Data Engineering Services for Scalable Analytics and AI
Faster data processing
Better data quality
Quicker analytics delivery
Get Started with Data Engineering Solutions
Data Engineering Services Designed for Real Business Outcomes
Modernize data pipelines, platforms, and governance to drive faster insights and better outcomes with Kanerika's end-to-end data engineering services. As your data engineering consulting partner, we scope each engagement to your platform, data volumes, and team structure, and deliver working pipelines.
Data Pipeline Development
- Ingest batch and streaming data reliably
- Built with alerting and retry logic
- Tested before reaching consumers
Data Quality and Observability
- Quality checks run on every pipeline
- Lineage tracked across data movements
- Issues flagged before users notice
Data Lakehouse Architecture
- Structured layers from raw to refined
- Schema enforcement at architecture layer
- Governed for query performance
ETL and ELT Modernization
- Legacy pipelines rebuilt for cloud
- Version controlled and testable
- Monitored end to end after deployment
DataOps and CI/CD for Data
- Every pipeline change reviewed
- Automated tests run on every build
- Data contracts enforced across workflows
Real-Time and Streaming Data
- Events processed as they actually happen
- Pipelines built for operational speed
- Dashboards reflect current data
Data Engineering Engagements Built Around Your Stack
From a one-time build to embedded engineering support, pick the model that fits your team.
Pipeline Audit and Design
- Map existing pipelines and gaps
- Define target architecture and tooling
- Deliver a build-ready technical blueprint
Build and Modernize
- Rebuild legacy ETL on cloud-native stack
- Implement lakehouse and streaming
- Hand off tested, production-ready pipelines
Ongoing Support
- Extend your team with dedicated engineers
- Monitor pipeline health and data quality
- Ship new workstreams as priorities shift
Data Engineering Results from Real Enterprise Deployments
Learn how Kanerika’s data engineering practice cut pipeline latency, reduced data debt, and unblocked analytics teams across industries.
Independent Microsoft Fabric Architecture Review Before a Dynamics 365 Go-Live
Impact:
- ~136 Findings documented before go-live
- ~30 Days estimated for pre-cutover remediation
- 6 Weeks to an independent, evidence-based go/no-go view
Bringing Live QAD ERP Data into Microsoft Fabric with Open MirroringÂ
Impact:
- 7 QAD data issues resolved before rollout
- 0 Duplicate records when data is reloaded
- 200+ QAD tables connected to Fabric
Detecting Rental Transaction Risk Early with AI-Powered Risk Intelligence
Impact:
- ~500 Risk findings surfacedÂ
- ~1.2M Financial exposure identified
- 6 Risk categories used to classify findings
IMPACT Methodology for Predictable Data Engineering Delivery
Our engineering methodology gives every project a clear path from architecture decisions to tested, deployed data infrastructure.
INNOVATE
Data Strategy Consulting Tuned to Your Industry
Why Choose Kanerika for Data Engineering Services?
Our team brings certified platform expertise and production-proven engineering to every data infrastructure project. Our data engineering consultants hold Microsoft, Databricks and Snowflake partner credentials and run production lakehouses.
Microsoft Solutions Partner status and an in-house MVP who contributes to Fabric mean recommendations come from real platform experience
Databricks Consulting Partner and Snowflake Select Tier status backed by production lakehouses and enterprise scale pipeline deployments
ISO 27001, 27701, 9001, SOC 2 Type II and CMMI Level 3 mean governance and audit controls are built in in all data engineering projects
Empowering Alliances
Our Strategic Partnerships
The pivotal partnerships with technology leaders that amplify our capabilities, ensuring you benefit from the most advanced and reliable solutions.
Frequently Asked Questions (FAQs)
01What do data engineering services include?
Pipeline design, development, and deployment across batch and streaming workloads. Lakehouse architecture implementation. ETL development services for modernizing legacy tools to cloud-native patterns. DataOps setup including CI/CD, testing, and monitoring. Data quality and observability frameworks. Kanerika scopes every engagement to your specific platform, data volumes, and team structure. You get working pipelines, not architecture diagrams.
02How long does a data engineering project take?
A focused data pipeline development project or ETL migration typically runs 2 to 4 months. A full lakehouse implementation with DataOps and governance runs 4 to 9 months depending on source system count, data volumes, and complexity. Milestones are set at the start and tracked throughout.
03How does data engineering differ on Snowflake vs Databricks vs Fabric?
Snowflake is SQL-first with virtual warehouses and Snowpipe for ingestion. Databricks is code-first with Spark, Delta Lake, and notebook-driven workflows. Fabric combines OneLake storage, Data Factory orchestration, and low-code options alongside Spark notebooks. The right choice depends on your team’s skills, workload types, and existing cloud investments. Kanerika engineers across all three.
04What is a data lakehouse and when do we need one?
A lakehouse combines the flexibility of a data lake (any format, any volume) with the structure of a warehouse (schema enforcement, ACID transactions, query performance). You need one when your data is too varied for a warehouse alone but too important to leave unstructured in a lake. Kanerika implements lakehouse patterns on Databricks (Delta Lake), Snowflake (Iceberg), and Fabric (OneLake).
05What is DataOps and why does it matter?
DataOps applies software engineering practices to data workflows: version control, automated testing, CI/CD, monitoring, and incident management. Without it, pipeline changes are manual, untested, and break in production. Kanerika sets up DataOps as a core part of every engagement, not as a separate initiative.
06How does data engineering relate to AI and ML?
ML models are only as good as the data feeding them. Feature engineering, training data preparation, and model serving all depend on well-built pipelines. If your pipelines deliver late, incomplete, or inconsistent data, your models produce unreliable results. Kanerika builds data engineering with AI readiness in mind.Â
07Can Kanerika modernize our legacy ETL pipelines?
Yes. Kanerika’s ETL development services cover migration from SSIS, Informatica, Talend, stored procedure chains, and custom scripts to cloud-native pipelines on Snowflake, Databricks, or Fabric. FLIP automates schema mapping, pipeline generation, and validation to reduce migration timelines, so your team reviews converted pipelines instead of rewriting them by hand.
08How does Kanerika handle data security in engineering projects?
Security is designed into the pipeline architecture from discovery: data classification, encryption in transit and at rest, access controls, lineage tracking, and audit logging. Kanerika holds ISO 27001, ISO 27701, SOC 2, and CMMI Level 3 certifications. For Azure environments, Purview and Defender are integrated into the governance layer.
09What industries does Kanerika serve for data engineering?
BFSI, manufacturing, logistics, retail, healthcare, insurance, pharma, and automotive. Pipeline requirements differ by industry: real-time fraud detection in BFSI, IoT ingestion in manufacturing, HL7/FHIR integration in healthcare, connected vehicle streaming in automotive.
10Why choose Kanerika over a Big Four firm for data engineering?
Two reasons. First, Kanerika is a data engineering company whose engineers build pipelines, while Big Four firms often staff projects with consultants who design architectures and subcontract the build. Second, Kanerika has built its own DataOps tooling (FLIP) from real project experience. The team advising you has actually operated at scale on the platforms they recommend. When you compare data engineering service providers, ask whether the team that advises you also builds and runs the pipelines.
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