NoSQL databases and other targets

Data modeling of document collections

Azure Cosmos DB is Microsoft's globally distributed, multi-model NoSQL database to elastically and independently scale throughput and storage across any number of Azure's geographic regions.

Hackolade was specially adapted to support the data modeling of multiple document types within one single collection. Each Document Type is modeled as a separate entity, so its attributes can be defined separately. We support both SQL API (formerly known as DocumentDB API) and MongoDB API.
To be added soon: Table API, Gremlin API, and Cassandra API.

Data modeling of fully managed cloud NoSQL database service

Amazon DynamoDB is a fast and flexible NoSQL database service for all applications that need consistent, single-digit millisecond latency at any scale. It is a fully managed cloud NoSQL database and supports both document and key-value store models. Its flexible data model and reliable performance make it a great fit for mobile, web, gaming, ad tech, IoT, and many other applications.

Hackolade was specially adapted to support the data modeling of DynamoDB tables including partition (hash) and sort (range) keys, supporting multiple regions as well.

Mobile app success made simple

The Google Firebase Realtime Database is a cloud-hosted database. Data is stored in JSON and synchronized in real time to every connected mobile or other client. It lets developers build rich collaborative applications, with data also persisted locally, to give users a responsive experience.

Hackolade was specially adapted to support the data modeling of data stored as a large JSON tree, with data nodes and their associated keys.

Note: the forward- and reverse-engineering of schemas are not currently available. They're being developed and will be released at a later time.

When you need random, realtime read/write access to your Big Data

Apache HBase is an open-source, distributed, versioned, non-relational (NoSQL) database modeled after Google's Bigtable. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware.

Hackolade was specially adapted to support the data modeling of HBase, whether you store your data in column families or as a JSON object. With our reverse-engineering function, you can easily discover, document, and enrich the structure of your column families and qualifiers, plus infer the structure of JSON documents you store in HBase.

ACID multi-model NoSQL DB for the enterprise

MarkLogic is designed from the ground up to make massive quantities of heterogeneous data easily accessible through search. MarkLogic is a leading 'multi-model' database, supporting JSON documents and RDF triples, all with ACID transactions capabilities.

Hackolade was specially adapted to support the data modeling of NoSQL database MarkLogic, including the JSON definition of model descriptors, geospatial structures, triples and quads, and sub-collections. The application closely follows the terminology of the database.

Note: forward-engineering of JSON Schema is available for use by xdmp.jsonValidate. Reverse-engineering of schemas is not currently available, and will be released at a later time.

Data modeling for graph databases

Neo4j is a graph database management system described as an ACID-compliant transactional database with native graph storage and processing.
Hackolade was specially built to support the data modeling of Neo4j node labels and relationship types. It provides a graph view with familiar circular node labels, as well as an Entity-Relationship Diagram view with permanent display of the attributes (or properties) of both node labels and relationship types.
Hackolade dynamically generates Cypher code as the model is created via the application. It also lets you perform reverse-engineering of existing instances, so you can enrich the model with descriptions and constraints, then produce a complete, clickable HTML documentation for distribution to all application stakeholders.
The application closely follows the terminology of the NoSQL, pioneering a new set of software tools to smooth the onboarding of NoSQL database technology in corporate IT landscapes, reduce development time, increase application quality, and lower execution risks.
Hackolade is not a graph visualization tool, but a tool for schema design of Neo4j graph databases.

Serialize data in Hadoop and stream data with Kafka

Apache Avro is a language-neutral data serialization system, developed by Doug Cutting, the father of Hadoop. Avro is a preferred tool to serialize data in Hadoop. It is also the best choice as file format for data streaming with Kafka. Avro serializes the data which has a built-in schema. Avro serializes the data into a compact binary format, which can be deserialized by any application. Avro schemas defined in JSON, facilitate implementation in the languages that already have JSON libraries. Avro creates a self-describing file named Avro Data File, in which it stores data along with its schema in the metadata section.

Hackolade was specially adapted to support the data modeling of Avro schema NoSQL databases. It closely follows the Avro terminology, and dynamically generates Avro schema for the structure created with a few mouse clicks. Hackolade easily imports the schema from .avsc or .avro files to represent the corresponding Entity Relationship Diagram and schema structure.

Data modeling for graph databases (OLTP) and graph analytics systems (OLAP)

Apache TinkerPop is an open source, vendor-agnostic, graph computing framework distributed under the Apache2 license. When a data system is TinkerPop-enabled, its users are able to model their domain as a graph and analyze that graph using the Gremlin graph traversal language.
Hackolade was specially built to support the data modeling of TinkerPop vertex labels and edge labels. It provides a graph view with familiar circular vertex labels, as well as an Entity-Relationship Diagram view with permanent display of the attributes (or properties) of both vertex labels and edge types.
Hackolade dynamically generates Gremlin code as the model is created via the application. It also lets you perform reverse-engineering of existing instances, so you can enrich the model with descriptions and constraints, then produce a complete, clickable HTML documentation for distribution to all application stakeholders.
Hackolade is not a graph visualization tool, but a tool for schema design of TinkerPop graph databases.

Visual schema design to serialize data in columnar format

Apache Parquet is a binary file format that stores data in a columnar fashion for compressed, efficient columnar data representation in the Hadoop ecosystem, and in cloud-based analytics.
Hackolade is a visual editor for Parquet schema for non-programmers, and specifically adapted to support the schema design of Parquet files. It supports the Parquet structure, data types, logical types, encodings, compression codecs, and all other standard metadata.
Hackolade dynamically generates parquet schema as the model is created via the application. It also lets you perform reverse-engineering of files on the local system, shared directories, AWS S3 buckets, Azure Blob Storage, or Google Cloud Storage.

Cloud-based data warehousing

Snowflake’s architecture is a hybrid of traditional shared-disk database architectures and shared-nothing database architectures. It supports the most common standardized version of SQL: ANSI.

Hackolade was specially adapted to support the data modeling of Snowflake, including schemas, tables and views, indexes and constraints, plus the generation of DDL Create Table syntax as the model is created via the application. In particular, Hackolade has the unique ability to model complex semi-structured objects stored in columns of the VARIANT data type. The reverse-engineering function, if it detects JSON documents, will sample records and infer the schema to supplement the DDL table definitions.