regardless of the underlying cloud storage location: Amazon S3, Google Cloud Storage, Azure Blob Storage or Azure Data Lake Storage. 

For hybrid architectures, we help integrate your on-prem systems with cloud platforms using federated query solutions built on Dremio or Presto.

Such a federated query solution enables us to iteratively migrate your data from on-prem to the cloud, while keeping the physical data location transparent to users, enabling a seamless experience for your data analytics consumers.

We find Google Cloud to be quite intentional about embracing multi-cloud.

Therefore, we can leverage Google Cloud solutions purpose-built for multi-cloud like Google BigQuery Omni, Anthos, Composer and Data Fusion to unify your data management and enable portability of your workloads across multiple cloud platforms.

We also leverage Immuta for consistent data access controls across multiple clouds.

Where applicable, we architect and build a suite of modular containerized components that encapsulate multi-cloud complexity and implement capabilities you want on any cloud platform.

We enable you to control such components via consistent APIs that you can invoke for any cloud platform.

Such a componentized approach gives you the flexibility to execute workloads on any cloud platform and optimize your data architecture as your needs evolve, while eliminating the risk of cloud vendor lock-in.

Finally, we leverage Terraform for automated and consistent infrastructure-as-code (IaC) across multiple cloud platforms.

With our deep hands-on expertise across all of the major cloud service providers, we are well-equipped to enable you to successfully navigate and thrive in a multi-cloud or hybrid architecture.

Yes, we can help you build a compelling business case and ROI model to demonstrate the value of cloud adoption.

We take both a top-down benchmarking approach and a detailed bottom-up analysis tailored to your unique needs and environment.

Our business case framework focuses on quantifying cloud value across four key dimensions:

1. Cost Savings: We estimate total cost of ownership (TCO) reductions by analyzing current fixed and variable infrastructure costs compared to cloud pay-per-use pricing. We highlight cost avoidance from eliminating on-premises refresh cycles.
2. Staff Productivity: We quantify potential efficiency gains on a task-by-task basis, highlighting time savings from automating operations and shifting focus to more strategic initiatives.
3. Operational Resilience: We benchmark potential SLA improvements and reductions in downtime/outages.
4. Business Agility: We quantify impacts like faster feature releases, easier scaling and lower change failure rates.

Our top-down benchmarks provide quick value indicators based on the performance of your peers.

Our bottom-up analysis leverages your organization's unique data for greater accuracy.

We tailor your business case to resonate with different stakeholders: For example, we emphasize TCO for your CFO, productivity for your CIO, resilience for your IT teams and agility for your product teams.

Our ROI models factor in transition costs to provide a complete view of your cloud economics.

We partner with you to build the most compelling business case tailored to your specific needs and audience.

Yes, cost optimization is a key focus area in our cloud data architecture advisory services.

We help optimize your cloud costs across data storage, compute and databases.

For storage, we advise you on the optimal cloud storage type — object storage vs block storage vs file storage — to best address your specific requirements.

For most use cases, we recommend starting with cloud storage, like Amazon S3 or Google Cloud Storage (GCS).

We help you pick the storage tiers that are best aligned to your data access patterns and performance/latency needs, as well as your durability, fault tolerance, availability and budget/cost requirements.

Where applicable, we implement automated tiering, like S3 Intelligent Tiering or GCS Autoclass, to further optimize your storage costs.

For compute, we help you choose between serverless, containerized or instances/VMs.

We maximize your compute cost-efficiency by implementing right-sized compute with automated elastic scaling.

For databases, we help you pick the best cloud data services that reduce your overhead and costs: From NoSQL databases, to relational databases, to a cloud data warehouse or a lakehouse.

We optimize your database sizing, read/write throughput and regional availability to minimize your costs.

We help you establish budgets, monitoring, alerts and reports to govern your cloud spend and reduce your total cost of ownership (TCO).

We architect your cloud data systems for virtually unlimited scalability to accommodate exponential data growth well into the future. 

Your data will leverage the same battle-tested cloud infrastructure that powers the global networks of some of the world's largest companies, such as Amazon, Google and Microsoft, as well as Netflix, Airbnb, ESPN, LinkedIn and Apple.

We design your data architecture for massive, web scale scalability: Your data infrastricture will elastically scale storage and compute, as your needs evolve.

For example, storing your data on Amazon S3 enables you to effectively scale your storage capacity infinitely, while reading data at up to 55,000 requests per second.

Likewise, serverless compute services like AWS Lambda enable you to automatically scale your compute capacity to hundreds of thousands of requests per second on demand.

We build in auto-scaling capabilities for your data architecture based on usage patterns, so your storage and compute scale out seamlessly during demand spikes and scale in during downtimes to optimize your costs. 

The pay-per-use economics of cloud infrastructure ensure you only pay for the resources that you use.

The future is unclear to us all, particularly so, in today's era of exponential change.

Therefore, we architect flexible and agile systems for you that seamlessly adapt to change.

Here are some key patterns we implement to design your cloud data architecture, so you are future-proof and embrace change as your needs and the cloud data solutions ecosystem evolve:

• Decoupled Architecture: We decouple your storage from your compute, by default. Specifically, we leverage cloud object storage; such as Amazon S3 or Google Cloud Storage (GCS); to persist your data separately from your computation layer. Likewise, we implement data processing and analytics engines that are distinct from your data storage. Such decoupled architecture enables you to: (1) Pick the best-of-breed data storage and data computation solutions that best address your specific needs; (2) Reduce your costs by right-sizing your storage and compute, scaling them independently and paying-per-use; and (3) Reuse your data across multiple data computation engines: Perform data processing and analytics on shared data, leveraging multiple engines in parallel. Additionally, such a decoupled architecture enables you to seamlessly leverage a cloud data warehouse (such as Snowflake, BigQuery or Redshift) or a cloud data lakehouse (like Databricks). In short, a decoupled architecture positions you to seamlessly adopt more powerful cloud data processing and analytics engines, as they emerge, without the need to perform a data migration.
• Capability-First Thinking: We design for the What before the How. Capability is the What. Technology is the How. We design for Capability first before Technology or Tooling. We decouple your capabilities from your technologies. For any given capability, there are potentially several technology options. Much of the time, the technologies are fungible. Therefore, we first decompose your business into a cohesive ecosystem of technology-agnostic loosely-coupled capabilities.
• Modular Architecture & API-First Design: We group each set of your closely-related capabilities into Application Programing Interfaces (APIs). Each API focuses on executing a set of closely-related capabilities well. For each of your APIs, we implement the best cloud data solution that executes the API's capabilities as efficiently and effectively as possible. Benefits of such API-first modular architecture include: (1) Speed: Build and deploy multiple capabilities in parallel; (2) Flexibility & Adaptability: Build, incrementally enhance and evolve your underlying cloud data solutions, while keeping such changes transparent to your API consumer; (3) Cost Savings: Avoid the under-provisioning (resource contention) or over-provisioning (overpaying) of monolithic systems. Pay-per-use for right-sized capability-optimized provisioning. Reuse capabilities across your business and avoid re-inventing the wheel.
• Microservices: One of our key patterns for implementing APIs is to leverage loosely-coupled collaborating microservices. Microservices enable the faster engineering and scaling of, as well as shorter time-to-market for large, complex data platforms. We implement serverless or containerized microservices for you, depending on your specific needs.
• Event-Driven Architecture: We leverage event streaming and message queues to link loosely-coupled services. Event-driven architecture facilitates asynchronous integration across distinct capabilities of your business. We leverage open-source systems like Apache Kafka or cloud-specific services, such as Amazon Kinesis or Google Cloud Pub/Sub, depending on the specific needs of your business.

We implement a cloud data strategy and architecture for you that positions you with confidence and flexibility to embrace change as your needs and the cloud data solutions ecosystem evolve.

WSaaS is a future-focused cloud, data and AI solutions firm.

We are continuously identifying and leveraging the latest, cutting-edge cloud data architecture patterns and solutions to maximize speed, satisfaction, flexibility, adaptability, innovation, cost savings, reusability and, ultimately, growth for our partners.

Partner with us to grow your business, streamline your operations and accelerate your innovation by leveraging the optimal cloud data architecture and solutions that effectively address your specific needs.

Yes, we offer highly-customized training sessions tailored to the specific needs and skill levels of your team, based on the outcomes and recommendations from our consultation engagements.

We deliver our training in a flexible format - from 1-on-1 sessions to interactive group workshops - and equip your team with the exact knowledge and hands-on experience they require to execute on the cloud data architecture strategy we recommend.

Our interactive sessions incorporate real-world examples and use cases targeted to the specific needs of your business.

Training curriculum can range from foundational cloud data architecture overviews to deep dives on advanced topics, such as performance optimization, security and leveraging the latest capabilities. 

We work closely with you to understand your team's current skills, identify knowledge gaps, define clear learning objectives and equip your team with the cloud data architecture expertise they require to deliver business impact based on the outcomes of our consultation.

Here are some best practices we recommend for implementing robust data governance, metadata management and ensuring high data quality in your cloud environment.

For Data Governance:

• Establish clear data ownership and stewardship roles and responsibilities.
• Clearly define policies for data security, entitlements/access control, retention, lineage and lifecycle management.
• Leverage dashboards to track metrics on data pipeline health, quality, lineage and usage.

For Metadata Management:

• Maintain a catalog of metadata, using AWS Glue Data Catalog, Google Cloud Data Catalog, Microsoft Purview or one of several third-party data catalog offerings, such as Alation, Amundsen, DataHub or Atlan.
• Track key metadata, like data definitions, ownership, lineage, quality metrics and a business glossary.
• Enable data discovery with search and collaboration features to find and understand your data.

For Data Quality:

• Monitor data quality metrics like freshness/recency, completeness and correctness, including domain, referential, product, feature and idiosyncratic data integrity.
• Perform data testing and enforce data quality requirements before moving data downstream.
• Fix quality issues as far upstream as possible, ideally at the root source.
• Leverage one or more of the tooling available to implement management of data quality, data testing and data observability, such as Amazon Deequ, BigEye, Databand, Datafold, Superconductive Great Expectations, Monte Carlo, Soda, Unravel, Accel Data and Metaplane.

Additionally, for large, complex organizations, consider adopting the data mesh paradigm and federating data product ownership back to the teams that produce the data.

Absolutely. We provide comprehensive guidance on robust controls and frameworks to safeguard your data security, privacy and compliance in the cloud.

We start by performing an in-depth data mapping assessment.

We classify your data based on sensitivity levels aligned to your business requirements.

We then design controls, access policies and data lifecycles tailored to your regulatory and compliance obligations.

For security, we advise on optimal patterns to encrypt your data both at rest and in-flight, using industry-standard algorithms.

We also recommend techniques to hash or tokenize your sensitive data, like personally-identifiable information (PII).

We implement role-based access controls (RBAC), attribute-based access controls (ABAC), policy-based access controls (PBAC) and automated monitoring, driven by your specific needs, to further harden your cloud data platform.

For privacy and compliance, our consultants stay current on regulations like GDPR, CCPA and CPRA.

We enable you to efficiently comply with user requests for data access, deletion and portability.

We also advise you on best practices to implement industry standards, such as ISO, SOC, PCI and HIPAA.

Yes, WSaaS can partner with your organization to establish a successful Cloud Center of Excellence (CCoE) tailored to your specific needs and objectives. 

A CCoE serves as an internal cloud strategy advisory and enablement team focused on driving cloud adoption and maximizing its business impact in your organization.

Here is an overview of how we can help:

Define CCoE Mission and KPIs

• Align CCoE objectives to your cloud strategy and business goals.
• Establish key performance indicators to track progress.

Structure and Staffing

• Advise on CCoE organizational structure, roles and responsibilities.
• Assess required cloud skills and help build an effective team.

Education and Training

• Develop customized cloud training programs tailored to your team's needs.
• Equip your staff with hands-on experience and cloud certifications.

Migration Planning

• Create application migration roadmaps aligned to business priorities.
• Advise teams on cloud-native architecture and engineering patterns and best practices.

Governance and Compliance

• Define cloud policies, controls and procedures.
• Help implement frameworks for security, compliance and cost governance.

Engineering Enablement

• Create accelerators like APIs, libraries, reference architectures, blueprints and templates to boost engineering productivity and effectiveness.
• Provide embedded, hands-on engineering enablement services to unblock and accelerate teams as needed.

Optimization and Innovation

• Identify opportunities to optimize performance and costs.
• Consult on adopting new cloud capabilities and services.

Measure Success

• Establish frameworks to track CCoE KPIs and demonstrate cloud ROI.
• Continuously improve CCoE delivery and business alignment.

With our solid experience enabling cloud operational excellence, performance efficiency and cost optimization globally, we are your trusted partner to establish a high-impact CCoE tailored to your organization's specific needs.