Data Engineering Services & Solutions | Polestar Analytics Data Engineering Services & Solutions | Polestar Analytics
x

    Data engineering by data & AI engineers

    AI runs on data.We engineer it with AI.

    Polestar Analytics builds the pipelines, lakehouses and data products your analytics, models and agents run on. AI agents do the heavy lifting on every build. Our engineers review and sign off every release.

    Talk to a data & AI expertSee what we build
    AI agents and engineers building a data platform: pipelines flowing into a layered lakehouse with analytics dashboards

    Platforms we build on

    Databricks Microsoft Snowflake Google Cloud Amazon Web Services Databricks Microsoft Snowflake Google Cloud Amazon Web Services

    What is changing in AI readiness?

    AI moved faster than the data underneath it

    15%

    of leaders say their data foundation is very ready for agentic AI 1

    46%

    name data silos as the top barrier to agentic AI 1

    60%

    of AI projects without AI-ready data will be abandoned through 2026 2

    Sources: 1. Harvard Business Review Analytic Services, sponsored by Reltio, survey of 325 business and technology leaders, February 2026. 2. Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” February 2025 (prediction).

    What we build

    Where does your AI foundation break?

    Get every source in, at the speed the use case needs

    Batch, CDC and streaming pipelines from SAP, SaaS apps, files and EDI, built on metadata-driven frameworks.

    AI in the buildAI agents profile each new source and draft the ingestion config. An engineer approves it before it runs.

    Foundation StudioData EngineeringDataOps

    Ingestion tab preview

    One lakehouse, with a quality gate between every layer

    Bronze, silver and gold layers on Delta or Iceberg, on the platform you already run.

    AI in the buildMoving off a legacy warehouse? AI translates the old logic by meaning, and engineers verify the output.

    Foundation StudioMigration/Modernization

    Lakehouse tab preview

    Give AI your definitions, not its guesses

    Enterprise and dimensional models with a semantic layer, so "net sales" means one thing to people and agents.

    AI in the buildAgents draft metric definitions from your existing reports. Business owners confirm them.

    Insight StudioData EngineeringAI & Data Governance

    Semantic layer tab preview

    Catch bad data before an agent acts on it

    Gates on every hop check completeness, validity, freshness and schema. Failing rows are quarantined, not loaded.

    AI in the buildAI drafts quality rules from the data profile, so coverage starts high on day one.

    Foundation StudioGovernance StudioAI & Data Governance

    Data quality tab preview

    Serve agents an endpoint, not a table

    Certified data products with data contracts and serving APIs that dashboards, models and agents can call.

    AI in the buildBuilt by engineers who also build the agents that call these endpoints.

    Foundation StudioAgent StudioData EngineeringAgent Engineering

    Data products tab preview

    One customer, one product, one vendor. Everywhere.

    Master data across customer, product, vendor and material domains, matched and merged into golden records every pipeline and agent can trust.

    AI in the buildAI proposes record matches with a confidence score. Data stewards approve the merge before it reaches a golden record.

    MDM StudioAI & Data GovernanceData Engineering

    Master data tab preview

    Make your documents usable by AI

    Extract contracts, invoices and SOPs, chunk by document type, embed, and manage the vector stores your RAG systems ground on.

    AI in the buildThe same team designs the RAG and agent systems that ground on these indexes.

    Agent StudioData EngineeringAgent Engineering

    Unstructured data tab preview

    Phase 1 is a read-only assessment

    You see the scope, the risk flags and a business case before anything moves.

    Book a data readiness assessment

    How we build

    We build data for AI. With AI.

    Every project runs on AI agents working beside our engineers. The agents take the slow, repetitive work. The engineers make the calls and own what ships.

    01

    Profile

    Agents scan every source and flag gaps, nulls and drift before design starts.

    02

    Map

    Agents propose source-to-model mappings. Engineers approve or fix them.

    03

    Translate

    Agents turn approved mappings into pipeline code and SQL. Engineers review every pull request.

    04

    Test

    Quality rules are drafted from the profile, then run as gates on every hop.

    05

    Document

    Lineage and business definitions get written while the pipeline is built.

    An engineer reviews and signs off every release. AI speeds up the build. It never ships unreviewed.

    Studio apps

    Faster, because we never start from zero

    A services team with 1Platform accelerators, where AI does the first draft and engineers own the result.

    Foundation Studio

    2-3 months

    not 8-10, to a working data foundation

    • Source dictionaryPre-modelled
    • Bronze-to-silver rules40+ ready to run
    • Data foundationWith cross-source lineage
    • Promote80–90% pre-built
    Explore Foundation Studio

    Migration toolingPart of Foundation Studio

    About 35%

    less migration time and cost

    • ScanEvery job, orphans flagged
    • TranslateBy meaning, not by syntax
    • OptimizeJobs bundled by source and target
    • PromoteThrough Git
    Explore Foundation Studio

    Governance Studio

    Every dollar

    of waste gets an owner and a status

    • Spend alertsReal time
    • Query policiesStop waste at the source
    • OptimisationRanked, with projected savings
    • Token wasteModel routing and caching
    Explore Foundation Studio

    1 Internal engineering target and test-bed result, August 2026. Not an audited benchmark.

    Making data work for a real-time lakehousezero-disruption migration

    Pair of sneakers inside a glass cube surrounded by plants

    Store teams now see POS performance in minutes, not hours.

    Global footwear and accessories retailer

    Batch reporting to a medallion lakehouse

    Read the case study →
    • 6–10 minPOS refresh, down from 30–120 min
    • 70–80%faster processing at larger volumes
    • 4,500files processed in 15 minutes
    Three construction workers reviewing building plans on site

    QlikView sign-in moved to Google SAML with no disruption in production.

    Building materials GCC, 70+ countries

    QlikView authentication migration

    Read the case study →
    • 0disruption during production migration
    • 6 weeksto complete the migration
    • 3environments moved: Dev, QA and Prod

    Studio apps

    Build the rest of your AI foundation

    Modern glass office building

    Data Readiness assessment

    A scored baseline of quality, structure and access before an AI project starts.

    Explore Data Readiness assessment →
    Executive reviewing financial charts on a tablet

    DataOps

    Keep pipelines tested, monitored and cost-efficient long after go-live.

    Explore DataOps →
    Connected global supply chain network map

    AI & Data Governance

    Ownership, catalog, lineage, privacy and responsible AI, on the data you just built.

    Explore AI & Data Governance →
    Talk to a data & AI expert

    In context

    Everything else we build reads from this.

    Every Studio app, every AI service and every industry solution runs on the foundation this team engineers. Get it right once and the rest ships faster.

    Data EngineeringStudio Apps (1Platform IP)Data Services
    AI ServicesIndustries

    Real Estate CPG Retail Pharma & LS Capital
    Markets     Private
    Equity     Agent
    Engineering     AI Readiness Migration/
    Modernization     AgentOps DataOps Data Readiness
    assessment     MLOps AI & Data
    Governance     Foundation
    Studio     MDM
    Studio     Governance
    Studio     Insight
    Studio     Agent
    Studio     ML Studio Data
    Engineering

    CXO AI roundtable series

    Where AI leaders compare notes

    A roundtable series on taking AI from pilots to production, hosted by Polestar Analytics with CIOs from complex enterprises.

    Watch the session
    CXO Roundtable: From AI pilots to enterprise impact

    Featured experts from

    Ajinomoto Nestlé Unilever Church & Dwight BIC Coca-Cola McCain Holcim BAT GoTo Foods Opella Kellogg’s Diageo PepsiCo Reyes Albertsons Mondelēz International

    Why Polestar Analytics for data engineering?

    The team behind the move

    850+

    man-years of data engineering

    70%

    of a typical migration is repeatable, and productised in MigrationStudio

    15+

    industry and technology accelerators

    87%

    repeat business

    • Platform-neutral

      Chosen from the workload, not a partner quota.

    • Scoped from usage

      Dormant tables and orphaned jobs don’t inflate the move.

    • Design before platform

      SAP extraction settled before anything is committed.

    • Certified secure

      ISO 9001 and ISO 27001. Code and data stay in your tenancy.

    Trusted partnerships

    Anaplan Intelligence Trusted AI Partner
    Databricks Brickbuilder Silver Partner
    Microsoft Managed Partner
    Google CloudPartner
    Amazon Web ServicesPartner
    SnowflakePartner

    Latest data engineering insights

    FAQ

    Questions data leaders ask

    FAQ questions are managed in admin: Services → Data Engineering → FAQ.

    Is your data ready for the AI you’re planning?

    Bring one use case. Our data and AI experts will show you what the data under it needs.

    Talk to a data & AI expert

    Fill the form

    Loading form…

    Talk to our Experts

    Saurabh Singh

    Saurabh Singh

    Senior Vice President

    LinkedIn
    Ankur Sharma

    Ankur Sharma

    Assistant Vice President

    LinkedIn
    Frequently Asked Questions

    Data engineering involves designing, building, and maintaining the infrastructure needed to collect, process, and store data at scale. It creates the foundation that AI systems rely on. Without proper data engineering, AI initiatives fail due to poor data quality, inaccessible data, or inefficient processing pipelines. Our data engineering services ensure your organization builds the robust data infrastructure necessary for successful AI implementation.

    Signs your data infrastructure needs improvement include: siloed data across departments, manual data processing bottlenecks, inconsistent data definitions, difficulty accessing historical data, and inability to process data in real-time. Our data readiness assessment evaluates your current infrastructure against AI implementation requirements, identifying gaps and providing a strategic roadmap to AI-ready data systems.

    Organizations typically struggle with data quality issues, scalability limitations, integrating diverse data sources, real-time processing capabilities, and governance concerns. Our specialized data engineering services address these challenges by implementing modern data architectures, automated quality controls, and scalable processing frameworks designed specifically for AI workloads.

    In addition to our own accelerator called 1Platform, we implement best-in-class tools including cloud data platforms (AWS, Azure, GCP), data processing frameworks (Spark, Kafka), data warehousing solutions (Databricks, Snowflake, BigQuery, Redshift), orchestration tools (Airflow, Dagster), and MLOps platforms. Our technology selections are customized to your specific needs, existing infrastructure, and future AI ambitions.

    Absolutely. Many organizations have valuable data in legacy systems that must be incorporated into modern data platforms. We specialize in creating integration layers that connect legacy systems to new data infrastructure without disrupting existing operations. Our approach preserves your past investments while enabling future capabilities.

    Talk to our Experts