Hackolade, with its mission to simplify schema design and smoothen the on boarding of NoSQL technology in corporate IT landscapes, is focused on producing user-friendly visual tooling.
Pioneering data modeling of NoSQL databases and the visual design of REST APIs, the solution includes schema design for document databases, key-value stores, property graphs, and column-oriented databases. Hackolade is the only data modeling tool for MongoDB, Neo4j, Cassandra, Couchbase, Cosmos DB, DynamoDB, Elastic search, HBase, Hive, Google BigQuery, Firebase / Firestore, MarkLogic, Neptune, TinkerPop, etc.
Also applying its easy and visual design to Avro, JSON Schema, Swagger and OpenAPI, the company is rapidly adding new targets for its data modeling engine.
When it comes to data governance and information management, which are particularly hard when dealing with unstructured and semi-structured data, the traditional data modeling tools being based on relational technology, don’t adequately represent hierarchical structures such as JSON. Hackolade is a unique tool providing visual data modeling and schema design built from the ground-up to support the nested and polymorphic nature of JSON.
With many Fortune 500 companies, as well as startups and independent developers of all sizes in between, as its customers, spread across a variety of industries – finance, insurance, healthcare, retail, tech, telcos, transportation, government, education – the startup is determined to make its presence known. Expecting to continue to grow quickly, Hackolade continues to re-invest heavily to develop innovative solutions responding to customer needs.
Hackolade Studio, a software tool providing visual data modeling for NoSQL databases and schema design for JSON, Avro, Swagger, and OpenAPI, is a client application running on Windows, Mac or Linux. It allows monthly or annual subscription pricing plans or a more traditional perpetual license with annual maintenance, and also offers concurrent licensing, tracking simultaneous users for larger deployments with a cloud server.
With Big Data getting bigger and bigger day by day – not just for the sake of storing data – enterprises today demand to leverage their data in a meaningful way, i.e. to feed AI and Machine Learning. To make sense of the data and ensure high data quality, companies realize the importance of mastering consistent and coherent data structures. Data modeling is even more important when formats like JSON are so flexible and powerful that data processing and storage can become inconsistent. Hackolade Studio is an intuitive and visual tool combining non-intrusive guard rails to leverage the flexibility of semi-structured data, with the needs for pragmatic data governance.
Hackolade’s Founder & CEO – Pascal Desmarets, originally hails from the world of relational databases. Having discovered the power of NoSQL, he realized that many established companies might be reluctant to adopt the technology unless additional tools contributed to the maturity of NoSQL. With this in mind, he designed the Hackolade software to combine the comfort and simplicity of graphic data modeling with the power of NoSQL databases, to reduce development time, increase application quality, and lower execution risks.
Listening to their customers, and constantly providing new solutions to address customer pain points, are key strategies that Hackolade believes its executives must apply to position the company for growth.
Given traditional Entity Relationship Diagram / Data Modeling tools – that are very mature and somewhat bloated, having been around for decades – dealing well only with flat and normalized structures, and also imposing a strict and heavy process of conceptual, logical and physical data modeling steps, Hackolade came up with a very unique approach: it kept the visual approach to entity design, but stretched the theory to accommodate hierarchical and polymorphic structures.
Hackolade’s tool fits well in an agile development environment, focusing mostly on physical schema design and code generation.
The company’s roadmap is strictly prioritized based on popular requests by customers and prospects. It combines 2 distinct tracks: one is to enrich the tool with new functionalities required by developers and data modelers to make their daily tasks more productive and accurate. And the other is to continue to quickly add new “targets” for the data modeling engine: new file formats (Thrift, ORC, Parquet, etc.) and additional databases (more NoSQL databases, but also RDBMS with JSON support.)