Connect Data
Capture records, events, documents, and media with source identity and checkpoints.
Big Data Engineering
Big Data Services for
Scalable Analytics and AI
For CTOs and data leaders, Codiste builds platforms that turn growing data volumes and varied sources into reliable inputs for analytics, machine learning, and AI. Connect your sources, choose the right processing model, and put performance, security, and operating costs into the design from the start.
When Data Growth Slows Decisions and Raises Costs
Jobs run longer as records accumulate. Yesterday’s batch overlaps today’s load, dashboards miss their refresh window, and adding compute only postpones the next bottleneck.
Transactions sit in databases; application events, documents, and sensor readings arrive elsewhere. Without consistent identifiers and ingestion rules, teams repeatedly prepare the same information before they can analyse it.
Unnecessary full-table scans, duplicated storage, and always-on clusters increase cloud costs.
What Are Big Data Services?
Big data services cover the design, development, integration, and operation of systems that process data whose volume, arrival speed, or variety exceeds the practical limits of an existing setup. Deliverables can include distributed pipelines, data lakes or lakehouses, governed datasets, and interfaces for analytics and machine learning.
The starting point is a measurable requirement: a reporting deadline, an event-response target, or a dataset that must support more users. If tuning the current database solves it, a distributed platform may be unnecessary.
Codiste’s Big Data Engineering Services
Choose a focused engagement or combine the services into one delivery programme. Each starts with a defined workload and ends with an agreed, reviewable output.
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Big Data Architecture and Platform Design
Our Big Data Technology Stack
Technology selection follows workload requirements and your team’s operating capacity. These are evaluation options; the final stack is agreed during architecture design.
Apache Spark
Apache Flink
Apache Beam
dbt
Python
SQL
Scala
Java
Apache Kafka
Apache NiFi
Debezium
Airbyte
Apache Airflow
Amazon S3
Azure Data Lake Storage
Google Cloud Storage
Apache Iceberg
Delta Lake
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
BigQuery
Amazon Redshift
Power BI
Tableau
Looker
MLflow
Great Expectations
DataHub
Unity Catalog
Our Process for Delivering Big Data Projects
Start with the workload that matters most. Validate its performance and operating cost before extending the approach to more sources, users, or business functions.
1. Assess the Workload
Review sources, volumes, deadlines, and the bottleneck. You provide: Sample data, workload history, and an owner. You receive: Architecture, priorities, criteria, and scope. Success check: The improvement target is clear.
2. Build Data Flow
Connect a priority source and build the processing path; use a PoC if feasibility is unclear. You provide: Access and user feedback. You receive: A working flow with checks. Success check: Quality and latency meet agreed targets.
3. Validate & Scale
Test peak load, late records, failure, and replay; reconcile migration outputs. You provide: Load assumptions and reviewers. You receive: Test results, settings, and a rollout plan. Success check: Recovery preserves correctness.
4. Launch & Handover
Deploy in stages, add monitoring, and train the team. You provide: A release owner and support contacts. You receive: Code, runbooks, training, and support duties. Success check: Your team can run the platform and explain costs.
Find What Is Slowing Your Big Data Platform
Bring your slowest workload, missed refresh target, or rising cloud bill. Codiste will help define the bottleneck and scope the right engineering next step.
Discuss your data bottleneckHow Our Big Data Architecture Works
The big data path
Ingest Data
Use batch or streaming paths; check schemas, duplicates, completeness, and arrival times.
Organise Data
Keep raw and curated data separate, with storage chosen for access and recovery needs.
Process Data
Distribute transformations and joins; allocate compute so jobs do not block each other.
Serve Data
Publish governed tables, metrics, features, and indexes through controlled interfaces.
Governance, Ownership and Lineage
Catalogues, ownership, business definitions, and lineage explain where data came from and how it changed.
Security, Access and Auditability
Role-based access control, encryption, secrets management, and audit logs protect access across the data path.
Quality Checks and Reconciliation
Validation, reconciliation, and quarantine rules prevent agreed critical failures from reaching consuming systems.
Cost, Capacity and Performance
Budgets, query monitoring, retention rules, and scaling limits make the cost of each workload visible.
Big Data Use Cases That Drive Better Decisions
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Customer 360 and Personalisation
Unify transactions, product activity, and support interactions into governed customer views. Identity resolution supports segmentation, recommendations, and service as teams track profile coverage and freshness.
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IoT and Sensor Analytics
Turn equipment telemetry and sensor events into operational signals. Streaming checks and historical data support anomaly investigation and maintenance, while teams review data gaps and alert relevance.
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Transaction and Risk Monitoring
Combine transaction streams with customer and historical context for risk teams. Traceable features and review paths support faster decisions while preserving reconciliation and agreed model evaluation.
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Demand Forecasting and Planning
Bring sales, inventory, pricing, and external signals into repeatable forecasting datasets. Give planners consistent history and refreshed inputs, then review forecast error and preparation time each cycle.
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Enterprise Search, RAG and AI Agents
Prepare documents, logs, and business records for retrieval. Extraction, indexing, refresh, and permission-aware access help applications find traceable information while teams monitor relevance and cost.
Big Data Engineering Planning by Industry
Data volumes, arrival rates, source formats, access requirements, and business deadlines shape the design. We assess these around the workloads your organisation needs to support.
How We Validate the Platform
We agree on a baseline and acceptance criteria, then retest the same workload with comparable data after implementation.
Plan Your Big Data Project With Codiste
Share your data sources, growth plans, and priority use case. Define a practical architecture, delivery scope, and cost assumptions before committing to the build.
Why Choose Codiste for Big Data Engineering?
Workload-led Architecture
We start with a measurable reporting, latency, or scale requirement. Architecture decisions account for your data formats, access patterns, operating capacity, and costs; tuning the current setup remains an option when it meets the need.
Validation Before Wider Rollout
We test a representative workload before expanding. Agreed quality, performance, recovery, and cost criteria give your team evidence to review before production rollout.
Clear Ownership and Operating Guidance
You receive source code, configuration, runbooks, and training. Support responsibilities are defined during handover, with ongoing optimisation or managed support included where agreed.
Big Data Services FAQs
Big data services turn demanding data workloads into systems your organisation can use and operate. Depending on scope, Codiste delivers architecture, integration pipelines, distributed processing, governed storage, analytics or ML datasets, monitoring, and documentation. Big data consulting services define the approach; big data implementation services build and validate it.
There is no universal size threshold. Assess the five Vs of big data: volume, velocity, variety, veracity, and value. The practical question is whether current systems meet your quality, cost, and response-time requirements. A slow dashboard may need better queries or modelling rather than a new distributed platform.
Big data describes workload demands; a data warehouse is one way to serve analytics. Modern warehouses can handle substantial scale. A data lake stores diverse data, while a lakehouse adds table management over lake storage. The choice depends on access patterns, formats, governance, and cost, not a rule that one must replace another.
Cost depends on source complexity, ingestion rates, retention, processing requirements, security controls, and migration scope. Codiste scopes delivery around defined workloads and acceptance criteria. Request a proposal that separates engineering fees from cloud infrastructure, licences, and ongoing support, with assumptions for both current usage and expected growth.
The schedule depends on access readiness, integration complexity, data quality, and production requirements. A focused workload has a different delivery path from a platform migration. Milestones are confirmed after assessment, with dependencies and review points made explicit. Existing infrastructure and usable source interfaces can materially reduce the work required.
Start with a PoC when throughput, latency, integration, or technical feasibility is uncertain. Give it measurable acceptance criteria and a decision to support. If the main risks are already understood, a scoped production release may be more useful. The first engagement should resolve uncertainty that affects the wider investment.
Use streaming when a decision loses value while it waits; use batch when scheduled updates meet the business need. Many platforms combine both. Sub-second latency is a requirement to benchmark across ingestion, processing, and serving, not a universal guarantee. Streaming also introduces operating complexity that the use case must justify.
Store original assets with their source metadata, then extract the information needed for the use case. That may involve parsing, OCR, transcription, classification, or embeddings. Validate extraction quality and preserve links to source assets. Images and video may require dedicated processing; they should not be treated as ordinary table rows.
Start with data classification, purpose, access requirements, and retention rules. Map controls with your security and compliance teams, including encryption, audit logs, deletion handling, and restricted access. HIPAA workloads also require appropriate business associate arrangements where applicable. Platform configuration supports compliance; it does not establish compliance on its own.
Track cost against useful units such as data processed, queries served, or completed jobs. Tune partitioning, file sizes, compute allocation, and retention before increasing capacity. Use scaling limits and budget alerts, review idle resources, and include data-transfer costs. FinOps reviews should preserve the performance and reliability the business actually needs.
Yes. It can provide governed source data, extraction pipelines, features, and searchable indexes for AI applications. Retrieval needs access filtering, source traceability, and refresh processes; agents also need controlled interfaces for actions. These foundations support the application, while retrieval quality and model behaviour still require their own evaluation.
Yes. An LLM can interpret supplied content, but it does not replace collection, deduplication, permissions, retention, or ongoing updates across an organisation. Large data estates still need systems that select and prepare relevant information economically. The model handles interpretation; the data platform provides the controlled, repeatable route to its inputs.
Discuss Your Big Data Requirements
Tell us about your source systems, data growth, priority workload, and target timeline. We will help define the assessment scope and the next steps for your project.