Hackolade download install video

Download and install Hackolade

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

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Quick tour overview video Hackolade

Hackolade quick tour

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

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Hackolade Studio Workgroup Edition overview

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.

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Data modeling reverse-engineering MongoDB video

Reverse-engineering of a MongoDB instance

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

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Create Hackolade model forward-engineering video

Create a model from scratch and perform forward-engineering

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

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SQL DDL reverse-engineering denormalization video

SQL DDL reverse-engineering and denormalization assistance

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

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re-usable object definitions video

Re-usable object definitions

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

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Hackolade naming conventions video

Naming Conventions

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

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collaboration version control video

Collaboration and Version Control

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

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Schema-centric API design Swagger OpenAPI video

Schema-centric API design with Swagger and OpenAPI

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

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Data Modeling is Dead... Long Live Schema Design Cassandra DataStax video

Data Modeling is Dead... Long Live Schema Design!

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

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ER2020 NoSQL Data Modeling in Practice video

NoSQL Data Modeling in Practice

CoMoNoS 2020: 1st Workshop on Conceptual Modeling for NoSQL Data Stores Co-located with the ER 2020 conference.
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Model-Driven API Generation video

Increase API Quality and Consistency with Model-Driven API Generation

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

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Cosmos DB Conference data modeling schema design video

Data modeling and schema design for Cosmos DB

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 Data Dictionary Integration

Collibra Data Dictionary Integration

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

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Studio3T data migration SQL to MongoDB

Migration from SQL to MongoDB with Studio3T

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.

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Amazon DocumentDB data modeling twitch

Twitch: how to use Hackolade for data modeling with Amazon DocumentDB

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.

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Migrating SQL Schemas for ScyllaDB: Data Modeling Best Practices

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

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In’s and Out’s of Polyglot Data Modeling

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...

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Schema design with JSON

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.

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NoSQL Document Database Schema Design

Understanding NoSQL Document Database Schema Design

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.

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How-To: reusable definitions

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.

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How-To: user-defined custom properties

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.

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How-To: pre-populate new entities using snippets

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.

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How-To: composite key constraints

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.

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How-To: CLI reverse-engineering (1 of 5)

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

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How-To: CLI model comparison (2 of 5)

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

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How-To: CLI generation of DDL script (3 of 5)

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.

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How-To: CLI documentation generation (4 of 5)

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

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How-To: CLI orchestration with batch script (5 of 5)

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.

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How-To Command-Line Interface Playlist (5 videos)

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.

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