Hackolade Studio is a powerful and flexible data modeling platform built for today’s complex data ecosystems. It supports a broad range of technologies, including relational SQL and NoSQL databases, cloud data warehouses, streaming platforms, APIs, and data exchange formats, making it a versatile choice for modern data architecture initiatives. The platform enables users to visually design, document, and evolve schemas across diverse systems such as Oracle, PostgreSQL, MySQL, SQL Server, BigQuery, Databricks, Snowflake, Redshift, MongoDB, Cassandra, DynamoDB, Neo4j, and Kafka with Confluent Schema Registry. It also supports API modeling for OpenAPI (Swagger) and GraphQL, along with native schema design for formats like Avro, JSON Schema, Protobuf, Parquet, and YAML. Hackolade Studio offers essential capabilities such as forward and reverse engineering, schema versioning, data type mapping, and model validation, helping teams ensure accuracy and consistency across environments. It integrates seamlessly with metadata management platforms like Unity Catalog and Collibra, enabling improved data governance, lineage, and compliance. With its intuitive interface and modular architecture, Hackolade empowers data architects, engineers, API designers, and governance professionals to collaborate effectively on clean, governed, and scalable data models. Whether designing new systems, managing evolving schema contracts, or aligning business and technical users around a shared understanding of data, Hackolade Studio accelerates delivery and fosters agility. In an era where data is a core asset, Hackolade Studio helps organizations unlock its full value by making data modeling and schema design a streamlined, iterative, and business-aligned process. A complementary product is the Hackolade Model Hub is a model-driven metadata collaboration platform that offers centralized, streamlined access to your Hackolade Studio data models, stored across one or more Git repositories. A model-driven metadata collaboration platform allows organizations to retake control of their data.