Big Data

AIGard builds platforms that store and process very large data volumes reliably and at reasonable cost. We design the architecture, pipelines and governance around how the data will be used.

The work typically covers:

• Big data architecture design
• Distributed processing pipelines
• Data lake and lakehouse setup
• Streaming ingestion
• Cost and performance optimization
• Security and access governance

Big Data is usually the right investment in one or more of these situations:

• Data volume has outgrown your current tools
• Processing jobs take hours or fail
• You need to combine many high-volume sources
• AI projects need large training datasets

On a 30-minute scoping call we confirm whether it fits your case and recommend a team shape and first milestones.

Not every organization needs a big data platform, and a well-tuned data warehouse is often enough. AIGard assesses data volume, velocity and intended use cases before recommending one.

A dedicated platform typically makes sense when data volume has outgrown your current tools, processing jobs take hours or fail, or many high-volume sources must be combined. If the assessment shows that a simpler setup will serve, we will say so plainly in the written proposal.

AIGard keeps big data processing costs under control through right-sized clusters, efficient storage formats, job tuning and cost monitoring.

Cost is treated as a design consideration alongside performance. We design the architecture and pipelines around how the data will be used, so that capacity matches the actual workload, and cost monitoring keeps spending visible so that further tuning can be directed where it matters most.

With the decisions the business needs to make, then the data required to support them.

We assess your sources, quality and existing platforms first, and tell you plainly what your data can and cannot support before recommending an architecture.

PII masking, encryption and audit logging are built into data work, and engineers operate inside your environment under least-privilege access.

Data-handling requirements are agreed during scoping and checked before release.

For Big Data, AIGard engineers typically work with Databricks, Spark, Kafka, Snowflake, BigQuery, AWS, Azure and Google Cloud.

The stack is selected to fit the systems you already run, and engineers are matched to it before they join.

Big Data is available through any of AIGard’s three engagement models: staff augmentation, where engineers join your team; a dedicated team that runs the workstream; or software outsourcing, where AIGard delivers a defined project.

The choice depends on how much of the delivery you want to manage, and it can change later under the same agreement.

Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.

We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.

AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.

The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.

NDAs are signed before any access is given, and engineers work inside your environment under least-privilege access, with permissions limited to their responsibilities.

Access is reviewed as roles change and removed when work ends. Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work. We review your access policies, data-handling needs and vendor requirements during scoping.

You do. Intellectual property assignment is written into every contract, in every engagement model.

That covers source code, designs, documentation, data, models and prompts produced for you. Third-party components and their licenses are identified, so ownership is clear at handover.