Data Platform Modernization Services

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    SMART SOLUTION
    Modernize Your Data Infrastructure for Faster Analytics and AI Adoption
    data platform modernization

    Data is a company’s biggest asset, right up until the platform supporting it stops performing efficiently. Then it creates the opposite: reports teams can’t rely on, departments making decisions on outdated numbers, and pipelines that lag behind business needs.

    PLANEKS is a data platform modernization services company with 10+ years of experience creating and rebuilding the Python-based backend infrastructure: pipelines, warehouses, integrations, and databases. AI-assisted tooling helps us accelerate repetitive modernization tasks, including schema mapping and code translation, while senior engineers verify the results and make the final technical decisions.

    Our Data Platform Modernization Services

    Every PLANEKS engagement starts with an audit that points to which of these nine services the platform actually needs.

    Data Platform Assessment & Modernization Strategy

    A comprehensive assessment of your current architecture: load-bearing pipelines, dependencies, bottlenecks, and risks. You get a modernization roadmap that ranks each fix by business impact so implementation starts with the highest-impact improvements.

    Legacy Data Platform Modernization

    Incremental replacement and refactoring of outdated databases, pipelines, and processing components. The platform improves in stages, each one shipped and validated on its own, reducing the risk associated with a single large rewrite.

    Cloud Data Platform Migration

    Migration of on-premises or legacy workloads to Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, selected based on data location, compliance requirements, workload characteristics, and existing infrastructure. The architecture is rebuilt to fit the cloud, so you move forward with infrastructure that scales without carrying over old limits into a new environment.

    Data Warehouse & Lakehouse Modernization

    Modernized storage, transformations, and analytical workloads, sized to real data volumes and how your teams actually query the platform. The modernized platform performs reliably under current reporting load and leaves room for the analytics and AI work coming next.

    Data Pipeline Modernization

    Replace fragile batch jobs and manual steps with maintainable ETL and ELT, orchestration, and streaming workflows. Pipelines run on a schedule the business can trust and detect failures early, before they get to a dashboard or a report.

    Data Integration Modernization

    Databases, APIs, SaaS tools, and internal systems connected through controlled integration pipelines. One failed connection no longer breaks unrelated reports, and adding a new source later becomes easier to manage and isolate.

    Data Quality & Governance Implementation

    Validation, lineage, access controls, metadata, and quality checks are built into the modernized platform as part of the migration. Problems get caught in the pipeline, so incomplete or inconsistent data never reaches production or the teams that use it. We incorporate these controls into the modernized platform, especially for insurance clients, where a single unvalidated field can skew an entire claims dataset.

    Data Platform Performance & Cost Optimization

    Storage, processing, queries, and infrastructure usage matched to actual load. We provision compute resources for current demand, reducing cloud spend that accumulates when storage tiers and instance sizing go unchecked after a migration.

    Analytics & AI-Ready Data Infrastructure

    Reliable data foundations for BI, ML, automation, and generative AI. Models and dashboards retrieve from clean, current, well-structured data sources, so the platform is ready for AI work with no further overhaul needed.

    Data Platform Modernization Across Industries

    With experience across 20+ industries, we adapt data platform modernization to each sector’s data volumes, workflows, and compliance requirements.

    FinTech and Banking

    Modernize transaction data, reporting, and integrations while preserving security, consistency, and compliance.
    Fintech Software Development Services

    Oil and Gas

    Upgrade pipelines and storage for high-volume operational and sensor data for oil and gas teams.

    Insurance

    Improve data quality, traceability, and integrations for insurance across claims, policies, and underwriting.
    Insurance Data Integration Services

    Healthcare

    Connect clinical, administrative, and billing data through reliable, controlled pipelines for healthcare organizations.
    Healthcare API Integration Services

    Hospitality

    Modernize the data behind reservations, payments, property management, and guest operations.
    Hospitality API Integration Services

    eSports

    Build scalable infrastructure for real-time match, player, audience, and event data.
    Sports API Integration Services

    Signs Your Data Platform Needs Modernization

    In our practice as a data platform modernization services company, clients come to us with concrete symptoms that slow performance and make business decisions harder. We identify what’s actually causing each problem and fix it.

    Fragmented data

    The same metric returns different numbers depending on which system you ask.

    Manual reporting

    Reporting still depends on manual exports and spreadsheets maintained by hand.

    Fragile pipelines

    Pipeline failures have become a recurring operational interruption.

    Outgrown architecture

    The architecture no longer supports required data volumes.

    Rising costs

    Infrastructure costs are rising faster than actual usage.

    Blocked integrations

    Legacy systems make planned integrations difficult or costly.

    Slow analytics

    Analysts wait days for data that should be available in minutes.

    Limited AI readiness

    The platform cannot support real-time processing, machine learning, or AI workloads.

    Our Approaches

    A common mistake among tech teams is committing to a full re-architecture when the platform only needs targeted optimization. We let the detected constraint guide the approach, and it often turns out to be a smaller fix than expected.

    1

    Optimize

    The architecture remains as it currently is. We fix the specific issue slowing things down, from query performance to compute sizing, so the platform runs better without a larger change.
    2

    Migrate

    The workloads move while the architecture stays close to what it was. This fits a pipeline that still works but sits on infrastructure the vendor is retiring or overpricing, letting you retain what performs and move away from what drives unnecessary cost.
    3

    Replatform

    The stack changes while the critical logic carries over, often moving a Django data layer onto managed PostgreSQL and Redis while the application code stays intact.
    4

    Re-architect

    The architecture itself is the limiting factor, so we redesign the data flows and storage model to remove it. A platform built for batch reporting moves toward real-time processing, so the design supports where the business is growing next.

    The least disruptive approach that efficiently handles the practical limitation is what we recommend. This has informed our work with 100+ clients, from startups to enterprise teams.

    What We Modernize

    Our data platform modernization company finds issues across several of these layers because issues in one layer often affect others.

    Databases and data stores

    Schema decisions made during launch often become a limit the business outgrows.

    Data warehouses

    Reporting slows down when the warehouse was sized for far less data than the business now holds.

    Data lakes and lakehouses

    Stored raw data loses much of its value when there’s no clear strategy to make it searchable.

    ETL and ELT pipelines

    Pipelines built as one-off scripts grow fragile and hard to maintain over time.

    Batch and real-time processing

    Misaligned processing schedules leave time-sensitive data out of date.

    Data APIs and integration layers

    Systems developed at different times can’t integrate cleanly without rework.

    Reporting and analytics infrastructure

    Reporting layers that lose the team's trust get worked around manually.

    Cloud data infrastructure

    Cloud-based infrastructure may remain sized for outdated usage patterns the business has outgrown.

    How We Modernize Data Platforms

    As we grew into an end-to-end data platform modernization firm, we developed a process designed to avoid downtime, data loss, and all-at-once switchovers. The legacy system stays running until the new platform is ready to take over. Each step below reduces cutover risk, where many modernization failures occur.

    01

    Audit the Existing Data Estate

    We take inventory of data sources, databases, pipelines, and hidden dependencies, including integrations that may not be documented or immediately visible.
    02

    Identify Bottlenecks and Migration Risks

    A risk in one area often explains a bottleneck in another, so we assess performance, quality, security, technical debt, cost, and dependencies to get a comprehensive view.
    03

    Design the Target Architecture

    Architecture choice follows workload patterns, data volume, latency, team capability, and budget. The right architecture matches the team that will run it at their operating scale.
    04

    Build the Modernization Roadmap

    High-impact areas are addressed first, divided into controlled phases scoped to a single pipeline or integration at a time.
    05

    Modernize Pipelines and Infrastructure

    New storage, transformation, integration, and orchestration components run behind the existing platform first, so live traffic is not shifted to new components until they have been tested.
    06

    Migrate and Validate Data

    Every migrated dataset undergoes reconciliation and validation before it is considered complete. Row counts and checksums catch silent data loss that a visual comparison misses.
    07

    Run Legacy and Modern Platforms in Parallel

    We run both systems side by side and compare outputs before switching over. Running them in parallel surfaces edge cases that standalone testing can't detect.
    08

    Optimize, Monitor, and Decommission

    Our data platform modernization company tunes performance and infrastructure costs against real production load, then retires legacy components once the new environment proves stable.

    Choose the Right Architecture for Your Data

    The right choice depends on real requirements: what the data is for, how much arrives and how fast, and who keeps it running. We weigh those before defining the architecture, so the platform fits the actual workload.

    Data Warehouse

    Structured, well-modeled data for BI and analytics. The right fit for enterprise reporting teams that run clean, predictable queries on a daily basis.

    Data Lake

    Large-scale raw and semi-structured data stored at low cost, suitable for teams still exploring how they'll use it.

    Data Lakehouse

    Warehouse-style management over lake-style storage, useful once a team needs governed BI tables and raw data in one platform.

    Batch Processing

    The right fit when results can be produced on a set schedule without affecting the business. Batch jobs are simpler to build and to recover when a failure occurs.

    Real-Time Data Processing

    Redis handles lighter needs; Kafka takes over at higher volume. Live inventory counts may require to moving off batch.

    Data Platform Modernization with Python

    Python provides a mature ecosystem for data platform work, with Pandas, SQLAlchemy, Celery, and Airflow covering the process end-to-end.

    Pandas and SQLAlchemy pipelines run against real samples before production.

    FastAPI supports high throughput and automatically generates OpenAPI documentation.

    Airflow manages data pipelines, including scheduling, dependencies, retries, and monitoring.

    Celery handles report generation, batch processing, and other long-running tasks outside the request-response cycle.

    Tools without a supported integration can be integrated through a custom connector.

    Pydantic and Cerberus validate incoming data against defined schemas and validation rules.

    From Legacy Data Infrastructure to a Modern Platform

    Your data platform does not need another workaround. We can assess your current architecture, identify the parts limiting performance and scalability, and define an incremental modernization roadmap.

    Our Data Platform Technology Stack

    As a data platform modernization company, we use these tools because they’ve delivered consistently and successfully across our projects.

    backend technologies stack

    Backend & Data Processing

    Django and FastAPI can both serve as the core backend, depending on the application’s requirements. We use Django when the project benefits from its batteries-included approach and mature data and business logic capabilities, while FastAPI fits lightweight APIs and async workloads. Celery handles background jobs that should run outside the request cycle.
    Backend Development Services
    database tools

    Databases

    PostgreSQL is our default for transactional workloads. When analytical queries begin to affect operational performance or data volumes outgrow the primary database, we move analytical workloads to platforms such as BigQuery, Snowflake, or ClickHouse, depending on query patterns and scale.
    Data Integration Services
    background processing tools

    Caching & Messaging

    Redis handles caching and task queues, reducing database load when the same data is read repeatedly.
    RPA Services
    cloud app platforms

    Cloud

    Cloud data platforms built on Amazon Web Services, Google Cloud Platform, or Microsoft Azure, chosen by data location, compliance requirements, existing infrastructure, and workload type.
    Cloud App Development Services

    Build a Data Platform Your Team Can Maintain

    By the time our clients bring us in, they’re usually running a platform designed by an earlier team that optimized for speed-to-market. We make maintainability a requirement from the start.

    Incremental modernization

    Staged rollouts ensure that no single release puts the whole platform at risk.

    Architecture at scale

    The architecture is sized to real growth, based on how the platform is used in practice.

    Data quality built in

    Quality checks run during the migration, identifying problems before production.

    Integration-first approach

    New components are designed to connect cleanly from their first deployment.

    Documentation and knowledge transfer

    Each stage is documented, so the next engineer starts with context already in place.

    Human-controlled AI-assisted engineering

    AI streamlines repetitive work, while a senior engineer reviews every change.

    Data Platform Modernization FAQ

    The questions clients ask most about our data platform modernization services company – answered directly below.

    What is data platform modernization?

    Data platform modernization means redesigning the databases, pipelines, and integrations that move and store company data, without putting daily operations on pause. Most data platform modernization consulting starts with an architecture assessment.

    What is included in data platform modernization services?

    A full engagement usually includes an assessment, a roadmap, migration, governance work, and post-launch tuning. As an end-to-end data platform modernization firm, PLANEKS scopes each engagement as a set of targeted solutions matched to the specific platform.

    What is the difference between data modernization and data migration?

    Migration moves data or workloads from one system to another. Modernization is broader, encompassing migration, architectural redesign, and governance work that make the new platform better than what it replaced.

    How long does data platform modernization take?

    A single component usually takes 2 to 6 months; a full platform, split into phases, runs 6 to 12 months. The top factor is typically the amount of data that has to be moved. Reach out to our consulting firm for a timeline scoped to your platform.

    Can you prepare our data infrastructure for AI?

    Yes. In practice, the work is mostly about data quality and access controls, so that models are built on reliable data. The best data platform modernization providers treat AI readiness as a data problem first.

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