Source Systems
Your operational systems, applications, databases, and files provide the source data.
Data Engineering Services
Data Engineering Services
for AI-Ready Data
We build the systems that move your data, clean it, and keep it right. So your reports can be trusted, and your AI has something real to work with.
How can you handle massive data
infrastructure?
As your business grows, data spreads across more systems, teams, and tools. The infrastructure that once supported daily operations starts demanding more manual work.
Reports take longer to prepare. Teams spend time reconciling figures before they can make decisions.
New integrations strain existing pipelines, while AI initiatives wait for reliable, properly governed data.
What is data engineering?
Data engineering is the work of building the systems that carry data from where it is made to where it is used. It covers moving data, storing it, cleaning it, setting rules for it, keeping it safe, and watching it for problems. It is not analytics, and it is not data science. Those two use the data. Data engineering builds the road it travels on.
When it is done, you own a set of pipelines, one place where everything lives, one shared meaning for every number, checks that catch bad data early, and alerts that reach you before your customers do. The newer part of the job is getting data ready for AI. Same road, a few extra lanes, so a model can use your data without guessing and without leaking.
Codiste’s Data Engineering Capabilities
Our Data Engineering Services Tech Stack
We do not push our favourite tools. We pick what your team already knows. If your engineers work in Azure, we are not going to move you to AWS to make our life easier. All of this runs on AWS, Azure or Google Cloud.
Apache Spark
Apache Airflow
Dagster
dbt
Great Expectations
MLflow
Python
SQL
Scala
AWS
Microsoft Azure
Google Cloud
Fivetran
Airbyte
Apache Kafka
Amazon Kinesis
Debezium
AWS Glue
Azure Data Factory
Snowflake
Databricks
BigQuery
Amazon Redshift
Delta Lake
Apache Iceberg
Unity Catalog
Soda
Collibra
Power BI
Tableau
Looker
Apache Superset
Neo4j
Pinecone
pgvector
Amazon SageMaker
How we move from data assessment to production
We deliver in stages, starting with a priority use case and validating it before expanding. Each stage has defined deliverables, giving your team clear visibility into progress, costs, and readiness for launch.
1. Assess & Plan
A review of your sources, data quality, and costs, with recommended platforms, milestones, and estimates.
2. Build & Extend
A priority source connected end to end, then further integrations, quality controls, and ready datasets.
3. Validate & Launch
Source-to-target reconciliation, a controlled rollout, operating documentation, and training for your team.
4. Support & Improve
Monitoring, issue resolution, and performance reviews, with support coverage agreed for your engagement.
Start with Your Highest-priority Data Use Case
Connect a priority source to the report, application, or AI workflow that needs it. We will assess the gaps and define a practical path from planning to a working pipeline.
Book a 30-min callHow We Engineer Your Data
Read it left to right. Your systems send data in, either in batches or the moment something changes. It all lands in one place. Then it gets cleaned and labelled, so every field has a clear meaning and a clear rule about who may see it. After that it splits: some goes to dashboards, some to your models, and some to your AI which can only ever read what the person asking is allowed to read.
The data path
Batch or Live Ingestion
Data arrives on a schedule or as changes happen, depending on the use case.
Shared Data Storage
Incoming data lands in a shared storage layer before downstream preparation.
Cleaning, Labelling and Access Rules
Data is cleaned and labelled so each field has a clear meaning and rules about who may use it.
Delivery to Analytics and AI
Prepared data feeds dashboards, ML models, and knowledge graphs. AI reaches it only through access-controlled lookup.
Data Quality Checks
Check each load for missing values, duplicates, and out-of-range data. Hold back records that fail the agreed rules.
Ownership and Access Controls
Assign dataset owners and apply access rules so downstream systems use the data permitted for each role.
Traceability and Shared Definitions
Trace numbers back to their sources and keep shared definitions consistent across reporting and applications.
Monitoring and Recovery
Use tests, alerts, and operating guidance to detect pipeline issues and support recovery under the agreed support scope.
Data Engineering for Your Business Workflows
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Trusted Reporting and Dashboards
Bring fragmented sources together and define metrics once, so teams spend less time reconciling numbers before making decisions.
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Live Operational Data
Use live feeds for stock, fraud checks, tracking, and customer-facing workflows where faster data changes a decision.
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Legacy Data Modernisation
Connect older databases, mainframes, files, and APIs. Reconcile source and target data before switching to the new environment.
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Data Foundations for AI
Prepare labelled data, fresh search indexes, knowledge graphs, and access rules for models, agents, and applications.
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Growing Data Volumes
Adapt storage and processing as data grows, with monitoring and cloud-cost reviews to keep the platform maintainable.
Data Engineering Planning by Industry
Source systems, access rules, reporting needs, and freshness requirements shape the architecture. We assess these in the context of your business workflows.
Case Studies
Plan Your Next Data Engineering Project
Start with the source systems, reporting needs, or AI use case you want to support. We will help define priorities, architecture, delivery scope, and costs before the build begins.
Why Choose Codiste for Data Engineering?
Built for Analytics and AI
We design data pipelines around how your teams, applications, and AI systems will use the data, with quality, access, and freshness requirements considered from the start.
Direct Technical Collaboration and Handover
Your team works with our engineers to review architecture, resolve integration challenges, and make informed decisions throughout delivery. You receive documented architecture, source mappings, and operating guidance, giving your team the knowledge to manage and extend the solution.
Support Beyond Deployment
We provide ongoing monitoring, maintenance, and pipeline improvements, with support coverage and response expectations agreed for your engagement.
Frequently asked questions
Data engineering builds the system. Analytics and data science use it. We move the data, clean it, and make sure every number means the same thing everywhere. Then your analysts answer business questions, and your data scientists build models. Without this layer, both of them end up doing it by hand.
One pipeline can be live in four weeks. A mid-sized setup with 10 to 30 sources takes four to five months, with something useful working by about week eight. Moving off an old system takes six to nine months. You get a firm number after the planning step, before the build starts.
We check the data every time it loads, not once a week. Each dataset gets rules with nothing missing, nothing duplicated, dates in range, numbers in range. Anything that fails is held back, not passed on. And every number can be traced back to where it came from, so you can see exactly where a bad value got in.
We mark the sensitive fields as soon as data arrives, lock them, and hide them from any system that does not need them. People only see what their role allows, right down to the row. For GDPR, HIPAA, CCPA and India’s DPDP we also set up the logs, the retention rules and the delete process, so you can show you follow the rules instead of just saying so.
Yes, and that is usually most of the job. Mainframes, in-house databases, file exports, old APIs, cloud apps, and the spreadsheet someone updates by hand. If a system cannot send updates on its own, we take snapshots or track changes another way, then check the totals against the source until they match.
Both, usually in the same setup. Scheduled runs are fine for daily reports. Live feeds make sense for stock, fraud checks, tracking, and anything a customer sees. Live costs more to run, so we use it where the speed actually changes a decision.
An AI is only as good as what it can read. We label your data so the model knows what each field means, keep the search index fresh, add a graph so it understands how things connect, and set access rules so it can never return a record the person is not allowed to see. Then we watch quality after launch, not just before.
Yes. We monitor the system, fix what breaks under an agreed response time, handle new sources and changes, keep an eye on cloud cost, and review the design every quarter. Most clients keep a smaller team on, because the data changes every time the business does.
Tell us what your data looks like today
We will come back with a plan and real costs. A data architect replies within one working day.