Videos

In this video, you see how to get going with Hackolade in barely more than 2 minutes.

This video provides a tour of the application, and a review of the user interface

Native integration with Git repositories to provide state-of-the-art collaboration, versioning, branching, conflict resolution, peer review workflows, change tracking and traceability. It allows to co-locate data models and schemas with application code, and further integrate with DevOps CI/CD pipelines as part of our vision for Metadata-as-Code.

The application performs a statistical sampling of each collection, followed by a probabilistic inference of the fields in the documents sampled. Finally, it transforms it into a Hackolade schema. You're now ready to enrich the model with comments and constraints. You can declare and document implicit relationships. In the end, you get a visual representation that lets you easily query the database and perform a better analysis of the data. More info

Create a model from scratch, then generate a MongoDB createCollection script with validation, a Mongoose schema, or a detailed documentation in PDF. More info

In this video, we reverse-engineer a DDL for the Northwind relational database, then we denormalize it in order to leverage the power of NoSQL document DBs and JSON. More info

In this video session, we explain in detail the handy feature of Hackolade to facilitate the work of data modelers: it provides the ability to create - once - object definitions that can be re-used in multiple places. More info Online documentation

In this video session, we explain in detail a new Hackolade feature that lets you maintain both a ‘business name’ and a ‘technical name’ for model objects. This feature can be useful if the database target has attribute length limitations for example, or if you wish to store abbreviated field names in the database, while keeping detailed names for documentation purposes. More info Online documentation

In this video session, we explain in detail how to leverage the capabilities of Git, to easily collaborate and manage changes in your Hackolade models. We will only go over very basic processes without covering Git advanced features such as forks, branches, pull requests, etc… which would be outside the scope of this video. More info Online documentation

In this video session, we explain how to visually design a REST API using Swagger or OpenAPI, using Hackolade's schema-centric approach. Online documentation

DataStax Accelerate conference: When it comes to managing data for modern, agile environments, is data modeling a roadblock? Or is it in fact one of the keys to achieving agility? If traditional data modeling will no longer support what businesses need today, what are the alternatives? Related blogpost

CoMoNoS 2020: 1st Workshop on Conceptual Modeling for NoSQL Data Stores Co-located with the ER 2020 conference.
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In this demo session, you’ll see how NoSQL schema designs based on access patterns can be converted into full API specifications with a few mouse clicks, to serve as a basis for API-driven development. A customizable API template lets users control the output configuration and create APIs that customers and developers will love. View slides

Facilitate migration from RDBMS to Cosmos DB through denormalization to leverage the scalability, flexibility, and easy evolution of schemas for Cosmos DB.

In this session, we will demonstrate how data modeling helps harness the power of Cosmos DB, resulting in reduced development time, increased application quality, and lower execution risks across the enterprise.

Collibra and Hackolade have teamed up to develop an integration of their solutions so that schema designs performed in Hackolade Studio and visual Entity-Relationship diagrams can be easily deployed to Collibra. Data Citizens add business context to create a unified view of all data assets, no matter the data storage or data exchange technology. Learn more

Studio 3T makes it possible to easily migrate either selected tables or an entire SQL database into MongoDB Atlas using the SQL Migration tool. And now, with Hackolade integration, both work in concert to enable easy, trackable, scheduled migrations from legacy SQL systems to modern data platforms like MongoDB Atlas.

Hosts Karthik Vijayraghavan and Cody Allen for the 14th episode of "Amazon DocumentDB Insider Hour". In this episode, Pascal Desmarets announces support for Amazon DocumentDB and explains how Hackolade can help with document modeling for Amazon DocumentDB workloads.

To maximize the benefits of ScyllaDB, you must adapt the structure of your data. Data modeling for ScyllaDB should be query-driven based on your access patterns– a very different approach than normalization for SQL tables. In this session, you will learn how tools can help you migrate your existing SQL structures to accelerate your digital transformation and application modernization. View slides

Polyglot persistence lets enterprises store different kinds of data in different data stores, depending on how data is being used. The trend complements the evolution towards microservices, and introduces a whole range of new questions about consistency of the data and consistency of the data structure. The same data is described over and over again, in different languages, different formats, and different data types. As a result, things get lost and out-of-sync, leading to data quality challenges...

Tens of thousands of organizations, from startups to the largest companies and government agencies, are looking to automatically generate JSON Schema easily. Using the latest NoSQL Data Modeling solutions, organizations can move faster and build applications with more confidence and speed than ever before.

NoSQL databases are designed so that you don’t have to create a schema to build a database. But it’s a common mistake to think that NoSQL databases don’t require any sort of schema design. Bottom line you still need a blueprint.

The purpose of reusable definitions is to increase productivity/convenience and consistency to achieve higher data quality and governance. It is a handy feature of Hackolade Studio to facilitate the work of data modelers: it provides the ability to create and maintain object definitions that can be re-used in multiple places.

Hackolade Studio is shipped with standard properties displayed in the Properties Pane. These properties are specific to each object in a data model. They are specific for each target technology, and can even be different for each data type.

Many users want to define their own properties, for example to track PII or GDPR characteristics, or to tie with metadata management and governance. Some power users create hooks to be leveraged in DevOps CI/CD pipelines or in code generation routines.

To avoid the repetitive task of adding the same set of attributes to new entities as you create a model, you may leverage reusable definitions. But there's another option to pre-populate new entities with the same structure, through JSON configuration, using the concept of snippets.

Composite keys are declared at the entity level, in the Properties Pane tab "Composite keys":. In general, you may give a name to the composite key constraint.

Reverse-engineer a database instance to fetch or infer the schema of the selected collections/tables

Compare two Hackolade models to detect differences, and optionally merge them.

Forward-engineer structure created with the application to dynamically generate the schema DDL of the selected entities. Or forward-engineer JSON Schema or a sample JSON data document.

Generate documentation for a Hackolade model, in HTML, Markdown, or PDF format.

The orchestration of the commands and arguments is typically scripted, then triggered by an event or a schedule. The sequence of steps depends on whether development has the upper hand or governance.

With its Command Line Interface (CLI), Hackolade supports an agile development approach leveraging the flexibility of NoSQL dynamic schemas. Some customers use this capability nightly in a batch process to identify attributes and structures that may have appeared in the data since the last run.