Organizations integrate big data with legacy systems by creating bridges between modern data platforms and older infrastructure, often using middleware, APIs, or incremental modernization. Legacy systems, such as mainframes or relational databases, weren’t designed to handle high-volume, unstructured data. To connect them, developers typically build interfaces that extract data from legacy sources, transform it into compatible formats, and load it into big data storage (like Hadoop or cloud-based data lakes). For example, a company might use Apache Kafka to stream transactional data from a legacy COBOL system into a distributed data platform for real-time analytics, ensuring minimal disruption to the existing system.
Data transformation and storage are critical. Legacy systems often rely on fixed schemas, while big data tools process unstructured or semi-structured data (e.g., JSON, logs). Developers use tools like Apache Spark or custom ETL pipelines to convert legacy data into formats like Parquet or Avro. Batch processing might handle historical data, while streaming frameworks like Flink integrate real-time data. For instance, a bank could modernize by extracting customer records from a DB2 database, flattening hierarchical records into JSON, and storing them in a cloud data lake for machine learning models without rewriting core banking software.
Security and governance require careful planning. Legacy systems may lack modern authentication or encryption, so integrating them with big data platforms often involves adding layers like API gateways or RBAC (role-based access control). Tools like Apache Ranger or Kerberos can enforce policies across hybrid systems. For example, a healthcare provider might use a middleware layer to anonymize patient data from an old EHR system before analysis in a Spark cluster, ensuring HIPAA compliance. Monitoring tools like Prometheus or Grafana help track performance and data flow between systems, ensuring reliability without overhauling legacy codebases.