Big Data Solutions

A big data solution is a combination of technologies, architectures and processes that store, process and analyze large, heterogeneous data volumes. Enterprises need one when traditional databases hit scaling limits � typically beyond terabyte-range data volumes, when real-time requirements emerge, or when structured and unstructured data from many sources must be unified. Concrete indicators include slow queries, isolated data silos and missing foundations for AI or analytics projects.

Modern big data architectures follow a layered model: The ingestion layer receives data from source systems via batch or streaming. The storage layer uses data lakes for raw data and data warehouses for curated data � increasingly combined as a lakehouse. The processing layer transforms data with engines like Apache Spark or Flink. The analytics layer provides prepared data for BI, machine learning and reporting. Cross-cutting concerns include data governance, security and metadata management. Current architecture patterns are data mesh for decentralized ownership and data fabric for automated integration.

Technology choices depend on the specific use case. For storage, cloud-native solutions like AWS S3, Azure Data Lake Storage or Google BigQuery are common. Apache Spark dominates batch processing, Apache Kafka and Flink handle real-time processing. For data warehousing, many enterprises use Snowflake, Databricks or Synapse Analytics. Open table formats like Delta Lake and Apache Iceberg enable lakehouse architectures. For data governance and cataloging, Apache Atlas, Alation and Collibra are established. ADVISORI selects technologies based on your existing infrastructure and requirements.

An initial productive proof of concept with a concrete use case is achievable in eight to twelve weeks. Building a complete enterprise-wide big data platform typically takes six to twelve months, depending on the number of data sources, integration requirements and target maturity level. ADVISORI recommends an iterative approach: start with a prioritized use case, validate the architecture, then scale incrementally. Organizations following this focused approach achieve three to four times higher success rates than comprehensive one-time implementations.

Big data consulting costs vary by scope: strategy development with an architecture concept is typically in the five-figure range, while full platform implementation falls in the six- to seven-figure range. ROI manifests in multiple dimensions:

60 to

80 percent savings on storage and processing costs through optimized technology choices, faster decision-making through real-time analytics, and new revenue potential through data-driven business models. Critical for ROI is defining concrete use cases with measurable business value before project start.

Data security starts at the architecture level: encryption at rest and in transit, role-based access control and network segmentation form the foundation. For GDPR compliance, data masking, pseudonymization and clear deletion concepts are required. Data lineage documents where data originates and how it is processed � a central proof for auditability. A data governance framework defines responsibilities, classification and access policies. ADVISORI integrates security and compliance into the architecture from the start, rather than retrofitting it.

Integration follows four approaches depending on the starting point: parallel installation where the big data platform runs alongside existing systems connected via APIs. Hybrid approach with gradual migration of individual workloads. Change data capture for real-time synchronization between legacy systems and the new platform. Data virtualization for a unified view without physical data movement. A clear integration plan defining data flows, dependencies and migration priorities is essential. ADVISORI has experience from over

520 projects integrating into heterogeneous enterprise landscapes.